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Electrical Engineering and Systems Science Signal Processing

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Sunday 13 September

Signal Processing

Combating Suppressive Jamming with Dynamic Agile Reconfigurable Intelligent Surface Antenna Array (DARISAA)

Yakun Ma, Qingli Yan and Hui-Ming Wang

Suppressive jamming is a severe challenge to digital receivers in wireless communications, since high-power jamming may cause analog-to-digital converter (ADC) overload or automatic gain control (AGC)-limited quantization blocking of weak desired signals. Analog beamforming in the spatial domain can be employed to combat suppressive jamming signals before they reach the ADC. In this paper, we introduce a comprehensive anti-jamming scheme considering both direction-of-arrival (DoA) acquisition and suppressive jamming elimination based on a Dynamic Agile Reconfigurable Intelligent Surface Antenna Array (DARISAA). DARISAA is a novel type of reconfigurable antenna array composed of many metamaterial elements, which can dynamically adapt its phase responses, enabling suppressive jamming cancellation at the antenna-end. Accurate DoA estimation schemes for jamming and desired signals are proposed based on a subspace approach by utilizing the dynamic agility of the DARISAA. By leveraging the spatial-domain preprocessing capability of DARISAA and the digital beamforming of the multi-channel receiver, an analog (antenna)-digital hybrid anti-jamming scheme is developed to effectively suppress high-power jamming and significantly improve the signal-to-interference-plus-noise ratio (SINR) even with low-resolution ADCs. Simulation results demonstrate that the proposed comprehensive anti-jamming scheme achieves significant SINR enhancement, highlighting the advantages of DARISAA-enabled spatial front-end processing for jamming suppression.
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Signal Processing · Cross-listed from cs.LG

Should All Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness

Salma Alsinan, Maksim Makarenko, Sixiu Liu et al. (+2 more)

Geophysical data collected from active field sites are often contaminated by complex and heterogeneous noise, obscuring weak seismic events, and complicating automated interpretation. Although deep learning offers promising solutions for seismic processing, its performance is highly sensitive to the nature of training noise, especially under out-of-distribution (OOD) conditions. This study investigates the influence of noise parameters, such as type, scale, and complexity on the performance, generalization and robustness of neural networks in two geophysical tasks: first break picking and denoising. We simulate seismic-while-drilling data and apply controlled input source noise augmentation using stochastic generators to vary the noise characteristics. Different neural networks are trained on fixed noise types and scales, then evaluated across both seen and unseen noise scenarios. We incrementally increase the complexity of the noise by introducing compound noise mixtures and assess the performance of the model under increasingly challenging OOD conditions. This yields a robustness matrix that captures the generalizability of each model relative to its training configuration. Results indicate that larger noise scales boost generalization, and that effective alignment between noise type, task complexity, and architecture is key for maximizing generalization gains. In addition, training with compound noises mitigate weaknesses associated with single-noise training, acting as an additional implicit regularizer to improving robustness. These findings highlight key factors influencing model resilience in noisy geophysical environments and offer guidance for developing deep learning models that generalize effectively across diverse and unpredictable noise conditions.
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All authors Salma Alsinan, Maksim Makarenko, Sixiu Liu, Ali Aldawood, Ibrahim Hoteit

Signal Processing · Cross-listed from cs.CV

PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization

Zhixuan Chen, Jialiang Lu, Zhong Ye et al. (+2 more)

Accurate 3D localization of unmanned aerial vehicles (UAVs) remains challenging for existing multimodal approaches due to sparse LiDAR geometry, modality-imbalanced fusion, and redundant feature transmission over constrained edge-to-server links. To address these limitations, we propose PRI-Net, an efficient and lightweight multimodal fusion framework for UAV localization that integrates point cloud splatting, residual attention fusion, and an information bottleneck. Specifically, a 3D point cloud splatting (3DPCS) strategy is introduced to transform sparse LiDAR observations into geometrically consistent dense depth maps. A residual attention fusion (RAF) module is then designed to alleviate modal bias by using an image branch for coarse estimation and a gated fusion branch for refinement. In addition, a multimodal information bottleneck (MIB) module compacts features by filtering task-irrelevant redundancy. Experiments show that PRI-Net achieves high localization accuracy with lightweight architectures, while reducing feature dimensionality and improving edge-to-server UAV sensing efficiency and robustness.
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All authors Zhixuan Chen, Jialiang Lu, Zhong Ye, Yinghui He, Guanding Yu

Signal Processing

Sum Secrecy Rate Maximization for Secure ISAC

Aleksandar Boljević, Mingjun Ying, Ahmad Bazzi et al. (+2 more)

In an integrated sensing and communications (ISAC) system, targets may intercept information. We address this specific security issue in a full-duplex ISAC system with malicious eavesdroppers aiming to intercept uplink (UL) and downlink (DL) communication exchanges between the dual-functional radar and communication base station and legitimate communication users. We formulate an optimization framework to maximize the sum secrecy rate for both DL and UL, considering power budget constraints for sensing and communications. The optimization problem is non-convex, so we introduce an iterative joint Taylor-block cyclic coordinate descent (IJTB) method to approximate it as a convex problem. The IJTB method alternates between sub-problems: one for UL beamformers and another for UL power allocation, artificial noise covariance, and DL beamforming, using Taylor approximations to simplify optimization. Simulations demonstrate the effectiveness of our approach compared to standard benchmarks.
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All authors Aleksandar Boljević, Mingjun Ying, Ahmad Bazzi, Theodore S. Rappaport, Marwa Chafii

Signal Processing

Vision Language Models for Radiation Patterns to Antenna Parameters

Pallaviram Sure and Chandra Mohan Bhuma

Observed radiation patterns often serve as a primary evidence of antenna's behavior, but translating them into meaningful interpretations is a nontrivial and expertise intensive task. This demand necessitates automated pattern interpretation, a diagnosis problem encountered in applications encompassing Radio Frequency (RF) surveillance, non cooperative emitter characterization and Over The Air (OTA) testing. This work addresses the incorporation of Contrastive Language Image Pre training (CLIP) and other recent vision language models to perform such a diagnosis. These models analyze the multimodal data and extract discriminative features directly from radiation pattern images, to train Machine Learning (ML) approaches for meaningful inferences on geometrical and performance parameters of the antenna. Performance of three such vision language models is demonstrated using RadPat 50K, a synthetic dataset of radiation pattern images generated for uniform linear arrays (ULAs). The radiation pattern images are processed by the vision language models to obtain discriminative features. The CLIP extracted features are processed by either ML classification models to infer antenna parameters number of elements, element spacing, weighting scheme, presence of grating lobe and steering angle, or ML regression models to infer parameters beam width, directivity and main lobe direction. ML models achieve accuracies exceeding 70 percent, highlighting the potential of multimodal Artificial Intelligence (AI) towards intuitive antenna analysis.
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Signal Processing

Low-Complexity Near-Field Channel Estimation and Subcarrier-Cooperative Hybrid Precoding for Wideband XL-MIMO OFDM Systems

Kangda Zhi, Tianyu Yang, Songyan Xue et al. (+2 more)

This paper addresses both accurate near-field channel acquisition and scalable precoding for wideband extremely large aperture MIMO (XL-MIMO) OFDM systems, with particular focus on reducing complexity. In the considered system, each MIMO multipath component is characterized by five continuous angle-distance-delay parameters, making conventional sparse recovery methods computationally infeasible while suffering from grid mismatch. We propose a decoupled off-grid channel estimation algorithm based on sequential sparse Bayesian learning (SBL). By exploiting the separable structure of the OFDM near-field atom, the five-dimensional joint search is replaced by a sequence of low-dimensional operations and active-set inference, avoiding multiplicative scaling with the per-dimension grid sizes. A continuous-domain Newton refinement is incorporated based on the exact marginal likelihood to mitigate the grid mismatch. Based on the estimated multipath parameters, we then develop a subcarrier-cooperative hybrid precoding framework for multiuser wideband transmission. A large-scale-matrix-inversion-free alternating optimization algorithm is established to maximize the sum user rate, together with a non-iterative low-complexity design that exploits the parameterized channel structure and provides an effective initialization. Simulation results demonstrate the accuracy of the estimation methods compared to several benchmarks and the effectiveness of the precoding algorithm based on estimated channel parameters.
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All authors Kangda Zhi, Tianyu Yang, Songyan Xue, Fangzhou Wu, Giuseppe Caire

Signal Processing · Cross-listed from eess.SY

Probe Sets for Nonlinear Kalman Filtering: Multi-Center Gains and Covariance Recalibration

Shida Jiang, Shengyu Tao and Scott Moura

Kalman filters (KFs) choose the gain as the product of the state-measurement cross-covariance and the inverse of the innovation covariance. In nonlinear systems, the predictive distribution generally changes shape, and these covariances require approximation. Conventional nonlinear KFs approximate them around a single center, the predicted state. They can produce inaccurate gains and overconfident covariance estimates when one local approximation does not represent the measurement geometry across the uncertainty region. To address this issue, we introduce probe sets, small covariance-scaled collections of states at which the filter repeats its local approximation. We then select the gain that minimizes the average covariance reported by these local approximations. The same construction is used in our previously proposed covariance recalibration step to improve covariance consistency. The construction applies to different KF variants without requiring a specific approximation rule. For quadratic measurements, we derive conditions under which probing yields lower normalized true error variance than the corresponding single-center gain when prediction uncertainty is sufficiently understated. We apply the framework to four KF variants and compare the resulting filters with multiple baselines across 300 randomized setups for each of two systems. The results show that probing preserves typical accuracy while substantially reducing covariance inconsistency and the rare large-error trajectories that dominate the root mean square of per-run errors. The code is available at https://github.com/Shida-Jiang/Probe_KF.
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Signal Processing

Gaussian-trigonometric functional link artificial neural network: design and analysis

Jie Wang, Lu Lu, Yi Yu et al. (+3 more)

This paper proposes a Gaussian function-based trigonometric functional link artificial neural network (GTFLN) filter for linear-in-the-parameters nonlinear filtering. Compared with the adaptive exponential TFLN (AETFLN) filter, the GTFLN filter provides smooth and localized basis functions with reduced computational complexity, where modeling advantages are theoretically established through the smoothness, reproducing kernel Hilbert space, approximation error, and operator theory properties. To maximize the modeling performance, an optimized scaling parameter for the GTFLN filter is derived, yielding the optimized GTFLN (OGTFLN) filter. Least mean square (LMS) adaptation is applied to the GTFLN and OGTFLN filters for nonlinear system identification, resulting in the GTFLMS and OGTFLMS algorithms, respectively. Moreover, the theoretical steady-state excess mean-square error of the GTFLN filter is analyzed. Simulations validate the effectiveness of the theoretical analysis and demonstrate the improved performance of the GTFLN and OGTFLN filters over the linear-in-the-parameters benchmarks in nonlinear system identification and nonlinear acoustic echo cancellation. Based on the GTFLN filter, the filtered-g LMS (FgLMS) algorithm is proposed for nonlinear active noise control. Simulations demonstrate improved stability and noise reduction performance compared to the benchmarks.
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All authors Jie Wang, Lu Lu, Yi Yu, Xiaodong Li, Chengshi Zheng, Rodrigo C. de Lamare

Saturday 12 September

Signal Processing

Inverse Maxwell-Based Wall-Aware OFDM-ISAC for Slow-Moving Target Sensing

Tri Nhu Do, Yosefine Triwidyastuti and Gunes Karabulut Kurt

In this paper, we study integrated sensing and communication (ISAC) for short-range indoor orthogonal frequency-division multiplexing (OFDM) systems in which the sensing path crosses a building wall. The target is a slow-moving user equipment behind the wall, and its echo is embedded in the wall reflection and static indoor clutter. Because the wall adds excess propagation length, attenuation, and internal reflections, a conventional delay transform reports an apparent range. We therefore formulate physical-range estimation as an inverse Maxwell problem for the known wall: the range operator is the distorted-Born Jacobian of the discretized one-dimensional Helmholtz equation, assembled offline from the calibrated wall and applied online as a per-snapshot range inversion, so that the range-Doppler map is indexed by physical rather than apparent range. We then prove that this operator carries a structural limitation: when the two-way wall factor has constant magnitude and linear phase, the wall-aware range image is identically the free-space image on a translated grid, for every ridge level, taper, and noise realization. A free-space branch translated by the same excess length is therefore a required comparison. Sensing-only numerical results for a representative layered wall, with every branch on one range grid and a common post-FFT CSI model, show that the proposed inverse removes the range bias that wall-unaware processing cannot, and that a scalar-corrected free-space inverse tracks it closely, the two differing only marginally in empirical range RMSE and detection probability across the tested sweep.
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Signal Processing

Fourth-Order Co-Channel Interference-Aware OFDM-ISAC Sensing in Cluttered Environments

Tri Nhu Do, Yosefine Triwidyastuti and Gunes Karabulut Kurt

In this paper, we develop a sensing-centric receiver for monostatic orthogonal frequency-division multiplexing integrated sensing and communication (OFDM-ISAC) in the simultaneous presence of passive clutter and a non-cooperative co-channel OFDM transmitter. We show that removing the known ISAC symbols maps target and clutter echoes to deterministic delay-Doppler components, while the non-cooperative waveform remains a random, non-Gaussian, generically full-rank residual. Guided by this distinction, we combine moving-target indication for zero-Doppler clutter with a centered diagonal fourth-order statistic for interference identification. To obtain an unbiased fourth-order estimate from phase-aligned proper snapshots, we derive additive and multiplicative finite-sample bias corrections and pair the corrected statistic with a second-order moment to separate interference from Gaussian noise. Fourth order provides identification and presence testing, whereas second order provides the diagonal disturbance-power proxy used for weighting. We further introduce diagonally loaded weights that enforce a range-Doppler aperture-efficiency floor. Simulations show that the interference estimate approaches its theoretical profile as coherent support grows and remains insensitive to deterministic echoes, that the presence test resolves interference well below the level at which weighting matters, and that loaded processing improves target detection while controlling point-spread-function degradation.
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Signal Processing · Cross-listed from stat.ME

Finite-Sample FDR Control for Greedy Aggregation over Networks

Mehrdad Pournaderi

Distributed multiple testing asks $N$ sites, each holding p-values for its own hypotheses, to control the false discovery rate (FDR) of the discoveries made across the whole network while communicating only a small number of bits. Greedy interval aggregation (Pournaderi and Xiang, IEEE TSIPN, 2023) meets the communication budget but controls FDR only asymptotically, and its FDR can exceed the target at finite sample sizes; the same interval counts both select the rejection regions and calibrate the stopping rule, which biases the selected interval densities upward. We propose budgeted BONuS-GA: each site mixes synthetic uniform p-values into its data, the center ranks candidate p-value intervals across sites using the pooled counts, and each site's false discoveries are estimated from its own synthetic counts under a per-site share of the level. The procedure needs no knowledge of the null proportions, uses every p-value for both selection and inference, and controls $FDR\le\alpha$ at every sample size for any fixed assignment of hypotheses to sites, assuming only that the null p-values are independent uniforms, independent of the non-null p-values. It keeps the original $O(\sqrt m\log m)$ communication, $m$ being the total number of p-values, when $N=O(\sqrt m)$. We also give a sample-splitting alternative, cross-fit greedy aggregation, with finite-sample control under a random-site model, and we quantify the selection bias behind the original procedure's failure. In a monitoring-network simulation the budgeted procedure retains most of the power of its heuristic counterpart at moderate-to-large site sizes.
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Signal Processing

Impact of PA Nonlinearities on AFDM Sensing: A Matched Filtering Perspective

Eya Gourar, Yahia Medjahdi, Laurent Clavier et al. (+2 more)

Affine Frequency Division Multiplexing (AFDM) is a promising waveform for integrated sensing and communications (ISAC) due to its Doppler resilience. However, such waveforms exhibit a high peak-to-average power ratio (PAPR), and they require highly linear Power Amplifiers (PAs), which conflicts with ISAC requirements, where high transmit power for sensing is always beneficial. This paper investigates the impact of PA nonlinearities on the sensing performance of AFDM. Using the Bussgang decomposition of the nonlinearly amplified AFDM signal, we analyze the delay-Doppler ambiguity function for the sensing matched filtering and show that AFDM's sidelobes remain nearly unaffected. Simulation results confirm that AFDM outperforms OFDM in target detection under high Doppler and nonlinear amplification, highlighting its suitability for the sensing task.
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All authors Eya Gourar, Yahia Medjahdi, Laurent Clavier, Abdul Karim Gizzini, Patrick Sondi

Signal Processing · Cross-listed from cs.NI

Towards a Connected Heterogeneous All-Medium Integrated Network (CHAIN) for Converged Connectivity Across Land, Sea, Air, and Space

Paul Anthony Haigh, Sujan Rajbhandari, Kyle Bottrill et al. (+12 more)

Next-generation connectivity depends on data traversing multiple physical media within a single end-to-end path, yet research in optical fibre, free-space optical and radio wireless, non-terrestrial networks, and underwater communications has advanced largely in isolation. This fragmentation has a wider adverse impact on communication performance, deployment and adaption as the most demanding open problems in next-generation connectivity, cross-medium channel characterisation, transport-layer protocol design across heterogeneous latency regimes, and cross-domain orchestration, sit at the boundaries between domains and can only be addressed by bringing these challenges together in cross-domain systems. Space-air-ground integrated network research has begun to treat three domains analytically, and its recent extension to the sea surface represents the most ambitious multi-domain framework proposed to date, yet neither has produced experimental results, a deployed system, or a standards engagement. The subsurface and maritime domain remains absent from both. This paper proposes the connected heterogeneous all-medium integrated network (CHAIN), a framework that treats the undersea, maritime, terrestrial, aerial and space domains as a single design space. We identify an all-photonic network backbone as the unifying physical infrastructure, Artificial intelligence (AI)-driven orchestration as the cross-domain control layer, and the joints between domains as a unification challenge. We survey the state of the art across all domains and the existing cross-domain literature, develop the CHAIN framework and its technical pillars, characterise the five core open problems the field must resolve, and set out a research roadmap from near-term measurement campaigns and testbed validation through cross-medium field trials to global-scale deployment.
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All authors Paul Anthony Haigh, Sujan Rajbhandari, Kyle Bottrill, Abderrahmen Trichili, Scott Watson, Hanaa Abumarshoud, David Benton, Sinan Sinanovic, Johannes Herrnsdorf, Peter Christopher, Mohsen Khalily, Rahim Tafazolli, Harad Haas, Martin Lavery, Wasiu Popoola

Signal Processing · Cross-listed from math.ST

Can one regularizer reproduce posterior means at two noise levels?

Kang Liu

Consider Gaussian denoising with a fixed prior and varying noise variance. When the prior has a density, maximum a posteriori estimation uses its negative logarithm as a fixed regularizer; only the weight of the quadratic data term changes with the noise variance. We ask whether a fixed regularizer can also recover the posterior mean. Although a suitable regularizer exists at each variance, we prove that no single regularizer works at two distinct positive variances for any compactly supported prior other than a point mass. This holds even for nonconvex and nonsmooth regularizers. A residual computed from two denoiser evaluations gives positive lower bounds on the uniform approximation error and excess mean squared error of estimators using a common regularizer. Numerical examples illustrate these bounds and the cost of reusing a regularizer across noise levels.
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Signal Processing · Cross-listed from cs.NI

Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks

Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán et al. (+13 more)

As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platforms, AI-native 6G networks operate across a dynamic, multi-domain edge-cloud continuum where data originates from heterogeneous sources including user devices, radio access networks, sensing infrastructures, and vertical applications. Centralizing this massive volume of data creates severe communication overhead, unacceptable latency bottlenecks, single points of failure, and complex cross-domain governance challenges. Consequently, decentralization becomes a fundamental architectural requirement for future 6G network intelligence and zero-touch operations. Security serves as the primary enabler of this decentralized paradigm. Critical security functions, such as real-time threat detection, physical-layer attack mitigation, slice protection, and intrusion detection, require immediate access to local context and telemetry before operational data loses its value. However, moving intelligence to the edge via collaborative paradigms like federated learning (FL) and decentralized FL (DFL) introduces complex trade-offs. System security cannot be addressed in isolation; it is deeply intertwined with equally important aspects like trustworthiness, explainability, and energy sustainability. Taking these aspects into account, this paper develops a unified perspective on decentralized intelligence for 6G, arguing that decentralization, trustworthiness, explainability, and sustainability must be designed jointly rather than treated as independent requirements
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All authors Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán, Fernando Torres-Vega, José María Jorquera Valero, Manuel Gil Pérez, Eunjeong Jeong, Nikolaos Pappas, Farah Abed Zadeh, Chamara Sandeepa, Bartlomiej Siniarski, Madhusanka Liyanage, Betül Güvenç Paltun, Leyli Karaçay, Ioannis Pitsiorlas, Marios Kountouris

Signal Processing

Channel Estimation for Movable Antenna Systems: Challenges, Solutions, and Opportunities

Linchu Chen, Zhendong Li, Zile Zou et al. (+5 more)

Movable antenna (MA) has emerged as a promising technology for future wireless networks by exploiting channel variation over local antenna movement regions. However, accurate and efficient channel acquisition in MA systems remains challenging due to the trade-off between estimation accuracy and computational complexity. In this article, the MA channel model and the associated estimation framework are first reviewed, where channel information over the movement region is reconstructed from finite measurements by exploiting shared path parameters. The structured dependence of MA observations across space, time, and frequency naturally motivates the adoption of tensor-based modeling for channel estimation. Subsequently, we discuss the tensor-based signal model and corresponding parameter estimation methods from multidimensional observations. These methods are further compared with conventional channel estimation methods in terms of estimation accuracy, computational complexity, and general applicability. Furthermore, a representative case study is provided to illustrate the performance and characteristics of different algorithms under MA channel estimation settings. Finally, some future research directions for tensor decomposition-based channel estimation in MA systems are outlined.
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All authors Linchu Chen, Zhendong Li, Zile Zou, Zhou Su, Lin Chen, Ruoyu Zhang, Qingqing Wu, Wen Chen

Friday 11 September

Signal Processing

Learning-Free Hierarchical Joint Estimation of AoA, Pointing Error, Receiver Jitter, and Turbulence in Multi-Aperture FSO Systems

Mahtab Dashtebozorgi, Meysam Ghanbari, Rula Ammuri et al. (+2 more)

This paper proposes a learning free hierarchical estimator for jointly recovering the angle of arrival (AoA), transmitter pointing error, receiver induced jitter, and per aperture turbulence coefficients in a multi aperture free space optical system. The proposed method exploits the distinct spatial signatures contained in quad photodetector measurements. First, normalized quadrant imbalance ratios provide an approximate closed form AoA estimate. Next, AoA compensated lens powers are transformed into a log linear regression model for non iterative pointing error estimation. Finally, receiver jitter and turbulence coefficients are directly reconstructed after compensating for the estimated angular losses. The method requires neither neural network training nor exhaustive multidimensional search and has computational complexity linear in the number of lenses. Monte Carlo results demonstrate robust AoA estimation under Gamma Gamma turbulence, reveal turbulence induced error floors in OLS based pointing and jitter estimation, and show improved turbulence reconstruction with larger arrays and explicit pointing compensation.
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All authors Mahtab Dashtebozorgi, Meysam Ghanbari, Rula Ammuri, Mazen Hasna, Khalid Qaraqe

Signal Processing

Progressively Attenuated Multi-Branch Reception for Inter-HAPS Optical Links

Meysam Ghanbari, Betul Beidas, Wasiu O. Popoola et al. (+3 more)

Inter HAPS optical links can experience receiver saturation at short separations and become signal to noise ratio (SNR) limited at longer distances. This work proposes a saturation aware spatial multi branch intensity modulation direct detection receiver using nonoverlapping apertures with progressive attenuation and selection of the nonsaturated branch with the highest instantaneous SNR. A Gaussian beam model with finite aperture pointing loss and common two dimensional pointing jitter yields closed form branch level expressions for saturation, insufficient SNR, usability, and outage probabilities. Results show that additional branches suppress short range saturation outage, shift the minimum outage point toward shorter separations, and enable progressive attenuation to control the saturation SNR tradeoff. Monte Carlo simulations closely match the analytical results. Overall, the proposed architecture provides a low complexity approach to extending the usable dynamic range of inter HAPS optical receivers.
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All authors Meysam Ghanbari, Betul Beidas, Wasiu O. Popoola, Mazen O. Hasna, Khalid A. Qaraqe, Mohammed Elamassie

Signal Processing

Noise-Domain OTFS: Statistical Delay-Doppler Signaling for Future Wireless Networks

Mahmoud Aldababsa

Orthogonal time frequency space (OTFS) usually conveys information through deterministic constellation symbols placed on a delay–Doppler (DD) grid. This article develops a different viewpoint, termed noise-domain OTFS (ND-OTFS), in which each message selects the covariance of a random DD-domain vector. The resulting statistical codeword can encode information through per-bin variance, equal-energy correlation structure, or separable DD covariance. We explain the transceiver principle, receiver implications, reliability–rate tradeoffs, and practical challenges. Representative results show that a matched-payload coherent OTFS reference retains a clear raw-reliability advantage, whereas ND-OTFS enables information recovery from covariance geometry even when competing states have identical average energy. Additional statistical observations reduce finite-sample overlap at the cost of latency and rate. ND-OTFS therefore complements, rather than replaces, deterministic OTFS by introducing a matrix-valued statistical signaling dimension across DD resources.
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Signal Processing · Cross-listed from eess.SY

From Winding-Fault Geometry to Reliability: Estimation and Prognosis of Stator Inter-Turn Faults

Bambang L. Widjiantoro, Syahrul Munir, Katherin Indriawati et al. (+1 more)

This paper develops an integrated framework for estimation, prognosis, and reliability assessment of stator inter-turn faults in induction motors. The fault is characterized by its severity, defined as the fraction of short-circuited turns, and its spatial orientation. A geometric fault model shows that the resulting output signature is affine in the fault severity and exhibits a fundamental spatial periodicity. Exploiting this structure, an augmented-state particle filter jointly estimates the nonlinear electromechanical state and the unknown fault severity while quantifying posterior uncertainty. The estimated degradation is then propagated using four prognostic models: linear trend, Holt exponential smoothing, Bayesian degradation, and particle-based forecasting. Their predictions are connected to threshold-crossing remaining useful life (RUL), first-passage reliability, degradation-dependent hazard reliability, and a Weibull lifetime benchmark, thereby providing both deterministic and probabilistic health assessments. Numerical results demonstrate accurate online fault estimation and output reconstruction, characterize the effects of degradation pattern and prediction horizon on prognosis, and show consistent reliability and threshold-crossing predictions. Moreover, estimation accuracy remains comparable across the distinct fault orientations induced by the spatial periodicity. The resulting framework provides a unified connection from physics-based inter-turn fault modeling to online diagnosis, degradation prognosis, and reliability assessment.
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All authors Bambang L. Widjiantoro, Syahrul Munir, Katherin Indriawati, Moh Kamalul Wafi

Signal Processing

Krum-Inspired Central Teacher Selection and Residual Channel Bottlenecks for Efficient DeepSC

Rami Eid, Mostafa Jammoul, Omar Kaaki et al. (+3 more)

Deploying transformer-based semantic communication models on edge devices requires compression that preserves semantic fidelity under channel variability. We study a compressed deep semantic communication (DeepSC) student trained by multi-teacher knowledge distillation and combine two ideas: (i) a residual channel bottleneck that splits the transmitted representation into a base stream and a residual stream with unequal power allocation, and (ii) a Krum-inspired, medoid-style centrality criterion that selects a single central teacher from a five-model ensemble, applied at the logit and intermediate-feature levels and combined with a feature-dominant distillation loss. On the EuroParl benchmark, a two-layer student recovers about 98% of the four-layer teacher's bilingual evaluation understudy (BLEU)-1 under additive white Gaussian noise, with 93% of its BLEU-4 and 96% of its sentence-BERT (SBERT) score, and about 89%, 77%, and 87% of the teacher's BLEU-1, BLEU-4, and SBERT, respectively, under Rayleigh fading, while reducing non-embedding parameters by 1.33x and single-teacher inference latency by 1.79x (9.0x relative to a five-teacher ensemble used here as an upper-bound reference, not a deployment baseline). Controlled ablations over three seeds indicate complementary contributions of about +8.4% BLEU-1 (+9.6% SBERT) from the residual bottleneck and +3.0% (+2.9% SBERT) from centrality-based selection over mean aggregation. A decoder-mode ablation shows that the base stream alone recovers about two-thirds of the full BLEU-1 while the residual stream alone collapses, supporting the role of the residual as a refinement on top of the base.
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All authors Rami Eid, Mostafa Jammoul, Omar Kaaki, Maria Slim, Mariette Awad, Hadi Sarieddeen

Signal Processing

Privacy-Preserving Deep Joint Source-Channel Coding with In-Loop Concept Erasure

Rami Eid, Maria Slim, Mariette Awad et al. (+1 more)

Deep joint source-channel coding (DeepJSCC) transmits learned semantic features efficiently but can leak sensitive attributes such as gender, race, or speaker identity. We propose LEAPSC (LEACE-in-the-loop privacy for semantic communication), whose core contribution is the integration of in-loop least-squares concept erasure (LEACE) within a variational information bottleneck (VIB) encoder. By periodically refitting the projection operator during training, LEAPSC couples the encoder dynamics to the erasure mechanism, driving attribute-conditional mean differences toward zero within each task-label group on the fitting sample. Additional components, namely conditional value-at-risk (CVaR) tail-sensitive privacy, feature-wise linear modulation (FiLM) signal-to-noise ratio conditioning, and Lagrangian dual ascent, improve robustness across channel conditions and over the high-leakage tail of samples. On CelebA, FairFace, and Google Speech Commands, LEAPSC reaches task accuracy of 0.862, 0.755, and 0.925 respectively, with attacker accuracy at or below the label-only floor on CelebA (0.548 vs. floor 0.580) and within 2 percentage points (pp) of chance elsewhere, improving over an information-bottleneck adversarial baseline (IBAL) at a matched 52-epoch budget by +3.6, +2.5, and +1.3 pp (Welch's t-test, p=0.019 on CelebA).
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All authors Rami Eid, Maria Slim, Mariette Awad, Hadi Sarieddeen

Signal Processing

Where to Defend? Layer-Wise Adversarial Training for Robust Transformer-Based Semantic Communications

Maria Slim, Razane Tajeddine, Mariette Awad et al. (+1 more)

Deep learning-based semantic communication (DeepSC), a Transformer-based encoder-decoder, achieves semantic fidelity over noisy channels but remains vulnerable to adversarial perturbations injected at multiple stages of the pipeline. We present a layer-wise robustness framework that compares fast gradient sign method (FGSM), projected gradient descent (PGD), and l2-normalized fast gradient method (FGM) defenses at the embedding output, and then uses PGD to analyze three attack and defense points: the embedding output, encoder output, and channel-encoder bottleneck. We evaluate reconstruction on Europarl and UK Hansard and sentiment classification on SST2 and YELP under additive white Gaussian noise (AWGN) and Rayleigh fading. A first-order damage budget, {epsilon} times the l1 norm of the clean-input loss gradient at each injection point, predicts the attack-severity ordering, and the transfer matrix reveals asymmetric defense transfer: the embedding defense transfers strongly to encoder attacks, whereas encoder defenses degrade robustness against upstream attacks. For reconstruction, encoder-point training yields the largest matched gain but fails severely under embedding attacks; joint embedding-plus-encoder training retains comparable gains under encoder attacks while mitigating this mismatch failure, and Rayleigh fading attenuates robustness gains and degradation. For classification, three of four defenses collapse to constant predictors; only the channel-encoder defense remains non-degenerate, suggesting a protective role for the 128D-to-16D bottleneck. At a signal-to-noise ratio (SNR) of 9 dB and perturbation budget {epsilon} = 0.3, the matched encoder defense recovers bilingual evaluation understudy (BLEU) from ~ 0.10 to ~ 0.64 on Europarl/AWGN, whereas the mismatched encoder defense yields a -0.444 BLEU change relative to the undefended baseline under an embedding attack.
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All authors Maria Slim, Razane Tajeddine, Mariette Awad, Hadi Sarieddeen

Signal Processing

Learning the Topology of a Simplicial Complex Using Noisy Simplicial Signals

Andrei Buciulea, Elvin Isufi, Geert Leus et al. (+1 more)

Graphs are a fundamental tool for modeling the irregular (non-Euclidean) structure of complex data. However, they are inherently limited to representing pairwise relationships, making them inadequate for datasets exhibiting higher-order interactions. Simplicial complexes (SCs) have emerged as a promising framework for capturing such higher-order dependencies. This paper focuses on the problem of identifying the topology of an SC from signals, which serves as the foundation for SC-based processing and learning schemes. We consider a setting where we observe noisy signals (features) associated with the nodes of the SC (0-simplices) and a subset of the edges (1-simplices). We assume the observed signals are smooth over the unknown SC topology, and that the higher-order interactions are sparse. Building on these assumptions, we formulate topology learning as a nonconvex optimization problem and propose an efficient block-coordinate descent (BCD) algorithm to solve it. A key step in our formulation is the modeling of the topology of the SC using binary edge and triangle selection vectors, combined with efficient greedy algorithms for optimizing such vectors. We establish theoretical convergence guarantees to a stationary point of a relaxed (penalized) version of the problem and discuss computational complexity. Multiple numerical experiments with both synthetic and real-world datasets validate the effectiveness of our approach, highlighting the capability of SC-learning methods to uncover and model higher-order relationships in complex datasets.
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All authors Andrei Buciulea, Elvin Isufi, Geert Leus, Antonio G. Marques

Signal Processing

Multi-Label 12-Lead ECG Classification on the PTB-XL Dataset: A Comparative Evaluation of Deep Learning Architectures and Heterogeneous Ensemble Approaches

Yunus Emre Mert, Ece Akdoğan and Hüseyin Üvet

This study aimed to compare the performance of different deep learning architectures and heterogeneous ensemble learning approaches for multi-label 12-lead ECG classification on the PTB-XL dataset. Five different models, namely 1D-ResNet18, Bidirectional Mamba, xLSTM, CWT-ViT-KAN, and the pre-trained ECGFounder, were evaluated. Utilizing the recommended split structure of the PTB-XL dataset, folds 1-8 were allocated as the training set, fold 9 as the validation set, and fold 10 as the independent test set. In addition to the individual models, three different ensemble approaches were investigated: Equal-Weight Soft Voting, Validation-Weighted Soft Voting, and stacking. Among the individual models, the highest performance was achieved by ECGFounder, with a Macro AUROC of 0.930 and a Macro AUPRC of 0.823. For the ensemble models, the highest values in the primary macro performance metrics were obtained by the stacking approach, achieving a Macro AUROC of 0.936, a Macro AUPRC of 0.836, and a Macro F1 of 0.763. The highest subset accuracy of 0.630 was achieved using the Validation-Weighted Soft Voting method. The findings indicate that heterogeneous ensemble models, which combine different representation learning approaches, can provide additional performance improvements over individual models in multi-label ECG classification.
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Signal Processing

Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment

Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh et al. (+2 more)

sEMG is vulnerable to various contaminants that distort signal morphology and spectral content. Accurate signal quality assessment (SQA) is essential for identifying such degradation and ensuring reliable clinical analyses and decisions. Recent neural network-based SQA methods achieve accurate quality estimation by learning complex contamination patterns, yet their black-box nature prevents clinicians from understanding or validating the reported scores and limits adaptability to application-specific quality definitions without retraining. To address these limitations, we propose Prism-SQA, an interpretable and adaptable neural framework that reformulates SQA as a physiology-aware source-separation and verification process. Prism-SQA decomposes each input signal into a clean sEMG component and five contaminant-specific components using a U-Net with bidirectional long short-term memory. Each separated contaminant component is examined by a Contaminant Fingerprint Verifier, which enforces physiological plausibility by comparing its temporal and spectral structure with canonical contaminant signatures. This design allows clinicians to inspect how each contaminant affects signal quality, grounding the assessment in transparent, signal-level evidence rather than opaque latent representations. Quality indices computed from the verified components further enable customization of quality criteria across clinical contexts without retraining. We evaluate Prism-SQA on continuous quality-score estimation using synthesized noisy sEMG from public Ninapro datasets and on binary quality classification using a clinical dysphagia dataset. Results show that Prism-SQA achieves competitive or better performance than contemporary black-box neural methods while providing explicit interpretability and adaptability, advancing toward practical and clinically aligned sEMG SQA.
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All authors Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh, Sheng-Yu Peng, Yu Tsao

Signal Processing

Unified Constrained Geometric Configuration Optimization for Source Localization Systems: A Riemannian Manifold-Based Approach

Xin Cheng, Feng Shu, Gangle Sun et al. (+3 more)

Time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), received signal strength difference (RSSD) and angle of arrival (AOA) are commonly used techniques for source localization. The positioning accuracy of these systems depends heavily on the geometric configuration of sensors, which is typically constrained by practical conditions. This paper presents a unified framework for optimizing sensor geometry across all five localization systems, explicitly incorporating both distance and angle constraints on sensor positions. First, the Cramér-Rao lower bounds (CRLBs) of these systems are transformed to obtain a unified expression. Based on this expression, a unified constrained geometric configuration optimization problem is formulated. The problem is then simplified into a compact form by replacing the sensor-target angles with an orientation matrix. Subsequently, a Riemannian manifold-based constrained geometric configuration optimization algorithm (RM-CGCOA) is proposed to optimize the sensor-target distances and the orientation matrix. This algorithm casts the orientation matrix onto a product manifold of unit circles. An adaptive pullback is further proposed to strictly enforce angle-related inequality constraints, ensuring that all iterates remain feasible when updating the orientation matrix via the Riemannian gradient. Within RM-CGCOA, analytical optimal distances are derived for TOA, TDOA, RSS and AOA, whereas for RSSD, the distances are updated using projected gradient descent (PGD) together with the orientation matrix. Experimental results demonstrate that the proposed RM-CGCOA consistently achieves a significantly lower position error bound (PEB) compared with the existing strategies and yields a similar PEB to the near-optimal search algorithm, but with a much faster running time.
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All authors Xin Cheng, Feng Shu, Gangle Sun, Xinrui Li, Yuqi Chen, Guangjie Han

Signal Processing · Cross-listed from q-bio.QM

Audiovisual diarization of overlapping click trains in sperm whale (Physeter macrocephalus) vocal sparring using a three-hydrophone array

Lara Berkenbaum, Hervé Glotin, François Sarano et al. (+4 more)

The individual attribution of sperm whale (Physeter macrocephalus) vocalizations during surface interactions constitutes a methodological challenge due to acoustic overlaps, multipath propagation, body shadowing, and indistinguishable inter-pulse intervals among similar-sized individuals. A multimodal audio-visual workflow is presented to deinterleave and attribute uncharacterized click trains produced by immature males during “vocal sparring” using a portable three-hydrophone array coupled with synchronized video. The approach combines spectro-temporal tracking, based on inter-click interval dynamics and the Constant-Q Transform, with spatial time-difference-of-arrival modeling projected onto the image plane for optical validation. Analysis of 2,655 manually validated clicks shows that purely acoustic clustering diarization errors remain low, peaking at 25.79% only during extreme temporal superpositions. Integrating the optical modality resolves residual spatial indeterminacies; although near-planar array geometry induces vertical ambiguities, the system achieves up to 100% horizontal visual concordance for primary emitters. Crucially, this framework successfully reconstructed and assigned 11 distinct, intertwined click trains totaling 882 clicks to specific focal individuals despite near-field tactile constraints. Because ethological descriptions remain incomplete without identifying the emitter, explicitly correlating these emissions with physical kinematics provides the fine-scale resolution required to define this socio-acoustic behavior.
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All authors Lara Berkenbaum, Hervé Glotin, François Sarano, Walter M X Zimmer, Véronique Sarano, Olivier Adam, Pascale Giraudet

Signal Processing

Secrecy Sum-Rate Maximization in Finite Blocklength IRS-aided Systems With Perfect and Imperfect CSI

Monir Abughalwa, Nguyen Van Huynh, Diep N. Nguyen et al. (+2 more)

The rapid growth of the Internet of Things (IoT) requires efficient and secure communication technologies to enable ultra-reliable low-latency (URLLC) communication applications. Intelligent reflecting surfaces (IRS) have emerged as a promising solution to enhance IoT network secrecy performance by improving signal quality for legitimate devices (Bob), while thwarting the eavesdropper (Eve) interception. However, ensuring secrecy across multiple users, given their diverse channels and locations, is challenging, especially under finite blocklength (FBR) constraints, which are common in IoT networks. This paper investigates the secrecy performance of IRS-aided URLLC systems under three channel state information (CSI) scenarios: perfect CSI, imperfect CSI, and unknown eavesdropper's CSI. We first formulate a non-convex optimization problem to maximize the system's sum secrecy rate (SSR) by jointly optimizing the transmitter's beamforming and the IRS's passive reflective elements, while maintaining FBR-related latency and transmission duration constraints. For imperfect CSI, the semi-infinite uncertainty constraints are transformed into finite linear matrix inequalities (LMIs) via a successive convex approximation (SCA)-based approach, and the proposed algorithm is proven to converge to a locally optimal solution with low computational complexity.
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All authors Monir Abughalwa, Nguyen Van Huynh, Diep N. Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz

Signal Processing · Cross-listed from physics.plasm-ph

Nonuniform Hydrodynamic Plasma-Wave Instability in a Gated Electron Channel

Yuhui Zhang

We report a theoretical and numerical study of plasma-wave instability in a current-driven InGaAs/GaAs gated electron channel. A finite drain current drives the channel away from a uniform plasma-wave cavity: the carrier density and plasma velocity decrease toward the drain, while the electron drift velocity increases. This current-induced spatial nonuniformity modifies both the propagation time and amplification of plasma waves and becomes increasingly important as the sonic regime is approached. Using a hydrodynamic description combined with the unified charge-control model, we determine the nonuniform steady state and analyze the resulting plasma-wave frequency and instability growth rate. The oscillation frequency is found to be governed by wave propagation through the entire channel rather than by the local drain conditions, leading to a pronounced current-induced frequency reduction that is not captured by a uniform-channel approximation. The instability results from the competition between boundary-induced plasma wave gain and distributed losses due to momentum relaxation and electron viscosity. The finite-viscosity theory shows good quantitative agreement with the simulation data over the subsonic regime and regularizes the singular behavior of the inviscid description near the sonic point. At higher currents, the dynamics become increasingly sensitive to the near-sonic structure and contact boundary conditions, indicating that the eventual suppression of the instability cannot be attributed to bulk hydrodynamics alone.
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