eess.SP — Pith

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cs.SD 2026-05-06

PHALAR model lifts stem retrieval accuracy by up to 70 percent with half the parameters

by Davide Marincione, Michele Mancusi +5 more

PHALAR: Phasors for Learned Musical Audio Representations

Contrastive framework adds pitch and phase equivariance via spectral pooling and complex head, trains seven times faster, and matches human

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Stem retrieval, the task of matching missing stems to a given audio submix, is a key challenge currently limited by models that discard temporal information. We introduce PHALAR, a contrastive framework achieving a relative accuracy increase of up to $\approx 70\%$ over the state-of-the-art while requiring $<50\%$ of the parameters and a 7$\times$ training speedup. By utilizing a Learned Spectral Pooling layer and a complex-valued head, PHALAR enforces pitch-equivariant and phase-equivariant biases. PHALAR establishes new retrieval state-of-the-art across MoisesDB, Slakh, and ChocoChorales, correlating significantly higher with human coherence judgment than semantic baselines. Finally, zero-shot beat tracking and linear chord probing confirm that PHALAR captures robust musical structures beyond the retrieval task.

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eess.SP 2026-07-03

Optimization boosts sum-rate in RIS-aided RSMA-SWIPT with movable antennas

by Muhammad Asif, Asim Ihsan +5 more

Robust Transmission Design for RIS-Assisted RSMA-SWIPT Systems With Movable Antennas Under Hardware Distortions

Decomposes the coupled problem into convex surrogates to handle CSI errors and hardware distortions while jointly tuning beamforming, reflec

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This paper investigates a robust transmission design for a multi-user rate-splitting multiple access (RSMA)-based simultaneous wireless information and power transfer (SWIPT) system empowered by movable antennas (MAs) and a reconfigurable intelligent surface (RIS) under channel state information (CSI) uncertainty and residual hardware impairments (HIs). The effective channels in MAs-enabled systems depend on antenna positions, causing CSI uncertainty to affect not only active and passive beamforming but also antenna position optimization. Furthermore, residual HIs distort the effective SINRs, creating additional coupling among beamforming, RIS reflection control, common-rate allocation, power-splitting ratio optimization, and antenna position optimization. Consequently, the joint impact of CSI uncertainty and HIs leads to a highly coupled and challenging resource allocation problem. To address this challenge, we propose a robust resource allocation framework that jointly optimizes common-rate allocation, transmit beamforming, RIS reflection coefficients, power-splitting ratios, and MAs positions to maximize the achievable sum-rate while satisfying practical system constraints. To obtain an efficient solution, the original problem is decomposed into active beamforming, RIS reflection design, power-splitting ratio optimization, and MAs position optimization subproblems, where tractable convex surrogate functions are constructed to handle the non-convex objective and constraints. Simulation results verify the effectiveness of the proposed framework and demonstrate substantial improvements in achievable sum-rate, robustness against CSI uncertainty and hardware impairments, and convergence performance compared with benchmark schemes.

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eess.SP 2026-07-03

Rotatable arrays raise multiuser rates by shaping phase and gain

by Xingxiang Peng, Qingqing Wu +3 more

Beyond Beamforming: Phase-and-Gain Channel Shaping via Rotatable Antenna Arrays

Joint pose and boresight control improves channel strength and user separation beyond fixed beamforming.

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This paper investigates geometry-reconfigurable transmission for multiuser communication systems enabled by a rotatable antenna array. In contrast to conventional fixed arrays, the proposed architecture jointly exploits array pose adjustment and element-level boresight steering, thereby reshaping both the array-induced phase responses and the direction-dependent channel gains. We formulate a weighted sum-rate maximization problem that jointly optimizes the transmit beamformers, array pose, and element boresights under practical visibility and steering constraints. To reveal the underlying design principles, we first provide a geometric interpretation via zero-forcing analysis, showing that the resulting rates stem from both channel-strength enhancement and spatial-separability improvement. Specifically, array-pose rotation improves inter-user channel orthogonality even with isotropic elements, whereas directional elements introduce a tradeoff between phase-based spatial separation and boresight-dependent gain alignment. Motivated by these insights, we develop an efficient optimization framework that jointly coordinates transmit beamforming, array-pose adaptation, and element-boresight steering to exploit the geometry-induced phase-and-gain channel-shaping capability. Simulation results demonstrate that the proposed joint design outperforms fixed-array, pose-only, and boresight-only benchmarks, with larger gains achieved under more directive element patterns and tighter boresight-steering constraints.

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eess.SP 2026-07-03

FFT preconditioning reduces neural feature error up to 50 percent

by Preston Pitzer, Anish Pradhan +1 more

Fourier Preconditioning for Neural Feature Learning

For stationary signals the transform packs dependence into dominant modes, cutting truncation error without extra training cost.

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Mutual information (MI)-inspired feature learning techniques are capable of generating low-dimensional embeddings that retain nonlinear dependence structures, but direct estimations of MI suffer from noisy probability distribution estimates in the low-data regime. The H-Score objective, computed from second-order statistics, provides a practical proxy metric for training feature extraction networks. We prove that H-Score is invariant to invertible transformations in the unrestricted functional setting, but becomes sensitive to input basis rotations under constrained approximation classes. Consequently, we study unitary preconditioning for H-Score networks and show that selecting an appropriate basis rotation reduces finite-width truncation error by concentrating predictive dependence into fewer dominant modes. We identify the fast Fourier transform (FFT) as an effective data-independent, low-cost preconditioner for approximately stationary processes, where spectral structure induces concentration of the cross-covariance singular value spectrum. We introduce training-free metrics based on spectral entropy and cumulative dependence energy to quantify basis suitability and predict downstream inference gains prior to network training. Experiments across eight multivariate datasets demonstrate that FFT preconditioning is particularly useful in resource-constrained regimes, achieving up to 50% normalized mean squared error (NMSE) reduction, while the proposed metrics correlate with observed performance gains and correctly identify cases where spectral preconditioning is detrimental.

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eess.SP 2026-07-03

Compressed LEO data locates GNSS jammers at high ratios

by Giacomo Pojani, Javier Tegedor +6 more

Complexity-Scalable Direct Geolocation and Cancellation of Terrestrial GNSS Jammers: Single-Satellite and Multi-Antenna Experiments in Low Earth Orbit

Quasi-direct geolocation processes quantized time-frequency samples from small satellites to track terrestrial jammers in near real time.

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Monitoring the radio-frequency (RF) spectrum from space imposes demanding requirements to satellite platforms in terms of communication bandwidth and computational resources, which are necessary for the downlink, the storage, and the processing of high-throughput I/Q samples. This paper analyzes in depth the quasi-direct geolocation (QDG) as a technique to enable the exploitation of satellites of opportunity in low Earth orbit (LEO) to sense the spectrum in the bands of global navigation satellite systems (GNSS). This is a technique of passive RF geolocation and consists of an ensemble of signal processing algorithms, which compress the I/Q samples and process the compressed data through fast delay-Doppler shift matching and interferometry in a quantized time-frequency domain. These algorithms speed up the exhaustive search of multiple RF sources in the position domain. The efficiency gain addresses the bottleneck that prevents the employment of satellites, which are limited in downlink capacity and on-board computational power. These satellites are usually constrained in size, weight and power (SWaP) and represent most of the spacecrafts in LEO. The ability to exploit assets as such for the geolocation of terrestrial GNSS jammers in near real time is instrumental the performance of a multi-constellation GNSS RFI monitoring system. The present work describes the mathematical framework and precision bounds, introduces single- and multi-antenna uses cases, combines different compression methods, and evaluates the geolocation accuracy with real data. The I/Q samples were collected by a repurposed GNSS reflectometry (GNSS-R) satellite, OPS-SAT PRETTY, in a dedicated test session during Jammertest 2025. The experimental results demonstrate the capability to geolocate GNSS jammers with different signal-to-noise ratios (SNR) with extremely high compression ratios.

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eess.SP 2026-07-03

Clustered THz HetNets outperform random models in coverage

by Hadeel Obaid

Coverage Analysis in Terahertz Clustered HetNets

Moderate spread of small base stations raises coverage probability when users cluster in hotspots.

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Terahertz (THz) transmission technologies hold significant potential for enabling ultra-broadband, short-range communication in next-generation networks. Despite the vast bandwidth, THz signals suffer from limited transmission range and a feasible scenario is to deploy THz within clustered heterogeneous networks (HetNets) to enhance coverage. This paper investigates THz communication in clustered HetNets, leveraging stochastic geometry for performance analysis. Specifically, we consider two tiers of macro base stations (MBS) and small base stations (SBS). The MBS tier is modeled as a Poisson Point Process (PPP), and both the SBS tier and users are modeled as a Poisson Cluster Process (PCP) to capture user clustering and network hotspots. We derive the analytical expressions for user association probabilities, the Laplace transform of interference, and the coverage probability. The derived coverage probability is validated through Monte Carlo simulation. The numerical results show that the coverage in THz PCP-HetNets is higher than that achieved in THz PPP HetNets. In addition, a moderate spatial spread of SBSs is beneficial for coverage.

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eess.SP 2026-07-03

Closed-form spatial correlation derived for cylindrical mMIMO arrays

by Shasha Liu, Abla Kammoun +1 more

Three-Dimensional Spatial Correlation Modeling for Cylindrical mMIMO Arrays in HAPS

Exact expression uses spherical harmonics and Fourier coefficients to handle arbitrary patterns and angles for HAPS.

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High-altitude platform stations (HAPS) are envisioned as a key component of future wireless networks, enabling ultra-wide coverage and providing direct connectivity to users with cylindrical massive multiple-input multiple-output (mMIMO) systems. Exploiting the channel degrees of freedom necessitates accurate modeling and characterization of three-dimensional (3D) channels in the presence of spatial correlation functions (SCFs). However, existing spatial correlation models are primarily developed for planar or linear antenna arrays and cannot be directly applied to cylindrical geometries commonly adopted by HAPS platforms. To address this limitation, this paper derives an exact closed-form expression for the SCF of 3D MIMO channels with antenna elements arranged in a cylindrical array. The proposed formulation is based on the spherical harmonic expansion (SHE) of plane waves and accommodates arbitrary antenna radiation patterns and angular distributions through the Fourier series (FS) coefficients of the power azimuth and zenith spectra. The derived SCF is validated through Monte Carlo simulations under standard-compliant settings.

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eess.SP 2026-07-03

Dual antenna powers brain implants and sends data at 32 Mbps

by Ali Khaleghi, Aminolah Hassanvand +1 more

Antenna System for Simultaneous Wireless Power and Information Transfer to Brain Implants

Inductive link supplies energy while backscatter returns high-rate signals without batteries or wires.

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Brain-Computer Interfaces (BCIs) have revolutionized neuroscience applications, from motor rehabilitation to neuroergonomics. Traditional implantable BCIs with invasive microelectrode arrays pose challenges, notably the need for wired connections and inherent implantation risks. This paper introduces a battery-free wireless BCI system, consolidating an implant and its external supporting system. Our design centers on a dual-function antenna system: firstly, an inductive coupling mechanism enables wireless power transfer, sufficiently powering the implant's Application-Specific Integrated Circuit (ASIC) for stimulation and readout without an implant battery. Secondly, a backscatter antenna in the implant facilitates battery-free, high-data-rate wireless connectivity (up to 32 Mbps). This system not only enhances the BCI experience by eliminating wires but also retains data fidelity and energy efficiency, promising a safer, more efficient interface for tasks like robotic arm control.

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eess.SP 2026-07-03

MIMO beamforming cuts IoT false wake-ups by over 50%

by Israa Khaled, Ammar El Falou +2 more

Integrated Wake-Up Radio and MIMO Solution for Cellular IoT Networks

Specific antenna count focuses wake-up signals, raising reliability and extending battery life in multi-cell networks.

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Wake-up radio (WUR) is a technology designed to enhance the energy efficiency of Internet of Things (IoT) networks and extend device battery life. While most studies focus on WUR performance with single-antenna base stations, this paper investigates the multiple-input multiple-output (MIMO) technology to improve device energy saving and extend the coverage of wake-up signals. By leveraging MIMO beamforming, the transmitted energy can be spatially focused toward the intended IoT devices, with high beamforming gain and minimal inter-device interference. We develop a preliminary analytical framework using stochastic geometry to evaluate the wake-up success probability of WUR-MIMO in multi-cell cellular IoT networks, when the number of antennas equals $2 \times (\text{number of devices}) - 1$. Monte Carlo simulations show that, relative to a single-antenna WUR baseline, MIMO beamforming significantly enhances wake-up reliability when this antenna configuration is applied, mitigates more than 50% of false activations across all settings, and thereby prolongs the lifetime of IoT devices.

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cs.NI 2026-07-03

Gain control makes additive noise fail for CSI simulation

by Aymen Bouferroum (FUN), Ildi Alla (uni.lu) +2 more

CSI Simulation: Why Additive Noise Fails and How to Fix It

M_QTC learns the multiplicative amplitude mapping from measurements and lets classifiers recover 93% of real jamming detection performance.

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Channel State Information (CSI) has become a widely used wireless channel sensing modality for applications such as indoor localization, activity recognition, and respiration monitoring. Because collecting labeled data under every target condition is impractical, training CSI-based models often relies on simulated data produced by adding noise or perturbations to recorded channel estimates, most commonly additive white Gaussian noise (AWGN). This practice assumes that the receiver chain between the antenna and the channel estimator is linear and gain-invariant. We test this assumption empirically using RF jamming as a controlled perturbation on 6 commodity receivers across 2 indoor environments. The assumption does not hold. Automatic gain control compresses the channel estimate multiplicatively before digitization, producing amplitude distributions that no additive noise variance can reproduce. To close the resulting fidelity gap, we propose M_QTC, a measurement-calibrated model that learns the per-subcarrier distribution transformation through quantile mapping, temporal filtering, and copula-based cross-subcarrier reordering. M_QTC reduces amplitude error 8-fold and closes 89% of the aggregate fidelity gap across four complementary dimensions. The improvement transfers directly to downstream tasks, where 5 classifiers from different families trained on M_QTC-simulated data recover 93% of real-data jamming detection performance, while AWGN-trained classifiers remain near random decision.

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eess.SP 2026-07-03

PINN-GNN builds accurate multipath RF maps from sparse points

by Lizhou Liu, Xiaohui Chen +3 more

Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

Electromagnetic constraints plus graph modeling let the method generate or complete maps across scenes better than image or diffusion baseli

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Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization. This paper proposes a unified RF map construction framework based on a physics-informed neural network (PINN) and a graph neural network (GNN), supporting both cross-scene generation and in-scene completion with 2D and 2.5D environmental representations. The PINN embeds electromagnetic propagation constraints to establish a physically consistent mapping from receiver locations to multipath parameters, including path gain, time of arrival, and angles, while the GNN enforces spatial consistency by modeling correlations among neighboring receivers. To comprehensively evaluate multipath reconstruction quality, we propose a peak-weighted dynamic time warping metric that jointly accounts for amplitude errors and peak delay misalignment in channel impulse responses. Extensive experiments demonstrate that the proposed method consistently outperforms image-based, diffusion-based, and interpolation baselines across both map-level and multipath-level metrics, achieving robust generalization and high-fidelity RF map construction under sparse observations.

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cs.CV 2026-07-03

LLM channel prompts cut localization error 40% in driving tests

by Wen Wang, Yaping Sun +6 more

LLM-Empowered Multimodal Fusion Framework for Autonomous Driving: Semantic Enhancement and Channel-Adaptive Design

Channel quality prompts let the model fall back to vision in noise and add radar when clear, shown on nuScenes and VIRAT.

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Vision-radar fusion is central to robust autonomous driving, combining dense visual semantics with precise range and velocity measurements from radar. However, real-world fusion quality is fundamentally challenged by dynamically varying input quality, stemming from occlusion, adverse weather, and channel noise. To address this, we re-frame the problem from static data fusion to channel-aware semantic reasoning and propose a Large Language Model-centric Semantic-layer Channel-aware Integrated Perception (LM-SCIP) framework. It places a Large Language Model (LLM) as a central reasoning core to fuse a local visual stream with a quality-varying external radar stream used to cover perception-blind spots. Concretely, LM-SCIP couples a hierarchical radar-vision encoder with a Channel-Adaptive Semantic Module (CASM) that maps link indicators into a "Channel Prompt" to dynamically gate external radar features. A parameter-efficient, LoRA-tuned LLM, in conjunction with a heterogeneous Mixture-of-Experts (H-MoE), then arbitrates between local visual cues and the channel-conditioned radar context. Finally, a decoupled multi-task decoder outputs localization, trajectory forecasting, and image reconstruction. Experiments on nuScenes and VIRAT validate our approach. On nuScenes, under a controlled toggle of radar input, LM-SCIP reduces localization RMSE by 40.0% versus a vision-only baseline. On VIRAT, the model attains a 0.214m localization RMSE and 0.179m minFDE (k=1). These results reveal that the proposed LM-SCIP enables a robust vision-dominant fallback at low SNR and synergistic fusion at high SNR.

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eess.SP 2026-07-03

Tighter surrogate raises multicell uplink weighted sum rates

by Zihan Jiao, Xinping Yi +2 more

Rethinking Fractional Programming for Joint Uplink Scheduling and Power Control in Multicell Wireless Networks

A reciprocal-inversion transform improves the lower bound on log-rate functions inside fractional programming while keeping closed-form per-

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This paper investigates the joint uplink scheduling and power control problem in a coordinated multicell wireless network, where at most one single-antenna user is allowed to access the single-antenna base station in each cell simultaneously. The resulting weighted sum-rate (WSR) maximization problem is a mixed discrete-continuous, nonconvex optimization problem that is notoriously difficult to solve directly. Classical fractional programming (FP) methods tackle this problem by leveraging the Lagrangian dual transform (LDT) followed by the quadratic transform (QT), yielding a tractable closed-form solution for scheduling and power control, with the LDT playing a crucial role in handling discrete variables. In this paper, we revisit the LDT from a minorization-maximization (MM) perspective and observe that its induced surrogate is somehow conservative due to the reciprocal-coordinate construction. Motivated by this observation, we propose a novel reciprocal-inversion transform (RIT) that constructs a tighter first-order Taylor expansion lower bound for the logarithmic rate function. The proposed RIT remains fully compatible with the QT, leading to a surrogate-enhanced FP (SEFP) algorithm for joint uplink scheduling and power control. The proposed SEFP algorithm retains the desirable per-cell separability of the classical FP framework and admits closed-form updates for the auxiliary variables, scheduling decisions, and transmit powers. Simulation results demonstrate that the SEFP algorithm consistently outperforms the classical FP method and other baselines for different network utilities.

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eess.SP 2026-07-03

Multicarrier optimization boosts underwater acoustic power transfer

by Jinheng Kang, Yizhe Zhao +2 more

Waveform Design for Underwater Simultaneous Acoustic Information and Power Transfer

Including transducer frequency response and rectifier nonlinearity in the design raises energy transfer efficiency in simulations.

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Simultaneous acoustic information and power transfer (SAIPT) plays a crucial role in enabling self-sustainable and maintenance-free Internet of Underwater Things (IoUT) networks. This paper studies a multicarrier underwater SAIPT system that jointly considers the frequency-dependent characteristics of acoustic transducers and the nonlinear behavior of rectifier circuits. The waveform vector is firstly optimized using the successive convex approximation (SCA) method under constraints on average and peak transmit power for acoustic power transfer (APT). Then, in the SAIPT scenario, both the power splitting factor and waveform vectors are jointly optimized through an alternating optimization (AO) framework based on SCA, subject to transmit power and achievable rate constraints. Simulation results demonstrate that incorporating the transducer's frequency response, rectifier nonlinearity, and the high peak-to-average power ratio (PAPR) of multicarrier waveforms leads to a significant improvement in acoustic energy transfer efficiency. The results also show that the energy harvesting DC output can be further enhanced by properly choosing system parameters, such as the number of subcarriers and subcarrier spacing.

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eess.SP 2026-07-02

Pilot-first method cuts CKM error 0.79-1.33 dB from sparse measurements

by Zhonghao Jiu, Fan Meng +4 more

Channel Knowledge Map Reconstruction From Sparse Measurements via Pilot-Anchored Layout-Conditioned Fourier Refinement

Stabilizing supported pilots before layout-conditioned refinement improves accuracy at 5-10% coverage in outdoor scenarios.

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Channel knowledge maps (CKMs) enable environment-aware wireless systems by providing location-specific channel knowledge, but long-term environmental variations, such as construction, traffic redistribution, and foliage changes, require periodic map refresh. In practice, channel measurements are often sparse and irregular, while environmental knowledge may be limited to coarse layout or topology descriptors. This paper studies CKM reconstruction from sparse measurements. We show that reconstruction pipelines that apply local aggregation or spectral operators directly to a zero-filled pilot grid can entangle the sampling mask with the channel field, allowing structural priors to act on mask-induced distortions before the measurements define a supported radio field. To address this issue, we propose Anchor-CKM, a measurement-first, knowledge-aided reconstruction framework. Anchor-CKM first uses support-aware partial convolutions to construct a pilot-supported representation, and then performs layout-conditioned dual-path Fourier refinement followed by coordinate-based heteroscedastic prediction of the CKM mean and per-location predictive variance. Experiments on transmitter-disjoint DeepMIMO scenarios cover missing ratios from 0.3 to 0.95, including stringent 5% to 10% pilot-coverage settings. In explicit-layout outdoor scenarios, Anchor-CKM reduces received-power root-mean-square error (RMSE) by 0.79 to 1.33 dB relative to the strongest reproduced baseline, while ablations identify pilot-support stabilization as the largest contributor and layout conditioning as beneficial for line-of-sight/non-line-of-sight (LOS/NLOS) boundary fidelity.

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eess.SP 2026-07-02

Wrist sensor framework spots eating episodes on new datasets

by Chunzhuo Wang, Emma De Schuyteneer +4 more

Generalizable framework of eating episode detection on free-living wrist-worn wearable data

Achieves F1 scores of 0.59 to 0.79 across varied sensors, hands, and even eating disorder groups.

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Accurate assessment of eating behavior is essential for understanding and managing conditions such as eating disorders, obesity, and diabetes. Wearable-based food intake detection has shown considerable promise; however, most existing approaches are trained and evaluated using internal validation on a single dataset with fixed sensor orientation and known wearing hand, which limits their generalizability to real-world settings. Furthermore, many existing approaches rely on both accelerometer (acc) and gyroscope (gyro) signals to achieve strong performance. However, gyro measurements may be unavailable in some real-world deployments due to battery constraints, and performance often degrades when only acc data are used. We propose a generalizable framework for orientation-invariant eating episode detection, with an acc2gyro module to improve performance in acc-only settings. The framework is trained using fine-grained wrist-worn datasets and externally validated across three heterogeneous datasets: the Clemson All-Day (CAD) and Capture-24 datasets, as well as Physio-ED, a dataset collected from individuals with eating disorders. Across external evaluations, the proposed framework demonstrates robust performance despite substantial variations in sensor modality, wearing hand, participant population, and annotation protocols. Specifically, the framework achieved F1-scores of 0.751, 0.592, and 0.793 on CAD, Capture-24, and Physio-ED, respectively, with CAD performance exceeding recent state-of-the-art methods evaluated using internal validation only. This study provides the first external validation of eating episode detection in an eating disorder population. Additionally, the acc2gyro module improves the performance in acc-only settings. These findings demonstrate the potential of orientation-invariant wearable sensing for scalable and clinically applicable assessment of eating behavior.

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cs.LG 2026-07-02

Hierarchical JEPA hits SOTA on ECG benchmark with low compute

by Siwon Kim

A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data

Pretrained on 180000 recordings, ER-JEPA reaches top ST-MEM scores using minimal resources and rapid computation.

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Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution. Under such circumstances, self-supervised learning (SSL) methods are highly effective for utilizing large datasets, making them a popular choice for electrocardiogram (ECG) analysis. This work presents the Event Reconstruction Joint-Embedding Predictive Architecture (ER-JEPA), a lightweight SSL framework for multivariate time series, whose name and two-fold hierarchical structure are inspired by the diagnostic approach of cardiologists. At its core, ER-JEPA features: (1) a two-stage structure that constructs representations for each time interval and subsequently processes these representations as a univariate time series, (2) the hierarchical integration of two Joint-Embedding Predictive Architectures (JEPAs), and (3) a Vision Transformer (ViT) backbone. The structural concatenation of two JEPAs categorizes the model as a Hierarchical JEPA (H-JEPA), designed to encode multiple levels of abstract representations for enhanced prediction on complex tasks. This study reports a successful application of H-JEPA to 12-lead ECG data as a multivariate time series alongside an analysis of the sensitivity of hierarchical representation during the pretraining stage. Pretrained on approximately 180,000 10-second recordings, the model achieves state-of-the-art downstream performance on the ST-MEM benchmark, with rapid computation and minimal resource usage.

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physics.ins-det 2026-07-02

Multifocal plenoptic system reaches 1 mm 3D resolution in scintillators

by Xiang Dai, Chi-Jui Ho +10 more

Plenoptic imaging of particle interactions in scintillation detectors

Design with varied focal lengths boosts depth sensitivity when photons are scarce, shown in prototype tests with O(100) photons.

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Accurate 3D localization of radiation interactions in scintillation detectors is essential for nuclear and particle physics, safeguards, and medical imaging, but remains difficult in light-starved regimes with limited photon statistics. We present PRISM, a multifocal plenoptic imaging system designed for millimeter-scale 3D position reconstruction in a single-volume scintillator. PRISM uses a multifocal microlens array with diverse focal lengths and high effective numerical aperture to balance photon collection with spatial and depth encoding. A Cram'er--Rao lower bound analysis shows that the multifocal design improves axial sensitivity over conventional unifocal plenoptic systems under photon-limited conditions. We build a prototype system, calibrate its optical response with a tunable light source, and form photon-limited measurements with $\mathcal{O}(100)$ detected photons. For sparse single-vertex events, we reconstruct interaction locations using an Alternating Descent Conditional Gradient-inspired algorithm and demonstrate an average 3D localization error of approximately 1 mm. We also provide an initial evaluation of double-vertex events, showing that localization improves as the axial separation between interactions increases. These results demonstrate that multifocal plenoptic imaging can mitigate the traditional trade-off between light collection and spatial resolution, providing a photon-efficient approach to 3D reconstruction in scintillation detectors and a foundation for future multi-scattering event reconstruction.

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eess.SP 2026-07-02

Sensing-aware reservation cuts PAPR and sidelobes in AFDM ISAC

by Eya Gourar, Abdul Karim Gizzini +3 more

Low-Complexity Sensing-Aware PAPR Reduction for AFDM-based ISAC Systems

Gradient minimization plus randomized local search improves both power efficiency and weak-target detection.

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Integrated sensing and communication (ISAC) has emerged as a key technology for future wireless networks by enabling communication and environmental sensing through a common waveform and hardware platform. Among the candidate waveforms for ISAC, Affine Frequency Division Multiplexing (AFDM) had attracted significant attention due to its robustness in high-mobility environments, but it suffers from a high peak-to-average power ratio (PAPR). In this paper, we propose a sensing-aware chirp-subcarrier reservation (CSR) framework that reduces PAPR while improving ranging performance. The proposed method combines low-complexity gradient-based PAPR minimization with a randomized local search that exploits the phase sensitivity of the AFDM autocorrelation function to suppress delay low-ambiguity-zone (LAZ) sidelobes. Numerical results show that the proposed scheme achieves significant PAPR reduction together with significant sidelobe suppression, resulting in improved weak-target detection performance.

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eess.IV 2026-07-02

Image tilt observations reduce UAV prediction error by 60 percent

by Minxing Sun, Yao Mao

Image-Domain Tilt Constrained Distributed Fusion for Maneuvering UAV Tracking with Multi-Camera Electro-Optical Observations

Apparent roll and pitch from rotorcraft images act as acceleration constraints in asynchronous multi-camera fusion

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Short-horizon prediction is essential for electro-optical UAV tracking, especially when the target is small, maneuvering, or intermittently observed. Image center, line-of-sight, and range measurements provide direct constraints on target position, but their constraints on acceleration are weak. As a result, prediction can lag during aggressive maneuvers. This paper proposes an image-domain tilt constrained distributed fusion method for maneuvering UAV tracking. The method uses the apparent roll and pitch of a rotorcraft target in the image as low-level maneuver cues. A weak-prior auto-labeling pipeline first generates oriented bounding box and image-domain tilt labels from synchronized video, gimbal IMU, and UAV IMU data. A YOLO-OBB detector is then trained to provide online target position and tilt measurements. The front-end Python implementation is publicly available at github.com/ShineMinxing/PythonYOLO. In the fusion stage, the UAV state is modeled by position, velocity, and acceleration. Image-domain roll and pitch are introduced as acceleration-related pseudo-observations. For distributed tracking, one mobile gimbal camera and two fixed ground cameras are fused asynchronously. Camera attitude error states are augmented into the filter to absorb extrinsic drift and cross-camera systematic inconsistency. A Mahalanobis gate with time-since-last-valid covariance widening is used to reject false detections and handle dropouts. In simulation, adding roll/pitch observations reduces the prediction RMSE from 1.991 m to 0.821 m and decreases the cumulative prediction error by 60.75\%. In real distributed experiments, a self-consistency evaluation shows an 18.10\% reduction in cumulative prediction error. The results show that image-domain tilt can provide useful acceleration constraints for robust short-horizon UAV prediction.

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eess.SP 2026-07-02

Joint optimization raises full-duplex fluid antenna rates

by Jingxuan Zhou, Yinchao Yang +3 more

Alternating Optimization for Joint Resource Allocation in Full-Duplex Multi-Sector Fluid Antenna-Enabled Near-Field Systems

Multi-sector system with antenna mobility and grouping outperforms half-duplex and fixed-position baselines in sum rate and efficiency.

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This paper proposes a full duplex fluid antenna near field system (FD-FANS) with a multi-sector antenna array that jointly exploits resource allocation, antenna mobility, and group-based transmitting (TX) and receiving (RX) partitioning. A spherical wave uplink downlink channel is established that accounts for residual self interference (SI), wireless energy transfer (WET), and geometric constraints on antenna motion. Within the FD-FANS framework, an efficient protocol is devised to enable simultaneous downlink energy transmission (DET) and uplink data transmission (UDT) at the base station (BS). Furthermore, we formulate, for both perfect and imperfect SI cancellation (SIC), a weighted sum rate (WSR) maximization problem over time power allocation, antenna positions, and binary group selection, under practical average and peak power limits, per antenna box constraints, minimum spacing, and a half TX half RX balance. To tackle the resulting non convex mixed integer design, we develop an efficient alternating optimization (AO) framework based on majorization minimization successive convex approximation (MM SCA). The proposed algorithm monotonically improves the objective and converges to a stationary solution of a continuous relaxation. Simulation results demonstrate that the proposed scheme achieves consistent performance gains over several benchmark designs, including half duplex FANS (HD FANS), FD fixed position antenna near field system (FD FPANS), non-grouped FD FANS, and far field counterparts, in terms of average sum rate (ASR), energy efficiency (EE), and user fairness, while exhibiting robustness to residual SI and channel uncertainty.

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eess.SP 2026-07-02

LEO satellites achieve under 10m positioning via standard signals

by Rainer Bachl, Muhammad Nabeel +1 more

Opportunistic Positioning with LEO Satellites based on SSB from NR NTN

NR NTN SSB provides pseudoranges whose ambiguities resolve geometrically for mean error below 10 meters in Starlink simulations.

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Forthcoming Low Earth Orbit (LEO) satellite networks such as Starlink's Mobile Satellite Service (MSS) will incorporate the New Radio (NR) Non-Terrestrial Network (NTN) standard. The Synchronization Signal Block (SSB) specified as part of NR is periodically broadcast for cell search and initial access. We propose to exploit the SSB for opportunistic receiver positioning. Doppler shift measurements are modeled and pseudoranges are derived from SSB while also taking into account the receiver's clock bias and drift. The resulting per satellite integer ambiguity in the pseudorange is resolved by geometry alone, without inter-satellite differencing or an a-priori position. Measurements are taken from SSBs of multiple satellites and at multiple occasions per satellite, whereby the SSBs are subject to different transmission timings and varying propagation delays. Finally, a simulation model is developed for positioning based on the actual Starlink constellation and the NR NTN standard to evaluate the positioning accuracy to be expected. The proposed approach achieves a mean positioning error of less than 10m without requiring any modification of the NR NTN standard.

0

eess.SP 2026-07-02

MiLAC compression cuts estimation complexity 1540 times

by Qiaosen Zhang, Matteo Nerini +1 more

Channel Estimation and Beamforming for Microwave Linear Analog Computers (MiLACs)-Aided Multiuser MISO Systems

Rank-deficient correlations let limited-RF-chain multiuser beamforming match digital performance

abstract click to expand

Microwave linear analog computers (MiLACs) have recently gained attention for future gigantic multiple-input multiple-output (MIMO) systems by enabling beamforming with greatly reduced hardware and computational cost. However, channel estimation for MiLAC-aided multiuser systems remains an open problem. Conventional channel estimation requires many radio-frequency (RF) chains to access full-dimensional received signals, followed by massive digital processing, which undermines the advantages of MiLAC-aided systems in reducing the number of RF chains and computational complexity. In this paper, we propose computationally efficient channel estimation and beamforming schemes for MiLAC-aided multiuser multiple-input single-output (MU-MISO) systems with a limited number of RF chains. We consider the general case where different user groups experience different channel correlation matrices. By exploiting the rank deficiency of these matrices, the proposed schemes use MiLAC to compress the full-dimensional received signals in the analog domain, making them compatible with the available RF chains while preserving the essential channel information. Then, in the digital domain, only low-dimensional channel estimation is performed based on these compressed observations, substantially reducing computational cost. We further show how regularized zero-forcing beamforming (R-ZFBF) can be efficiently realized from the low-dimensional channel estimates through a cascade of two MiLACs, which offers greater computational flexibility than a single MiLAC. Numerical results show that the proposed schemes reduce computational complexity up to $1540\times$ and $16108\times$, for channel estimation and beamforming, respectively, while achieving performance comparable to digital baselines.

0

eess.SP 2026-07-02

Lightweight vision model tracks mmWave beams across environments at 84% accuracy

by Mengyuan Ma, Ahmed Alkhateeb +3 more

Lightweight Vision-Aided Beam Tracking for Cross-Environment mmWave Communications

Cuts parameters by 52x and complexity by 79x versus ResNet while generalizing on real data from two distinct scenarios

abstract click to expand

Sensing-aided beam tracking is a promising approach to reduce the overhead for millimeter-wave beam management. However, real-world application remains challenging due to rapid channel variations and substantial environmental differences across deployment scenarios. Developing low-complexity sensing assisted approaches that generalize to diverse environments can alleviate the problem. With this motivation, this paper proposes a lightweight vision-aided model for cross-environment beam tracking. The task is formulated as a sequence-to-sequence classification problem, where the model jointly predicts the current and future optimal beams from past visual observations. We develop a low-complexity model based on depthwise separable convolutions and introduce hierarchical data augmentation and beam power-based label smoothing to improve robustness and generalization. Experimental results on real-world images from two geometrically distinct DeepSense 6G scenarios show that the proposed strategies consistently improve cross-environment beam prediction accuracy up to 84% across the current and three future time slots, outperforming the state-of-the-art solution. Notably, this performance is achieved while reducing the number of model parameters and computational complexity by factors of approximately 52 and 79, respectively, compared with the high-capacity ResNet baseline.

0

eess.AS 2026-07-02

CNNs turn 4-mic covariance into 32-mic acoustic images

by Marianthi Adamopoulou, Parthasaarathy Sudarsanam +6 more

CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging

Models trained on real recordings achieve lower error than random guessing and produce sound maps nearly identical to those from a full 32-c

abstract click to expand

Acoustic imaging visualization is a core methodology in acoustics, enabling spatial analysis of sound sources and acoustic scenes. However, limited sensor availability in practical systems motivate approaches that enhance spatial resolution without increasing the hardware complexity. In this paper, we focus on upsampling virtually a tetrahedral 4-microphone array to a spherical 32-microphone array by estimating the covariance matrices of the channels employing deep learning techniques. Five neural network architectures are investigated for covariance upsampling for acoustic imaging using the real-world STARSS23 dataset. These models are developed to estimate a 32-microphone, time-frequency covariance matrix from a 4-microphone input covariance representation. The proposed architectures are based on 2D convolutional layers to capture the underlying spatial-spectral structure of covariance matrices, and are further enhanced with frequency dynamic convolution to model their frequency-dependent properties. The proposed architectures are evaluated in terms of root mean square error (RMSE) and using delay-and-sum beamforming acoustic imaging. Quantitative results show that all models outperform a random-guess baseline, which yields an RMSE of 0.548, with the best-performing architecture achieving an RMSE of 0.432. We analyze qualitatively the performance of the proposed models through beamforming heatmap visualizations derived from the 4-channel input covariance, the 32-channel ground truth, and the predicted 32-channel covariance matrices. These results demonstrate that covariance upsampling significantly enhances the effective performance of the 4-channel microphone array, producing sound maps that closely resemble those obtained with the 32-channel array.

0

cs.IT 2026-07-02

Outage probabilities derived for ISAC over Rician channels

by Marziyeh Soltani, Mahtab Mirmohseni +2 more

Fundamental Limits of Random Downlink Integrated Sensing and Communication over Rician Channels

SJB and LB schemes yield expressions and scaling laws showing K-factor impacts communication more than sensing.

abstract click to expand

This paper studies the stochastic performance of a downlink multiple-input multiple-output integrated sensing and communication (ISAC) system over Rician fading channels. Rician fading is important in line-of-sight (LoS)-dominated deployments, where a deterministic propagation component can strongly affect sensing and communication reliability. The base station (BS) simultaneously serves a user and senses a target. The BS-user channel contains LoS and non-line-of-sight components. The user LoS angle may be fixed or random, and the target angle may follow an arbitrary distribution potentially correlated with the user angle. Compared with Rayleigh fading, the deterministic LoS component introduces angle-dependent terms and leads to generally independent but non-identically distributed random vectors, requiring new analysis. We analyze two beamforming strategies: subspace joint beamforming (SJB), optimal for the shared waveform structure, and linear beamforming (LB), a practical alternative using separate sensing and communication beamformers. For both schemes, we derive communication outage probability (OP) and sensing OP based on the Cramer--Rao bound (CRB). We also identify special cases with simpler expressions. For LB, we derive upper and lower bounds on sensing OP and a tractable approximation. We characterize large-system and high-power scaling laws. LB without dirty paper coding (DPC) is interference-limited at high power due to radar self-interference. Results show the Rician K-factor affects communication more strongly than sensing, with non-monotonic behavior across regimes. LB with DPC achieves the best overall performance in strong LoS environments and is the only scheme achieving ultra-high communication reliability in Rayleigh fading, while SJB provides a robust lower-complexity alternative across operating conditions.

0

eess.SP 2026-07-02

Meta-transfer cuts mmWave beam parameters by 17x

by Ahmet Nuri Cevik, Sinem Coleri

Meta-Transfer Learning for mmWave Beam Alignment

Frozen backbone plus meta-learned adapters match full fine-tuning accuracy in new environments while using far fewer parameters and epochs.

abstract click to expand

Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam prediction models to rapidly adapt to unseen environments; however, existing meta-learning approaches adapt the entire network and are trained from random initialization, leading to a large number of updated parameters and a high meta-training cost, while transfer learning approaches restrict adaptation to part of the network but do not exploit episodic meta-learning, which explicitly trains the model over multiple tasks, to optimize the adaptation process itself. To overcome these limitations, we propose MTL-BA, a meta-transfer learning framework for beam alignment in millimeter-wave multiple-input single-output (MISO) systems that freezes a pre-trained convolutional backbone and meta-learns only lightweight Scale-and-Shift (SS) adapters together with a classifier head. Warm-starting from the pre-trained model and restricting adaptation to the SS adapters and classifier head reduce both the adaptation cost and the meta-training budget without sacrificing prediction performance. Simulation results on the DeepMIMO ray-tracing dataset show that MTL-BA matches the accuracy and spectral efficiency of full fine-tuning across various SNR levels despite updating approximately $17\times$ fewer parameters than both full fine-tuning and Model-Agnostic Meta-Learning (MAML), outperforms last-layer fine-tuning while updating a comparable number of parameters, and approaches MAML's performance while requiring $60\%$ fewer meta-training epochs.

0

cs.LG 2026-07-02

Brier score and MCC best track spelling rate in ERP BCIs

by Okba Bekhelifi, Naoual El Djouher Mebtouche

Which Metric Reflects the Spelling Rate Accuracy in Event-Related Potential-Based Brain-Computer Interfaces?

Two datasets show these metrics handle class imbalance better than accuracy when measuring actual characters spelled per minute.

abstract click to expand

For predictive models, the often-reported performance metrics are the loss and accuracy. In synchronous Brain- Computer Interface (BCI) systems, these metrics are informative for most BCI paradigms; however, for Event-Related Potential (ERP) applications the spelling rate, which measures the number of characters correctly selected is more important as it influences the estimation of information transfer rate (ITR) and any related metric measuring spelling performance. Moreover, ERP-based BCIs hold imbalanced data class distributions, which require reporting metrics that can handle the imbalance, such as the area under the receiver operating characteristic curve (ROC AUC). In this work, we study the correlation of the spelling rate with 13 metrics to identify which among them best reflect user spelling performance and how they are affected by trial repetition. The Results of two datasets (a private LARESI ERP dataset and the public OpenBMI ERP dataset) favor the Brier score, Matthews Correlation Coefficient (MCC), and the metrics that account for class imbalance in binary classification: ROC AUC, area under the Precision-Recall curve (PR AUC), Average Precision (AP), and partial AUC (pAUC). These findings encourage researchers and practitioners to report those metrics in ERP-based BCI experiments.

0

eess.SP 2026-07-02

Pacemaker RF signals track heart chamber volumes and valves

by A. Khaleghi, J. Bergsland +1 more

Assessing Cardiac Dynamics through RF Sensing for Hemodynamic Monitoring in Pacemakers

Temporal variations between RV-RA leads and subcutaneous receivers align with cardiac rhythm for real-time hemodynamic estimates.

abstract click to expand

This paper examines the use of radiofrequency (RF) channels for hemodynamic monitoring in cardiac pacemakers. It analyzes RF signal variations between intracardiac transceivers in the right ventricle (RV) and right atrium (RA), as well as subcutaneous receivers, to determine their correlation with cardiac dynamics. The study shows that temporal RF signal variations closely align with cardiac rhythm, allowing for the estimation of parameters such as chamber volume, valve behavior, and pressure changes. These results underscore the potential of RF-based sensing as a novel method for real-time cardiac monitoring in pacemaker systems.

0

eess.SP 2026-07-02

AFDM-ISAC frame uses one chirp subcarrier per ISAC symbol

by Qu Luo, Zilong Liu +4 more

Frame-Based AFDM-ISAC Waveform Design With Chirp-Enabled Pulse Compression

Analog down-mixing captures compression gains and GCE-BEM Kalman filtering tracks high-mobility channels without full-duplex hardware.

abstract click to expand

This paper proposes an Affine frequency division multiplexing (AFDM)-empowered integrated sensing and communications (ISAC) design, referred to as AFDM-ISAC. We first design a novel AFDM-ISAC frame structure that consists of both ISAC and pure data symbols. Each ISAC symbol consists of a single chirp subcarrier for both sensing and channel estimation, while the remaining subcarriers are allocated for communication. Building upon this structure, we present an analog-domain sensing receiver that down-mixes the received echo with a local chirp to fully exploit \textit{chirp compression} gains avoiding the need for full-duplex hardware. In addition, a sensing fusion algorithm, guided by AFDM modulation parameters, is further proposed in the digital domain. Leveraging the distinct features of the proposed AFDM-ISAC frame, we present a low-complexity channel estimation scheme for high mobility channels based on a generalized complex exponential basis expansion model (GCE-BEM), along with an optimal power allocation strategy between pilot and data symbols. Moreover, to support frame-based AFDM communications, a GCE-BEM-based Kalman filter is also employed for robust intra-frame channel estimation.

0

cs.NI 2026-07-02

Bayesian optimization delivers 72.8% farm coverage with 3 base stations

by Gourav Prateek Sharma, Durgesh Singh +1 more

Robust Base Station Placement in Agricultural IoT via Bayesian Optimization

Uses under 50 ray-tracing simulations to beat other methods by 4.6 points across crop seasons.

abstract click to expand

Precision-agriculture networks based on private 5G NR should ensure reliable connectivity for IoT sensor nodes throughout the crop growing season, yet the propagation environment changes dramatically as vegetation grows and matures. We formulate $K$-base-station~(BS) placement as a \textit{maximin seasonal coverage} problem that maximizes the worst-case coverage fraction across all crop growth stages. Since each objective evaluation requires expensive ray-tracing simulations across all stages, we adopt a Gaussian-process Bayesian optimization~(GPBO) framework that builds a probabilistic surrogate of the robust objective using ray tracing. On a $1\,\text{km}^2$ multi-crop farm with three distinct crop zones at $3.5\,\text{GHz}$, the proposed scheme achieves $72.8\%$ worst-case coverage with $K{=}3$ BSs in fewer than fifty ray-tracing evaluations, outperforming budget-matched state-of-the-art approaches by at least $4.6\,\text{pp}$ across all four seasonal stages.

0

eess.SP 2026-07-02

Close-in model fits urban A2G path loss better than 3GPP

by Bin Ao, Jianhua Zhang +4 more

Measurement-Based Characterization and Statistical Modeling of 6G Urban Low-Altitude A2G Channels across FR1 and FR3

NLoS raises path-loss exponents; LoS delay spread falls from 93 ns to 47 ns as frequency rises from 2.85 to 7.25 GHz

abstract click to expand

Unmanned aerial vehicle (UAV) communications have been recognized as a key component of future sixth-generation (6G) space-air-ground-sea integrated networks. Accurate characterization and modeling of air-to-ground (A2G) channels are essential for the design and optimization of low-altitude communication systems. This paper presents a wideband A2G channel measurement campaign in an urban environment at 2.85 and 4.6~GHz in FR1 and 7.25~GHz in the FR3 frequency band, each with a bandwidth of 250~MHz. To enable reliable line-of-sight (LoS) and non-line-of-sight (NLoS) propagation state identification, a weakly supervised method is developed by fusing geometric priors, channel features, and spatial consistency constraints. Furthermore, based on the measured data, A2G channel characteristics are extracted and analyzed under LoS/NLoS conditions across different frequency bands, including path loss (PL), shadow fading (SF), power delay profile, root-mean-square delay spread (RMS-DS), and Rician $K$-factor. The results show that the close-in model fits the measured PL more accurately than the 3GPP reference model, and that NLoS propagation leads to larger path loss exponents and stronger SF than LoS propagation. For channel delay characteristics, higher-frequency channels exhibit fewer effective MPCs and weaker delay dispersion, indicating increased channel sparsity. Specifically, the mean RMS-DS under LoS conditions decreases from 93.11 to 46.84~ns, while the mean Rician $K$-factor increases from 9.16 to 12.88~dB. The statistical results further show that the RMS-DS and the Rician $K$-factor can be well characterized by lognormal and normal distributions, respectively. Moreover, the movement of the receiver in a complex scattering environment intensifies the spatial non-stationarity of the A2G channel.

0

eess.SP 2026-07-02

B2X networks close wireless loops for embodied agents

by Yuanwei Liu, Xu Gan +6 more

B2X Networks: Joint Design of Communication and Control for Embodied Intelligence

Redesigning uplink for urgent states and downlink for shared commands enables Pareto analysis of transmission-control trade-offs.

abstract click to expand

This article proposes the concept of \emph{brain-body-to-everything (B2X)} networks to facilitate the integration of wireless networks and embodied intelligence. In this framework, the \emph{brain} refers to the intelligence functions for reasoning, planning, and decision-making, the \emph{body} denotes the physical embodied agent that senses and acts in the real world, and \emph{X} represents the surrounding ecosystem involved in the brain-body interaction loop. Two B2X architectures with \emph{distributed} and \emph{centralized} brains are introduced to characterize different placements of intelligence across the body, base station, and core network. The uplink and downlink designs of B2X networks are then discussed under a representative base-station-side brain setting. For the uplink, communication is redesigned for B2X state acquisition under event urgency, sensing volume, and simultaneous multi-body access. For the downlink, communication is redesigned to coordinate command delivery and conventional service under shared radio resources. Based on these uplink and downlink considerations, a communication-control Pareto boundary is further used to characterize the loop-level trade-off between wireless transmission performance and control quality in B2X networks. Finally, several open research problems are discussed to guide future B2X network design.

0

cs.IT 2026-07-02

Bounds close at high SNR for MIMO phase-modulated channels

by Hengyu Cui, Ru-Han Chen +4 more

Performance Evaluation of A Certain Transceiver Architecture for Multiple-Input Multiple-Output Phase-Modulated Channels

Row-echelon transform yields scalar annulus sub-channels whose capacity is bracketed tightly by geometry and entropy-power arguments.

abstract click to expand

For multiple-input multiple-output (MIMO) channels with phase modulation, we recently proposed a method of unitarily transforming the channel matrix into a certain row-echelon form, by which the original MIMO channel can be converted into a certain number of scalar sub-channels with two phase inputs, thereby forming an annulus constellation geometry, and corrupted by both the additive white Gaussian noise and weak self-interference. In this paper, several bounds are derived to evaluate the fundamental limit of such a specific transceiver architecture. Two upper bounds are obtained by upper-bounding the capacity of a scalar channel with an annulus support constraint from the perspective of the convex geometry, while a lower bound is obtained by the standard entropy power inequality. Numerical results show that the gaps between these bounds are small at high signal-to-noise ratios for the MIMO phase-modulated channels over the Rayleigh fading and the single-input multiple-output symbiotic communication system assisted by a reconfigurable intelligent surface.

0

eess.SP 2026-07-02

Semantic metric unifies four stages of embodied agent networks

by Yaheng Wang, Rui Meng +8 more

Semantic-based Internet of Embodied Intelligence: Visions and Frontiers

Meaning-based exchanges replace bulky data transfers and tie reasoning to physical limits in multi-agent setups.

abstract click to expand

Recent advances in generative artificial intelligence (AI) and embodied intelligence (EI) enable autonomous agents to interact with the physical world. However, scaling these systems into networks of multiple agents, namely the Internet of EI (IoEI), faces critical bottlenecks. These include the overhead of massive multimodal data transmission and the decoupling of logical reasoning from physical constraints. To address these challenges, we envision the Semantic-based IoEI (SIoEI), which leverages semantic information as a unified metric throughout the agent lifecycle. We systematically define four key dimensions of EI: perception, intelligence, control, and communication. We further elaborate how semantic empowerment revolutionizes environmental perception, cognition and task planning, action generation and robust control, and communication and networking. We also present a case study to verify that, the semantic-empowered end-to-end process significantly improves channel robustness and reduces end-to-end latency for EI. Finally, we outline critical open research directions for the SIoEI paradigm.

0

eess.SP 2026-07-02

Intellicise networks turn complex systems into intent-driven intelligent ones

by Ping Zhang, Rui Meng +24 more

Evolving Intelligent Complex Systems via Intellicise Networks: Architecture, Technologies, and Pathways

Architecture with cross-layer evolution and six planes derives simplicity from high-level intelligence via closed-loop information flows.

abstract click to expand

Future engineering infrastructures are evolving into large-scale, open, heterogeneous, and wirelessly interconnected complex systems. These systems present significant challenges in optimizing network resource utilization, managing high-dimensional information spaces, and accommodating diverse business requirements. Intellicise networks, characterized by Intent-driven operation, semantic-native capability, and distributed intelligence, offer a promising paradigm for enabling such intelligent complex systems. We provide a systematic exploration of future intelligent complex systems from the perspective of intellicise networks. Specifically, we propose a cross-domain intelligent communication network architecture based on intellicise networks, grounded in information theory, systems theory, game theory, and cybernetics. The architecture comprises a cross-layer organizational framework, multi-functional planes, and novel information flows. The cross-layer framework defines the vertical evolution from perception and cognition to decision, while the control, user, data, computation, intelligence, and security planes deliver horizontal intellicise capabilities. Moreover, data, knowledge, model, and task flows interconnect the various layers and planes, forming a closed-loop process that derives simplicity from high-level intelligene while concurrently pursuing enhanced. Building on this architecture, we review key enabling technologies, tracing their evolution from semantic extraction to intent understanding, from heterogeneous resource integration to self-configuration and self-optimization, from generative artificial intelligence (AI) to agentic AI, and from embodied AI to symbodied AI. Additionally, we present a case study on intellicise networks for embodied agent communications and discuss representative applications and services for intelligent complex systems.

0

eess.IV 2026-07-02

Polarimetric time-series SMI retrieves mine soil moisture at R²=0.67

by Oleg Antropov, Alireza Hamedianfar +6 more

Polarimetric SAR Model Fitting for Soil Moisture Retrieval: Study of PALSAR-2 data over a Heterogeneous Mine Environment in Finland

Sediment-calibrated generalization to [T3] matrix beats single-pol versions and matches ML benchmarks in quarry setting

abstract click to expand

This paper examines several model based approaches for retrieving surface soil moisture from ALOS-2 PALSAR-2 quad-pol imagery, over a lime stone quarry in southeastern Finland. The study primarily targets physically interpretable semi-empirical modeling approaches, with generic ML modeling used as a benchmark. Along with common polarimetric observables, we propose a generalization of the SAR time series based TU Wien soil moisture index (SMI) retrievals examined across several representational spaces derived from polarimetric coherency matrix $[T3]$. This study was conducted over a closed tailing storage facility and a landfill, with a set of 9 repeat pass PALSAR-2 images. The best semi-empirical configuration combining temporal context SMI and current observation PolSAR parameters achieved $R^2=0.67$ and RMSE $=5.65$ volumetric \% units. The strongest $SMI_{[T3]}$ approach with sediment-specific calibration, achieved $R^2=0.66$ and RMSE $=5.67$ vol. \%, which was considerably better than using $SMI_{HH}$ or $SMI_{VV}$. The proposed approach was sensitive to representations: dB-based projection outperformed linear or trace-normalized $[T3]$ representation. Factoring in sediment information dramatically improved retrieval performance compared to using global model fitting. Machine learning results closely approached but not outperformed semi-empirical model based methodologies. Similarly, they highlighted the need for sediment-specific modeling as well as the importance of including time-series/temporal backscatter dynamics during SSM retrieval. Our study demonstrated the utility of physics based SSM retrieval approaches in the complex multi-sediment mine environment under relatively scarce reference data conditions.

0

eess.SP 2026-07-01

ADC-aware training keeps 87% accuracy with one-bit ADCs in DMA sensing

by Philipp del Hougne

ADC-Aware End-to-End Optimization of a Dynamic Metasurface Antenna with Strong Mutual Coupling for Monostatic Scene Classification

Ignoring quantization and mutual coupling drops performance from 95% to random guessing; modeling both restores usable classification with e

abstract click to expand

Dynamic metasurface antennas (DMAs) enable programmable wave-domain signal processing that can be jointly optimized with downstream digital processing in an end-to-end manner. Existing studies, however, typically assume ideal analog-to-digital conversion (ADC) and often rely on simplified electromagnetic models. Here, we study ADC-aware end-to-end optimization of a monostatic sensing pipeline based on a DMA with strong mutual coupling (MC). We model the wave domain using an MC-aware multiport-network model whose parameters were experimentally estimated for a fabricated chaotic-cavity-backed DMA with 96 one-bit-programmable meta-elements. We perform ADC-aware end-to-end optimization of the DMA configurations and digital classifier, either with awareness of a fixed uniform ADC or, optionally, with jointly learned ADC decision thresholds, and compare against baselines that assume an ideal ADC and/or ignore MC. Our results show that ADC awareness is essential in low-resolution ADC regimes: with one-bit ADCs and eight DMA configurations, deploying an ideal-ADC-trained system with a uniform one-bit ADC reduces the test accuracy from 95.5% to 56.0%, whereas ADC-aware training with the same fixed uniform one-bit ADC achieves 87.2%. We also show that without MC awareness the accuracy drops to the random-guess level. Learning non-uniform ADC thresholds provides at most modest additional gains over fixed uniform ADCs in the considered DMA-based sensing pipeline.

0

cs.LG 2026-07-01

Mixture models conditioned on activity and metadata improve biosignal layout transfer

by Geeling Chau, Ran Liu +6 more

Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts

Device Passport outperforms learned baselines when pretrained and target sensor arrangements differ, tested on subset and ear-EEG transfers.

abstract click to expand

New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they are able to generalize to new layouts effectively. To improve cross-layout transfer, we study how different channel embedding techniques behave when pretraining layouts differ substantially from the downstream decoding layout. We propose Device Passport, a new channel embedding technique that learns experts and mixture models that take each channel's functional activity and metadata as input. This contrasts with prior embedding methods, which typically use only functional information or only metadata to look up learned or fixed positional embeddings. Across controlled subset-transfer experiments and realistic transfer to ear-EEG, Device Passport is competitive overall and improves over the strongest learned baseline in the layout-transfer regimes that motivate this work. These results suggest that channel embedding design is a key consideration when reusing large-scale pretrained biosignal models on new devices.

0

eess.SP 2026-07-01

Ziv-Zakai bound tightens sensing limits for pinching antennas

by Hao Jiang, Chongjun Ouyang +3 more

Pinching Antennas-Assisted Sensing: A Ziv-Zakai Bound (ZZB) Perspective

It accounts for likelihood ambiguity to give a reliable error bound across signal strengths where other bounds loosen.

abstract click to expand

The sensing capability of the pinching-antenna system (PASS) is analyzed from a Ziv-Zakai bound (ZZB) perspective, motivated by the sensing ambiguity arising from the multimodal observation model inherent to PASS. In comparison to other Bayesian sensing bounds, the ZZB provides a lower bound on the mean-squared error (MSE) across a broad range of signal-to-noise ratios (SNRs) and accounts for ambiguity in the likelihood functions. First, an observation model is developed for an uplink sensing scenario where a single sensing target transmits uplink pilots to a single-waveguide PASS receiver equipped with multiple pinching antennas (PAs). Building on this model, general ZZB expressions are derived for arbitrary prior distributions of the target's position, and are then specialized to the Gaussian and uniform cases. Second, the asymptotic ZZBs in low- and high-SNR regimes are characterized, and the relationship between the ZZBs and the conventional Bayesian Cram\'er-Rao bound (BCRB) is further studied by introducing the concept of an ambiguity function. Furthermore, to reduce the high computational complexity of direct evaluation of the ZZB, SNR-free and SNR-aware surrogate objective functions are proposed to facilitate ZZB-based optimization for enhancing sensing performance. Numerical results demonstrate that: i) Compared with the BCRB, the ZZB provides a tight sensing performance lower bound over a wide range of SNRs, ii) the ambiguity-awareness of the ZZB can address the multimodality-induced ambiguity in sensing, thereby yielding a reliable lower bound on the MSE, and iii) the proposed surrogate objective functions enable effective ZZB minimization with a lower computational complexity.

0

cs.IT 2026-07-01

DFDD lowers error floors in RIS differential detection

by Jiawei Qiu, Harry Leib

Decision Feedback Differential Detection for Reconfigurable Intelligent Surfaces

The scheme keeps error rates falling at high SNR where standard differential receivers plateau in varying channels

abstract click to expand

This work considers a Differential Reflecting Modulation (DRM) scheme for Reconfigurable Intelligent Surfaces (RIS) not requiring channel state information (CSI). When operating over time-varying fading channels, such schemes with Conventional Differential Demodulation (CDD) receivers experience high error floors and performance degradation. To address these issues, we propose a Decision Feedback Differential Detection (DFDD) technique for DRM. We explore the application of DFDD for RIS DRM and conduct extensive Monte-Carlo simulations to analyze performance. Results demonstrate the viability of our DFDD technique across various RIS scenarios and highlight the importance of proper parameter selection to achieve good performance. The DFDD scheme is also compared with uncoded and Differential Space-Time Modulation (DSTM) coded DRM using CDD based receivers. We observe that at low SNR, the DFDD scheme performs almost as well as the DRM with CDD scheme, but worse than the DSTM coded DRM. As the SNR increases however, both CDD-detected systems encounter high error floors while the error rate of DFDD based scheme continues to improve until it reaches a relatively low error floor. It is shown that the chief merits of employing DFDD receivers in such RIS systems is the low error floors they provide over time varying fading channels, albeit at expense of a small increased complexity.

0

cond-mat.mes-hall 2026-07-01

Straintronic MTJ amplifies AC voltage with tunable gain

by Cael Johnson, Rahnuma Rahman +1 more

An analog ac voltage amplifier based on a single straintronic magnetic tunnel junction

Biasing inside the linear conductance region lets an external supply set the gain instead of fixed internal parameters.

abstract click to expand

Magnetic tunnel junctions (MTJs) are known for their digital applications (memory and logic). A special class of them called "straintronic" magnetic tunnel junctions (s-MTJ) has lately emerged as a potential platform for analog applications because their conductance can be varied continuously with mechanical strain generated with a gate voltage. The conductance versus gate voltage (transfer) characteristic always has a linear region and that can be leveraged for a variety of analog applications. Here, we discuss one such application, namely analog voltage amplification. If the s-MTJ's gate voltage is fixed around the midpoint of the linear region and a small ac voltage is superimposed on it, then the ac voltage can be amplified without distortion as long as its amplitude is small enough to avoid gate voltage excursion beyond the linear region. Unlike a transistor-based voltage amplifier whose amplification is determined solely by the transistor's internal parameters - namely the transconductance and Early resistance - here the amplification can be varied by an external power supply voltage. We examine the maximum allowed amplitude and frequency of input signal for distortion-free amplification by modeling the magnetization dynamics and derive an expression for the amplification.

0

cs.IT 2026-07-01

Dual-regime chain yields optimal AoII push thresholds

by Ismail Cosandal, Sennur Ulukus +1 more

Dual-Regime Absorbing Markov Chain Theory in Remote Estimation: Age-Minimizing Push Policies

A new absorbing Markov construction gives exact SMDP parameters for multi-threshold policies that minimize weighted AoII cost plus energy un

abstract click to expand

For a remote estimation system, we study the optimization of age of incorrect information (AoII), which is a recently proposed semantic-aware information freshness metric. In particular, we assume an information source that observes a discrete-time finite-state Markov chain (DTMC), and occasionally transmits status update packets to a remote monitor which is tasked with remote estimation of the source. For the forward channel from the source to the monitor, we assume the channel delay to be modeled by a general discrete-time phase-type (DPH) distribution, whereas the reverse channel from the monitor to the source is assumed to be perfect, ensuring that the source has perfect information on the AoII and the remote estimate at the monitor, at all times. Push-based transmissions are initiated when AoII exceeds a threshold depending on the current estimation value, i.e., multi-threshold policy. In this very general setting, our goal is to minimize a weighted sum of the time average of a polynomial function of AoII, depending on the remote estimate, and energy consumption from transmissions. We formulate the problem as a semi-Markov decision process (SMDP) with the same state-space of the original DTMC to obtain the optimal multi-threshold policy, whereas the parameters of the SMDP are obtained by using a novel stochastic tool called dual-regime absorbing Markov chain (DR-AMC), and its corresponding absorption time distribution named as dual-regime DPH (DR-DPH). The proposed method is validated with numerical examples using comparisons against other policies obtained by exhaustive search, and also various benchmark policies.

0

cs.NI 2026-07-01

Relay extracts semantic meaning from latent codes without source data

by Yalin E. Sagduyu, Tugba Erpek +2 more

Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications

This exposes a privacy flaw in semantic comms; adversarial training widens the accuracy gap at the relay while preserving receiver performan

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Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw data, enabling efficient and robust communication under resource and channel constraints. In this paper, the privacy implications of relay-assisted SemCom systems are studied, where the intermediate relay node operates directly on learned latent representations. It is shown that the relay, even without access to source data, can reliably infer semantic meaning and reconstruct signals with performance comparable to that of the legitimate receiver, revealing a fundamental privacy vulnerability of semantic representations. To address this issue, an iterative adversarial training framework is proposed in which a strong, adaptively trained eavesdropper at the relay is explicitly accounted for. The proposed approach alternates between optimizing the relay's eavesdropping function and the legitimate system, resulting in representations that preserve semantic decoding performance at the intended receiver while degrading semantic inference at the relay. The semantic accuracy gap between the legitimate receiver and the eavesdropper is significantly enlarged across channel conditions. Importantly, this protection is achieved in a stealthy manner, with high reconstruction fidelity maintained while semantic leakage is selectively suppressed.

0

eess.SP 2026-07-01

Subcarrier selection trims OFDM sensing data volume for ISCC

by Ziqi Ye, Yinghui He +3 more

Toward Efficient Sensing in Multi-Device ISCC by Removing Frequency Domain Redundancy

Local removal of frequency redundancy cuts transmission and processing load in multi-device setups while holding accuracy

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Integrated sensing, communication, and computation (ISCC) is envisioned as a key enabler for intelligent services in future wireless networks. However, in multi-device ISCC systems, directly offloading full orthogonal frequency division multiplexing (OFDM) sensing data to the edge may incur excessive overhead, thereby limiting sensing performance under practical resource constraints. In this paper, we propose a subcarrier selection-based sensing framework for multi-device ISCC systems, where frequency-domain redundancy in OFDM sensing data is removed during local preprocessing to reduce sensing data transmission and processing overhead. Based on the proposed framework, we establish analytical models for sensing accuracy, delay, and energy consumption, and formulate a sensing accuracy maximization problem under practical resource constraints. To solve this problem, we develop an alternating direction method of multipliers (ADMM)-based algorithm. Experiments on commodity wireless devices validate the effectiveness of the proposed framework and show that it consistently outperforms three baseline schemes under various resource constraints.

0

eess.SP 2026-07-01

WAX adaptation enables complexity-performance trade-offs in gigantic MIMO

by Juan Vidal Alegría, Joao Vieira +1 more

Trade-Offs in Decentralized Gigantic MIMO with Hard-Boundary Constraints

Non-cooperating modules with hard boundaries allow practical scaling of 6G antenna arrays.

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To maintain the antenna apertures offered by 5G massive MIMO systems operating at the sub-6GHz band, known as FR1, 6G base stations (BSs) using the upper-mid band, FR3, should increase the number of antennas by a factor 4-8, giving rise to gigantic MIMO. This poses challenges in terms of processing complexity and interconnection bandwidth. The WAX framework, previously introduced for exploring trade-offs in decentralized architectures, may offer the flexibility needed to tackle these challenges. However, no results have been established on the applicability of this framework in the presence of hard-boundary constraints. The current work explores gigantic MIMO implementations based on a novel adaptation of the WAX framework, where the decentralized processing is performed by non-cooperating hardware modules. These modules may be implemented through state-of-the-art massive MIMO baseband units (BBUs). The results show the potential of the proposed framework towards exploiting trade-offs between complexity and performance in practical gigantic MIMO implementations.

0

eess.SP 2026-07-01

Spatial coupling lifts SCMA minimum distance via factor graph spectral gap

by Yiming Gui, Zilong Liu +2 more

Spatially Coupled Sparse Code Multiple Access (SC-SCMA): A Spectral Graph Approach

Coupled factor graphs create higher effective dimensionality that improves error rates in overloaded access without added complexity

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This paper presents a spatially coupled sparse code multiple access (SC-SCMA) framework to overcome the performance and scalability limitations of conventional SCMA systems. By analyzing the pairwise error probability associated to multi-user error patterns, we show that spatial coupling projects the superimposed SCMA codewords into a higher-dimensional effective signal space, leading to a strictly improved minimum Euclidean distance (MED) compared with conventional SCMA, while simultaneously enhancing the coding gain through global message propagation and the diversity gain through inter-block resource spreading. Such a distance gain is shown to be governed by the effective access dimensionality (EAD) induced by the coupled factor graph. With the aid of spectral graph theory, we establish a direct relationship between the spectral gap of the factor graph and a lower bound on the EAD, providing a computable structural metric that guarantees MED improvement under various error patterns. Building upon these theoretical insights, we introduce a low-complexity structure-aware codebook design approach, including a spectral-gap-oriented construction of spatially coupled factor matrices and a localized codebook optimization strategy that exploits the dominant error-inducing local user group. Simulation results validate the analysis and demonstrate that the proposed SC-SCMA consistently outperforms conventional SCMA in overloaded massive access channels.

0

cs.IT 2026-07-01

GLBP tracks unresolved measurements at O(m n 2^n) cost

by Augustin A. Saucan, Florian Meyer +1 more

Gaussian Belief Propagation for Tracking With Unresolved Measurements

The algorithm approximates exact marginalization over object partitions while using only a fraction of the computation required by direct en

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Unresolved measurements occur in many inference problems where two or more hidden processes may, at times, jointly generate a single measurement. For instance, such phenomena are encountered in multiobject tracking owing to the limited resolution capabilities of practical sensors; or in camera-aided autonomous driving due to shadowing or occlusions. Substantial performance degradation, such as track losses, are incurred when unresolved measurements are not accounted for. In this paper, we address multiobject tracking under a generalized unresolved measurement model, where any subset of objects may generate a single unresolved measurement according to a probabilistic model. Our innovation lies both in modeling and algorithm-design directions. First, we develop a probability distribution for object partitions based on a model of pairwise coupling of objects and subsequently a probability distribution for object-to-measurement association variables. This generic model incorporates sensor resolution capabilities, sensor detection, and sensor noise characteristics for object groups. Second, a generic Loopy Belief Propagation (LBP) method as well as a specialized Gaussian-LBP (GLBP) algorithm are proposed that perform object state inference under the aforementioned model. In contrast to direct marginalization methods, which involve a computational complexity of $O(m^n)$, for $m$ measurements and $n$ objects, the proposed GLBP algorithm achieves a computational complexity on the order of $O(m n 2^{n})$. Numerical results demonstrate the effectiveness of our proposed GLBP, with estimation performance that closely matches that of exact marginalization for only a fraction of the computational resources.

0

eess.SP 2026-07-01

Age fusion raises WiFi sensing accuracy with fewer samples

by Abolfazl Zakeri, Nhan Thanh Nguyen +1 more

Resource-Efficient WiFi CSI Sensing via Exploiting the Age of Samples

Encoding and multiplicatively fusing sample age improves activity and identity recognition by up to ten points when CSI acquisition is restr

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WiFi channel state information (CSI) sensing must coexist with data communications, which constrains the acquisition rate of fresh CSI measurements. To model this, we formulate CSI-based human activity and identity recognition under a sensing rate constraint that limits the fraction of time slots, within a measurement session, where CSI samples are available. This framework captures sensing-communication resource sharing and uncontrolled packet loss or traffic-driven irregularity. To satisfy the sensing constraint, two fixed CSI sampling policies are considered: a deterministic policy and a stochastic Bernoulli policy. We propose a low-cost age-aware WiFi sensing framework that explicitly incorporates sample freshness into the model training. The age of each retained CSI sample is first encoded and then fused with the CSI embedding via multiplicative fusion. On the NTU-Fi human activity recognition and person identification datasets, the proposed model consistently outperforms both a CSI-only baseline and the state-of-the-art time-aware attention model from the UniFi benchmark. For example, it yields up to a 10-percentage-point improvement over the UniFi method for person identification, with the largest gains observed under strict sensing budgets.

0

eess.IV 2026-07-01

Joint k-space model corrects EPI distortions better at high b-value

by Wenqi Huang, Zhitao Li +9 more

Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives

Gaussian-primitive representation of field and image from raw data outperforms sequential correction on brain diffusion scans.

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Echo Planar Imaging (EPI) is the standard acquisition technique for diffusion and functional neuroimaging, enabling rapid imaging but suffering from geometric distortions caused by B0 field inhomogeneities. Existing correction methods first reconstruct distorted images using parallel imaging, then estimate the B0 field and correct the distortion in the image domain. In this sequential process, reconstruction artifacts at high acceleration factors and low SNR at high diffusion b-values degrade B0 estimation and limit the overall correction quality. We propose a physics-informed framework that jointly estimates the B0 field and distortion-free image directly from k-space data, without depending on an intermediate parallel-imaging reconstruction for the correction. The image and the B0 field are each represented as a superposition of Gaussian primitives embedded within an MRI physics forward model. The explicit, continuous parameterization captures both smooth regions and tissue boundaries and supports rotated-view EPI acquisitions without interpolation. The diffusion-weighted image is modeled as real and non-negative, with the image phase absorbed into a per-shot phase factor. Rotated views distribute distortions across multiple phase-encoding orientations, improving point spread function isotropy and providing stronger constraints for B0 estimation. On in vivo brain diffusion EPI, the proposed method attains the closest brain-boundary agreement with a distortion-free structural reference, with the largest improvement over sequential methods at high b-value and high acceleration. Extensive visual comparisons further show improved detail fidelity and noise suppression.

0

eess.SP 2026-07-01

Von Mises parameters plug directly into radar trackers

by Vinay Kulkarni, V. V. Reddy

Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets

Ensemble outputs mean angle and concentration that supply closed-form likelihoods for association without extra approximations.

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This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar, focusing on probabilistic modeling and downstream integration. A circular-statistics-based von Mises (VM) ensemble (ENS) is compared with an evidential deep learning (EDL) framework based on a normal inverse gamma formulation, yielding a Student t predictive distribution in the Euclidean domain. The ENS framework produces angular predictions parameterized by (mu, kappa), enabling interpretable uncertainty aligned with directional geometry. Performance is evaluated under in distribution and multiple out-of-distribution conditions using risk coverage and ROC or AUROC analyses. Results indicate that ENS achieves lower uncertainty under nominal conditions and exhibits stronger sensitivity to severe perturbations, whereas EDL provides smoother uncertainty variation and slightly improved ranking consistency. Importantly, the ENS representation enables direct probabilistic integration into association modules via closed form VM likelihoods, facilitating a unified detection tracking pipeline. These findings highlight a trade-off between geometric consistency and statistical generality in uncertainty-aware DOA estimation.

0

eess.SP 2026-07-01

Joint optimization of UAV predicted positions and base station resources maximizes…

by Yifan Jiang, Qingqing Wu +4 more

Sensing for Reliable UAV Communication: Robust Trajectory and Resource Optimization in Low-Altitude Networks

Two geometric approximations turn intractable outage constraints into tractable joint trajectory and allocation problems for multiple cellul

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In low-altitude wireless networks, sensing-aided communication has emerged as a promising integrated sensing and communication (ISAC) paradigm for unmanned aerial vehicle (UAV) tracking and communication. This paper investigates reliable sensing-aided communication for multiple cellular-connected UAVs under mobility uncertainties. Specifically, we maximize the minimum outage capacity among UAVs by jointly optimizing their real-time predicted positions, as well as the base station (BS) transmit power and bandwidth allocations. To address the non-convex and intractable maximum tolerable outage probability (OP) constraints, two robust optimization schemes are proposed based on a continuous confidence ellipse (CE) and discretized inverse-whitened sectors (IWSs), respectively. For the CE-based scheme, an efficient algorithm is proposed to optimize the predicted UAV positions individually via block successive convex approximation, followed by convex resource allocation. For the IWS-based scheme, an IWS-based OP approximation is proposed to facilitate the robust optimization, based on which a low-complexity IWS selection method is proposed to decouple the optimization variables. Then, a similar sequential optimization algorithm is proposed based on the projected gradient descent approach. The two algorithms are further unified into a common trajectory-resource optimization framework, revealing a low-complexity structure for robust UAV trajectory and resource management. Simulation results validate the effectiveness of our proposed OP approximation, demonstrate the significant outage capacity improvement of the proposed robust optimization schemes over benchmark schemes, and illustrate the superiority of the IWS-based scheme over the CE-based scheme.

0

eess.SP 2026-07-01

Graph semantics cut visual data by 99.1% for 6G safety tasks

by Soheyb Ribouh, Phil Polo Ditsia Di Ngoma

Towards a Joint Task-Oriented and Generative Semantic Communication Framework for 6G Networks

Scene graphs transmitted over 3GPP channels feed both collision prediction and image reconstruction while beating JPEG and HEVC on efficienc

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Semantic Communication (SC) has emerged as a key enabler for 6G wireless systems by transmitting task-relevant meaning rather than raw data, thereby significantly reducing bandwidth consumption while preserving communication intent. In this work, we propose an end-to-end OFDM-based semantic communication framework that integrates a semantic encoder-decoder pipeline with a neural receiver operating over a 3GPP vehicular channel. The semantic encoder extracts the underlying meaning of a visual scene by transforming it into a graph-based representation consisting of object-level features and relational structure. At the receiver, the reconstructed scene graph is processed by a spatio-temporal graph neural network (ST-GNN)-based module for collision-risk estimation, enabling task-oriented inference. In parallel, a diffusion-based semantic decoder reconstructs the visual scene from the recovered semantics, providing dual functionality: safety prediction and image reconstruction. The proposed framework is evaluated in a MIMO configuration under varying SNR conditions. Experimental results show that it achieves up to 99.1% data compression relative to pixel-domain transmission, outperforming conventional compression-based methods (JPEG and HEVC) while preserving downstream inference performance. Furthermore, the diffusion-based reconstruction attains significantly lower frechet inception distance (FID) scores than existing semantic communication approaches, reflecting superior semantic and perceptual fidelity.

0

eess.SP 2026-07-01

Denser antennas raise cell-free MIMO energy efficiency

by Ozan Alp Topal, Özlem Tuğfe Demir +1 more

Rethinking Energy Efficiency in Cell-Free Massive MIMO: The Role of Processing and Optical Fronthaul

Power model shows fronthaul and processing costs fall relative to performance when radio units spread more widely.

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Cell-free massive MIMO promises uniformly high performance by combining densely distributed radio units, coherent transmission, and centralized processing. Unlike earlier radio generations, it depends on dense fronthaul connectivity and a virtualized cloud-RAN architecture. In this setting, energy use is no longer driven primarily by active radio components; instead, fronthaul and processing play a dominant role, calling for a fresh perspective on what defines energy efficiency. This work introduces a modular power model that captures the interplay between radios, fronthaul, and cloud processing. The analysis highlights how design choices, such as functional splits and precoding strategies, shape both fronthaul data load and total power consumption. Centralized precoding provides stronger performance with less resource utilization, while flexible activation of radios and processing elements avoids unnecessary overhead. Overall, the energy efficiency of cell-free massive MIMO grows as antennas are more densely distributed across the coverage area, particularly when combined with end-to-end resource allocation.

0

eess.SP 2026-07-01

Refinement cuts backscatter channel error by 8.9 dB

by Hanyeol Ryu, Nohgyeom Ha +1 more

Transformer-Hypernetwork-Controlled Deep-Unfolded Phase-Aware Channel Estimation Refinement for Phase-Drift-Robust Backscatter Links

Unfolded Gauss-Newton steps with transformer context suppress the phase-drift error floor left by first-order compensation.

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This paper proposes a transformer-hypernetwork-controlled deep-unfolded phase-aware channel estimation refinement (THUNDER) for phase-drifting backscatter links. Residual carrier-phase drift across the pilot block renders the backscattered observation phase-nonstationary, and a closed-form phase-aware channel estimation (PACE) compensates only the first-order phase component, leaving a deterministic high signal-to-noise ratio (SNR) error floor. THUNDER suppresses this floor by initializing from PACE and refining the estimate through unfolded Gauss-Newton steps on the exact phase-exponential model. A transformer extracts pilot-wide phase context, and a hypernetwork generates bounded controls and pilot-reliability weights. Evaluations show an 8.9 dB normalized mean square error gain over the strongest learning-based channel estimation baseline.

0

cs.LG 2026-07-01

Parameter sharing across IQ channels cuts generalization error

by Yurui Zhao, Xiang Wang +4 more

Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis

Dualformer applies shared real-imaginary weights to modulation recognition and signal parsing while retaining full model capacity.

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Designing effective feature extractors is critical for blind signal analysis tasks such as automatic modulation recognition (AMR), signal scheme recognition (SSR), and \color{black} signal structure parsing (SSP). In this work, we propose dual-channel neural network (DualNN) that efficiently exploits complex-valued signals through parameter sharing across IQ channels. Unlike traditional real-valued or complex-valued models, DualNN is a groundbreaking framework which shares the network parameters for processing the real and imaginary parts of the complex-valued signals, and is theoretically shown to reduce generalization error while preserving expressive capacity. Specifically, we propose a novel Transformer-based architecture to implement DualNN, called Dualformer. The Dualformer segments input signals into patch-level tokens and captures multi-granularity features, enabling robust performance across diverse signal analysis tasks. Furthermore, we conduct extensive experiments comparing Dualformer with three Transformer-based baselines and four conventional DL-based approaches. Results demonstrate consistent performance improvements on AMR, SSR, and SSP tasks. Besides, the modular design of DualNN allows it to generalize well to blind signal processing tasks such as blind source separation and low-SNR spectrum sensing. This work paves the way for a broader application of DualNN architectures in unsupervised and weakly supervised complex-valued signal analysis scenarios.

0

eess.SP 2026-07-01

Pressure teacher adapts sEMG gestures across subjects with 5% labels

by Yurui Liu, Xiao-Cong Zhong +4 more

PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition

Cross-modal distillation transfers stable physical semantics to unlabeled sEMG domains, reaching 58% accuracy and matching supervised baseli

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Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction. However, its practical deployment remains challenging due to severe performance degradation caused by feature distribution discrepancies across different subjects and recording sessions. Although domain adaptation (DA) techniques are commonly employed to mitigate such discrepancies, conventional methods often struggle to effectively aligning sEMG features, primarily due to their inherent stochasticity and the scarcity of labeled data. To address these limitations, this paper proposes a novel Pressure-Guided Unsupervised Domain Adaptation (PGUDA) framework, which leverages the robustness and stability of pressure signals to introduce a cross-modal knowledge distillation strategy that transfers consistent physical semantics across modalities. Specifically, a teacher network trained on pressure signals guides an sEMG student network on unlabeled target domains, thereby regularizing the representation learning process with transferable and modality-invariant knowledge. Extensive experiments conducted on a self-collected multimodal dataset involving eleven subjects validate the effectiveness of the proposed PGUDA framework. The results demonstrate that our proposed PGUDA achieves leading performance in both cross-subject and cross-session classification tasks, achieving average accuracies of 58.08% and substantially outperforming existing DA approaches. Notably, PGUDA exhibits remarkable label efficiency: it attains classification accuracy comparable to fully supervised benchmarks while requiring only 5% of labeled data for teacher network training. This framework offers a robust and data-efficient solution that can significantly reduce the calibration burden in practical sEMG-based gesture recognition systems.

0

physics.optics 2026-07-01

Phase signal encodes cumulative temperature change in coherent OTDR

by Roman Ermakov (1), Huwei Wang (1) +11 more

Fundamentals of Optical Fiber Sensing Schemes Based on Coherent Optical Time Domain Reflectometry: Signal Under Dynamic Temperature Conditions

Model separates cumulative phase from local amplitude, enabling detection and reconstruction algorithms tested on standard fibers.

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We present a theoretical, algorithmic, and experimental study of temperature sensing using $\phi$-OTDR with coherent detection. A physics-based model is developed to relate the measured Rayleigh backscattered signal to temperature variations along the fiber, showing that the phase evolution encodes the cumulative temperature change between the interrogator and the sensing location, while the amplitude exhibits only local sensitivity. Based on this insight, we propose robust algorithms for temperature-event detection and temperature-profile reconstruction. Experimental results demonstrate reliable recovery of temperature-induced perturbations in standard single-mode fibers using coherently detected $\phi$-OTDR.

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eess.SP 2026-07-01

QSAoI metric minimizes semantic delays via quantization tuning

by Huanyu Zhang, Yulin Hu +3 more

Minimizing Quantized Semantic Age of Information (QSAoI) in Foundation Model-Based Semantic Communications

Joint blocklength and precision optimization adapts to fading and cuts expected QSAoI in low-latency links.

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The emerging techniques of semantic communications and edge computing in 6G networks necessitate a paradigm shift toward co-designed semantic-aware and adaptive resource allocation for short-packet transmissions. However, there is a fundamental gap between the semantic layer and the physical layer under low-latency finite blocklength (FBL) effects. To bridge this gap, we introduce the Quantized Semantic Age of Information (QSAoI), a novel metric that rigorously captures the trade-offs among freshness and semantic efficiency of high-level features in real-time communication in the FBL regime. Guided by this metric, we propose a novel foundation model-based efficient co-designed framework to minimize the expected QSAoI over wireless fading channels in latency-constrained semantic communication. Specifically, we formulate a non-linear joint optimization problem to dynamically optimize the block-wise mixed-precision quantization (MPQ) strategy and the physical blocklength. To efficiently resolve this complex problem, we develop a high-efficiency low-complexity algorithm based on fixpoint inspection and bisection search. Extensive simulations validate that our proposed algorithm dynamically adapts the semantic quantization precision to varying channel conditions, effectively minimizing the expected QSAoI compared to baselines.

0

cs.IT 2026-07-01

Gaussian signals near capacity in quantized MIMO ISAC

by Hossein Atrsaei, Mireille Sarkiss +1 more

Fundamental Limits of Quantized MIMO ISAC under Gaussian Signaling

Bounds tighten at low SNR and saturate at high SNR; LMMSE saturates based on spatial combining ratio.

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We study a quantized multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system in which the communication and sensing receivers each apply analog spatial combining followed by scalar subtractive dithered quantization. This quantization model leads to an additive effective-noise representation with non-Gaussian noise. We derive upper and lower bounds on the capacity of this channel. Numerical results show that these bounds are tight at low signal-to-noise ratios (SNR) and saturate at high SNR due to finite-resolution quantization. They also show that, despite the effective noise being non-Gaussian, independent and identically distributed (i.i.d.) isotropic Gaussian signaling achieves rates close to capacity. Focusing on i.i.d. Gaussian signaling, this paper also presents a closed-form expression for the linear minimum mean-squared error (LMMSE) achieved under a Kronecker sensing-channel model. Numerical results show that the LMMSE also saturates at high SNR, where the saturation level increases as the spatial combining ratio decreases, and for combining ratios below one, saturation occurs even without quantization.

0

cs.LG 2026-07-01

Bayesian filters learn Lagrangian dynamics from noisy measurements

by Kundan Kumar, Shreya Das +1 more

A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements

Joint maximum-likelihood estimation of neural parameters and states works on pendulum and Duffing examples where standard LNNs do not model

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This paper proposes a Bayesian filtering-based approach for learning the dynamics of a physical system from partial, noisy measurements. We model the system dynamics using a Lagrangian mechanics formulation. As in Lagrangian neural networks (LNNs), we parameterize the kinetic and potential energies with neural networks. The unknown external forces in the Lagrangian formulation are modeled as white Gaussian noise. The corresponding Euler--Lagrange equations then yield a continuous-time stochastic state-space model (SSM) that describes the system dynamics. The neural network parameters and system states are then jointly learned via a maximum-likelihood method using Gaussian-approximation-based Bayesian filters. The effectiveness of the proposed method is demonstrated on pendulum and Duffing oscillator examples, and its performance is compared with conventional LNNs and with approximate Bayesian filters using known system models.

0

eess.SP 2026-07-01

Rate-splitting with knowledge graphs cuts UAV semantic energy use

by Sicheng Wang, Tiankui Zhang +2 more

Rate-Splitting Multiple Access Enabled Probabilistic Semantic Communication in UAV Networks

Joint optimization of flight path, power, and compression outperforms NOMA and SDMA while preserving more semantic triples.

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This article proposes an uncrewed aerial vehicle (UAV) downlink semantic communication framework, where probabilistic knowledge graphs (PKGs) are employed to model user equipment (UE) semantics and decompose semantic information into shared and private components. Leveraging the capability of rate-splitting multiple access (RSMA) in addressing such semantic structures, a PKG-assisted RSMA transmission scheme is developed to efficiently deliver multi-user semantic information under severe energy constraints and fast-varying UAV channels. To characterize the strongly coupled energy costs of communication, computation, and flight, a weighted energy minimization problem is formulated to jointly optimize the UAV trajectory, power allocation, beamforming design, and semantic compression ratio. The resulting non-convex problem is efficiently solved using an iterative semantic-aware weighted energy optimization (SWEO) algorithm that integrates Lagrangian dual decomposition and successive convex approximation. Furthermore, a semantic accuracy metric is proposed to quantify the reliability of reconstruction by assigning importance-based weights to informative KG triples. Extensive simulation results verify that the proposed framework achieves superior energy efficiency, enhanced semantic preservation, and consistently better performance than conventional RSMA, non-orthogonal multiple access (NOMA), and space division multiple access (SDMA) schemes in benchmarks across various network parameters.

0

eess.SY 2026-06-30

Federated updates keep tinyML accurate on wireless end-devices

by Prasoon Raghuwanshi, Vimal Bhatia +3 more

TinyML for On-Device and Edge Analytics in Wireless Networks: A Survey of Deployments, Opportunities, and Concept-Drift Mitigation

A procedure using federated learning and intermittent support resolves concept drift for both battery-powered and batteryless setups.

abstract click to expand

Ubiquitous intelligence is essential for enabling real-time, adaptive, autonomous, and scalable operations in the next generation of wireless networks. However, this poses significant challenges in data management and energy consumption on the end-device/edge side, specially under dynamic environmental conditions. This has driven the adoption of tiny machine learning (tinyML), which offers data-driven optimization at the end-device/edge side. In this work, we survey and thoroughly discuss various tapped/untapped deployment possibilities of tinyML in wireless networks. We identify existing frameworks, accustomed to design tinyML algorithms, that could be utilized to solve a range of wireless network problems. We present a federated learning-based tinyML model update procedure, for both battery-powered and batteryless end-devices, to resolve the concept drift problem faced by tinyML models. Furthermore, we discuss the update-aware checkpointing, fault-tolerant bootloader, and intermittent-aware modify operation, which could support federated learning-based tinyML model update in the case of batteryless end-devices. Overall, this paper spells out several areas where end-device/edge intelligence can be utilized in the next generation of wireless systems, as well as ways to mitigate the concept drift problem faced in the case of end-device intelligence.

0

cs.IT 2026-06-30

Belief in age and state guides which sensor to query

by Ismail Cosandal, Sennur Ulukus +1 more

When and Which Sensor to Observe? Timely Tracking of a Joint Markov Source

Model predictive control on the joint distribution cuts weighted age of incorrect information plus sampling costs over delayed erasure links

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We investigate the problem of remote estimation (at a monitor) of a discrete-time joint Markov process with individual components which can be observed with dedicated sensors. At a given time slot, the monitor has the option of staying idle or sending a pull request to one of the sensors to obtain a partial state value, while the sensors are assumed to have heterogeneous sampling costs. Our goal is to develop a monitor pull policy, i.e., determining when and towards which sensor to send a pull request, in order to minimize a weighted sum of average age of incorrect information (AoII), or in short age, and sampling costs. As the communication model, we assume an erasure channel with a fixed one-slot delay from each sensor to the monitor. In this setting, the monitor does not perfectly know either the state of the process or the age, at any given time. We first obtain a sufficient statistic, namely belief, representing the joint distribution of the age and the current state of the observed process, by using the history of all pull requests and observations. Then, we formulate the optimization problem as a continuous state-space Markov decision process (MDP), namely belief-MDP, for the solution of which we propose two model predictive control (MPC) methods, namely MPC without terminal costs (MPC-WTC), and reinforcement learning MPC (RL-MPC). The effectiveness of the proposed methods is validated by numerical examples.

0

cs.NI 2026-06-30

Low-power trigger selectively backdoors one semantic transmitter

by Yalin E. Sagduyu, Tugba Erpek +2 more

Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel

Adversary contaminates training with waveform to alter inference for one user while sparing the other in multiple access semantic system.

abstract click to expand

Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless channels. The adoption of SemCom in shared-access wireless networks introduces new vulnerabilities for multi-user semantic inference. This paper considers a SemCom system for two transmitters communicating with a common receiver over a multiple access channel. Each transmitter maps source information into latent semantic representations, while the receiver jointly reconstructs and classifies the semantic information for both transmitters. A selective over-the-air backdoor (Trojan) attack is presented in which an adversary transmits a low-power trigger waveform over the air and injects it into the shared received signal during training. By transmitting the trigger again during testing, this stealthy, low-power attack selectively manipulates the semantic inference for one transmitter while minimally affecting the inference of the other transmitter. To mitigate this vulnerability, a trigger-aware defense mechanism is developed to preserve correct semantic labels under trigger-contaminated wireless observations. The results demonstrate both the vulnerability of shared-access SemCom systems to selective over-the-air backdoor attacks and the effectiveness of trigger-aware robust training for semantic protection.

0

eess.SP 2026-06-30

SDR prototype achieves real-time OTFS cooperation at 33% higher efficiency

by You-Yu Huang, Yu-Ming Yeh +1 more

GPU-Accelerated Real-Time Software Defined Radio-Based Orthogonal Time Frequency Space Network-Coded Cooperation System: Hardware Implementation

Five-node TDD tests with GPU acceleration show zero packet loss over 60 seconds on 112x64 grids under vehicular fading.

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While Orthogonal Time Frequency Space (OTFS) modulation offers robust reliability for 6G vehicular networks, standalone links suffer from blockages, and existing Software-Defined Radio (SDR) testbeds are bottlenecked by complex Delay-Doppler (DD) equalizers. This paper presents a real-time Decode-and-Forward Network-Coded Cooperation OTFS (OTFS-NCC) prototype implemented on consumer hosts and USRP B210 SDRs. Operating over a five-node TDD (Time-Division Duplexing) topology, our framework improves spectral efficiency by 33% over conventional relaying while mitigating error propagation via an enhanced Gaussian Approximate Message Passing Algorithm (GA-MPA). To support a 2 MHz baseband rate, we devise a hardware-algorithm decoupled GPU (Graphics Processing Unit) architecture using 1D memory mapping and transcendental function clipping, compressing the simulated Real-Time Factor (RTF) from 4.37 to 0.89. RF-conducted Hardware-in-the-Loop validation under 3GPP EVA70 (Extended Vehicular A model) fading confirms sustained zero-packet-drop real-time demodulation over 60-second test runs across a large 112-by-64 DD grid.

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cs.LG 2026-06-30

Margin selection limits labels to 3.4% while holding accuracy in network failure detection

by Yousuf Moiz Ali, Jaroslaw E. Prilepsky +5 more

Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection

Hybrid active-online updates adapt to drift in optical networks with negligible added latency.

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We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-based selective labeling, our method achieves nearceiling accuracy and AUC scores while querying only 3.4% of streaming samples, with negligible latency overhead compared to static inference.

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cs.IT 2026-06-30

SDP voting method recovers binary signals below m=k limit

by Ece Abay, Burhan Gulbahar +1 more

Binary Signal Recovery in Undersampling: Iterative SDP with Majority Voting and Successive Interference Cancellation

For n=100-144, complexity budget of 2 times 10 to the 10 yields exact recovery at m/k ratios down to 0.4 as sparsity falls to 0.1

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Binary compressive sensing (BCS) seeks to recover a $k$-sparse binary vector of length $n$ from $m$ linear measurements. Classical CS guarantees break down for $m < k$ and convex/greedy BCS algorithms with random Gaussian sensing matrices perform poorly. We introduce ISDP-MVSIC, which combines randomized semidefinite programming (SDP) sampling, majority voting (MV) and successive interference cancellation (SIC) across $L \ll n$ stages, wrapped in a residual-cost driven retry loop. The method exposes a tunable complexity--performance trade-off: for $n=100, 144$, raising the worst-case complexity $\mathcal{C}_{max}$ from $7.9 \times 10^9$ to $2.0 \times 10^{10}$ enables empirical exact recovery over $m/k \in [0.4,5.0]$ as the sparsity ratio $s=k/n$ decreases from $0.5$ to $0.1$, by practically targeting the undersampled regime.

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cs.SD 2026-06-30

Two-step scheme lifts audio transfer scores at fixed inference cost

by Ludovic K. Tuncay (IRIT-SAMoVA), Etienne Labbé (IRIT-SAMoVA) +1 more

BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations

Decomposing masked prediction into context and prediction stages improves overall benchmark transfer without extra runtime compute.

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Self-supervised learning enables audio representations that transfer across domains and tasks. We present BEST-RQ-2, an evolution of BEST-RQ that retains frozen randomprojection-based discrete targets while introducing a two-step contextualize-then-predict pretraining scheme. A ViT context encoder processes only the unmasked spectrogram regions, and a lightweight predictor infers targets for the masked regions; the predictor is discarded after pretraining. Replacing the original Conformer encoder with a ViT shifts performance across domains, slightly reducing speech performance while improving music and environmental sounds, with comparable average scores. The main improvement comes from decomposing masked prediction into separate contextualization and prediction stages. On the X-ARES and XARES-LLM benchmarks, BEST-RQ-2 consistently outperforms one-stage baselines in overall transfer while keeping inference compute unchanged. Code and model checkpoints are publicly available.

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eess.SP 2026-06-30

DQN recovers 54% of 5G outages with 11 times better rate

by Sajjad Hussain

Joint Outage Detection and Compensation for Self-Healing 5G RAN via Deep Reinforcement Learning

The agent learns to fix base station failures using less energy than rule-based methods and prefers antenna adjustments.

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Self-healing radio access network (RAN) requires autonomous detection and compensation of base station (BS) failures. This letter proposes an end-to-end framework combining three-class cell outage detection (COD), distinguishing normal, failed, and collaterally degraded cells, with a deep Q-Network (DQN) based deep reinforcement learning (DRL) agent that jointly controls power and antenna tilt for cell outage compensation (COC). Evaluation results show that the proposed DQN agent achieves 99.1% coverage and 54% full-recovery rate, an 11$\times$ improvement over the best heuristic, while consuming less compensation energy than heuristic baselines and learning, without explicit geometric input, to prefer tilt-only compensation for centre-cell outage.

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physics.app-ph 2026-06-30

Electrical outputs realized for spin-wave Rowland circle spectrometer

by Johannes Greil, Maximilian Hofschen +4 more

Design and Realization of Broadband Magnonic Spectrometers With Local Electrical Outputs

Micrometer concave gratings in YIG deflect waves by frequency for local detection, matching predictions.

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Microscopic radio-frequency (RF) devices based on propagating spin waves (SWs) are promising for compact, energy-efficient RF signal processing, but their implementation is impeded by fabrication complexity and the lack of efficient electrical readout. In this work, we demonstrate a SW-based Rowland circle spectrometer with electrical input and local electrical output transducers. The device is realized using a scalable fabrication process based on sputter deposition and wet-chemical etching of Yttrium-Iron-Garnet (YIG), forming concave grating structures with micrometer-scale features. The device functionality is confirmed by combined electrical and magneto-optical measurements, which show that the deflection of SW wavefronts at different input frequencies closely follows the analytically predicted behavior. The linear excitation of SWs via two input tones further confirms the spectrometer operation for simultaneously propagating waves. Beyond the single-device demonstration, we propose a concept for scalable architectures comprising multiple Rowland circles with tunable operating points. When combined with broadband parallel electrical readout, this approach enables control over bandwidth and spectral resolution, which are relevant to spectral occupancy detection in wireless communication systems.

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eess.SP 2026-06-30

Multiple sensors enhance drone detection around critical sites

by Reiner Thomä, Gerd Sommerkorn +2 more

Multi-Sensor Integrated Sensing and Communication for Critical Infrastructure Protection

Multistatic architecture using distant base stations and local sniffers improves coverage and precision over single-sensor setups.

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Integrated Sensing and Communications (ISAC) will become a service in future mobile communication networks. It enables the detection and recognition of passive objects and environments using radar-like sensing. One promising first application is the protection of critical infrastructure (CI), for example by monitoring the lower airspace above sensitive sites or facilities to prevent unauthorized drone overflights. Our proposal is based on the concept of a distributed multi-sensor (MS)-ISAC. We assume deploying three or more additional passive sniffing sensors near the protected site (PS) of a CI. The sniffers are connected via Downlink (DL) / Uplink (UL) to the distant illumination base station (BS). Multistatic range-Doppler estimation, including synchronization, is performed according to the Cooperative Passive Coherent Location (CPCL) principle. The multistatic architecture has several advantages over the often considered quasi-monostatic architecture where one sniffer is located close to the base station. We discuss the advantages and disadvantages of both approaches and compare their performance for the considered use case in terms of coverage and geometric dilution of precision (GDoP)

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cs.SD 2026-06-30

Child-adapted SSL models improve voice anonymization

by Pranav Tushar, Xiao Xiao Miao +1 more

Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models

Experiments on MyST data show better speech quality and privacy for kids in solo and mixed-speaker recordings.

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Voice anonymization aims to protect speaker identity while preserving linguistic content and speech usability. However, most anonymization systems are developed on adult speech, leading to degraded performance when applied to child speech. This paper investigates child-centric anonymization by adapting a self-supervised learning (SSL) based anonymization pipeline to the child speech domain. The system is adapted using child speech from the MyST corpus and evaluated under both single-speaker and two-speaker mixture conditions. Experimental results show that child-domain adaptation improves intelligibility and perceptual quality while maintaining strong privacy protection. Extending the approach to multi-speaker further demonstrates that combining target speaker extraction with child-adapted anonymization provides privacy protection while preserving conversational structure. These findings highlight the importance of child-specific adaptation for practical speech anonymization systems.

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eess.SP 2026-06-30

Uncertainty maps guide sampling for better channel gain maps

by Yunzhe Zhu, Xuewen Liao +3 more

Active Learning for Channel Knowledge Map Construction via Bayesian Inference Diffusion Models

Diffusion models supply epistemic uncertainty that, with spatial uniformity, picks the next measurement locations for CGM construction.

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Channel knowledge maps (CKMs) are regarded as key enablers of environment-aware communications in future wireless networks, as they provide location-specific channel information by establishing an explicit connection between wireless devices and the physical propagation environment. As a representative CKM, the channel gain map (CGM) characterizes the spatial distributions of large-scale fading to support wireless environment awareness and network optimization. Existing CGM construction methods generally lack a well-defined sampling-point acquisition strategy, which may result in a limited number of sampling points being allocated to spatially redundant or highly predictable regions, thereby degrading CGM reconstruction performance in complex propagation environments. In this paper, we propose an active-learning-based diffusion framework for efficient CGM construction. By combining Bayesian inference with the diffusion model, the proposed method estimates epistemic uncertainty without retraining the model. Two uncertainty quantification algorithms are further developed along the reverse diffusion process to generate element-wise epistemic uncertainty maps. Furthermore, an uncertainty-aware sampling strategy is designed to determine new observation locations by jointly considering epistemic uncertainty and spatial distribution uniformity. Experimental results on both static and dynamic CGM datasets demonstrate that the proposed method achieves better reconstruction performance than baseline methods. These results indicate that the proposed method can effectively improve the utilization efficiency of limited sampling points and enhance the accuracy of CGM construction in complex wireless propagation environments.

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eess.SP 2026-06-30

Calibrated model predicts needed decoder depth from SNR in JSCC

by Kaiwen Yu, Gang Wu +3 more

Effective Depth in Joint Source-Channel Coding: An Implicit Equilibrium Analysis

Implicit equilibrium iterations plus kernel analysis give the refinement steps required at each channel quality after one-time fitting.

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A fundamental design question in deep joint source-channel coding (Deep JSCC) remains insufficiently explored: given a channel signal-to-noise ratio (SNR), what effective computation depth is required for semantic reconstruction? Existing Deep JSCC systems typically employ fixed-depth neural architectures selected through empirical hyperparameter tuning, which may lead to unnecessary computation under favorable channel conditions and insufficient refinement under severe channel noise. This paper proposes \emph{Implicit-JSCC}, an implicit equilibrium framework in which semantic encoding and decoding are formulated as fixed-point equilibrium processes. The effective encoder and decoder depths are determined by residual-based solver convergence rather than manually predefined layer numbers, while parameter sharing across equilibrium iterations enables depth-independent parameter complexity. To analyze the resulting effective-depth behavior, we develop a Gaussian-process-inspired kernel evolution framework that models equilibrium iterations as an effective-depth propagation process. Since channel noise is injected between the encoder and decoder, the analysis tracks channel-induced representation perturbations across receiver-side equilibrium iterations and derives a theory-guided depth--SNR relationship. After offline calibration of the system-specific parameters, the resulting model characterizes the required receiver-side refinement depth under different SNRs. Extensive experiments show that Implicit-JSCC achieves competitive reconstruction performance while enabling residual-based adaptive inference and controllable computation--quality tradeoffs. The depth--SNR model further provides a characterization of the SNR-dependent refinement depth required to reach a prescribed perturbation tolerance.

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eess.SP 2026-06-29

Early exits reduce wireless FM FLOPs by 93% and improve OOD accuracy

by Omar Mashaal, Hatem Abou-Zeid

Fast Wireless Foundation Models with Early-Exits

Lightweight heads at intermediate layers of a frozen encoder deliver faster inference and better transfer to unseen tasks than full-depth ex

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While wireless foundation models (FMs) are demonstrating strong potential to enable AI-Native 6G networks, their high computational cost remains a critical barrier to deployment. The large computational cost stems from the rigid, full-depth execution of the FM backbone for every task, a process we show is not only inefficient but can also degrade performance on unseen out-of-distribution (OOD) tasks. In this paper, we propose a novel early-exit FM framework that attaches lightweight, per-task heads, at the most appropriate exit-stage of a frozen wireless FM encoder, enabling variable-depth inference tailored to each task's preferred representation depth. Our results demonstrate that these intermediate-layer features not only speed-up inference significantly (up to 93% fewer FLOPs), but also provide more transferable representations that exceed the full encoder accuracy on unseen tasks. We further demonstrate that a simple fixed-exit strategy per task is more effective than traditional early-exiting policies that route different samples to different exits based on their perceived difficulty levels.

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cs.IT 2026-06-29

Binary latents reach optimal neural compression rates

by Ezgi Ozyilkan, Sharang M. Sriramu +3 more

SoftBinary Coding: A New Information-Theoretic Neural Compression Paradigm

Stochastic binary space plus fast channel simulation avoids mismatch and bias, proves rate optimality, and beats TCQ on Gaussian vector quan

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Neural compression is currently dominated by Nonlinear Transform Coding (NTC), which maps data to real-valued latents via continuous transforms. Despite its success, NTC suffers from train-test mismatch due to non-differentiable quantization, a ``smoothness bias" inherent in continuous transforms that precludes optimality for certain sources, and a loss of ``shaping gain" due to the complexity of including high-dimensional vector quantization. We propose SoftBinary Coding (SBC), an end-to-end learning paradigm that bypasses these limitations by using a stochastic binary latent space. In the spirit of vector quantization, SBC employs discrete representations and compresses them through a novel fast binary channel simulation scheme, for which we provide a proof of rate optimality. Experimental gains on information-theoretic sources provide both theoretical and practical closure to NTC's limitations, establishing discrete binary structures as a viable path toward reaching optimal rate--distortion bounds. Surprisingly, SBC also achieves state-of-the-art performance on vector quantization of i.i.d. sources, exceeding Trellis Coded Quantization of the Gaussian source.

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cs.IT 2026-06-29

Ergodicity equates time averages to ensemble averages in LEO satellite networks

by Chang-Sik Choi, Francois Baccelli

Dynamical System Characterization of Heterogeneous Walker Satellite Networks: An Orbit-Aware Stochastic Geometry Perspective

Rational independence of rotation speeds allows computing downlink SINR coverage and throughput from the invariant measure for typical recei

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Heterogeneous and in particular multi-altitude low Earth orbit (LEO) satellite constellations exhibit complex spatial and temporal structures, which require new modeling tools for their performance analysis. In this paper, we develop an orbit-aware stochastic geometry framework modeling today's LEO satellites on various orbits and various altitudes. In particular, we characterize such a system as the superposition of multiple Walker point processes and formulate it as a dynamical system determined by an initial condition and the rotation speeds of satellites and Earth. We show that when the speeds are rationally commensurable, the proposed satellite system is periodic. Then, we show that the system is ergodic when the speeds are rationally independent, establishing a theoretical link between time averages of the system and the expectation of it under the invariant measure. We derive the nearest-satellite distance distribution of a typical receiver at a given latitude and analyze the signal to interference-plus-noise ratio (SINR) coverage probability of the typical receiver. We then derive the ergodic throughput of the downlink communication to the typical receiver. Overall, the proposed framework offers a rigorous and tractable tool for analyzing downlink performance in Walker-type heterogeneous LEO satellite networks.

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eess.SP 2026-06-29

Neural augmentation repairs LLRs from any MIMO-OFDM receiver

by Ory Eger, Nir Shlezinger

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction

The compact network improves soft outputs for decoding without needing to know the source of impairment or detector type.

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The growing demands for higher throughput and cost-efficient wireless communications drive the need for receivers that are both simple to deploy and robust to hardware impairments and nonlinear environments. While classical model-based receivers and recently proposed deep neural network ( DNN) architectures provide complementary benefits, they either rely on simplified linear Gaussian assumptions, require considerable computational resources, or are tailored for a given setting and modulation. In this work, we propose a compact and modular DNN augmentation that universally refines the soft outputs of existing receivers (model-based or data-driven), addressing two distinct operating regimes: structurally incomplete soft information arising from reduced-complexity detectors, and degraded soft outputs caused by hardware impairments and synchronization errors. A key property of the proposed framework is its task-agnostic nature: operating without any knowledge of the specific source of unreliability, it produces well-calibrated log-likelihood ratios (LLRs) suitable for channel decoding. Our design leverages an element-wise scaled convolutional neural network tailored to perform learned interference cancellation across users and neighboring subcarriers, combined with a training algorithm that encourages accurate LLR s for soft channel decoding. Numerical results demonstrate that the proposed augmentation consistently improves diverse receiver algorithms in challenging channel conditions while incurring minimal overhead.

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physics.geo-ph 2026-06-29

Sensor-dropout training during optimization

by Isao Kurosawa

Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data

Ablations on three real datasets show training recipe accounts for most noise tolerance while architecture contributes little

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Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise. These two failure modes are routinely conflated, and architectural complexity is often credited with robustness it may not deserve. We assemble a unified binary event-detection benchmark from three physically distinct real sources -- Hi-net seismic waveforms, Utah FORGE 2024 borehole DAS, and MAFAULDA industrial vibration -- each mapped to a common 8-channel, 256-sample representation, and evaluate a fault-tolerant detector (CEPHALON) trained with per-sample sensor-dropout against standard detectors (a 1D convolutional network, a temporal convolutional network, and a compact Transformer) trained with an identical recipe. On clean data every model is near-perfect (AUC ~ 0.99). Under progressive sensor loss, simple models with sensor-dropout are already robust and CEPHALON holds no advantage. Under additive noise, however, CEPHALON degrades far more gracefully: at -2.5 dB its overall AUC is 0.939 versus 0.532-0.572 for the convolutional baselines. Same-architecture ablations isolate the cause: disabling internal redundancy at inference reduces the low-SNR advantage only modestly, whereas removing sensor-dropout training collapses it (0.899 to 0.603 at -5 dB). The training recipe is therefore the dominant cause and parallel redundancy only secondary. We release a complete, numbered, reproducible pipeline so that every figure can be regenerated.

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