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Electrical Engineering and Systems Science Signal Processing

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Sunday 27 September

Signal Processing

DDPG-Based Intelligent Handover For STAR-RIS-Assisted 6G Networks

Amr Mansour, Hanan Hussein, Marya Albert et al. (+2 more)

Intelligent handover management is a key capability for future 6G networks. This paper investigates enhanced handover strategies in systems assisted by Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS). In addition, it explores the optimal placement of multiple base stations (BSs) to further improve network performance. To eliminate coverage dead zones, a Deep Deterministic Policy Gradient (DDPG) framework is utilized to enforce a Max-Min Fairness objective, directly maximizing the worst-case Signal-to-Noise Ratio (SNR). This optimized topology is evaluated via a dynamic mobility stress test simulating 180 pedestrians executing A3-event handovers under stochastic transient blockages. Results demonstrate that the DDPG deployment outperforms random baselines by a 3.17 dB mean gap, achieving a 24 dB worst-case SNR and a 0.963 Jain's fairness index. Crucially, this optimal static placement drastically improves dynamic mobility resilience, ensuring a 100% successful handover rate and reducing post-failure outage time to a negligible 0.01%.
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All authors Amr Mansour, Hanan Hussein, Marya Albert, Nada Elsharkawy, Mohamed Shalma

Signal Processing

A Low-Dimensional Riemannian Alternating Optimization Algorithm for BD-RIS-Assisted Multiuser Beamforming

Weitao Xia, Zheyu Wu and Ya-Feng Liu

This paper investigates joint active and passive beamforming for a multiuser downlink system assisted by a fully-connected beyond-diagonal reconfigurable intelligent surface (BD-RIS). Since the number of tunable components grows quadratically with the number of RIS elements, directly optimizing the full scattering matrix becomes increasingly computationally expensive. To address this challenge, we first derive an equivalent low-dimensional formulation that replaces the full scattering matrix with a variable whose number of columns equals the number of users. We further establish a smooth manifold structure on a dense subset of the equivalent feasible set, based on which we develop a low-dimensional Riemannian alternating optimization (LD-RAO) algorithm, where the passive variable is updated using a single Riemannian gradient ascent step at each iteration. The convergence of LD-RAO is established under mild assumptions. Numerical results demonstrate that the proposed algorithm achieves the same performance as the existing state-of-the-art (SOTA) full-dimensional methods with significantly less CPU time, particularly for large-scale BD-RIS.
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Signal Processing

Robust Transceiver Design for SIM-Assisted Integrated Sensing and Covert Communications

Ahmed M. Benaya, Ali A. Nasir, Khaled M. Rabie et al. (+2 more)

Stacked intelligent metasurfaces (SIMs) have emerged as a promising wave-domain processing technology for sixth-generation integrated sensing and communication networks, reducing reliance on complex radio-frequency hardware. Nevertheless, imperfect channel state information (CSI) can compromise sensing performance and communication reliability. Moreover, ensuring covert transmission introduces a further challenge, as the system must sense potential targets while concealing communication activity from unauthorized wardens. These challenges motivate the development of robust SIM-assisted integrated sensing and covert communication (ISACC) systems. In this paper, we investigate the robust transceiver design of a multi-user, multi-target ISACC system under imperfect CSI. We jointly optimize the transmit and receive beamformers, sensing covariance matrix, and SIM phase shifts to maximize the worst-case minimum sensing signal-to-interference-plus-noise ratio. The formulation enforces constraints on bounded channel uncertainties, user quality-of-service requirements, transmit-power budget, SIM phase shifts, and target detection probabilities. Since the formulated problem is highly non-convex, we develop an alternating optimization framework based on the S-procedure, successive convex approximation, and semidefinite relaxation. Furthermore, to reduce computational complexity, we derive exact lower-dimensional representations of the robust sensing linear matrix inequalities (LMIs) involved in transmit/receive-beamformer designs. We further develop a low-complexity phase-update method that avoids the large sensing LMIs. Numerical results demonstrate that the proposed design outperforms random phase shift and fully digital-beamforming benchmarks, while the low-complexity scheme achieves comparable performance to the LMI-based scheme at substantially lower computational cost.
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All authors Ahmed M. Benaya, Ali A. Nasir, Khaled M. Rabie, A. Abdelaziz Salem, Daniel B. da Costa

Signal Processing

A Majorization-Minimization Framework for Activity Detection in Mixed Near-and-Far-Field Random Access

Xinjue Wang, Zhi-Yong Wang, Sergiy A. Vorobyov et al. (+3 more)

Grant-free random access in massive machine-type communications requires detecting a small active set from length-L uplink pilots received at an M-antenna base station. Classical covariance-based detectors are largely built on the far-field (FF) model, where the M antenna-domain observations are treated as independent snapshots, and inference reduces to an L $\times$ L covariance description. This model becomes inadequate in mixed near-field (NF) and FF access, where NF devices induce device-specific structured spatial covariances and the aggregate observation no longer fits the FF snapshot structure. In this paper, we develop a unified covariance-aware Rician framework for activity detection that treats FF and NF devices by a single likelihood model. Within this framework, we propose a majorization-minimization projected gradient descent (MM-PGD) detector for the resulting relaxed likelihood. For scalable exact likelihood evaluation, we further derive a Kronecker-Woodbury implementation that exploits the mixed NF/FF covariance structure, and avoids factorizing the full LM $\times$ LM covariance matrix. Numerical results show that MM-PGD is on par with the strongest coordinate-wise baseline in the all-FF limit, while its advantage becomes more pronounced as the fraction of NF users increases under the tested regimes.
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All authors Xinjue Wang, Zhi-Yong Wang, Sergiy A. Vorobyov, Esa Ollila, Gayan Amarasuriya Aruma Baduge, Mojtaba Vaezi

Signal Processing

Index Modulation Aided Phase-Rotated AFDM for Efficient Integrated Sensing and Communication: Framework, Optimization and Performance Analysis

Shiqi Cui, Zeping Sui, Tianqi Mao et al. (+7 more)

Affine frequency division multiplexing (AFDM) has emerged as a competitive waveform for integrated sensing and communication (ISAC) in high-mobility scenarios, but its data-dependent high peak-to-average power ratio (PAPR) and ambiguity function (AF) sidelobes may degrade the power-amplifier efficiency as well as the sensing performance. To mitigate the issues, one promising candidate is the phase-rotation optimization philosophy, which necessitates auxiliary side information (SI) to guarantee successful data recovery at the receiver. However, using additional signalling overhead to deliver the SI degrades the spectral efficiency. Against the background, this paper proposes a power level index modulation (PLIM)-aided phase-rotated AFDM (PLIM-PR-AFDM) scheme, which embeds the phase-rotation SI within the transmitted block. At the transmitter, the proposed scheme jointly optimizes the waveform by coordinating the PLIM power allocation, phase rotation, and embedding of SI. Specifically, subcarriers are flexibly allocated with different transmit power levels. High-power subcarriers are phase-rotated to suppress the PAPR and fast-slow-time AF sidelobes. By decomposing the fast-slow-time AF sidelobe energy into per-block periodic autocorrelation function (PACF) terms, the sensing objective can be formulated as a PACF-weighted integrated sidelobe level (WISL) minimization problem, while selected low-power subcarriers embed the phase bits as SI through constellation labels. Furthermore, the PLIM power patterns of each transmitted block can convey additional index bits, which compensates for the spectral efficiency loss caused by SI transmission. Moreover, the error performance is analyzed by deriving an upper bound of the average bit error probability (ABEP) and identifying the condition for full diversity.
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All authors Shiqi Cui, Zeping Sui, Tianqi Mao, Yuanshuo Gang, Guangyao Liu, Fan Zhang, Miaowen Wen, Dezhi Zheng, Christos Masouros, Zhaocheng Wang

Signal Processing

Communication-Centric Symbol-Level Precoding for Fronthaul-Limited Networked ISAC

Shu Cai, Amin Li, Yiming Zhang et al. (+3 more)

This paper studies communication-centric symbol-level precoding (SLP) for fronthaul-limited networked integrated sensing and communication (ISAC). SLP is attractive for exploiting constructive interference, but its data-dependent nature makes fronthaul-aware waveform design difficult because the transmit covariance used in conventional linear-precoding compression models is unavailable before the symbol block is formed. To address this issue, we develop a compression-aware SLP framework that relates the fronthaul requirement to the realized block energy. We further formulate a sensing-oriented joint design. To solve the resulted high-dimensional and nonconvex problem, we propose a tractable lower-bound method with a performance bound. Simulations show that the proposed SLP scheme improves sensing performance over linear-beamforming baselines under fronthaul constraints.
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All authors Shu Cai, Amin Li, Yiming Zhang, Jun Zhang, Qi Zhang, Ya-Feng Liu

Signal Processing · Cross-listed from cs.LG

ECG-Scroll: A Long-Horizon, Streaming Benchmark and Agent Environment for Interpretation of Ambulatory Electrocardiograms

Haitao Li, Chenglin Li, Zhengyao Ding et al. (+3 more)

Multimodal large language models (MLLMs) can now interpret a standard ten-second, twelve-lead electrocardiogram (ECG) with clinically grounded, reward-verified reasoning. Real cardiac monitoring is different. Ambulatory (Holter) and telemetry recordings span hours to days and are read as they stream in, and their clinically decisive findings are paroxysmal, brief episodes buried in an otherwise unremarkable trace. Such a recording cannot be held in one context at diagnostic resolution, and its future has not yet happened, so a reader must work online, deciding what to measure now, committing evidence to memory as it passes, and reporting events as they occur. We recast long-duration ECG interpretation as a long-horizon, online (streaming, causal) sequential decision process and introduce ECG-Scroll. As a benchmark, long ambulatory recordings are streamed to an agent chunk by chunk, and it must localize, quantify, and promptly flag paroxysmal events without access to future signal; because the underlying signal is retained, every answer is checkable against objective ground truth, giving rule-based rather than judge-based rewards, and the streaming formulation adds a metric batch evaluation cannot express, the detection latency between an event's onset and the moment the agent records it. As an agent environment, it is a fixed, gym-style interaction layer that exercises three competencies single-glance ECG models never touch: Memory, Tool use through signal-grounded measurement rather than reading pixels, and Planning of what to measure now and when to commit. We release 390 whole-recording instances spanning 2,536 hours of two-lead ambulatory ECG and evaluate a signal-threshold rule agent alongside off-the-shelf LLM agents online, characterizing how they use memory, tools, and planning and where the benchmark's head-room lies.
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All authors Haitao Li, Chenglin Li, Zhengyao Ding, Ziyu Li, Yiheng Mao, Zhengxing Huang

Signal Processing · Cross-listed from cs.LG

Simulation-Free Learning of GP-SDEs from Irregular Observations

Zhidi Lin, Yuhao Liu, Ying Li et al. (+2 more)

Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertainty quantification, but learning and inference from noisy and irregular observations remain computationally challenging. To address this issue, we propose GP-SDE Matching, a simulation-free variational framework for Bayesian GP drift learning and continuous-time state smoothing. We analytically marginalize the sparse GP posterior to derive a tractable drift-matching objective that accounts for both the posterior mean and uncertainty of the unknown drift. To handle irregular observations, we further introduce an irregular-time-aware variational state posterior that incorporates the actual observation times during both encoding and continuous-time marginal querying. Experiments on the stochastic Lorenz–63 system demonstrate substantially improved drift recovery and state reconstruction under irregular observations, while five system identification benchmarks show robust forecasting under increasing observation sparsity and competitive performance against existing latent-SDE and state-space methods.
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All authors Zhidi Lin, Yuhao Liu, Ying Li, Edwin Fong, Petar Djurić

Signal Processing · Cross-listed from math.OC

Plug-and-Play Methods Provably Converge Even with Improperly Trained Denoisers: Convergence by Architectural Design

Henry Pritchard and Rahul Parhi

Plug-and-Play (PnP) methods replace proximal operators with learned denoisers, which produce state-of-the-art reconstruction quality, but sacrifice the variational interpretation and convergence guarantees of proximal methods. Learned Proximal Networks (LPNs) address this problem by designing the denoiser architecture so that it is exactly the proximal operator of a regularizer. In this work, we clarify the theoretical foundations of LPNs and extend the framework to a broader class of activation functions. We then study two practical mechanisms for controlling the learned prior: (i) averaging the LPN with the identity, a common heuristic in PnP methods, and (ii) directly scaling the implicit regularizer induced by the proximal operator. In particular, we characterize the regularizer induced by averaging and develop a convergent method for evaluating the scaled proximal operator.
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Saturday 26 September

Signal Processing

Spherical ESPRIT by Paired Small Circles and Infinitesimal Rotations

Albert Fannjiang

We study the recovery of a finite three-dimensional point cloud from its spherical Fourier signal subspace at a fixed wavenumber. Two ESPRIT constructions are analyzed. The first uses finite shifts between paired small circles; the second uses angular derivatives on the whole sphere. Both recover the same commuting coordinate matrices, either through their finite exponentials or directly from a coupled linear system. We prove that the continuous generator system is injective for every distinct point cloud and that one guard harmonic degree preserves the retained equations exactly. For $s$ targets, the cutoff $K\ge s$ gives generic finite identifiability, but not uniform conditioning. We also derive a finite-cutoff perturbation bound. The accuracy of the coordinate estimates is a separate issue. A one-target perturbation calculation shows that the original generator estimate can retain a nonzero bias as the wavenumber increases. To address this bias, we use the generator eigenbasis to separate approximate atoms and recover their locations from amplitude-normalized phases. Under pointwise atom dominance, the resulting error is $O(\kappa^{-1})$; for a dominant two-atom mixture at fixed separation, it is $O(\kappa^{-2})$. Experiments on structured point clouds with smooth perturbations compare the two constructions and their use as initializers for a common MUSIC refinement.
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Signal Processing · Cross-listed from cs.CR

Coordinated Electromagnetic Side-Channel Attacks for Voter–Ballot Linking: A Case Study of the Brazilian E-Polling System

Leandro Hyeda, Kleber V. Cardoso, Antonio Oliveira-Jr et al. (+1 more)

In this work, we show how two adversaries (Eve and Mallory) can coordinate an electromagnetic side-channel attack to recon- struct the vote displayed on an e-voting machine (EVM) and link it to a specific voter (Alice). Assuming that Eve has access to a place adjacent to the e-polling room (e.g., restroom, unsupervised room), she performs vote reconstruction by intercepting un- intended electromagnetic emanations associated with the video signal. Meanwhile, Mallory observes when Alice is voting and reports this information to Eve over the Internet. To validate the most critical stage of the attack, we conduct a software-defined radio experiment using the official Brazilian e-voting interface and demonstrate through-wall vote reconstruction. Although conventional monitors are employed rather than official EVMs, our findings provide insights into information forensics and support security recommendations for government authorities responsible for e-voting systems.
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All authors Leandro Hyeda, Kleber V. Cardoso, Antonio Oliveira-Jr, Saulo Queiroz

Signal Processing

EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review

Mohammad Hossein Koohi Ghamsari, Seyede Fatemeh Ghamkhari, Siavash Bayat et al. (+1 more)

Brain-computer interfaces (BCIs) have emerged as transformative technologies that enable direct communication between the brain and external devices. Among various BCI paradigms, EEG-based motor imagery (MI) has gained prominence due to its simplicity, non-invasiveness, and potential to restore motor function and facilitate rehabilitation for patients with motor impairments. This paper presents a comprehensive review of the most practical processing algorithms developed over the past decade for decoding brain sensorimotor cortex signals. Specifically, this paper discusses the integration of artificial intelligence (AI)-based algorithms, particularly machine learning and deep learning techniques, and their contributions to improving the performance and efficiency of MI-BCI systems in detail. Furthermore, the paper reviews state-of-the-art hardware platforms and emerging converging technologies, including system-on-chip (SoC) architectures, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), wearable devices, the Internet of Things (IoT), and augmented/virtual reality (AR/VR), and discusses their integration with advanced signal processing algorithms to enable next-generation MI-BCI systems. By highlighting current achievements of EEG-based MI-BCI technology and predicting future research directions that could further enhance real-time capabilities, this paper aims to provide valuable insights for researchers and practitioners, fostering innovation in high-performance EEG-based MI-BCI systems.
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All authors Mohammad Hossein Koohi Ghamsari, Seyede Fatemeh Ghamkhari, Siavash Bayat, Ahmed Hemani

Signal Processing · Cross-listed from cs.AI

TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements

Saad Masrur, Saeed R. Khosravirad and Ismail Guvenc

Wireless digital twins (DTs) rely on 3D environment models to predict radio propagation and support wireless-network decisions, yet these models are often initialized from imperfect 3D maps. Errors in building position, height, footprint, and orientation can therefore cause a high-fidelity propagation engine to simulate the wrong physical environment. In this paper, we study how a deployed wireless network can repair an existing DT using its own radio frequency (RF) measurements. In particular, we introduce Twin Residual Alignment and Calibration Engine (TRACE), a physics-grounded learning-based self-calibration framework that treats twin maintenance as residual alignment between the physical world and the current DT. Using the same sensing configuration as the physical measurements, TRACE ray-traces the current DT, coherently backprojects the measured and simulated RF onto a common world grid, and extracts the same local region around each building's current DT position. A multi-view corrector then fuses evidence across sensing nodes and neighboring buildings to predict a gated six-parameter correction per building, without relying on absolute layout or sensor ordering, and supports iterative correction through re-rendering. On 5,400 held-out samples from unseen simulated scenes at 28 GHz, TRACE reduces 3D position RMSE from 2.202 m to 0.302 m and yaw RMSE from 4.978{\deg} to 0.894{\deg}, outperforming ViT and U-Net baselines under changes in layout, building count, sensing-node count, and SNR. On measured 28 GHz RF data from the NIST outdoor courtyard, a model trained only on synthetic RF reduces mean planar wall-position error from 1.00 m to 7.8 cm, without measured-data fine-tuning or geometric labels. These results show that the discrepancy between measured and twin-rendered RF can serve as a learning signal for repairing a wireless DT.
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Signal Processing

Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning

Xiaojie Li, Yu Han, Han Fang et al. (+4 more)

Radiomap blind prediction aims to infer radiomaps from observable representations of the propagation environment and base station configuration without field measurements. In practice, the observable representations are inherently incomplete. Thus, the target radiomap is not fully determined by the inputs when generalizing to unseen configurations or environments. Under incomplete observation, we establish a population-level theory of deterministic radiomap blind prediction that identifies the conditional mean as its optimal target and separates prediction error into reducible predictor approximation and irreducible uncertainty caused by missing physical information. The framework further characterizes the train-test risk gap and the uncertainty reduction enabled by observation enrichment. Building on it, we reveal the dual role of propagation priors: they provide physically grounded guidance, yet their implementable forms may bias the attainable predictor. This motivates RadioDecomp, which treats a prior-guided predictor as a correctable base and learns its remaining predictable discrepancy through residual refinement. To evaluate RadioDecomp across distinct propagation-prior designs, we instantiate it with a feature-guided monolithic base and a LoS-Shadow structured base, yielding RadioFR and RadioLSR, respectively. Across random, cross-configuration, and cross-environment settings, experiments confirm the benefit of propagation-related representations and show that both instantiations improve upon their respective bases. Further controlled studies on base capacity, training-support coverage, and observation coarsening corroborate the proposed analysis.
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All authors Xiaojie Li, Yu Han, Han Fang, Shangqing Liu, Guangxu Zhu, Shi Jin, Chao-Kai Wen

Signal Processing

Characterizing the Effects of Mixed Division Waveform Schemes in MIMO Radar

Oliver P.R. Kirkpatrick, Santiago Ozafrain, Christopher Gilliam et al. (+1 more)

Direction of departure estimation in Multiple-Input-Multiple-Output (MIMO) radar is based on transmitting a set of orthogonal (or at least separable) waveforms, one per transmit element. Assuming orthogonality is preserved, these waveforms can then be separated into different channels at the receiver before beamforming. In practice, however, MIMO waveforms are not perfectly orthogonal, and at different points in the processing chain, this can manifest as phase errors in the steering manifolds used in beamforming. Furthermore, due to resource constraints, either by the channel or the sensing scenario, a mixture of orthogonality techniques, mixed division strategies, are often employed, resulting in further phase errors. Naturally, such errors will interfere in the beamforming process, spoiling the beam, introducing direction finding errors, or increasing sidelobe levels, and consequently reducing SNR. In this paper, we demonstrate how mixing division strategies significantly degrades orthogonality of otherwise orthogonal waveforms resulting in degraded beamforming performance. Specifically, we show that waveforms which exhibit favourable sidelobe levels may still induce large direction of departure errors and vice versa. Additionally, through multiple exhaustive searches of potential mixed division strategies with combinations of time-, Doppler- and code-based division techniques we show that large numbers of the possible waveforms exhibit poor beamforming qualities.
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All authors Oliver P.R. Kirkpatrick, Santiago Ozafrain, Christopher Gilliam, Beth Jelfs

Signal Processing · Cross-listed from cs.LG

Benchmarking EEG Foundation Models at Scale: Lessons from 20,000 Evaluations

Zhige Chen, Shu Peng, Chengxuan Qin et al. (+4 more)

Electroencephalography (EEG) foundation models (FMs) promise transferable neural representations, yet their advantages over strong supervised baselines and their prospects for further scaling remain unclear. To address these questions, we introduce EEG-Arena, an open-source benchmark covering 30 EEG FMs and 25 supervised baselines evaluated on 57 downstream tasks from 23 public datasets. Through more than 20,000 evaluations across five experimental protocols, we assess downstream performance, pretraining benefits, model size scaling, pretraining data scaling, and robustness to channel configuration. We find that (1) EEG FMs outperform strong task-specific supervised baselines on most evaluated tasks, particularly under non-bipolar settings; (2) compared with architecture-matched supervised training from scratch, pretraining improves both early optimization and final downstream performance, with larger and more consistent gains as more labeled downstream data become available; (3) existing EEG FMs do not exhibit a consistent positive relationship between parameter count and downstream performance; (4) under a fixed architecture, increasing the pretraining data scale yields sustained downstream gains; and (5) channel-flexible FMs achieve higher absolute performance than channel-constrained models across most evaluated channel configurations. Together, these findings demonstrate the downstream value of EEG FMs and identify pretraining data expansion as a promising direction for further progress. To support continued research, we release EEG-Arena as an open-source evaluation framework that provides shared infrastructure for reproducible benchmarking, model comparison, and community-driven development.
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All authors Zhige Chen, Shu Peng, Chengxuan Qin, Rui Liu, Rui Yang, Kay Chen Tan, Jibin Wu

Signal Processing · Cross-listed from cs.CV

Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection

Zhiyuan Ji, Junjun Yin and Jian Yang

Superpixel copula models provide stable regional evidence for heterogeneous remote sensing change detection, but a single label per region limits localization within mixed superpixels. This letter develops a region-local copula evidence fusion method that retains the regional decision structure while introducing spatially varying local dependence anomalies. Independently fitted local models characterize departures from unchanged cross-image relationships. Reference ranking and an upper-tail gate transform these anomalies for fusion with continuous regional confidence. We derive the resulting regiondependent local decision threshold and identify a condition under which gating is equivalent to reparameterizing ungated fusion. On Lake and UK, whole-image optimized configurations achieve kappa coefficients of 0.78136 and 0.90817 and improve mixedregion and boundary decisions. Four-fold retrospective spatial validation over ten training subsets confirms complementary local information, with ungated reference fusion increasing mean kappa by 0.00693 and 0.01793. Fixed gating yields a larger UK gain of 0.03353 but only 0.00041 on Lake. These results support regional-local dependence interaction, while showing that calibration and gating have scene-dependent benefits.
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Signal Processing

Secure Near-Field ISAC with Pinching-Antenna Systems: Exploiting Constructive-Destructive Interference

Dimitrios Bozanis, Vasilis K. Papanikolaou, Sotiris A. Tegos et al. (+3 more)

This paper investigates secure near-field integrated sensing and communication (ISAC) with pinching-antenna systems (PASS), where a base station serves legitimate users while localizing a target that may also eavesdrop on the transmitted data. We jointly design symbol-level precoding (SLP) and the transmit and receive pinching-antenna positions to minimize the Cartesian position error bound (PEB) averaged over the data symbols. Constructive interference ensures reliable reception at the legitimate users, whereas destructive interference drives the target's observation toward incorrect symbol regions without sacrificing the illumination required for sensing. We derive the PEB for unknown complex target reflectivity and establish an exact factorization into transmit-illumination and receive-geometry terms. This decomposition reveals the distinct sensing roles of the two apertures, enables separate receive-placement and joint transmit-placement/SLP optimization, and provides analytical insights into local position identifiability, the range-information limitations of compact arrays, sensing-oriented receive placement, and in-waveguide attenuation. Building on these results, we develop an efficient algorithm by combining geometry-informed receive initialization, one-dimensional transmit-position searches, and convex per-symbol precoding. Numerical results demonstrate sub-millimeter localization, erroneous symbol decisions at the target, an expanded feasible operating region, and substantial gains over fixed-placement PASS and fully digital extremely large-scale MIMO (XL-MIMO) benchmarks.
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All authors Dimitrios Bozanis, Vasilis K. Papanikolaou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, Christos Masouros, George K. Karagiannidis

Signal Processing

Realistic modeling of Photonic Terahertz Communication based on a real-world 30 km 30 Gbps experimental demonstration

Mingxu Wang, Jianjun Yu, Xianming Zhao et al. (+9 more)

Experimental studies have demonstrated that THz links can support multi-kilometer, gigabit-per-second wireless transmission. However, few studies have effectively validated channel models through high-capacity, long-distance experiments. In this work, we experimentally demonstrate a 30 km ultra-long-haul photonic THz wireless communication system. 16 Gbaud quadrature phase shift keying (QPSK) signals are transmitted and tested, achieving a maximum data rate of 30 Gbps. For the first time, we validate two widely used THz channel models using a fully implemented 30 km experimental setup, providing a systematic and thorough analysis that integrates theoretical modeling with experimental results. We also perform an analysis of power consumption in photonic THz communication systems and provide a detailed comparison between simulated and experimental power consumption results. Finally, a detailed performance comparison between the simulation and experimental results, including signal-to-noise ratio (SNR), bit error rate (BER) and data rate, are provided. Our work provides essential insights for the design and energy-efficient deployment of future high-capacity THz networks, establishing a benchmark for subsequent research in channel modeling, experimental verification, and system-level energy efficiency.
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All authors Mingxu Wang, Jianjun Yu, Xianming Zhao, Jiali Chen, Ye Zhou, Xin Lu, Hansong Ma, Chengzhen Bian, Wen Zhou, Kaihui Wang, Weiping Li, Iman Tavakkolnia

Signal Processing · Cross-listed from eess.SY

Schur-Neural KF: Learned Schur-Consistent Corrections to the Extended Kalman Filter

Min Kim, Lianghao Cao, Soon-Jo Chung et al. (+1 more)

We present Schur-Neural KF (SN-KF), a learning-based correction to the extended Kalman filter (EKF) that preserves the probabilistic conditioning interpretation of the EKF. The method perturbs the predictive state-measurement cross-covariance and the Cholesky factor of the measurement noise covariance so that the resulting joint predictive covariance is always positive semidefinite. The positive semidefiniteness is ensured by a Schur complement-based parametrization. We instantiate the parametrization with a recurrent neural architecture whose matrix outputs are modulated by amplitude gates. We prove that incorporating a measurement does not increase the filter's state uncertainty, and show that no measurement can induce an arbitrarily large state correction relative to its statistical surprise. We also present a perturbative analysis suggesting SN-KF's structural strength in the data-scarce regime. We provide two numerical experiments to illustrate the practical benefits of SN-KF. In a two-radar experiment, enforcing Schur-consistency provides a much broader failure-free hyperparameter region and reduces RMSE for small training subsets, consistent with our theoretical analysis in the data-scarce regime. In the unicycle experiment, SN-KF achieves the best precision, recall, false alarm rate, and gated RMSE under innovation-based sensor-fault rejection.
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All authors Min Kim, Lianghao Cao, Soon-Jo Chung, Andrew M. Stuart

Signal Processing

A Novel Dynamic Ray-Tracing Channel Model for 6G LEO Satellite-to-Ground Communication Systems

Songjiang Yang, Cheng-Xiang Wang, Yinghua Wang et al. (+4 more)

The sixth generation (6G) communication systems envision a space-air-ground integrated network, making accurate satellite-to-ground (S2G) channel modeling crucial for communication systems design. Currently, the majority of S2G channel models rely on stochastic methods, which cannot provide multipath information based on realistic satellite orbits and environments. In this paper, we propose a novel low Earth orbit (LEO) S2G channel model based on dynamic ray-tracing to overcome the challenges of tracking rays for long-distance propagation by setting a virtual transmission plane (VTP). Moreover, since the line-of-sight propagation mechanism contributes the most power to satellite communications, the VTP is constructed based on the first Fresnel zone to generate parallel rays close to the ground. The realistic satellite orbit and environmental information are used in the proposed LEO S2G ray-tracing channel model to compute channel characteristics. The results show that the proposed LEO S2G ray-tracing channel model can accurately generate the channel characteristics based on the satellite orbits. The channel characteristics of LEO S2G communications computed by the proposed ray-tracing method can well match the channel measurements.
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All authors Songjiang Yang, Cheng-Xiang Wang, Yinghua Wang, Jie Huang, Yuyang Zhou, Wei Feng, el-Hadi M. Aggoune

Signal Processing

From Electromagnetic Physics to Intelligent Optimization: A Deterministic Modeling for Indoor Pinching-Antenna Systems

Qiushi Zhao, Zihan Feng, Ximing Xie et al. (+1 more)

Pinching-antenna systems (PASS) enable flexible wireless transmission by activating pinching antennas (PAs) along dielectric waveguides. However, existing PASS analyses commonly rely on tractable analytical or statistical channel abstractions that do not jointly capture in-waveguide field evolution, sequential PA power extraction, and site-specific indoor multipath propagation. This paper develops a physics-driven deterministic framework for end-to-end modeling of indoor PASS. Specifically, the parabolic wave equation (PWE) resolves the position-dependent electromagnetic field inside the dielectric waveguide, and coupled-mode theory (CMT) maps the local guided field to the radiation power and phase of each PA. Ray tracing (RT) subsequently models the site-specific indoor electromagnetic propagation from each PA to the users, including LoS, reflected, and diffracted components. To enable efficient repeated evaluations, we combine a reusable PWE-CMT field representation with a physics-assisted neural-network-based RT surrogate that preserves propagation geometry, visibility, and phase information. The proposed framework achieves a complex-channel NMSE of -19.14 dB while reducing the end-to-end single-link evaluation time from approximately 2.5 s to 77 ms. Its application to single- and multi-user deployment demonstrates that the physical model can be efficiently reused for PA-position and power-allocation optimization while preserving the underlying position-dependent propagation physics.
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All authors Qiushi Zhao, Zihan Feng, Ximing Xie, Hao Qin

Signal Processing

Integrated Communication and Computing with Index Modulation

Shreesal Shrestha, Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou et al. (+1 more)

We propose an integrated communication and computing (ICC) architecture that repurposes the inactive antennas of a spatial index modulation (IM) transmitter to perform over-the-air computation (AirComp). While IM delivers excellent spectral efficiency by selectively activating a subset of transmit antennas, the unused antennas are conventionally completely deactivated, forgoing spatial degrees of freedom (DoF) that can be exploited without additional bandwidth. By instead transmitting a pre-equalized, low-power computing stream over these idle antennas, the proposed architecture achieves simultaneous data transmission and computation without requiring orthogonal frequency or time resources. Alignment is performed entirely at the transmitters, such that the pre-equalization occurs leveraging local channel state information (CSI), while at the receiver, a vector Gaussian belief propagation (VGaBP) detector recovers the data payload under the discrete IM codebook constraint before the target function is estimated from the residual. Numerical results against exact maximum likelihood (ML) baselines confirm near-optimal detection under the adopted statistical model, and reveal that the number of antennas assigned to data transmission governs a direct trade-off between modulation robustness and AirComp accuracy.
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All authors Shreesal Shrestha, Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu

Signal Processing · Cross-listed from cs.IT

A DSP Framework for ISAC with random signals: From Physical Transceiver to Periodic Models

Junjie Chen

Communication-centric integrated sensing and communication~(ISAC) directly employs random data-bearing communication signals for both information transmission and environmental sensing. Existing studies generally describe pulse-shaped transmission in continuous time or directly adopt periodic discrete-time sensing models, leaving the digital signal processing~(DSP) operations connecting the physical transceiver and the periodic models unclear. This paper develops a DSP-oriented transceiver framework for single-antenna communication-centric ISAC. A common transmitter is constructed through modulation, cyclic-prefix~(CP) insertion, upsampling, and pulse shaping. For communication reception, the physical transceiver chain is reduced to a symbol-rate equivalent linear channel, which becomes circulant after CP removal and thereby supports frequency-domain equalization. For sensing reception, a high-rate reference waveform is extracted from the transmitted waveform and equivalently represented by circular pulse shaping. CP removal at the receiver then converts physical target delays into circular shifts of this reference waveform, leading to a periodic matched-filtering model and the corresponding range profile. Numerical results validate the developed framework through target range estimation and the symbol error rate performance of pulse-shaped CP-OFDM over frequency-selective Rayleigh fading channels.
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Signal Processing

Curv-Tail: Lightweight Long-Tailed Encrypted Traffic Classification with Discrete Packet-Length Encoding and Lorentz Prototypes

Yankun Wang, Jun-Jie Huang, Lin Liu et al. (+6 more)

Long-tailed encrypted traffic classification requires accurate recognition of infrequent classes under limited computational budgets. We propose Curv-Tail, a lightweight, end-to-end packet–byte framework trained without a separate pretraining stage. Mixed-resolution tokenization preserves exact packet-length identities within a bounded range and coarsens larger values to limit the vocabulary. An auxiliary objective predicts observed length tokens from contextual packet features before pooling, encouraging length-token retention beyond flow-level supervision. Compact temporal encoders process packet sequences and directional byte patches, and Lorentz prototypes with a shared learnable curvature magnitude classify their fused representation. On NUDT-Mobile and DataCon-Website under natural class frequencies, Curv-Tail achieves three-seed mean Tail-F1 scores of 85.70% and 46.30%, exceeding the strongest evaluated baselines by 2.91 and 1.89 percentage points, respectively. In 300-class profiling on an RTX 4090 with FP32 and batch size 256, Curv-Tail uses 98.48% fewer parameters and achieves 10.2 times the batch inference throughput of MM4Flow.
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All authors Yankun Wang, Jun-Jie Huang, Lin Liu, Xiaodong Lei, Yi Chen, Yeqing Yan, Lin Liu, Jiangyong Shi, Yongjun Wang

Friday 25 September

Signal Processing

Runtime Assurance Under Measurement Attack: Necessary and Sufficient Observability Conditions for Learned Control in Radio Access Networks

Yasser Al Eryani

Runtime assurance pairs a verified fallback with an untrusted controller and a switching monitor, and is the leading route to admitting learned policies into safety-relevant network control. Its guarantee rests on a condition it assumes rather than requires: that the monitor's estimation error is zero, or at worst stochastic with a characterisable rate. In mobile networks many measurements originate at untrusted endpoints, where the error is neither. We state this assurance precondition and prove it: at zero measurement noise the guarantee survives an adversary controlling $q$ channels if and only if the plant is $2q$-sparse observable with respect to the safety-relevant output. This is functional observability over attack supports, strictly weaker than full-state observability: on our topology a per-cell outage condition doubles the budget and one stated over total offered load triples it. The converse is a constructive attack making a safe and an unsafe trajectory observationally identical; it defeats every monitor on those channels, not one detector, at any noise level. As the trust split is static and known before deployment, confining the adversary to one family yields a confinement threshold, above which trusted channels alone resolve the state and all untrusted channels may be corrupt at once. Three uninfluenceable counters cross it here, lifting the budget from two to six and making placement the dominant lever. Below the precondition the monitor faces a frontier, not a dichotomy; a set-valued monitor is optimal and carries sufficiency into non-zero noise at the residual test's false-rejection rate. An adversary reaching the trigger radius owns the switch regardless. The same argument bounds any monitor reading agent messages rather than measurements. A rank test decides the budget and identification does not preserve it, so a published budget must name its margin floor.
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Signal Processing · Cross-listed from cs.LG

Learned Compression of SAR Phase-History Data: A Rate-Honest Feasibility Study on GOTCHA

Alizishaan Khatri

On-board compression of synthetic aperture radar (SAR) phase history is bandwidth-critical, and block-adaptive quantization (BAQ) remains the operational standard. We test whether a small convolutional autoencoder, with its encoder on the sensor, can compete with BAQ on complex phase-history patches from the AFRL GOTCHA collection. Every method is charged for all transmitted bits, rates are reported in bits per complex sample (b/cs), and detection is scored by one-to-one matching of CA-CFAR detections. The autoencoder (28,656 encoder parameters) loses at every rate. At 16 b/cs it reaches -2.87 dB NMSE, against -35.5 dB for 8-bit BAQ with $\pm 3\sigma$ clipping and -41.0 dB with a tuned clipping range. It also loses to a $16 \times 16$ block Karhunen-Loève transform (KLT), a local linear coder with a tenth of its encoder cost (-5.39 dB). Running the network in a companded Fourier domain helps, but its detection F1 remains bounded at 33%. The evidence points to this model, its normalization, and its objective, not to a fundamental limit of learned coding. Per patch, the data have modest lag-1 coherence ($|\rho| \approx 0.3$) and patch-specific spectral concentration. Two findings concern evaluation itself. First, 97% of CFAR crossings on raw $64 \times 64$ patches are border artifacts of the zero-padded detector. Second, on interior cells BAQ's clipping range decides detection: 8-bit BAQ keeps 69% F1 with tuned clipping but 17% at $\pm 3\sigma$, and at 8 b/cs or less adaptive FFT thresholding preserves more detections than BAQ. We close with an evaluation protocol for learned radar compression.
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Signal Processing · Cross-listed from eess.AS

Perceptually Motivated Alignment and Interpolation of Pitch-Aligned Time-Frequency Representations

Shahan Nercessian, Jeff Sontag and Alejandro Koretzky

We propose a framework for the alignment and interpolation of pitch-aligned time-frequency representations. Building on the tonal interval vector, we introduce a series of extensions that reformulate it as an invertible operator, culminating in a new feature extractor that embeds perceptual consonance priors within a pitch-aligned representation. Accordingly, we develop methods for aligning and interpolating between musical structures. For alignment, we cast the problem as a permutation search under perceptually weighted distances, enabling robust matching without explicit key detection, which is inherently ambiguous in music information retrieval. For interpolation, we employ optimal transport to generate musically meaningful transitions between pitch distributions, proposing a circular formulation over the Circle of Fourths/Fifths to respect harmonic structure. Finally, we learn a low-dimensional geometric representation of scale structure aimed to factorize scale color and density. Experimental results demonstrate the effectiveness and musicality of the proposed methods. Together, our contributions provide a principled, purely signal processing approach to modeling pitch structure in audio and symbolic music signals.
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Signal Processing

Stacked Fluid Metasurfaces: Mutual-Coupling-Aware Modeling and Optimization

Giovanni Iacovelli, Chandan Kumar Sheemar and Symeon Chatzinotas

Stacked intelligent metasurfaces process the transmitted field layer by layer, and fluid antennas make the position of every radiator a design variable. Combined, they pack radiators at sub-wavelength spacings within and across layers, where mutual coupling governs the physics that current models omit. This paper develops a coupling-consistent model of a fluid transmit layer illuminating a stack of passive fluid beyond-diagonal layers. The multiport impedance matrix of any port constellation is shown to be the impedance kernel sampled at the three-dimensional separations, in one closed spherical-Hankel form whose in-plane restriction is intra-layer coupling and whose axial restriction replaces the scalar Rayleigh–Sommerfeld propagator. Only the transmitter is driven: the layer currents are induced, and the familiar cascade is the single-pass limit of one matrix inversion. In the wavenumber domain the light circle still separates radiation from reaction, now with a plane-wave propagator attached, every Fourier mode sees a transmission line loaded by the layers, and an efficiency identity shows that what the stack costs in efficiency is set by its total induced-current norm, not by its layer count. Because the load is never assumed layer-diagonal, interconnections may join atoms on different sheets, and vertical networks repeated at every site are spectrally local. Alternating optimization designs precoders, positions, and loads with closed-form gradients. Numerically, fluid ports on a $\lambda/8$ grid perform within a few per cent of continuously movable ones, while a design based on the conventional cascade model can do worse than open-circuiting the stack.
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Signal Processing · Cross-listed from cs.LG

Online Learning via Learned Latent Bayesian Tracking

Guy Gerson, Tomer Raviv, Nir Shlezinger et al. (+2 more)

Online learning in non-stationary environments requires models to adapt rapidly from streaming data under strict computational constraints. A principled approach casts online learning as Bayesian state tracking, where model parameters are updated sequentially via Bayesian filtering. However, applying Bayesian filters directly to modern deep models is computationally prohibitive due to the high dimensionality of parameter space, forcing existing methods to rely on restrictive approximations or manually designed low-dimensional subspaces. In this work, we identify the absence of a suitable low-dimensional dynamical representation as the core bottleneck in Bayesian filtering-based online learning. Accordingly, we propose Adaptive Update through Representation Adaptation (AURA), a meta-learning framework that learns offline a low-dimensional latent state-space model governing the evolution of optimal model parameters under distribution shift. Online adaptation is then performed via extended Kalman filtering in this learned latent space followed by reconstruction of the full model parameters through a learned lifting map, enabling efficient single-step online adaptation while preserving model expressiveness. Evaluated on online adaptation of neural wireless receivers under time-varying channels and on non-stationary image classification, AURA shows substantial improvements in adaptation speed, accuracy, and computational efficiency over existing online learning and Bayesian filtering baselines, demonstrating that an adaptation-aware latent geometry is beneficial for effective Bayesian online learning in high-dimensional models.
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All authors Guy Gerson, Tomer Raviv, Nir Shlezinger, Tirza Routtenberg, Osvaldo Simeone