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Electrical Engineering and Systems Science Signal Processing

eess.SP Watch Signal Processing

Sunday 9 August

Signal Processing · Cross-listed from cs.IT

Mean Gain Is Not a Guarantee: Risk-Controlled Open-Loop Scheduling for Ka-Band LEO Semantic Downlinks

Milad Bafarassat

A low-Earth-orbit satellite has only minutes to downlink imagery on a limited power budget, and some moments in a pass carry far better channels than others. Spending more power on the good moments and skipping hopeless ones should deliver more images, but the channel cannot be measured in time, so the schedule is planned in advance from weather statistics using a safety margin. That margin is sized so the forecast is statistically reliable, and the scheduler is judged by whether it beats uniform power on average. Both targets are wrong for a satellite that gets one attempt per pass: on real Sentinel-2 imagery, a scheduler gaining one to two images per pass on average still delivers fewer than uniform power on a quarter to a third of individual passes, and a genie knowing the weather exactly does no better. We instead size the margin by measuring, on recorded passes, how often it would have backfired, keeping only settings that provably backfire rarely. The procedure declines to schedule our learned codec and certifies scheduling for codecs with a sharper cliff.
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2608.08635 · 9 Aug · The author withdraws this paper because the conclusions in Section 4.3 concerning the limitations of perfect-CSI scheduling are not established by the reported experiments. The evaluated genie-CSI policy does not establish a limitation applying to other scheduling policies, so the claimed attribution of the observed shortfall to the codec's delivery response is insufficiently supported

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Signal Processing

Low-Complexity Channel Estimation for Reconfigurable Fluid Antenna System

Alireza Fadakar and Andreas F. Molisch

Electromagnetically reconfigurable fluid antenna system (ER-FAS) provides additional electromagnetic (EM)-domain degrees of freedom by enabling dynamic control of per-element radiation patterns, thereby improving power efficiency in wireless communications. The effective exploitation of these capabilities, however, critically depends on accurate channel estimation, which has received limited attention in prior studies. This paper presents a low-complexity channel parameter estimation framework for downlink wideband systems that leverages available channel sparsity. A synthesis-based reconfigurability model is considered, where each antenna generates desired beampatterns using a finite set of EM basis functions. Based on this model, a joint optimization of digital and EM precoders is formulated with the objective of minimizing the Cramér-Rao lower bound of the channel parameters. Simulation results demonstrate that the proposed hybrid beamforming and estimation approach enables accurate recovery of channel parameters while maintaining low computational complexity.
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Signal Processing

Transfer Learning-Enabled Distortion Compensation for Amplitude-Phase-Time Block Modulation-Based Nonlinear Single-Carrier Wireless Communications

Guoxing Duan, Min Fan, Cheng Yi et al. (+4 more)

Power amplifier (PA) nonlinearity and memory effects significantly limit the spectral compliance, reliability, and energy efficiency of communication systems. To address this, we propose a transfer-learning-enabled, fully digital transceiver-cooperative method for amplitude-phase-time block modulation (APTBM)-based nonlinear single-carrier transmission under adjacent channel leakage ratio (ACLR) constraints. At the transmitter, iterative clipping and filtering (ICAF) and static digital pre-distortion (SDPD) act jointly to reduce signal peaks and suppress spectral regrowth without requiring wideband feedback. At the receiver, the inherent amplitude-phase constraints of APTBM provide weakly supervised prior knowledge for offline inverse-model pretraining, which is followed by the online few-shot adaptation of a lightweight digital post-distortion (DPoD) network. Subsequently, a cascaded DPoD and clipping-noise cancellation scheme systematically compensates for residual distortions induced by both the PA and ICAF. Simulation and measurement results demonstrate reliable transmission at an input back-off of approximately 2 dB under a 30-dBc ACLR constraint. Furthermore, the proposed DPoD approach significantly reduces online training time and computational overhead, delivering a performance gain of over 2 dB compared to conventional learning-based DPoD schemes.
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All authors Guoxing Duan, Min Fan, Cheng Yi, Bensheng Yang, Wei Xu, Haiming Wang, Xiaohu You

Signal Processing · Cross-listed from cs.IT

Memory AMP: Overflow Avoidance, Complexity Reduction, and Comparative Analysis

Shunqi Huang, Lei Liu and Brian M. Kurkoski

Approximate message passing (AMP)-type algorithms are widely used for signal recovery in high-dimensional noisy linear systems. Recently, a framework called memory AMP (MAMP) was introduced, offering a new approach to incorporating memory terms within AMP algorithms. Building on this, a low-complexity gradient descent MAMP (GD-MAMP) was proposed for right-unitarily invariant matrices. In this paper, we first address an overflow problem in GD-MAMP caused by intermediate variables exceeding the floating-point range, which typically occurs when the condition number is large. Second, we propose two low-complexity variants of GD-MAMP: one replaces full-length memory with partial memory, while the other reduces the number of matrix-vector products per iteration by $1/3$ (from three to two). Neither degrades the convergence speed notably. Third, we develop a general gradient-based formulation for designing MAMP algorithms. This formulation recovers warm-started conjugate gradient VAMP (WS-CG-VAMP) as a special case. Furthermore, we show that the computation of the orthogonalization parameters in this formulation can suffer from catastrophic cancellation, which explains the finite-precision instability of WS-CG-VAMP. Finally, we derive an equivalent reformulation, termed WS-CG-VAMP(r), which reduces the number of matrix-vector products by up to $50\%$. Measured by matrix-vector products, GD-MAMP converges faster for small condition numbers, whereas WS-CG-VAMP(r) converges faster for large ones under high-precision arithmetic but may diverge in IEEE double precision due to catastrophic cancellation.
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Signal Processing

Hybrid Beamforming in Non-Terrestrial Networks: Architectures, Design Challenges, and Opportunities

Thuan Van Le, Nguyen Cong Luong, Huy T. Nguyen et al. (+6 more)

Hybrid analog-digital beamforming (HBF) has emerged as a key enabling technology for non-terrestrial networks (NTNs), where large antenna arrays are required to compensate for severe propagation loss but fully digital beamforming is often impractical due to radio-frequency (RF) chain cost, power consumption, and payload limitations. Compared with terrestrial networks, NTN platforms such as low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) impose distinctive HBF design challenges, including high mobility, Doppler effects, sparse line-of-sight-dominant channels, stringent on-board energy budgets, and, for UAVs, the additional coupling between beamforming and controllable platform placement or trajectory. This survey provides a systematic review of HBF techniques for NTN systems, with emphasis on LEO satellite and UAV communications. We first introduce common HBF architectures, signal models, channel representations, analog and digital precoder designs, and learning-aided approaches that form the shared technical foundation of existing works. We then survey both platforms under a common set of five categories, which cover system architecture and precoding design, time-varying beam management, network-level cooperation and scheduling, sensing capability and reconfigurable surfaces, and security and multiple access. Their platform-specific content differs most sharply in the second one, since the dominant time-varying mechanism is traffic-driven beam hopping on an LEO payload but mobility-aware beam tracking on a UAV. Finally, we discuss open research challenges and future directions toward scalable, robust, and hardware-efficient HBF in next-generation NTNs.
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All authors Thuan Van Le, Nguyen Cong Luong, Huy T. Nguyen, Trong-Dai Hoang, Xiaojing Huang, Peiyuan Qin, Tran Thien Thanh, Vo Nguyen Quoc Bao, Ngo Hoang Tu

Signal Processing · Cross-listed from cs.CV

DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing

Navid Hasanzadeh and Shahrokh Valaee

Motivated by the IEEE 802.11bf effort to standardize advanced WLAN sensing, interest in Wi-Fi Channel State Information (CSI) for passive, device-free, and privacy-preserving activity and gesture recognition has grown rapidly. Recent studies have shown that Doppler velocity projections extracted from CSI, which directly reflect human-motion velocity, enable more robust human activity recognition (HAR) and stronger generalization across users and unseen conditions. Nevertheless, reliable generalization under real-world variability remains a major challenge that hinders the adoption of Wi-Fi sensing. To address this challenge, we build on Doppler Radiance Fields (DoRF), adapting the concept of neural radiance fields (NeRF) from computer vision to Wi-Fi sensing. DoRF treats the extracted Doppler velocity projections as sparse virtual-camera views of human motion. From these views it recovers a regularized latent 3-D motion descriptor whose projections along learned effective Doppler directions explain the CSI observations. The recovered motion is then re-projected onto an equiangular grid of directions on the unit sphere, yielding a spherical representation of the underlying activity. Because DoRF produces a signal defined on the sphere, this paper introduces DoRF++, a spherical representation-learning model that applies quadrature-aware spherical Transformers directly to the spherical field for activity classification. Experiments on our collected hand-gesture dataset show that DoRF++ noticeably outperforms state-of-the-art Wi-Fi-based HAR methods in cross-user generalization accuracy, especially for difficult gestures when only a single multi-antenna receiver access point (AP) is available.
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Saturday 8 August

Signal Processing

Uplink Hardware Impairments in OFDM-Based Cell-Free Massive MIMO With Impaired Wireless Fronthaul

Özlem Tuğfe Demir

This paper studies the uplink performance of OFDM-based cell-free massive MIMO systems in the presence of hardware impairments affecting both low-cost access points and wireless fronthaul transceivers. We consider a centralized architecture with functional split option~8, where the sampled baseband signals are forwarded from the access points to the central processing unit through wireless fronthaul links operating at mmWave or sub-THz frequencies. A unified analytical framework is developed to characterize the aggregate impact of hardware distortions across the access and fronthaul links. In particular, closed-form expressions are derived for channel estimation and uplink spectral efficiency under hardware-impaired transmission and reception. To mitigate the resulting performance degradation, distortion-aware linear receive combining is considered for the wireless fronthaul. Moreover, a max-min fair resource allocation framework is developed to jointly optimize the uplink transmit powers, fronthaul transmit powers, and fronthaul time expansion factor.
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Signal Processing · Cross-listed from cs.RO

Tolerance-Dependent Inspection Disagreement Between a Fixed CMM and a Portable Articulated-Arm CMM

Md Manjurul Ahsan, Hamidreza Samadi and Shivakumar Raman

Fixed coordinate measuring machines (CMMs) and portable articulated-arm CMMs are often assigned to the same inspection task, but their nominal accuracy specifications do not show whether a change of instrument will preserve the disposition of a part. The question is not simply how far the two results differ, but whether that difference crosses the tolerance boundary. We examined this issue with recorded measurements of cylindrical, cubic, and spherical features under nominal 20 {\deg}C and 30 {\deg}C conditions. Repeated records and two roughness profiles without sufficient acquisition information were removed, leaving six dimensional and four form profiles. For each dimensional feature, the distances of the two system means from nominal define the exact tolerance interval in which the systems receive opposite direct labels. The fixed-CMM stream was approximately 11.2 {\mu}m higher than the articulated-arm stream at both conditions. All four form profiles fell on opposite sides of the recorded 10 {\mu}m upper limit. The dimensional disagreement intervals also overlapped strongly; their mean widths were 6.573 {\mu}m at 20 {\deg}C and 4.995 {\mu}m at 30 {\deg}C. The results clarify why an average difference between instruments is not, by itself, a measure of substitution risk. The proposed tolerance map identifies the feature-tolerance combinations for which instrument choice can change the recorded inspection label and, therefore, where a controlled equivalence study and a task-specific uncertainty budget are needed before substitution.
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Signal Processing · Cross-listed from cs.LG

Sheaf-Based Federated Representation Learning

Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino et al. (+4 more)

Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures, latent dimensionalities, and local learning objectives. To address this challenge, we propose Sheaf-based Federated Representation Learning (SFRL), a general framework that jointly optimizes local objectives with a manifold-constrained geometric alignment regularizer based on learnable sheaf restriction maps. Unlike most existing approaches, SFRL does not assume a shared global latent space. Instead, global consistency emerges from the alignment of neighboring latent representations through orthogonal transformations and isometric embeddings. This alignment is enforced by a quadratic gluing regularizer induced by the sheaf Laplacian, whose learnable restriction maps adapt the geometry to the observed data. The penalty is evaluated on a small set of shared pilot samples, ensuring scalability and communication efficiency. We develop a decentralized algorithm for solving SFRL, termed Sheaf-FRL, which alternates between gradient updates of the local models and closed-form Procrustes updates of the edge-wise restriction maps. We further establish convergence of Sheaf-FRL to first-order stationary points in both deterministic and stochastic settings. As an application, we consider a cooperative classification task in the context of semantic communication, under model and data heterogeneity. Our results show that Sheaf-FRL outperforms baseline approaches in terms of local and post-communication classification accuracy across different levels of local distribution shift and exhibits greater robustness to latent-space dimensionality compression.
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All authors Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino, Mario Edoardo Pandolfo, Leonardo Di Nino, Sergio Barbarossa, Paolo Di Lorenzo

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

HIPNO: Symmetry-Aware Physics-Informed Neural Operators for Noninvasive Hemodynamic Inference

Yunbei Pan, Jiahang Sha, Simon A. Lee et al. (+3 more)

Continuous hemodynamic monitoring guides treatment decisions in surgery and intensive care. However, gold-standard signals are only measured in severe cases due to risks associated with invasive measurement. In this work, we introduce HIPNO (Hemodynamic Inference via Physics-informed Neural Operators) to recover hemodynamic state from ubiquitous, non-invasive signals and expand access to advanced monitoring. HIPNO addresses a problem of scale symmetry in physics-informed hemodynamic inference, where different combinations of flow, resistance, and compliance can generate the same observed pressure. We identify the symmetry group of the observation model and parameterize the network in its quotient space. For the 3-element Windkessel model, the quotient coordinates are the compliance-normalized flow $U=Q/C$, the decay time constant $\tau_{WK}=R_2 C$, and the characteristic-impedance coordinate $\kappa=R_1 C$. Across 945499 intraoperative windows from 2562 patients, HIPNO predicts $\tau_{wave}$, a proxy for vascular decay derived from pressure, with 32% lower error on the log scale than a population baseline while preserving mean arterial pressure accuracy. Because vascular decay and flow drive occupy separate coordinates, counterfactual perturbations produce the expected directional responses in at least 90% of windows in almost all prespecified scenarios, a separation unavailable to pressure-only baselines. The coordinates are also used as inputs to a calibration model for monitored cardiac output. Finally, the formulation identifies the external compliance or flow reference required to recover absolute physical scale.
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All authors Yunbei Pan, Jiahang Sha, Simon A. Lee, Maxime Cannesson, Wei Wang, Jeffrey N. Chiang

Signal Processing

Semi-Blind Fluid Antenna System: Port Selection via Statistical Analysis

Tianyu Han, Yongxu Zhu, Kai-Kit Wong et al. (+3 more)

The fluid antenna system (FAS) enables position reconfigurability. A potential drawback of real-time FAS, however, is that it requires complete channel state information (CSI) for each FAS port at every communication time slot, an approach referred to as ideal-FAS. Recognizing the difficulties of achieving ideal-FAS, we propose a FAS scheme based on incomplete CSI, referred to as semi-blind FAS. This paper first introduces the spatial-temporal framework of FAS, upon which the proposed semi-blind FAS is developed. The proposed semi-blind FAS is lightweight and computationally efficient, scalable to an arbitrary number of ports and time slots, and operates without pre-training or deep learning structures. The scheme effectively exploits incomplete historical CSI to estimate the conditional distribution across all FAS ports at the desired time slot, thereby identifying the statistical optimal port for signal reception. Generally, the key idea of semi-blind FAS is to select the optimal port through conditional distribution analysis, from a statistical perspective, with optimality defined according to the scenario of interest. Inspired by information-theoretic entropy, we further develop the residual entropy power ratio to characterize how physical parameters influence the performance gap between semi-blind FAS and ideal-FAS. Our analysis reveals that estimation performance depends not only on the number of sampled ports and time slots, but also on the specific indices of ports with given CSI at each time slot, i.e., the port sampling strategy. This critical factor has been largely overlooked in existing port estimation studies. Numerical results demonstrate that the proposed semi-blind FAS achieves performance comparable to, and in some cases indistinguishable from, that of ideal-FAS, while requiring significantly fewer port CSI measurements and lower port switching speeds.
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All authors Tianyu Han, Yongxu Zhu, Kai-Kit Wong, Gan Zheng, Chan-Byoung Chae, Xiaohu You

Signal Processing

Split-Gate Pooled-Evidence Stochastic-Rollout Scheduling for Timely Progressive Edge Inference

Sai Xu, Yinbo Yu, Yanan Du et al. (+2 more)

This paper investigates causal radio scheduling for progressive edge inference, with the goal of maximizing timely inference throughput under job-specific deadlines. Specifically, a multi-tenant system is modeled in which each job alternates between wireless transmission and graphics processing unit (GPU) computation. The model captures time-varying uplink service, inter-stage precedence, variant-aware batching, non-preemptive execution on two GPU streams, and heterogeneous deadlines. To account for delayed radio-GPU coupling, a split-gate pooled-evidence stochastic-rollout (SGPE-SR) scheduler is proposed. Candidate and fallback policies are selected on one set of sampled futures, after which admission is evaluated on an independent held-out set; common random numbers are retained within each held-out candidate-fallback comparison. The override is executed only when its pooled nominal/recent-history held-out advantage is sufficiently positive and an independent recent-history replication statistic is nonnegative. System-level simulations using measured GPU profiles and paired random instances show that, in a prespecified long-horizon evaluation over 30 previously unused seeds and 3,379 offered jobs, SGPE-SR improves timely completions over the strongest rate-based baseline by 4.27%, with a mean paired gain of 3.00 jobs per run and a 95% bootstrap interval of [1.60, 4.43].
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All authors Sai Xu, Yinbo Yu, Yanan Du, Xusheng Zhu, Gaojie Chen

Signal Processing

Uplink-Completion-Triggered Edge-GPU Inference for Multi-Agent Cooperative Perception

Sai Xu, Yanan Du, Chong Tang et al. (+1 more)

This paper investigates the coupling between wireless input completion and graphics processing unit (GPU) execution in centralized multi-agent cooperative perception. Specifically, beyond the conceptual treatment of completion-triggered overlap, a complete execution path is realized and validated on a physical GPU for a cooperative-perception deep neural network (DNN). Each encoder branch is released immediately upon completion of its corresponding input transmission, while the original fusion dependencies and inference mapping are preserved. The resulting release-triggered communication computation coupling (RTCC) propagates validated wireless completion events through host-to-device (H2D) staging, CUDA synchronization, and dependency-preserving branch dispatch, while remaining compatible with causal wireless schedulers. Experiments combining trace-driven wireless arrivals, physical GPU execution, and measured-DAG evaluation show that RTCC reduces complete-detection latency across different communication loads and schedulers, while preserving identical detection outputs and average-precision performance relative to conventional execution.
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All authors Sai Xu, Yanan Du, Chong Tang, Gaojie Chen

Signal Processing · Cross-listed from cs.LG

Machine-Learning-Based Diagnostic Framework for Passive Ultrasonic Detection of Railway Wheel Defects

Aashish Shaju, Steve Southward and Mehdi Ahmadian

Reliable identification of railway wheel defects is important for safety and maintenance. This study develops a machine-learning-based diagnostic framework for multi-class defect identification using passive air-coupled ultrasonic acoustic emission signals. Data were collected from eleven full-scale railway wheelsets representing nine health states. Time- and frequency-domain features were evaluated using Kruskal-Wallis statistical testing and mutual-information analysis to identify the most discriminative indicators. A Random Forest classifier was then trained using the selected features with stratified 5-fold cross-validation. The model achieved a balanced accuracy of approximately 0.66 and a Macro-F1 score of 0.65 across the nine classes. Decay rate, kurtosis, skewness, and envelope low-frequency power emerged as the most influential features, while a compact subset of features retained most of the classification performance. The results demonstrate the feasibility of combining passive ultrasonic sensing, statistical feature selection, and supervised machine learning for non-contact railway wheel defect classification and provide a foundation for future field-deployable inspection systems.
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Signal Processing

Toward Intelligent Skies: Signal Processing and AI Foundations of Low-Altitude Wireless Networks

Weijie Yuan, Geng Sun, Jiacheng Wang et al. (+7 more)

The rapid growth of low-altitude aerial services and applications, driven by uncrewed aerial vehicles (UAVs), calls for a new class of digital infrastructure beyond conventional terrestrial networks. The low-altitude wireless network (LAWN) has been proposed as dynamically reconfigurable three-dimensional architectures that integrate aerial and ground nodes to provide connectivity, sensing, and control in open, safety-critical airspace. This tutorial presents a comprehensive treatment of LAWNs from the joint perspectives of artificial intelligence (AI) and signal processing. We first review the historical evolution and architectural foundations of LAWNs, introducing altitude-based layers and functional planes, and summarizing the regulatory and standardization landscape. Building on this system view, we then discuss signal processing fundamentals for LAWNs, including 3D channel and system models, performance metrics, waveform and receiver design, localization and tracking, and multi-functionality co-design. Next, we survey AI techniques for LAWNs, covering discriminative and generative models for perception, control, resource management, and security, as well as emerging paradigms such as foundation models, large language models, and digital twins for mission planning and closed-loop optimization. To illustrate AI-signal processing integration in practice, we provide a case study of an AI-driven multi-tier LAWN with hybrid satellite, high-altitude, and ground nodes. The tutorial concludes by outlining key research challenges in architecture design, signal processing-AI co-design, safety and security, experimentation, and standardization, and by highlighting opportunities for LAWNs to evolve into dependable, AI-native infrastructure for the intelligent skies.
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All authors Weijie Yuan, Geng Sun, Jiacheng Wang, Jun Wu, Yuanhao Cui, Jiahui Li, Wei Zhang, George K. Karagiannidis, Sumei Sun, Yonina C. Eldar

Signal Processing

Characterization and Mitigation of Polyphase-Code Artifacts in 5G NR ISAC

Xingkang Li, Shengheng Liu, Ziguo Zhong et al. (+4 more)

Target sensing utilizing 5G NR reference signals has emerged as a prominent research direction in both academia and industry. However, non-ideal factors in practical deployments exert a significant detrimental impact on target sensing performance, manifesting as artifacts in the RV spectrum. These artifacts mask weak targets and cause severe false alarms. To address these challenges, this paper establishes a theoretical model of artifacts and constructs a data-physics-driven deep learning paradigm for artifact mitigation. First, the origin of artifacts and their characteristics are theoretically derived. These analyses demonstrate that the artifacts are associated with polyphase codes, e.g., Zadoff-Chu sequences, and reveal their characteristics, including periodic extensions in the range domain and spectral spreading in the velocity domain. Then, the physical priors of artifacts are formalized as temporal continuity and spatial consistency, informing the design of the training mechanism for the proposed network. Guided by these insights, we propose a PIAENet. At its core is a multi-frame selective-masked encoder-decoder module, explicitly designed to incorporate the above priors. Specifically, temporal continuity is implemented via a multi-frame mechanism to capture features across consecutive RV spectra. Meanwhile, spatial consistency is realized through a selective masking mechanism to enhance reconstruction of artifact-affected regions. Extensive validation is conducted using real-world measured data collected with commercial mmWave equipment. The polyphase-code-related characteristics of the artifacts are experimentally validated. Meanwhile, the experimental results demonstrate that the proposed PIAENet not only effectively reduces the false target count but also improves the detection probability from 79.58% to 98.88%.
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All authors Xingkang Li, Shengheng Liu, Ziguo Zhong, Fanfei Xu, Qingji Jiang, Dazhuan Xu, Yongming Huang

Signal Processing

WiFo-INR: A Wireless Foundation Model Based on Implicit Neural Representations

Boxun Liu, Xuanyu Liu, Shijian Gao et al. (+2 more)

Wireless foundation models are emerging as a promising paradigm for AI-native physical-layer design. However, existing methods typically model channel state information (CSI) as image-like discrete tensors with generic token decoders that may struggle to capture complex high-frequency variations efficiently and often produce high-dimensional, size-dependent representations. In this paper, we propose WiFo-INR, an implicit neural representation (INR)-based wireless foundation model that represents CSI as a coordinate-conditioned neural function. A Transformer encoder maps partial or coarse CSI to fixed-dimensional modulation tokens that adapt a SIREN-based decoder, and a compression autoencoder enables quantized CSI feedback. It adopts a two-stage self-supervised pretraining scheme, where mixed masking and denoising improve channel reconstruction and compression-enhanced pretraining enables accurate CSI feedback at low compression ratios. Extensive experiments demonstrate that WiFo-INR learns efficient, compact, and CSI-size-independent implicit wireless representations. Compared with existing foundation models, WiFo-INR improves channel reconstruction and CSI feedback performance while substantially reducing inference latency. It also transfers efficiently to diverse wireless tasks with minimal fine-tuning overhead and achieves zero-shot generalization to unseen CSI sizes.
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All authors Boxun Liu, Xuanyu Liu, Shijian Gao, Xiang Cheng, Liuqing Yang

Signal Processing

Dual-polarized RIS versus dual-polarized network-controlled repeater

Özlem Tuğfe Demir

We study the achievable rate performance of dual-polarized passive reconfigurable intelligent surfaces (RISs) and dual-polarized network-controlled repeaters (NCRs) in a point-to-point wireless link. While RISs rely on passive beamforming with large array gains, NCRs exploit active amplification across polarization branches. We develop a unified analytical framework that captures the impact of amplification, noise propagation, and deployment geometry under different power-budget models. Closed-form rate expressions are derived, and the optimal one-stream and two-stream transmission strategies for NCRs are characterized. The results reveal that RISs are competitive in short-range scenarios and benefit from large array sizes, whereas NCRs become preferable in long-range deployments due to their ability to compensate for pathloss through amplification.
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Signal Processing

Energy-Efficient Integrated Access and Fronthaul for Cell-Free Massive MIMO with Adaptive Quantization Resolution

Özlem Tuğfe Demir

Cell-free massive MIMO with wireless fronthaul is a promising architecture for energy-efficient 6G networks, but the access and fronthaul links must then share the same scarce spectrum, and, under the fully centralized (option-8) functional split, the fronthaul rate is dictated by the finite quantization resolution used at the access points (APs). This paper develops a network energy-efficiency (EE) maximization framework for the uplink of such a system, jointly optimizing the integrated access and fronthaul (IAF) resource split, the adaptive per-AP quantization resolution, and the fronthaul powers, and treating the time-division (TD) and frequency-division (FD) operating modes in a unified manner. Each AP may be switched off (put to sleep) when it is not worth activating, so the resolution allocation is inherently coupled with AP selection. The resulting mixed-integer, nonconvex fractional program is solved by an alternating-optimization algorithm with per-block optimality guarantees—a closed-form optimal time split, bandwidth bisection, and optimal per-AP bit selection—that applies verbatim to both modes. While the design relies on the tractable additive quantization noise model, the reported performance is obtained end-to-end with the actual Lloyd–Max quantizers and a Bussgang decomposition-based achievable-rate bound.
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Signal Processing

Parametric Electromagnetic Information: Field-Manifold Geometry and the Stability of Learned Field Representations

Marco Donald Migliore

In many electromagnetic systems, the set of radiated or scattered fields is controlled by only a few physical parameters and therefore forms a low-dimensional manifold embedded in a high-dimensional observation space. This paper extends Electromagnetic Information Theory (EIT) to such parametric field families by separating two descriptors that classical linear NDF analysis merges: the Electromagnetic Intrinsic Dimension (EID), $d$, which counts the locally independent directions of field variation, and the Metric Stretching Exponent, $\nu$, which governs the electrical-size scaling of the intrinsic metric volume. Using Kolmogorov $\varepsilon$-entropy and $\varepsilon$-capacity, we derive lower bounds showing that stable non-linear representations depend not only on dimension but also on metric-volume growth, which reappears as a decoder-sensitivity burden. Under additive Gaussian noise, the pullback metric is proportional to the Fisher Information Matrix, linking the same geometry to Cramér–Rao estimation bounds. Physics-constrained autoencoders provide an operational estimate of the latent dimension required to achieve a prescribed reconstruction accuracy and an empirical proxy for the associated normalized decoder sensitivity. Array and scattering benchmarks show that systems with the same intrinsic dimension can exhibit different metric-growth laws depending on the physical modulation or scattering regime, while a dedicated steering-arc experiment provides finite-sample evidence that the best observed decoder-sensitivity proxy scales nearly linearly with metric length across electrical apertures, consistently with the predicted lower-bound trend.
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Signal Processing

The Impact of Demand Forecasting on Delay and Jitter in DVB-Based Beam-Hopping LEO Networks

Yekta Demirci, Guillaume Mantelet, Stéphane Martel et al. (+2 more)

In LEO satellite networks utilizing beam hopping (BH), resource allocation plans must be committed well in advance. This inherent operational delay necessitates predicting future user demand during the planning phase. Such predictive agility is particularly crucial for military applications, where unpredictable tactical environments demand low-latency, resilient communication links. However, existing forecasting models are typically evaluated based on standalone accuracy, ignoring their cross-layer impact on overall network performance. To address this gap, we evaluate two distinct demand forecasting solutions within a comprehensive, full-stack LEO satellite simulation compliant with DVB-S2X standards. Beyond prediction accuracy, we examine how incorporating user demand forecasts into BH plan generation impacts key network metrics, particularly delay and jitter. We evaluate these forecasting solutions alongside a static allocation baseline. Our results demonstrate that forecast-based dynamic planning reduces delay by 10-40% across the beams under certain load conditions compared to static allocation methods. Crucially, marginal improvements in predictive accuracy do not translate into proportional network metric gains. While the evaluated forecasting solutions differ by 14-16% in Normalized Mean Square Error (NMSE), this discrepancy yields less than a 1% reduction in delay and produces nearly identical jitter characteristics. These findings suggest that when designing user demand forecasting solutions for practical LEO deployments, prioritizing system scalability may be more valuable than chasing minor accuracy enhancements.
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All authors Yekta Demirci, Guillaume Mantelet, Stéphane Martel, Jean-François Frigon, Gunes Karabulut Kurt

Friday 7 August

Signal Processing

Distributed Acoustic Sensing Over Deployed Telecommunication Fiber Networks: Opportunities and Challenges

Élie Awwad, Maria Freire Hermelo, Diane Prato et al. (+5 more)

In this paper, we highlight opportunities and challenges of deploying Rayleigh-based distributed optical fiber sensing, also known as distributed acoustic sensing (DAS), over existing optical telecommunication networks. We review some of the prominent recent advances in this area and we shed light on future directions. First, we briefly review classifications of DAS systems and we focus on the advantages of using a continuous-wave constant-power DAS probe signal for in-band coexistence between DAS and data transmission over the same fiber. Second, we emphasize on the important role of the mechanical coupling between the fiber, its hosting cable and their surrounding environment in the interpretation of the extracted strain information. Third, we show results on the estimation of distributed birefringence information using a polarization-diversity DAS interrogation. Finally, we study the impact of laser phase noise on continuous-wave constant-power DAS interrogation using phase-encoded probing sequences and attempt to mitigate it.
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All authors Élie Awwad, Maria Freire Hermelo, Diane Prato, Pierre Pruvost, Pallab Kumar Choudhury, Yves Jaouën, Renaud Gabet, Heming Huang

Signal Processing

CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals

Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire et al. (+3 more)

Cardiovascular AI models can classify clean elec- trocardiogram (ECG) signals, but real wearable signals change because of motion, breathing, posture, sensor contact, and true clinical deterioration. This paper asks when a model should keep its prediction, change it, or flag uncertainty. We propose a physiologic stability framework, called PECS, that compares changes inside the model with measurable changes in the signal. ECG is treated as the main cardiac signal, photoplethysmography (PPG) adds pulse and vascular information, and respiration is used only when ECG and PPG disagree. We test the framework on PTB-XL at pilot and full scales and on synchronized BIDMC and MIMIC waveform cohorts. The PTB-XL pilot and full- scale analyses selected different domain pairs, and the strongest cross-modal pair also changed across BIDMC and MIMIC, showing that adding every available signal is not always the best choice. PECS outperformed the evaluated drift-detection baseline implementations, reaching drift classification accuracy (DCA) of 0.8786 on expanded BIDMC and 0.9560 on MIMIC. The MIMIC results also showed that respiration can help during disagreement cases, but it should be used selectively rather than as an automatic override. Overall, the results support PECS as a candidate monitoring framework for wearable cardiovascular AI while highlighting the need for scale-aware domain selection and interpretable trust routing
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All authors Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire, Kosisochukwu Ogbuanya, Sanaa Reeves, Sandy Akoy

Signal Processing

RIS-Aided mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprinting

Md Tarek Hassan, Dmitry Zelenchuk and Muhammad Ali Babar Abbasi

Accurate user equipment (UE) localization is critical for beam management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) based sixth-generation (6G) networks, especially if the direct base-station-UE links are unavailable. This paper proposes a beam-domain fingerprint framework that maps the received signal-to-noise ratio (SNR) across a small set of predefined RIS reflection states to the UE azimuth angle and range, without requiring channel state information (CSI). Crucially, we extend the framework to a realistic interference-impaired scenario in which a nearby cross-link interferer (CLI) corrupts the clean SNR fingerprint, yielding a signal-to-interference-plus-noise ratio (SINR) fingerprint; an interference-to-noise ratio (INR)-constrained calibration strategy keeps the interference level physically interpretable. Four machine-learning (ML) regressors are evaluated under both conditions. Simulation results at 28 GHz with a 20x20 RIS show that k-nearest neighbors (KNN) achieves the lowest angle MAE of 0.37 degrees and range MAE of 4 cm under clean conditions, rising to 1.4 degrees and 7.6 cm under interference. A key finding is that interference degrades angle estimation substantially more than range estimation across all models, a consequence of the asymmetric encoding of location information in the beam-domain fingerprint.
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Signal Processing · Cross-listed from cs.IT

Efficient Discrete Position Design for Movable Antenna Systems: Low Complexity and Robustness

Haonan Wang, Xianghao Yu, Rui Wang et al. (+2 more)

Building on advances in reconfigurable antenna techniques, movable antennas (MAs) can dynamically reshape antenna arrays and introduce additional spatial degrees of freedom (DoFs), thereby further improving communication performance. Despite these benefits, existing MA design algorithms often entail prohibitively high computational complexity from discrete positioning selection, which prevents practical implementations of MAs. In this paper, we investigate efficient solutions for the mutual information (MI) maximization problem of a multi-user multiple-input multiple-output (MU-MIMO) uplink communication system aided by discrete MAs. To this end, we first formulate the discrete MA positioning problem with the assumption of perfect channel state information (CSI). Then, we prove that the design problem falls into the category of monotone submodular maximization subject to a 2-system constraint. Accordingly, we propose a low-complexity distance-constrained submodular position search algorithm, which is theoretically shown to achieve at least 1/3 of the optimum. Furthermore, we extend our approach to scenarios with imperfect CSI, and show that the proposed submodular optimization-based design remains robust against channel estimation errors. Numerical results demonstrate that the proposed scheme can achieve at least 90% of the optimal solution's MI gain under both perfect and imperfect CSI assumptions. Remarkably, the algorithm achieves orders-of-magnitude complexity reduction (e.g., 34.4x faster than the branch-and-bound approach) while maintaining significant MI gains.
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All authors Haonan Wang, Xianghao Yu, Rui Wang, Ang Li, Ying-Jun Angela Zhang

Signal Processing · Cross-listed from cs.LG

FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivity

Deepank Girish, Yi Hao Chan, Yubin Zheng et al. (+2 more)

Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to generalize well. While Federated Learning (FL) offers a privacy-preserving paradigm for collaborative training, standard approaches continue to struggle with statistical heterogeneity. In particular, site differences pose a key challenge in multi-site data settings. Additionally, existing FL approaches for fMRI rely on static Functional Connectivity ( FC), omitting dynamic information in brain networks. To address this, we propose FedDOSE, a novel framework that explicitly decomposes site differences for analysis of dynamic FC (dFC). FedDOSE introduces a Modularity-Guided Tucker Decomposition block to encode high-dimensional dFC tensors and capture modular-level spatio-temporal patterns efficiently. Class-specific prototypes are generated across all sites and subsequently aligned at the global level by using a combination of Optimal Transport (OT) barycenter formulation and Procrustes analysis. Extensive experiments for diagnosing Autism Spectrum Disorder (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) on three multi-site resting-state fMRI datasets: ABIDE-I, ABIDE-II, and ADHD-200, demonstrate that FedDOSE outperforms state-of-the-art methods in ASD and ADHD detection. Our results highlight its effectiveness in learning robust representations from multi-site datasets for reliable analysis.
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All authors Deepank Girish, Yi Hao Chan, Yubin Zheng, Sukrit Gupta, Jagath C. Rajapakse

Signal Processing · Cross-listed from cs.LG

Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations

Ioannis Ziogas, Ensieh Khazaei, Bilal Taha et al. (+4 more)

Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing. However, existing approaches do not operate as full-stack wearable processors, i.e., they do not simultaneously address task-specific classification performance, disentangled and interpretable representation learning, fusion, and generative modeling of highly heterogeneous multi-modal time series. To address this gap, we introduce Omni-modal Variational Decomposition Autoencoders (OmniDecVAEs), a framework that efficiently learns multi-purpose representations in a unified and scalable manner from arbitrarily many modalities. OmniDecVAEs extend DecVAEs by learning modality-conditioned time-frequency latent subspaces through a multi-view self-supervised decomposition loss and a shared asymmetric autoencoder (AE) architecture. Results on a challenging omni-modal human activity recognition (HAR) setting with up to thirty modalities, demonstrate the ability of OmniDecVAEs to learn full-stack wearable representations. When compared to transformer-based and VAE-based methods, OmniDecVAEs full-stack disentangled representation properties lead to accuracy improvements of 1.01% and 6.75% in activity and identity recognition, respectively. Furthermore, OmniDecVAEs synthesize realistic omni-modal time-frequency data that manifest with enhanced reconstructions (mean absolute error improves by 76.84%) and distributional similarity between real and synthetic data (maximum mean discrepancy improves by 13.85%). Our results highlight OmniDecVAEs potential as a lightweight model suitable for intelligent edge wearables and clinical healthcare, unifying processing requirements and abilities in a single model, through its enhanced representational capacity, modality-invariant spatial complexity (4.1M parameters), and real-time latency.
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All authors Ioannis Ziogas, Ensieh Khazaei, Bilal Taha, Aamna Al Shehhi, Ahsan H. Khandoker, Leontios J. Hadjileontiadis, Dimitrios Hatzinakos

Signal Processing · Cross-listed from eess.AS

How Much AI Is in This Track? Quantifying the Proportion of AI-Generated Stems in Hybrid Music Mixtures

Fernando Garcia de la Cruz, David López-Ayala, Pablo Zinemanas et al. (+2 more)

AI-generated music is increasingly used at the stem level, with producers integrating synthetic drums, basslines, or vocals alongside human-performed instruments. However, current AI music detection systems are binary, treating tracks as either fully AI or fully human. In this paper, we reformulate AI music detection as a regression problem on a continuous AI energy ratio, alpha in [0, 1]. We propose a methodology that leverages a multi-track music dataset to assemble mixtures of human-performed and AI-reconstructed stems (obtained using a neural audio codec) with known proportions of each content type. Using this approach, we first show that a CNN-based model trained on fully AI-generated or human-performed tracks, which achieves >99% accuracy as a binary detector, when faced with mixed content, yields an output that rises with the AI stems' energy contribution, acting as a noisy and miscalibrated estimator. Our analysis of the influence of different stems shows that detection sensitivity depends on the instrument and reflects its frequency content: drums and guitar carry strong codec-artifact signatures, while vocals and bass are less detectable. Based on these insights, we train a similar CNN-based model for regression of alpha, achieving MAE = 0.076 and R^2 = 0.85 on held-out mixtures from the same pipeline. These results suggest that the regression formulation is an initial promising step towards AI-music detection in realistic music production workflows.
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All authors Fernando Garcia de la Cruz, David López-Ayala, Pablo Zinemanas, Emilio Molina, Martín Rocamora

Signal Processing

Token Communication for Multimodal Large Language Model

Jingkai Ying, Zhijin Qin, Yuan Shen et al. (+1 more)

With the broad success of the Transformer architecture, token is becoming a new basic information processing unit. This trend is especially evident in multimodal large language models (MLLMs), where both visual and textual information are represented and processed as tokens. With the rapid deployment of MLLMs, the efficient transmission of tokens has become increasingly important. This paper investigates how to reduce the amount of transmitted data during interactions with MLLMs while preserving their multimodal understanding performance. To address this problem, we propose a token communication framework tailored to MLLMs. In the proposed framework, a neural codec is integrated into the vision tokenizer to control the number of transmitted bits. At the receiver, the decoded latents are processed through two paths. The decoder reconstructs image as a reconstruction prior, while the adapter converts latents into visual tokens and injects them into an intermediate layer of the vision tokenizer. To make the injected tokens suitable for MLLMs, we further design a two-stage visual-language semantic alignment training scheme. The adapter is first warmed up by a distillation loss and then aligned with textual semantics through an alignment loss. An adaptive adapter is also introduced through feature-wise linear modulation, allowing one adapter to support multiple codec rates. Extensive simulations on various MLLM benchmarks show that, under the same amount of transmitted data, the proposed scheme achieves better task performance than other image processing schemes for MLLMs.
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All authors Jingkai Ying, Zhijin Qin, Yuan Shen, Khaled B. Letaief

Signal Processing

Sub-Sampling for Positioning Privacy in ISAC: Deception by Aliasing via Sparse Arrays and Pilots

L. Yashvanth, Christos Masouros, Suraj Srivastava et al. (+2 more)

Integrated sensing and communications (ISAC) enables simultaneous communication and sensing using shared spectrum and hardware resources in wireless systems. However, securing the sensing functionality against unauthorized receivers remains a fundamental challenge. In this paper, we propose a sub-sampling based sensing-privacy framework for communication-centric (CC)-ISAC systems that jointly exploits sparse arrays and sparse pilot allocations to induce controlled aliasing in the spatial and frequency domains, respectively. By interpreting antenna arrays and pilot subcarriers as spatial and frequency sampling mechanisms, respectively, we show that spatial-frequency undersampling naturally distorts the range-angle multiple-input multiple-output (MIMO) ambiguity function (AF) observed by an unauthorized receiver. To this end, we first derive a closed-form expression for the range-angle MIMO-AF, and subsequently characterize the ghost targets that arise due to spatial and frequency-domain aliasing. Next, we establish a sufficient condition under which these ambiguities jointly translate into positioning ambiguity and show that, for sufficiently large spatial and frequency sub-sampling factors, an unauthorized receiver inevitably positions a target at incorrect ghost positions. Finally, we show that the proposed sub-sampling framework preserves the native legitimate ISAC performance without introducing additional trade-offs. Numerical results verify the analysis and show that sparse arrays and sparse pilots naturally enable sensing and positioning privacy through deception by aliasing.
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All authors L. Yashvanth, Christos Masouros, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo