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Electrical Engineering and Systems Science Signal Processing

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Friday 14 August

Signal Processing

Explainability Boosted Anomaly Detection Framework for O-RAN based NextG Networks

Nurullah Aksu, Ali Fuat Sahin and Semiha Tedik Başaran

The wireless networks have historically faced significant security vulnerabilities, necessitating advanced anomaly detection mechanisms, especially as networks evolve towards 6G and beyond. This study introduces an advanced anomaly detection framework that leverages explainable artificial intelligence to enhance the security of next-generation (NextG) cellular networks. By implementing and evaluating a variety of artificial intelligence models, the framework demonstrates high accuracy and efficient runtime performance in identifying malicious traffic within a realistic Open Radio Access Network (O-RAN) testbed. A key innovation of this work is the integration of post-hoc explainability methods to identify the most critical key performance metrics (KPMs), which enables a significant 80% reduction in dataset complexity without compromising detection accuracy. Additionally, explainability analyses identify several critical attack traffic characteristics, such as protocol type, bandwidth, interval, and duration, to prevent upcoming network attacks. The resulting framework effectively balances computational efficiency, accuracy, and explainability, underscoring its practical applicability for enhancing security in next-generation cellular networks.
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Signal Processing

Handover Analysis for Vehicular Communication with Explainability on the Fly

Ali Fuat Sahin, Semiha Tedik Başaran and Tufan Kumbasar

Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions. While machine learning (ML) approaches can improve HO detection by capturing complex relationships among various key performance indicators (KPIs), their black-box nature limits interpretability and operator trust. To address this, this paper investigates HO detection from an explainability-on-the-fly perspective using inherently interpretable models based on the functional analysis of variance (fANOVA) framework. The proposed models are evaluated using two real-world operator datasets and compared against a Long Short-Term Memory baseline augmented with post-hoc SHAP explanations. Unlike post-hoc approaches, the proposed framework enables immediate interpretation of model decisions without incurring additional computational overhead. This capability is particularly critical for latency-sensitive vehicular networks. The results show that fANOVA-based models achieve competitive detection performance while providing significantly reduced explanation latency compared to conventional post-hoc methods. Furthermore, feature ranking and visualization analyses reveal physically meaningful relationships between KPIs and HO occurrences that align with standardized HO mechanisms. These results demonstrate that inherently interpretable models provide an efficient and transparent solution for HO detection in next-generation vehicular networks.
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Signal Processing

Impulse Response Estimation via Laguerre-Fourier Expansion

Tamás Dózsa, Art J. R. Pelling and Matthias Voigt

The empirical transfer function estimate (ETFE) is a widely used method for system identification of linear time-invariant (LTI) systems in engineering disciplines such as acoustics, audio engineering, seismography, and tomography. However, ETFE suffers from numerical limitations when the excitation signal is band-limited or vanishes at certain frequencies, which is a common physical constraint of the excitation in practice. In such cases, division in the frequency domain becomes heavily ill-conditioned, and small measurement disturbances or numerical inaccuracies can degrade the solution. This paper presents L-ETFE, a generalization of ETFE based on Laguerre-Fourier expansions that addresses these limitations. After a suitable transformation, the method can yield a well-conditioned circulant problem even when the original ETFE system is ill-conditioned. Solving this problem via classical ETFE yields the discrete Laguerre-Fourier coefficients of the system's transfer function. The desired impulse response (IR) of the system-to-be-identified can then be recovered by a subsequent transformation pipeline. We derive novel and efficient algorithms for performing these transformations and analyse the conditioning of the transformed problem, explicitly characterizing its dependence on the input and a parameter used in the Laguerre-Fourier expansion. We evaluate the method on two simulated discrete-time LTI systems of varying complexity. The experiments demonstrate accurate IR recovery for spectral-zero and band-limited excitation, where standard ETFE fails.
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Signal Processing

The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data

Jiajie Chen and Jinfeng Li

Real-world EV charging data exhibit three interlocking pathologies: hardware fragmentation (network timeouts and billing resets split sessions), physical violations (independent energy/duration models produce impossible states like 50 kWh in 10 min on a 7 kW charger), and collider bias (clustering on post-treatment outcomes opens backdoor paths for price elasticity). We propose the Note-Chord-Voice framework, a music-inspired, axiom-driven pipeline that separates data cleaning (Repair Chords), structural pattern discovery (Harmonic Chords), descriptive source separation (NMF Voices), and causal inference into distinct, falsifiable stages. Key innovations: (i) falsification gates (A1-A5, G3, G10) that test data suitability before modeling; (ii) Gamma-initialized NMF with input rescaling for convergence stability from STL decomposition; (iii) tag-based coupon grading (A/B/C/D) to isolate quasi-random treatment from night-time confounders and targeted promotions; (iv) separate per-voice OLS to avoid simplex collinearity; (v) Foote novelty curves for structural regime detection. Applied to the Jiangmen dataset (495,707 sessions, 20 stations, from July 2024 to March 2025), all core axioms pass except G3 (no strong 168 h cycle). NMF achieves R^2=0.9921; the physically constrained duration model yields aggregate R^2=0.5409. Two voices are price-sensitive (beta = -11 to -14 min, p<0.001), of which one is stable (Voice 3, beta=-14.16) and one treatment-driven (Voice 1, beta=-11.10); only the stable voice supports causal claims. Counterfactual simulation shows targeting discounts to price-sensitive voices recovers 52.8% of discount expenditures (~0.85M CNY/year); restricting to the single stable price-sensitive voice yields a more conservative estimate.
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Signal Processing

EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals

Yulong Peng, Yijian Pan, Yuqi Yang et al. (+4 more)

Human motion is governed by a hierarchical motor system where the brain provides high-level intentions and lower-level structures coordinate detailed dynamics. Existing brain-computer interfaces (BCIs) typically oversimplify this into constrained classification or low-dimensional control, failing to capture the richness of natural movement. Bridging this gap to achieve open-vocabulary, full-body motion synthesis remains challenging due to the substantial cross-modal divergence between sparse neural signals and high-dimensional kinematics, as well as the lack of large-scale paired EEG-motion datasets. To address this, we introduce EEG2MOTION, the first EEG-motion-text dataset for human motion synthesis, comprising nearly 20,000 paired samples across thousands of motions. Using this dataset, we first demonstrate via multimodal contrastive learning that non-invasive EEG embeddings can be effectively aligned with text, video, and motion representations to decode high-level semantics. We then propose EEG-conditioned Masked Motion Model (EMMM), a generative framework that unites an EEG encoder with a motion decoder to synthesize continuous, full-body human motions directly from brain activity. Experimental results show that EMMM generates coherent and realistic motion sequences from non-invasive brain signals. To the best of our knowledge, this is the first work to generate diverse full-body human motions from non-invasive brain signals, opening a new direction toward generative and open-vocabulary motor BCIs. See our project page: https://yulom.github.io/EEG2MOTIONdemopage/.
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All authors Yulong Peng, Yijian Pan, Yuqi Yang, Nenggan Zheng, Weidong Chen, Xiaoling Hu, Shaomin Zhang

Signal Processing

Sensing-Aided Ordered Reliability Bits Guessing Random Additive Noise Decoding

Yu Ge, Lukas Rapp, Ken R. Duffy et al. (+1 more)

Integrated sensing and communication (ISAC) is a key enabler for future wireless systems, providing environmental information that can support tasks beyond conventional data transmission. However, its impact on channel decoding remains less explored. This paper studies sensing-aided ordered reliability bits guessing random additive noise decoding (ORBGRAND) over single-input single-output narrowband fading channels. Environmental information is used to construct a geometry-based prior for the channel coefficient, which is fused with pilot observations via linear minimum mean square error (LMMSE) estimation. The resulting posterior channel estimate and uncertainty are used to compute the log-likelihood ratios (LLRs) supplied to ORBGRAND, improving the reliability ordering that drives its noise-guessing process. Simulation results demonstrate improved block error rate and reduced average query complexity, with the largest gains in pilot-limited regimes.
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All authors Yu Ge, Lukas Rapp, Ken R. Duffy, Muriel Médard

Signal Processing · Cross-listed from eess.IV

UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI

Mahdi Saberi, Toygan Kiliç and Mehmet Akçakaya

MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task by combining the MRI forward model with learned image regularization within algorithm-unrolling frameworks. However, most existing PD-DL methods reconstruct complex-valued images directly, thereby implicitly coupling magnitude and phase within a single learned representation. This coupled regularization may be suboptimal in reconstruction settings where accurate phase modeling plays an important role, such as partial Fourier (PF) imaging, where recovery of the omitted asymmetric k-space measurements depends on the underlying image phase. In such scenarios, explicit modeling of magnitude and phase as separate components may reduce the reliance on externally estimated or predefined phase information. To this end, we propose UMPIRE-Net (Unrolled Magnitude-Phase In REgularization Network), a PD-DL method that introduces separate learned regularizers for magnitude and phase components, together with a novel data-fidelity formulation that enforces measurements consistency. We evaluate UMPIRE-Net for accelerated MRI with PF across different datasets and acceleration factors. Experimental results demonstrate that our proposed method improves reconstruction quality compared with a conventional complex-valued PD-DL baseline, yielding sharper images and reduced artifacts. Code available at: https://github.com/MahdiSaberii/UMPIRE-Net
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Signal Processing

Multi-Agent Reinforcement Learning for Joint Handover Management and Power Allocation in Multi-Orbit Satellite Networks

Yassine Afif, Ashutosh Balakrishnan, Philippe Martins et al. (+3 more)

Future sixth-generation non-terrestrial networks are expected to combine low Earth orbit (LEO), medium Earth orbit (MEO), and geostationary Earth orbit (GEO) satellites, whose complementary layers must be coordinated through joint user association, power allocation, and handover management under fast LEO dynamics. This paper studies this problem by formulating it as a mixed-integer nonlinear program and decomposing it into a multi-agent reinforcement learning (MARL) policy that selects the associations and a convex power-allocation subproblem solved exactly at each time slot that defines the reward of the MARL part. The association policy is trained with multi-agent proximal policy optimization (MAPPO) and the targeted multi-agent communication (TarMAC) mechanism, and is made aware of the orbital layer through a state that encodes layer-dependent handover penalties. Evaluated on a realistic multi-constellation scenario built from real two-line element data over Nairobi, Kenya, the proposed policy reaches 92% of the throughput of a greedy signal-to-noise ratio (SNR) maximizing scheme while triggering more than four times fewer handovers, and improves throughput by roughly 14% over a conservative stay heuristic. Compared to an LEO-only learned policy of identical architecture, it attains slightly higher throughput with fewer handovers by offloading a fraction of the users to the MEO and GEO layers, an emergent multi-orbit behavior that drives its favorable throughput and handover trade-off.
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All authors Yassine Afif, Ashutosh Balakrishnan, Philippe Martins, Mohammed Almekhlafi, Antoine Lesage-Landry, Gunes Karabulut Kurt

Signal Processing

Lightweight Beam Index Map Using Coupled Gaussian Mixture Models

Amar Kasibovic, Franz Weißer and Wolfgang Utschick

This paper addresses the beam alignment problem in MIMO systems from a decentralized, mobile terminal (MT)-centric perspective. We propose a lightweight machine learning approach that leverages position information to perform beam selection without relying on exhaustive search or strong base station coordination. Specifically, we model the joint distribution of MT positions and channel observations using a coupled Gaussian mixture model (GMM), enabling the construction of a beam index map (BIM) that directly associates spatial locations with codebook entries. To account for practical hardware constraints, we introduce a refinement procedure that adapts the learned statistical model to fixed codebooks. The resulting method is computationally efficient and suitable for deployment on resource-constrained devices. Simulation results on the DeepMIMO and QuaDRiGa datasets demonstrate that the proposed approach outperforms clustering-based fingerprinting methods and achieves competitive performance compared to exhaustive search, while significantly reducing complexity and overhead.
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Signal Processing

LLM-Assisted LDPC Decoding via Syndrome-Verified Semantic Priors

Sojeong Park, Hyeonsu Lyu, Jaehyun Choi et al. (+1 more)

Semantic communication exploits the meaning of the payload, which bit-level processing discards. When channel decoding fails on a natural language payload, the errors appear as corrupted characters in the recovered text. A large language model (LLM) infers the intended characters from the semantic context, but it can also produce incorrect corrections. Applying them directly introduces new bit errors when the LLM modifies characters incorrectly. In this paper, we propose an LLM-assisted decoding framework for low-density parity-check (LDPC) codes. Rather than trusting LLM predictions, the decoder evaluates the modified characters jointly against the parity-check constraints and admits only the accepted corrections as verified semantic priors. These priors are injected as soft updates to the channel log-likelihood ratios, preserving the original channel evidence without modifying the decoder. A subsequent belief propagation pass distributes the injected evidence across the check nodes, recovering not only the injected bits but also the residual errors that the LLM fails to correct. Simulations demonstrate a 73% bit error rate reduction over a conventional decoder at 2.0 dB, whereas doubling its iterations to the same budget yields only 21%. The verification maintains an injection precision above 0.88 despite inaccurate LLM predictions, demonstrating that semantic knowledge can be reliably translated into physical-layer reliability gains.
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All authors Sojeong Park, Hyeonsu Lyu, Jaehyun Choi, Hyun Jong Yang

Signal Processing

Why Performance Metrics Overpromise in Auditory Attention Decoding: an Information-Theoretic Reappraisal

Nicolas Heintz, Simon Geirnaert, Tom Francart et al. (+1 more)

Auditory attention decoding (AAD) algorithms are predominantly evaluated in a steady state where a listener continuously attends to the same speaker, using metrics such as accuracy and information transfer rate. However, such metrics fail to account for the (in-)dependence of an AAD prediction with respect to previous predictions. In this paper, we argue that failing to take this dependence into account in the algorithm evaluation can lead to severe misrepresentations of the true performance of an AAD algorithm. We therefore introduce the relative Incremental Mutual Information (rIMI); the rate at which a new prediction removes the remaining uncertainty about the identity of the attended speaker. This allows us to track how much new, useful information a prediction actually generates on top of the information already obtained from previous predictions. By investigating the rIMI and the behaviour of AAD models around attention switches, we demonstrate that recent direct-classification AAD algorithms are not superior to traditional AAD algorithms based on stimulus reconstruction, despite what accuracy alone may suggest. We also demonstrate how these direct-classification AAD predictions are severely influenced by irrelevant feature drifts, which artificially inflates accuracies by leaking information across windows, and even across trials.
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All authors Nicolas Heintz, Simon Geirnaert, Tom Francart, Alexander Bertrand

Signal Processing · Cross-listed from cs.LG

KV Cache Compression Through the Lens of Transform Coding

Hannah Laus, Claudio Mayrink Verdun, Hao Wang et al. (+2 more)

The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference. Existing quantization methods address this bottleneck by representing the KV cache uniformly with lower-precision data types and designing quantization schemes to minimize reconstruction error in the cache itself, without accounting for how that error propagates through attention mechanisms. We prove that, under a white-noise quantization model, the expected attention-aware distortion decomposes into additive key and value contributions that factor across tokens and channels. Building on transform coding and reverse water-filling, which are classical tools from signal processing and rate-distortion theory, we introduce Attention-Aware Transform Coding (AATC), which allocates bits over a calibration set to minimize attention-aware distortion. On Llama-3.1-8B-Instruct and Qwen-2.5-7B-Instruct, evaluated across LongBench, RULER, GSM8K, MMLU-Pro, and MATH-500, our method achieves near-lossless accuracy at approximately $5.8\times$ compression, whereas each baseline degrades in at least some settings.
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All authors Hannah Laus, Claudio Mayrink Verdun, Hao Wang, Flavio du Pin Calmon, Felix Krahmer

Signal Processing

Budget-Aware Federated Dual-Side Channel Estimation for Hybrid mmWave Massive MIMO

Jiawei Chen, Ruining Fan, Mouli Chakraborty et al. (+2 more)

This work studies communication-constrained federated dual-side channel state information (CSI) estimation in hybrid millimeter-wave (mmWave) massive multiple input multiple output (MIMO) systems. Accurate CSI recovery is challenging because hybrid beamforming yields compressed and noisy observations, while repeated model exchange in federated learning (FL) makes communication efficiency strongly dependent on estimator size. Rather than developing a new federated optimization algorithm, we focus on estimator design under standard federated averaging (FedAvg) and study how to use a limited parameter budget effectively under repeated model exchange. Based on this perspective, we propose a budget-aware recalibrated refinement network (BARRNet), which combines a compact residual backbone with lightweight channel-wise recalibration for dual-side CSI refinement. Simulation results show that BARRNet achieves a better normalized mean squared error (NMSE)–communication tradeoff than the backbone-only control and heavier convolutional neural network (CNN) baselines. At 5 dB SNR, for the -13 dB DL NMSE target, it reduces the cumulative communication required by 28.9% relative to the architecture-matched backbone-only control. These results indicate that communication-efficient federated CSI estimation depends not only on model compactness, but also on how limited model capacity is used under repeated model exchange.
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All authors Jiawei Chen, Ruining Fan, Mouli Chakraborty, Avishek Nag, Anshu Mukherjee

Signal Processing

An HFM-Inspired Random Access Preamble Design for NTN under High Doppler

Mehwish Bibi, Saira Rafique, Ahmed Naeem et al. (+1 more)

Non-terrestrial networks (NTNs) are a key enabler of ubiquitous 6G connectivity, but the high orbital velocity and long propagation distances in low-Earth orbit (LEO) NTN operation introduce large Doppler shifts and substantial delay uncertainty that challenge New Radio (NR) physical random access channel (PRACH) design. Conventional Zadoff Chu (ZC) and linear frequency modulated (LFM) preambles are particularly vulnerable, as Doppler induced ambiguity and delay Doppler coupling degrade timing estimation and preamble identification. This paper proposes a hyperbolic frequency modulation (HFM) inspired PRACH preamble for robust synchronization and reliable identification under uncompensated or unknown Doppler, detected with a conventional matched filter receiver so that the gains reflect the preamble design. A unified delay Doppler ambiguity function framework characterizes the self and cross ambiguity behavior of ZC, LFM, and HFM inspired preambles, and a scaling-factor based codebook ensures multi-user separability. Simulation results under NTN channel conditions confirm higher detection probability, lower timing root mean square error (RMSE), and improved peak to sidelobe and integrated sidelobe levels compared with ZC and frequency-domain superposed LFM baselines.
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All authors Mehwish Bibi, Saira Rafique, Ahmed Naeem, Huseyin Arslan

Signal Processing

Integrated Sensing, Communication, and Computing in Multi-Tier Systems: Joint Hybrid Beamforming Design and Computation Resource Allocation

Peng Liu, Zesong Fei, Xinyi Wang et al. (+4 more)

This paper proposes a novel integrated sensing, communication, and computing (ISCC) framework over a cloud-edge-device collaborative architecture, where passive sensing is enabled by reusing uplink offloading signals to extract sensing information directly at the edge without incurring additional transmission overhead. Nevertheless, such signal reuse introduces an inherent tradeoff between communication efficiency and sensing coverage. To address this challenge, we adopt a hybrid beamforming architecture under practical hardware constraints. In addition, the integration of sensing tasks creates significant resource contention at the mobile edge computing (MEC) server, where latency-sensitive device tasks and computation-intensive sensing inference tasks compete for limited processing capacity. To alleviate this computation burden, we introduce a split inference mechanism that strategically partitions intelligent sensing tasks between the edge and the cloud. Building upon this framework, we formulate a joint optimization problem to minimize the average computation latency of all device tasks subject to strict sensing performance constraints. To tackle the high non-convexity of the formulated problem, we develop an efficient alternating optimization algorithm. In particular, we design a two-layer framework to jointly determine the optimal DNN splitting point and computation resource allocation and employ a weighted minimum mean square error (WMMSE)-based approach with manifold optimization for hybrid beamforming design. Numerical results demonstrate that the proposed framework achieves a superior tradeoff between sensing accuracy and computation latency compared to the benchmark schemes.
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All authors Peng Liu, Zesong Fei, Xinyi Wang, Qiao Qi, Zhaohui Yang, Meng Hua, Arumugam Nallanathan

Signal Processing

Standard-Compliant Neuromorphic Integrated Sensing and Communications Aided by an Intelligent Reflecting Surface

Jiho Park, Jiechen Chen, Joonhyuk Kang et al. (+1 more)

Neuromorphic computing enables event-driven, low-power inference and is therefore an attractive technology for jointly carrying out communication, sensing, and processing at resource-constrained wireless devices. Impulse radio ultra-wideband (IR-UWB) signaling, which is standardized in the IEEE 802.15.4 family and widely deployed for ranging and short-range communication, is naturally matched to neuromorphic processing, since both operate through sparse temporal events, or spikes. In this context, this paper studies a standard-compliant neuromorphic integrated sensing and communications (N-ISAC) system in which a single spiking neural network (SNN) receiver jointly demodulates digital data and detects a passive radar target directly from a common IEEE 802.15.4z high-rate-pulse-repetition-frequency (HRP) UWB waveform. Unlike prior N-ISAC work, which assumed a basic pulse-position-modulation interface and a simplified multipath channel, we consider the full standardized physical layer together with a realistic ray-traced channel aided by a reconfigurable intelligent surface (RIS). The model accounts for the frequency-selective response of a metamaterial-based RIS, which disperses the wideband IR-UWB pulses and can degrade both communication and sensing. Numerical experiments quantify the impact of RIS frequency selectivity on UWB ISAC and characterize the trade-off between ISAC performance and receiver computation energy.
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All authors Jiho Park, Jiechen Chen, Joonhyuk Kang, Osvaldo Simeone

Signal Processing

Conjugate Equivariant Neural Network for Precoder Learning

Shiyong Chen, Mingyu Deng and Shengqian Han

Exploiting mathematical properties of wireless policies in deep neural network (DNN) design can improve learning performance and generalizability while reducing training complexity. Permutation equivariance and permutation invariance have been incorporated into DNN architectures. In this paper, we investigate conjugation equivariance (CE) and propose a general conjugation-equivariant neural network (CENN) framework for precoder learning. We first establish that the optimal policies for a unified class of precoding problems satisfy CE, i.e., when the channel matrices are conjugated, the conjugate of an optimal precoder remains optimal. We then show that, for DNNs with linear processing functions, enforcing CE restricts their ability to learn optimal precoding policies. To overcome this limitation, we develop a general nonlinear construction and prove that it converts an arbitrary base function into a CE processing function while preserving the base function's original equivariance properties. This construction enables existing equivariant networks to incorporate CE without adding learnable parameters. Simulations for fully digital and RIS-aided precoding show that the resulting CE-enhanced networks improve learning and generalization performance while requiring fewer training samples and shorter training time than their original counterparts.
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Signal Processing

Omni-Photonic Base Station: A Three-Functional-Domain Photonic Architecture for Evolutionary 6G Wireless Infrastructure

Dapeng Wang, Xiaoxiong Song, Ziling Fu et al. (+7 more)

6G mobile communications impose immersive communication demands for higher data rates and massive connectivity, while emerging integrated sensing-computing-intelligence scenarios require base stations to concurrently enhance computing capability, guarantee service latency, and realize sensing. We propose the Omni-Photonic Base Station-a progressive evolutionary architecture that introduces photonic technologies into three functional domains, namely baseband processing, fronthaul transmission, and the RF front-end, and obtains system-level gains through cross-domain co-design. By introducing photonics, Omni-PBS harnesses the inherent physical advantages of ultra-broad bandwidth, ultra-low propagation latency, and native parallelism to transcend the aforementioned electronic bottlenecks and fulfill the compound demands of 6G scenarios. The optical baseband computing domain employs photonic accelerators to perform linear computation-intensive tasks, improving the energy efficiency of AI inference by one to two orders of magnitude. The analog optical fronthaul domain replaces digital fronthaul with analog radio-over-fiber , which eliminates the ADCs/DACs and digital intermediate-frequency chips in the remote unit and substantially reduces fronthaul bandwidth requirements. The microwave-photonic RF domain breaks through the bandwidth and frequency limitations of electronic RF front-ends via optical true-time-delay beamforming, programmable photonic filtering, and optical heterodyne frequency conversion. The three domains yield system-level gains beyond single-domain summation through four co-design principles: end-to-end optical-domain continuity, cross-domain co-design, optical computing resource scheduling, and joint optimization of functional splitting.
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All authors Dapeng Wang, Xiaoxiong Song, Ziling Fu, Wenyang Cheng, Jiayao Wang, Xiaogang Yan, Ze Wang, Min Zhang, Jingdi Liu, Nan Li

Signal Processing

Dual-Layer Over-the-Air Federated Learning in LEO Satellite Networks: Architecture, Key Technologies and Applications

Zhendong Li, Shaojie Wang, Zhou Su et al. (+4 more)

Low Earth orbit (LEO) satellite networks are emerging as a pivotal infrastructure for global edge intelligence. In this context, integrating over-the-air (OTA) computation with adaptive beam hopping (BH) provides an innovative framework that seamlessly merges physical-layer analog aggregation with dynamic resource orchestration. This effectively overcomes the stringent bandwidth and power constraints of space platforms while extending federated learning (FL) to pervasive Internet-of-things (IoT) deployments. In this article, we first outline the fundamental principles of the dual-layer OTA model and introduce the adaptive BH mechanism designed for time-varying topologies. Then, we summarize the distinct advantages of this learning-centric architecture, which include decoupling aggregation latency from device density, optimizing spatio-temporal resource efficiency, and balancing data freshness with channel quality. Several application scenarios are explored to highlight the framework's potential across diverse vertical industries. Furthermore, a specific case is studied to demonstrate the practical efficacy of the proposed scheduling policy. The results reveal substantial performance gains in terms of model convergence speed and data utilization for satellite-based FL systems. Finally, we discuss the implementation challenges and outline future research directions, aiming to provide insights for the evolution of ubiquitous non-terrestrial intelligence.
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All authors Zhendong Li, Shaojie Wang, Zhou Su, Tom H. Luan, Ruijin Sun, Ying Wang, Wen Chen

Thursday 13 August

Signal Processing

Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures

Dongyi He, Bin Jiang, Xiangkai Wang et al. (+4 more)

Cross subject emotion decoding from electroencephalography EEG requires representations that accommodate individual variability while preserving spatial spectral structure for interpretation. This study introduces EmoDiPyraTrans, a differential graph Transformer that integrates adaptive graph recurrence, differential attention, pyramid fusion and distribution regularization over sequential relative power spectral density graphs. Across SEED, FACED, MAHNOB HCI, DEAP and DREAMER, the model achieved the highest participant mean accuracy and positive class F1 among the evaluated methods, with accuracy and F1 both reaching 0.928 on SEED. On DEP EEG, positive versus neutral accuracy reached 0.802 within healthy controls and 0.704 within participants with depression, compared with 0.591 under healthy to depression transfer and 0.581 with mixed population development. Complementary SEED analyses identified distributed spatial weighting and an alpha centred spectral preference, while configurations averaging six channels retained near full performance. These findings link generalization assessment with model derived candidate signatures to support interpretable EEG emotion decoding, with code available at https://github.com/hdy6438/EmoDiPyraTrans.
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All authors Dongyi He, Bin Jiang, Xiangkai Wang, Yun Zhao, Hongjie Yan, Wai Ting Siok, Nizhuan Wang

Signal Processing

Quadratic Unconstrained Binary Optimization for Sparse Magnetoencephalography Source Localization

Arim Ryou and Kiwoong Kim

Magnetoencephalography (MEG) source localization is an ill-posed inverse problem because distinct cortical source configurations can produce similar sensor-level fields. We formulate sparse multi-source localization as a quadratic unconstrained binary optimization (QUBO) problem combined with residual-aware candidate screening. Candidate source-location groups are generated from the sensor-space residual, fixed sensor-space templates are estimated for the resulting candidates, and active templates are jointly selected using data-fit, pairwise template interactions, and soft-cardinality terms. We evaluate the method using classical simulated annealing in controlled synthetic MEG simulations, primarily under a two-source condition, and compare it with MNE, dSPM, MxNE, LCMV, and RAP-MUSIC. Across 100 main-benchmark trials, QUBO achieved a mean cardinality-aware localization error of 8.45 mm, compared with 22.35 mm for MxNE, the best-performing baseline according to this metric, corresponding to a 62.2% reduction. The composite metric adds a 50 mm penalty per unit of source-count mismatch before normalization by the true source count. Because MxNE returned only one source in 35 trials, the reported reduction reflects both spatial localization and source-count performance. In separate sensitivity experiments, QUBO remained competitive across the tested sensor-noise and source-count conditions, although RAP-MUSIC performed comparably to or better than QUBO in some low-noise and three-source settings. The present experiments use classical simulated annealing and do not evaluate quantum hardware or claim quantum advantage. The resulting binary quadratic objective admits a direct Ising representation, enabling future evaluation on quantum-annealing and hybrid backends.
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Signal Processing

gmsEDA: Decomposition of Electrodermal Activity Signals Using Matrix Separation

Xuemei Chen, David MacQueen, Wendy Donlin Washington et al. (+4 more)

Electrodermal activity (EDA) signals, which reflect sympathetic nervous system arousal through changes in skin conductance, are widely used in psychological and behavioral research. Decomposing an observed EDA signal into its slowly varying tonic baseline and stimulus-driven phasic component is an important preprocessing step; however, existing methods process signals in isolation and remain highly sensitive to noise and motion artifacts. This work introduces gmsEDA, a new decomposition method based on generalized matrix separation whose model is designed to cope with noise and motion artifacts. Our method analyzes multiple recordings jointly rather than one at a time, taking advantage of patterns shared across signals to produce more accurate and robust results. Numerical experiments on both simulated and real data shows that this approach outperforms existing standard tools.
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All authors Xuemei Chen, David MacQueen, Wendy Donlin Washington, Mark Lammers, Owen Deen, Sean Carey, Margot Ledford

Signal Processing

Compact Microstrip Triplexer Using Square Open-Loop Resonators with High Isolation for 5G Sub-6 GHz Applications

Bhaskarareddy N C, Rashmi Ravi and Augustine O. Nwajana

The rapid proliferation of multi-standard 5G New Radio sub-6 GHz systems have intensified the demand for compact, high-isolation multiplexing components capable of simultaneously routing multiple frequency channels through a single shared antenna port without cross-band interference. This paper presents the design and full-wave electromagnetic simulation of a compact microstrip triplexer operating at 2.2, 2.6, and 3.0 GHz for 5G sub-6 GHz applications. The proposed triplexer employs square open-loop resonators arranged as three independent three-pole Chebyshev bandpass filter channels, yielding nine resonator poles in total. Each channel is synthesised from the standard normalised Chebyshev lowpass prototype. The three channels are integrated at a common input port via a T-junction, with connecting transmission line stubs dimensioned to enforce high inter-channel isolation. Full-wave EM simulation results demonstrate return losses of 21.1 dB, 23.1 dB, and 22.8 dB; insertion losses of 1.08 dB, 1.01 dB, and 0.98 dB; and inter-channel isolations of 45.7 dB, 45.2 dB, and 45.7 dB, respectively. The achieved inter-channel isolation exceeding 45 dB represents a significant improvement over the majority of recently reported microstrip triplexer designs.
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Signal Processing

Energy Efficient Multi-User Beamforming and 3D Position Optimization for SIM-Assisted UAVs

Chandan Kumar Sheemar, Giovanni Iacovelli, Sourabh Solanki et al. (+3 more)

This paper studies energy-efficient downlink multi-user transmissions with unmanned aerial vehicle (UAV) communication systems equipped with stacked intelligent metasurfaces (SIM), enabling wave-domain analog beamforming through multiple cascaded metasurface layers, while low-dimensional digital precoding is carried out using a limited number of transmit radio-frequency chains. This architecture enables flexible electromagnetic wave manipulation with reduced hardware complexity, making it particularly suitable for energy-constrained aerial platforms. We formulate a hardware-aware energy-efficiency (EE) maximization problem aiming to jointly optimize the digital precoder, the phase shifts of all SIM layers, and the three-dimensional UAV position under transmit-power, SIM operation, and UAV deployment constraints. The resulting problem is highly non-convex due to the fractional objective, the cascaded SIM structure and the unit-modulus phase constraints of the constituent metasurface layers, as well as the non-linear UAV-dependent channel. To address these challenges, we develop a transform-based alternating optimization framework that combines Dinkelbach's method, dual and quadratic transforms, to enable closed-form digital beamforming, Riemannian manifold optimization for SIM phase shifts, and successive convex approximation (SCA) for UAV positioning. Convergence and complexity analyses are provided to characterize the proposed algorithm. The presented numerical results showcase that the proposed joint design significantly improves EE compared with fully digital and maximum ratio transmission benchmark schemes, while revealing important design trade-offs among transmit power, SIM size, and the number of its constituent stacked layers.
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All authors Chandan Kumar Sheemar, Giovanni Iacovelli, Sourabh Solanki, Wali Ullah Khan, George C. Alexandropoulos, Symeon Chatzinotas

Signal Processing · Cross-listed from physics.optics

In-situ Adjoint Wave Control in Reconfigurable Non-Hermitian Nonlinear Systems

John Guillamon, William Tuxbury, Cheng-Zhen Wang et al. (+3 more)

Complex multipath environments are usually avoided in wave-based information processing because repeated scattering creates many interfering propagation paths, obscuring controllability and generating extreme sensitivity to perturbations. The addition of nonlinear mechanisms fundamentally alters the wave-control landscape by breaking the superposition principle that underpins most wave-management strategies. Here, we show that these two apparent impediments – multipath complexity and nonlinearity – can instead be harnessed as key resources for physical optimization. We demonstrate an in-situ adjoint optimization protocol in a wave-chaotic platform incorporating a single localized nonlinear defect, in which the system itself performs both the forward and the adjoint propagations required for gradient evaluation. Recurrent multipath returns repeatedly expose the wave to the defect, producing from a minimal hardware a rich nonlinear input-output map with many pathway-mediated degrees of freedom. At the same time, a suitable adjoint excitation enables direct extraction of the sensitivities from measurements alone, without a digital twin or conventional numerical backpropagation. We experimentally validate the protocol on a minimal nonlinear multipath platform composed of incommensurate coaxial cables connected via T-junctions, one of which hosts a diode-loaded cavity. Our approach opens a route to adaptive wireless communications, imaging and analog intelligence in complex, partially unknown environments where conventional modeling is impractical.
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All authors John Guillamon, William Tuxbury, Cheng-Zhen Wang, Owen Miller, Zin Lin, Tsampikos Kottos

Signal Processing

On-Off Digital Noise Modulation with Fluid Antenna Systems over $\kappa$-$\mu$ Fading Channels

Daniel C. Araújo, André A. dos Anjos, Hugerles S. Silva et al. (+2 more)

This letter proposes the integration of on-off digital noise (OODN) modulation with fluid antenna systems (FASs). A unified analytical framework is developed to evaluate the performance of FAS- assisted OODN receivers over additive white Gaussian noise and generalized ąppa}-{\mu} fading channels. The analysis incorporates fluid antenna port selection, the number of available ports, and spatial correlation. Analytical expressions for the average bit error probability are derived and the achievable diversity order is characterized. All expressions are validated through Monte Carlo simulations. Results demonstrate that the spatial diversity provided by the FAS significantly enhances the reliability of OODN transmissions while preserving their inherent low-complexity, non-coherent operation without carrier-phase recovery. The proposed framework establishes a new research direction for energy-efficient Internet of Things (IoT) and machine-type communication systems.
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All authors Daniel C. Araújo, André A. dos Anjos, Hugerles S. Silva, Constantinos Psomas, Robson D. Vieira

Signal Processing

Low-Complexity Channel Parameter Estimation for Coprime HRIS-Assisted MIMO Systems

Jean C. S. Ferreira, Melryllin G. O. Sousa and Fazal-E Asim

This paper proposes a parameter estimation scheme for uplink MIMO systems assisted by a Hybrid Reconfigurable Intelligent Surface (HRIS). To reduce hardware complexity, the HRIS employs a small number of active elements arranged in a sparse coprime geometry for local sensing, while the remaining elements passively reflect signals. This simultaneous sensing and reflection significantly improves accuracy over fully passive architectures. The method leverages Khatri-Rao and Kronecker factorizations to efficiently decouple the cascaded channel at the base station. Furthermore, spatial smoothing resolves the coprime array rank deficiency, enabling robust angular extraction via Root-MUSIC. Simulations demonstrate highly resilient estimation performance across scenarios.
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Signal Processing · Cross-listed from cs.CL

Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection

Serli Kopar, Sam Gijsen, Abner Hernandez et al. (+2 more)

Self-supervised learning (SSL) speech representations achieve strong performance for Parkinson's disease (PD) detection within individual corpora. However, it remains unclear whether these models capture disease-related characteristics or exploit dataset-specific confounds, particularly since most SSL backbones are pretrained exclusively on healthy speech. To investigate this question, we perform a layer-wise analysis of nine SSL speech backbones using a low-capacity logistic regression probe across three languages. We structure the evaluation as multiple scenarios that progressively introduce distribution shifts in participant identity, recording conditions, language, and pathology. Our results reveal two key findings. First, layer selection is highly corpus-dependent: the optimal representation layer is determined primarily by the source dataset rather than by the SSL architecture itself. Second, the transferred discriminative signal lacks pathological specificity: classifiers trained to detect PD assign similarly high probabilities to both PD and dementia speech in the target corpus. These results highlight critical limitations that must be addressed before speech-based pathology recognition models can be reliably deployed in clinical settings.
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All authors Serli Kopar, Sam Gijsen, Abner Hernandez, Paula Andrea Perez-Toro, Kerstin Ritter

Signal Processing

Heterogeneity-Aware Belief Synchronization for Semantic Communication in AI-Native 6G Networks

Muhammad Hannan Akram, Muhammad Abubakar Rashid, Wassi Haider Kabir et al. (+3 more)

6G networks will not be serving as communication infrastructures only; rather, they are expected to evolve into intelligent systems, where thousands of autonomous artificial intelligence (AI) agents are interconnected. The agents are deployed across a wide range of platforms including low Earth orbit (LEO) satellites, high-altitude platforms (HAPs), unmanned aerial vehicles (UAVs), edge servers, and terrestrial devices. These agents continuously observe their environment and exchange information. Semantic communication provides an efficient mechanism for exchanging meaningful information instead of raw data. However, its effectiveness depends on the communicating agents having sufficiently aligned beliefs to correctly interpret and decode the transmitted messages. This assumption becomes difficult to satisfy in the 6G network where heterogeneous AI models operate under diverse computational constraints and continuously acquire different knowledge from their local environments. This article presents a heterogeneity-aware belief synchronization framework for 6G AI-native networks. It uses latent translation models deployed on multi-access edge computing (MEC) servers. These models translate belief updates from one agent to agent-specific knowledge without requiring joint training and a homogeneous architecture of models. By exchanging compact belief updates through a latent translation model only when necessary, the framework preserves privacy, reduces synchronization cost, and minimizes local knowledge drift. We validate the framework through a case study on a multi-layered terrestrial/non-terrestrial network. Results demonstrate that it maintains low synchronization cost, measured by the number of parameters transmitted, and low belief alignment error across the heterogeneous agents in the case study.
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All authors Muhammad Hannan Akram, Muhammad Abubakar Rashid, Wassi Haider Kabir, Haejoon Jung, Kapal Dev, Syed Ali Hassan

Signal Processing

A Physically Consistent Assessment of Nearfield Beamfocusing into Occluded Regions

Georg Schwan, Alexander Stutz-Tirri and Christoph Studer

Recent work has explored elaborate beamfocusing techniques for the radiative nearfield, with some studies suggesting that certain beamshapes, such as Airy beams, can enable efficient electromagnetic (EM) wave transmission behind obstacles. In this letter, we ask whether the added complexity of such techniques is justified. We distinguish between partially and fully occluded regions. In the partially occluded region, where Airy beams are commonly employed, we show that a simple line-of-sight (LoS) strategy, which activates only antennas having an unobstructed view of the receiver, is near-optimal and outperforms Airy beams at substantially lower complexity. In the fully occluded region, we argue that accurate beamfocusing requires a physically consistent EM wave propagation model that captures propagation effects such as diffraction. Once such a model is available, however, the optimal beamfocusing strategy has a closed-form solution and can be computed directly. These results suggest that elaborate techniques, such as Airy beams, offer little benefit over simpler alternatives for beamfocusing into occluded regions.
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