Systems and Control · Cross-listed from math.OC
Data-driven approximation of regions of attraction via an LP-based selection of PWA Lyapunov functions
Oumayma Khattabi, Matteo Tacchi-Bénard, Martin Gulan et al. (+1 more)
This paper presents a method to approximate regions of attraction of unknown nonlinear dynamical systems from data. Assuming point-wise evaluations of the vector field and known Lipschitz bounds, a polyhedral uncertainty set of admissible dynamics is constructed. This uncertainty description enables the synthesis of a continuous piece-wise affine Lyapunov candidate via a linear program, enforcing a robust decrease condition for all admissible vector fields. The approach allows certification of a region of attraction consistent with the available data. Numerical examples illustrate the effectiveness of the proposed method in extracting certified regions of attraction from sparse data.
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All authors Oumayma Khattabi, Matteo Tacchi-Bénard, Martin Gulan, Sorin Olaru
Systems and Control · Cross-listed from cs.DC
DAG-Based QoS-Aware Dynamic Task Placement for Networked Multi-Stage Control Pipelines
Thien Tran, Jonathan Kua, Thuong Hoang et al. (+3 more)
Current Physical AI (PAI) relies heavily on closed-loop visual-servoing pipelines, whose perception and planning stages may become computationally intensive onboard due to complex models embedded on
robots. In practice, offloading the perception task to on-site edges statically is inappropriate for latency-sensitive, precise industrial settings over a standardized industrial network. This emphasizes the importance of Control-Communication-Computing (3C) co-design in industrial automation: monolithic local execution saturates AI-accelerated machine and
robot hardware, while static edge offloading exposes the control loop to network jitter. Existing adaptive task placement (ATP) controllers can partially address the gap by relocating a single pipeline stage on binary threshold rules, without a multi-stage model and an explicit cost on placement switching. In this paper, we propose a directed acyclic graph (DAG) based quality-of-service (QoS)-aware dynamic task placement (DTP) framework for sensing-perception-planning-control pipelines in networked robotics. This pipeline is formalized as a DAG with task-level and node-level attributes for compute cost, communication delay, and feasible placement sets; over a small interpretable candidate set (fully local, static offload, hybrid), a window-based cost function combines tail end-to-end latency, deadline violation rate, hardware utilization, and a Hamming-distance switching penalty, and a DTP
algorithm with hysteresis and a minimum dwell-time bounds placement chatter. Our work presents the theoretical framework, a structured qualitative analysis, and a two-phase simulation plus hardware-in-the-loop validation roadmap.
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All authors Thien Tran, Jonathan Kua, Thuong Hoang, Minh Tran, Yuemin Ding, Jiong Jin
Image and Video Processing
Set Shaping Theory as a Complementary Payload-Shaping Layer for Steganography
Aida Koch, Logan Lewis, Lily Scott et al. (+1 more)
This paper studies the use of Set Shaping Theory (SST) as a reversible payload-shaping layer for least significant bit (LSB) image steganography. The proposal is not intended to replace existing steganographic methods or to compete with them as a new
embedding scheme. Instead, SST is positioned as a complementary preprocessing stage that makes an existing
embedding method easier to apply with lower statistical disturbance. The SST transformation increases the message length by K symbols and is implemented with the approximate and fast transformation
algorithm developed by Glen Tankersley. Although the embedded payload is lengthened from N to N+K bits, the selected representation can reduce D_KL(P||Q) and therefore make the subsequent steganographic insertion less detectable under histogram-based criteria. Across 1,800 controlled simulations on four synthetic cover-image models, SST reduced D_KL(P||Q) by an average of 25.16 percent relative to a fair N+K LSB baseline, with a 95 percent confidence interval of +/- 1.22 percent. For K=8, the average reduction reached 42.81 percent. Additional robustness simulations with keyed random
embedding paths confirmed the effect across several distances: at K=8, SST reduced KL divergence by 42.44 percent, Jensen-Shannon divergence by 29.62 percent, total variation by 12.41 percent, and symmetric chi-square distance by 28.30 percent. An additional image-based
matrix-
embedding/STC-like simulation showed that SST also reduces the minimum weighted insertion cost: relative to the unshaped K=0 reference, K=8 reduced the cost by 6.93 percent.
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All authors Aida Koch, Logan Lewis, Lily Scott, Agi Weber
Systems and Control · Cross-listed from cs.LG
A Closed-loop, State-centric, Multi-agent Framework for Passenger Load Estimation from Heterogeneous Data Streams
Yiyao Xu, Hao Zhou, Yuhang Wang et al. (+1 more)
To support operations and passenger-facing services, transit agencies need reliable passenger load trajectories. Currently, load estimates are typically inferred from imperfect sensing systems rather than fully observed, and the accuracy of modern automatic passenger counting (APC) systems still varies with station layout, flow intensity, and operating conditions. To address the challenges of robust passenger load estimation from heterogeneous data streams, including incremental count errors, evidence conflicts, and context-dependent sensor reliability, we propose a closed-loop, state-centric, multi-
agent framework. This method enforces physical feasibility at every step, allocates trust dynamically among evidence sources, and feeds physics-derived violation residuals back into training for robustness improvement. The architecture consists of a unified stop-event backbone, a coupled Perception–Physical–Fusion loop for stop-by-stop inference, and optional trip-level macro-correction and closed-loop calibration modules.
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All authors Yiyao Xu, Hao Zhou, Yuhang Wang, Jingran Sun
Audio and Speech Processing · Cross-listed from cs.SD
Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation
Zhifei Xie, Kaiyu Pang, Haobin Zhang et al. (+4 more)
Despite rapid advances in automatic
speech recognition (ASR) and large audio-
language models, robust recognition in real-world environments remains limited by an "acoustic robustness bottleneck": models often lose acoustic grounding and produce omissions or
hallucinations under severe, compositional distortions. We propose Mega-ASR, a unified ASR-in-the-wild framework that combines scalable compound-data construction with progressive acoustic-to-semantic optimization. We introduce Voices-in-the-Wild-2M, covering 7 classic acoustic phenomena and 54 physically plausible compound scenarios, and train Mega-ASR with Acoustic-to-Semantic Progressive Supervised Fine-Tuning and Dual-Granularity WER-Gated Policy Optimization. Extensive experiments demonstrate that Mega-ASR achieves significant advantages over prior state-of-the-art systems on adverse-condition ASR
benchmarks (45.69% vs. 54.01% on VOiCES R4-B-F, and 21.49% vs. 29.34% on NOIZEUS Sta-0). On complex compositional acoustic scenarios, Mega-ASR further delivers over 30% relative WER reduction against strong open- and closed-source baselines, establishing a scalable paradigm for robust ASR in-the-wild.
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All authors Zhifei Xie, Kaiyu Pang, Haobin Zhang, Deheng Ye, Xiaobin Hu, Shuicheng Yan, Chunyan Miao
Systems and Control · Cross-listed from cs.NI
Motion-Coupled Sensing: When the State Change Powers Its Own Sensing
Muhammad Tahir, Muhammad Mubbashar Baig, Umer Irfan et al. (+2 more)
Batteryless IoT systems have largely followed two paths: ambient-energy sensing, where energy arrival is decoupled from the event being monitored, and kinetic event telegrams, where a user actuation powers a short report of the actuation itself. Mechanically gated states expose a third case: the access motion is not only an event to report, but the moment at which a latent physical state may have changed and must be measured. We show that routine hinge motion can supply enough energy for one bounded wake-sense-transmit transaction, including ultrasonic sensing and a long-range LoRa uplink. We call this principle motion-coupled sensing and instantiate it with an open-source compact electromagnetic harvester that retrofits to bins, doors, and cabinets with no structural modification. We size the platform for the most demanding workload, waste-bin monitoring, where each actuation must power both an ultrasonic measurement and a long-range LoRa uplink. Across five campus locations and 5,945 lid actuations, the bin deployment achieves 99.3% per-event transmission reliability. Field deployments on room doors with 1,870 actuations and office cabinets with 1,636 actuations achieve 92% and 94% transmission success respectively, demonstrating that the same energy envelope transfers across hinge geometries without hardware redesign. These results show that mechanical access can be treated as a self-powered sensing transaction, removing periodic polling and scheduled battery maintenance for IoT deployments.
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All authors Muhammad Tahir, Muhammad Mubbashar Baig, Umer Irfan, Muhammad Ahad, Naveed Anwar Bhatti
Signal Processing
Channel Estimation for Beyond Diagonal RIS-Aided Multi-User mmWave Systems
Linyu Peng, Tian Qiu, Cunhua Pan et al. (+3 more)
Beyond diagonal reconfigurable intelligent surface (BD-RIS) represents a promising architecture for advancing millimeter-wave (mmWave) communications. However, its intricate inter-element connections invalidate the conventional decoupled mathematical structure, thereby severely complicating cascaded channel estimation. In this paper, we formulate a novel block-Kronecker-structured cascaded channel model for a group-connected BD-RIS-aided multi-user (MU) mmWave system equipped with uniform planar arrays (UPAs). By exploiting the cascaded channel sparsity, an efficient three-stage estimation protocol is proposed. Specifically, Stage I acquires the common angles of arrival (AoAs) at the base station (BS) via a discrete
Fourier transform (DFT)-based approach. Stage II leverages the block-Kronecker structure alongside orthogonal matching pursuit (OMP) and correlation-based least squares (LS) to extract the complete cascaded channel for a designated typical user. Finally, Stage III utilizes a Hierarchical Block OMP (HBOMP)
algorithm to estimate the other users' channels. This structurally reconstructs the common and user-specific components, which fundamentally reduces the computational complexity and substantially reduces the pilot overhead. Numerical simulations verify that the proposed protocol yields improved channel estimation accuracy while maintaining a relatively low pilot overhead.
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All authors Linyu Peng, Tian Qiu, Cunhua Pan, Jiangzhou Wang, Taihao Zhang, Hong Ren
Signal Processing
Practical RIS Gain without the Pain via Randomization and Opportunistic Scheduling in 5G NR Wireless Systems: Theory and Experiments
Nekkanti Guna Sai Kiran, L. Yashvanth, Raju Malleboina et al. (+3 more)
In this paper, we theoretically analyze and experimentally demonstrate the performance gains achievable by integrating an in-house built reconfigurable intelligent surface (RIS) with a 5G new
radio (NR) system implemented using the OpenAirInterface (OAI) software stack. Unlike conventional RIS-assisted systems that rely on explicit channel state information (CSI) estimation followed by RIS phase configuration optimization, we adopt a low-complexity approach in which the RIS phase states are randomly switched among predefined configurations. The resulting channel fluctuations are opportunistically exploited by the inherent proportional fair (PF) scheduling mechanism of 5G NR. We develop a theoretical framework that characterizes the interaction between RIS switching dynamics and PF scheduling. Based on this framework and the associated analysis, we provide design guidelines for selecting the RIS switching time $T_s$ and the PF throughput averaging window $T_c$ that maximize the system throughput. Experimental evaluations on the 5G NR testbed demonstrate improvements in key performance metrics, including reference signal received power (RSRP), block error rate (BLER), modulation and coding scheme (MCS) index, and throughput. Our key takeaway is that randomly configured RIS operation with appropriately chosen system parameters can achieve performance comparable to optimized RIS designs, with no additional overhead compared to a conventional 5G NR system. More importantly, it requires no coordination between the RIS and the 5G NR system.
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All authors Nekkanti Guna Sai Kiran, L. Yashvanth, Raju Malleboina, Venkatareddy Akumalla, Debdeep Sarkar, Chandra R. Murthy
Signal Processing
DAFT-s-AFDM Enabled ISAC Systems: Ambiguity Function Analysis and Waveform Design
Shiqi Cui, Tianqi Mao, Fan Zhang et al. (+3 more)
Discrete affine
Fourier transform spread affine frequency division multiplexing (DAFT-s-AFDM) is a promising waveform for integrated sensing and communication (
ISAC) due to its low peak-to-average power ratio, robustness to Doppler shifts, and reduced multiuser interference in the uplink transmission. This paper presents a comprehensive ambiguity function (AF) analysis of DAFT-s-AFDM and derives the closed-form expression for the AF magnitude expectation. Several key insights into the impact of DAFT-s-AFDM parameters on
ISAC performance are revealed, thus providing concrete guidance for the subsequent waveform design. Building on these insights, a novel probabilistic constellation shaping (PCS) framework is proposed for
ISAC waveform enhancement, where the communication throughput and the sensing AF characteristics are jointly optimized by addressing a multi-objective problem. An efficient
algorithm based on a closed-form bit error rate expression is developed to obtain the Pareto-optimal solutions. Extensive simulations validate the theoretical results and that the proposed PCS-enhanced DAFT-s-AFDM can significantly outperform the classical counterparts, achieving a superior and highly controllable tradeoff between the dual-functional performances.
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All authors Shiqi Cui, Tianqi Mao, Fan Zhang, Zeping Sui, Christos Masouros, Zhaocheng Wang
Audio and Speech Processing
Cross-Talk Speech Reduction, by Separation, for Separation
Zhong-Qiu Wang and Samuele Cornell
In conversational
speech separation and recognition tasks, close-talk microphones are typically attached to each speaker during training data collection to capture near-field, close-talk mixture signals, in addition to using far-field microphones to record far-field mixture signals. Each such close-talk mixture exhibits a reasonably high energy level for the wearer and could intuitively serve as weak supervision for training far-field
speech separation models directly on real-recorded far-field signals. However, they are not sufficiently clean for this purpose, as they often contain strong cross-talk
speech from other speakers in addition to background noise. To address this, we propose cross-talk reduction (CTR), a task aiming to isolate the wearer's
speech from each close-talk mixture, and a novel method called CTRnet, which can be trained directly on real-recorded pairs of close-talk and far-field mixtures to accomplish CTR. Building on CTRnet, we further propose pseudo-label based far-field
speech separation (PuLSS), which uses CTRnet's estimated clean
speech as pseudo-labels to train models for separating far-field mixtures. A key advantage of the proposed framework is that both CTRnet and PuLSS can be trained on real-recorded data from the target domain, addressing the generalization gap commonly observed when models are trained exclusively on simulated data. On the CHiME-6
dataset, our framework achieves state-of-the-art ASR performance under both oracle and estimated speaker diarization, surpassing all CHiME-{7,8} challenge submissions. To our knowledge, it is the first neural
speech separation method that substantially outperforms guided source separation on real conversational "
speech-in-the-wild" data.
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Systems and Control
Equalized Coverage in Motion Control Performance Prediction for Self-Adaptive Road Vehicles
Ole Reuter, Richard Schubert, Marvin Loba et al. (+1 more)
Automated driving systems require monitoring mechanisms to ensure operation as intended, especially when system elements degrade and/or fail. Hence, capability monitoring is crucial in order to evaluate the system's remaining performance and implement capability-based behavior. In this paper, we investigate the dynamics of a highly over-actuated automated vehicle under actuator degradations and failures, affecting the vehicle's motion control capabilities. We propose a lightweight prediction model based on conformalized
quantile regression that predicts whether an automated vehicle can be controlled with sufficiently low lateral deviation from a planned trajectory under nominal, degraded, and failed actuator conditions. We recognize that statistical guarantees should hold not only across all data (marginal coverage) but also for different regimes within the data (conditional coverage). We therefore employ equalized coverage methods to address this challenge. During runtime behavior generation our predictor can provide a heuristic for determining the admissible action space. Its application and limitations are discussed in this paper.
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All authors Ole Reuter, Richard Schubert, Marvin Loba, Markus Maurer
Image and Video Processing
Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
Giovanni S. Alberti, Damiana Lazzaro, Serena Morigi et al. (+2 more)
Deep generative models have emerged as state-of-the-art for solving inverse problems, but applying them to inverse problems for PDEs, like electrical impedance
tomography (EIT) remains challenging. Because physical domains are naturally discretized as unstructured meshes rather than regular grids, standard convolutional architectures are often inadequate. In this paper, we propose a novel framework that extends
diffusion posterior sampling (DPS) to graph-structured data. We develop an unconditional score-based
diffusion model directly on a 2D triangular mesh to learn an accurate prior over the physical solution space. Furthermore, we introduce a regularized variant, RDPS, which incorporates explicit regularization terms, such as total variation and generalized Tikhonov, to complement the implicit
diffusion prior and mitigate severe ill-posedness. Extensive experiments on synthetic and real 2D EIT
datasets demonstrate that RDPS produces stable, physically plausible reconstructions. Our approach generalizes well to out-of-distribution inclusion geometries, is highly robust to measurement noise, and outperforms current state-of-the-art solvers (e.g., GPnP-BM3D, DP-SGS) in reconstruction accuracy and artifact reduction.
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All authors Giovanni S. Alberti, Damiana Lazzaro, Serena Morigi, Matteo Santacesaria, Shibo Wang
Signal Processing
How Many Independent Modes Does a Fluid Antenna Have? A Closed-Form Outage Analysis via Equivalent Degrees of Freedom
Tuo Wu, Junteng Yao, Kai-Kit Wong et al. (+5 more)
In a fluid
antenna system (FAS), a single reconfigurable
antenna is able to activate one of $N$ correlated ports to exploit spatial diversity. However, outage analysis is challenging because exact evaluation requires an $N$-dimensional multivariate integral, while existing closed-form approximations based on block-correlation models tend to underestimate the true outage probability. This paper shows that the spatial correlation
matrix of a FAS with a normalized linear aperture length $W$ has at most $K^{*}=2\lceil W\rceil+1$ significant eigenmodes, regardless of the number of deployed ports. This is a spatial counterpart of the Slepian-Landau-Pollak spectral concentration
theorem and reveals that the spatial degrees of freedom are determined by aperture size rather than port count. Motivated by this result, we derive an equivalent degree of freedom (EDoF) approximation, under which the outage probability can be expressed in closed form as that of selection combining over $K^{*}$ independent branches. We propose a refined weighted independent modes (WIM) approximation, to incorporate
eigenvalue-dependent branch weights $\{\beta_k\}$ and yield a product-form closed-form expression with improved accuracy at moderate signal-to-noise ratio (SNR). Both approximations achieve the exact diversity order, become asymptotically exact at high SNR, and provably never underestimate the true outage probability by Anderson's inequality. The proposed framework is further extended to obtain closed-form expressions for ergodic capacity, characterize multi-user fluid
antenna multiple access (FAMA) with explicit interference-limited outage floors. Besides, we analyze two-dimensional planar FAS, for which the diversity order scales multiplicatively with the aperture dimensions.
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All authors Tuo Wu, Junteng Yao, Kai-Kit Wong, Jie Tang, Maged Elkashlan, Baiyang Liu, Kin-Fai Tong, Hyundong Shin
Systems and Control
MagCeptor: Encoding Broadcast-Addressable Logic into Magnetic Receptors
Sishen Yuan, Baijia Liang, Tangyou Liu et al. (+4 more)
Selectively operating multiple components while simplifying actuation and control hardware is a common challenge in multifunctional mechanical and fluidic systems. Here, we introduce MagCeptor, an addressable
magnetic-cluster architecture in which a shared external field conveys commands and supplies actuation energy. Fixed stators establish local
magnetic-field gradients, while field-induced mover reorientation modulates the resulting forces to select guide-constrained mechanical outputs. Field-dependent position-orientation energy profiles explain target actuation, non-target retention and passive reset after field removal. The evaluated configuration achieves a maximum normalized force output of 385 mN/mm3. By coupling finite, addressable strokes to mechanical transmissions, accumulators and locks, we demonstrate coordinated rotation and multistep manipulation with retained task states. Hierarchical node-and-channel addressing further enables selective model-fluid release from distributed receivers in ex vivo tissue. These results establish a receiver-side architecture that combines shared
magnetic inputs with task-specific mechanisms to organize mechanical work, retain operation states and access spatially distributed functions.
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All authors Sishen Yuan, Baijia Liang, Tangyou Liu, Yiqing Huang, Haoxuan Wu, Shuo Xu, Hongliang Ren
Signal Processing
Hyperbolic Frequency Multicarrier Modulation for Wideband Linear Time-Varying Channels
Xuehan Wang, Jinhong Yuan, Jintao Wang et al. (+2 more)
Numerous multicarrier modulation schemes have been proposed recently to enhance the performance in narrowband doubly dispersive channels for emerging high-mobility applications. However, the ultra-reliable modulation framework in wideband linear time-varying (LTV) channels remains an open problem, where the time dilations and contractions brought by the high mobility cannot be ignored for the baseband signal to obtain the constant Doppler shift across the whole transmission band. To solve this problem, we propose the
hyperbolic frequency multicarrier (HFMC) waveform in this paper based on the inspiration from affine frequency division multiplexing (AFDM) modulation, where the delay and Doppler shift are absorbed into a 1D shift in the affine domain to provide a compact characterization of doubly dispersive discrete-time channels. By adopting the passband representation of wideband LTV channels and
hyperbolic frequency modulated (HFM) signals, we reveal that the Doppler scaling factor brought by the relative mobility can be absorbed into an equivalent delay. The basic principle of HFMC modulation is established by investigating the approximate orthogonality among HFMC subcarriers, which are generated from a basic HFM signal by utilizing uniformly spaced equivalent delay. The spectrum of HFMC subcarriers is also analyzed to evaluate the system capacity, where the overlapping nature in the frequency domain can be observed. The input-output characterization in wideband LTV channels is then executed to confirm the 1D integration of time delay and Doppler scaling factor for each path, which demonstrates the ability to exploit potential multipath diversity. The parameter optimization based on the input-output relation and spectrum analysis is finally developed to balance the efficiency and reliability.
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All authors Xuehan Wang, Jinhong Yuan, Jintao Wang, Zhi Sun, Jinxing Hao
Signal Processing
DJSCC-Enabled Multi-User Semantic CSI Feedback for Hybrid Beamforming in Dual-Polarized cmWave Massive MIMO
Ziqi Han, Ziwei Wan, Hengwei Zhang et al. (+5 more)
Driven by the ultra-high throughput requirements of 6G, wireless communications are migrating to centimeter wave (cmWave) bands to overcome the limitations of current spectral resources. Massive multiple-input multiple-output (
MIMO) and orthogonal frequency division multiplexing (OFDM) systems aim to achieve high spectral efficiency in cmWave regimes but are often constrained by the heavy overhead of downlink channel state information (CSI) feedback. This paper proposes a deep learning scheme based on the multi-axis multi-layer perceptron for image processing (MAXIM) architecture for joint semantic CSI feedback and hybrid beamforming in multi-user cmWave
MIMO-OFDM systems, which maximizes the downlink sum rate by end-to-end optimization. Specifically, distributed encoders at multiple user equipments (UEs) perform limited CSI feedback, while the decoder at the base station (BS) jointly designs the hybrid beamforming
matrices without explicit CSI reconstruction. The uplink transmission is implemented via deep joint source-channel coding (DJSCC) to enhance CSI compression efficiency and noise robustness. Furthermore, considering the high correlation between vertical and horizontal polarization channels in dual-polarized massive
MIMO systems, a cross-polarization interaction module is introduced at the UEs to exploit polarization correlations for joint CSI compression. Simulation results demonstrate that the proposed method improves the downlink sum rate under various signal-to-noise ratio (SNR) conditions with a limited number of feedback symbols, validating its robustness and superiority in multi-user dual-polarized cmWave
MIMO-OFDM systems.
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All authors Ziqi Han, Ziwei Wan, Hengwei Zhang, Keke Ying, Chabalala S. Chabalala, Dapeng Li, Wei Wang, Zhen Gao
Signal Processing
Sparse Fluid Antenna Arrays: Continuous Position Design Beyond Classical DOF Limits
Tuo Wu, Jie Tang, Ye Tian et al. (+3 more)
Fluid
antenna system (FAS), which continuously repositions a single physical element across a deployment region $[0, D]$, breaks this limit by freeing
antenna positions from the discrete grid entirely. This paper establishes the theoretical foundations of sparse FAS design for direction-of-arrival (DOA) estimation and shows that continuous position freedom unlocks three compounding advantages over the classical designs. First, we derive a universal dual DOF bound and prove that FAS-optimized positions can approach it, growing the DOF linearly with $D/\lambda$ , where $\lambda$ is the signal wavelength, rather than saturating at $O(N^2)$. Second, the CRB scales as $O(1/D^{2L})$ for $L$ sources, a $(D/(N^2 d_0))^{2L}$ improvement over the best grid design, with $d_0 = \lambda/2$ and D-optimal positions admitting closed-form solution for single sources and efficient Frank-Wolfe
algorithm for multiple sources. Third, we propose a two-stage FAS-MUSIC approach that combines coarray MUSIC disambiguation with full-aperture local maximum likelihood (ML) refinement to track the CRB, overcoming the grating-lobe ambiguity inherent in large-aperture non-uniform arrays. Robustness to minimum spacing constraints, mutual coupling, and finite position accuracy is also analyzed. Extensive simulations show that FAS-MUSIC achieves $17.5\times$ lower root mean squared error (RMSE) than uniform linear array (ULA) MUSIC and that FAS with $4$
antennas outperforms MRA with $8$
antennas, gains that are unattainable by any grid-constrained design.
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All authors Tuo Wu, Jie Tang, Ye Tian, Cheng Zeng, Matthew C. Valenti, Hing Cheung So
Signal Processing
Measurement Selection Strategies for Position Estimation in Indoor Environments
Neetu R. R, Shrihari Vasudevan and Ranjani H.G
Time-based indoor positioning techniques rely on multiple access points (APs) and measurements between the user equipment (UE) and the APs. In dense indoor environments, occlusion-induced non-line-of-sight (NLoS) propagation introduces significant delays in these measurements, thereby degrading position estimation accuracy. To address this challenge, this paper proposes measurement selection strategies to improve position estimation accuracy. A ray-tracing (RT) simulator is employed to characterize the propagation environment and derive AP neighborhood information, which is subsequently used to design and evaluate different measurement selection strategies. The approaches explored include AP neighborhood-based cardinality selection, intersection and union of measurements from AP neighborhoods, and fixed measurement selection. Experiments demonstrate the efficacy of the proposed measurement selection strategies in environments under significant NLoS conditions.
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Image and Video Processing
Perception-Aware Video Semantic Communication
Yinhuan Huang and Zhijin Qin
Ultra-high-resolution streaming and emerging immersive services are driving rapidly increasing wireless
video traffic. However, perceptually pleasing
video transmission over bandwidth-limited and latency-constrained wireless links remains challenging for conventional separated source-channel systems, which primarily target bit-level reliability and often suffer performance degradation under short-blocklength transmission. In addition, pixel-level distortion optimization does not necessarily align with human perception, while existing learned
video codecs may incur high complexity and raise deployment issues. This paper proposes PVSC, a perception-aware
video semantic communication framework for real-time wireless
video transmission. PVSC eliminates explicit motion-vector transmission and exploits spatio-temporal feature coding to generate compact and channel-robust symbol streams. It also specifies side-information formatting, reference-buffer management, and lightweight rate control, enabling stable receiver-side reconstruction and bandwidth-adaptive inference with a single model. Extensive experiments demonstrate that PVSC achieves superior performance across diverse
datasets, resolutions, GOP configurations, and channel conditions. Compared with the engineered “VTM + 5G LDPC” baseline, PVSC saves up to about 75% and 87% bandwidth at comparable LPIPS and DISTS, respectively, while enabling real-time inference on a single NVIDIA RTX 4090 GPU.
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Signal Processing
Quantum-Native Maximum Likelihood Detection in Random Access Channel with Overloaded MIMO
Hyoga Iizumi, Naoki Ishikawa, Shunsuke Uehashi et al. (+3 more)
In this paper, we propose a
quantum-native formulation of maximum likelihood detection (MLD) for overloaded multiple-input multiple-output (
MIMO) systems in a random access channel, where numerous user terminals share the same channel resource and asynchronously transmit signals. Classical linear detectors suffer from significant performance degradation in this scenario, whereas the exhaustive-search MLD achieves the optimal performance but incurs an exponential computational complexity. To overcome this trade-off, we formulate the MLD as a binary optimization problem and solve it via Grover adaptive search (GAS) – a
quantum exhaustive search
algorithm offering quadratic speedup in fault-tolerant
quantum computing. We then introduce a search space reduction technique to substantially decrease the required computational resources. In addition, we investigate efficient parameter settings for GAS through probability analysis to improve convergence performance. We demonstrate that the proposed detector achieves the optimal detection performance while reducing the required Grover rotation count to reach the solution by up to approximately 65% compared with the conventional GAS, showing its potential as a viable solution for future
quantum-accelerated wireless systems.
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All authors Hyoga Iizumi, Naoki Ishikawa, Shunsuke Uehashi, Kota Nakamura, Shusaku Umeda, Toshiaki Koike-Akino
Audio and Speech Processing
Fast Multichannel NMF with Block-Diagonal Spatial Covariance Matrices for Efficient Blind Source Separation Using Distributed Microphone Arrays
Hirotaka Nishikori, Nobutaka Ito, Kouei Yamaoka et al. (+2 more)
Distributed microphone arrays composed of multiple subarrays enable blind source separation over a wide spatial area. Directly applying fast multichannel nonnegative
matrix factorization (FastMNMF) to all subarrays can exploit observations from all subarrays, but it requires repeated inversions of large
matrices spanning all microphones, causing the computational cost to increase rapidly as the number of microphones grows. In contrast, applying FastMNMF to one subarray reduces the
matrix size but cannot exploit observations from other subarrays. We propose distributed FastMNMF, which imposes a block-diagonal structure on the source spatial covariance
matrices, so that
matrix inversions are performed within subarrays. The NMF-based source spectrogram model is shared across subarrays, allowing the method to aggregate source activity information while discarding inter-subarray covariance. In synchronized, noiseless simulations with fixed room and array/source geometry, the method required less computation time than conventional FastMNMF using all subarrays, achieved a higher average source-to-distortion ratio than conventional FastMNMF using one subarray, and was applicable in the tested five-source condition, where each four-microphone subarray was locally underdetermined.
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All authors Hirotaka Nishikori, Nobutaka Ito, Kouei Yamaoka, Norihiro Takamune, Hiroshi Saruwatari
Signal Processing
CDiT: Conditional Diffusion Transformer for Geometry-Aware Terahertz Cross Far- and Near-Field Channel Generation
Zhengdong Hu and Chong Han
Accurate channel modeling is fundamental to design and evaluation of Terahertz (THz) ultra-massive multiple-input multiple-output (UM-
MIMO) systems. However, existing model-based approaches typically rely on simplified assumptions, such as sparsity or predefined parametric structures, which are insufficient to capture the complex spatial variations and cross far-/near-field propagation characteristics of practical THz channels. In this paper, a conditional
diffusion transformer (CDiT) framework is proposed for high-fidelity THz channel generation. By leveraging the state-of-the-art hybrid planar-spherical wave model (HPSM), THz channel modeling is formulated as a geometry-aware conditional generative learning problem in the sparse beamspace domain. Position information is incorporated as a conditioning signal within a
diffusion-
transformer architecture, enabling effective learning of the spatially dependent channel distribution. By combining the strong distribution modeling capability of
diffusion models with the global dependency modeling strength of
transformers, the proposed framework achieves controllable and high-fidelity THz channel synthesis. Extensive experiments on realistic THz channel
datasets demonstrate that the proposed framework converges stably and significantly outperforms representative
benchmark methods. The proposed framework provides a promising data-driven paradigm for THz channel modeling in next-generation wireless systems.
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Systems and Control
Revisiting angle stability in power systems with grid-forming power converters
Régulo E. Ávila-Martínez, Javier Renedo, Luis Rouco et al. (+2 more)
This letter presents a comprehensive analysis of the stability phenomenon related to the ability of generators to remain in synchronism when subjected to small or large disturbances, in power systems with both synchronous machines and grid-forming voltage source converters (GFM-VSC). This phenomenon is associated with two stability classes in the IEEE/PES classification, namely, rotor-angle stability (when involving synchronous machines and slow-interaction converter-driven stability (when involving power converters). However, this work shows that this phenomenon is fully characterised with the slow dynamics of the angle difference between the voltage sources connected to the power system, regardless of whether they are synchronous machines (with rotors) or GFM-VSCs. Therefore, we suggest using the term angle stability to refer to this phenomenon, while slow-interaction converter-driven stability should only include slow interactions of different nature involving power converters.
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All authors Régulo E. Ávila-Martínez, Javier Renedo, Luis Rouco, Aurelio Garcia-Cerrada, Lukas Sigrist
Image and Video Processing
Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction
Yilmaz Korkmaz and Vishal M. Patel
MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes more severe under high acceleration, where pixel-domain continuous predictors tend to average over feasible reconstructions and suppress high-frequency anatomy. We address this limitation by moving reconstruction to discrete multi-scale latent space and posing it as autoregressive next-acceleration-scale prediction. Leveraging discrete priors proven effective in visual autoregressive modeling, our method restricts the solution to compact sequences of codebook
tokens, enabling sharp reconstructions even from extremely sparse measurements. This discrete autoregressive formulation also aligns naturally with modern large
language model post-training techniques. Building on this observation, we introduce on-policy privileged information
distillation for visual autoregressive modeling, where a teacher is provided training only privileged context that is unavailable at inference, in our case fully sampled acquisitions, and supervises a student trained on its own rollouts, leading to consistent reconstruction gains. Through extensive experiments on the fastMRI
benchmark, we show that our approach delivers improved reconstruction performance across diverse sampling patterns under extreme undersampling. Project website is \href{https://yilmazkorkmaz1.github.io/discrete-mri-reconstruction-opd/}{here}.
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Signal Processing · Cross-listed from cs.LG
An Objective Performance Evaluation of the LSTM Networks in Time Series Classification
Sooraj Sunil and Balakumar Balasingam
The rapid adoption of deep learning has increasingly led to data-driven models replacing classical model-based
algorithms, even in domains governed by well-understood physical laws. While data-driven models, such as long short-term
memory (LSTM) networks, have become a popular choice for time-series analysis, their performance relative to model-based approaches in structured environments is rarely evaluated objectively. This paper presents a performance evaluation framework comparing an LSTM classifier against a model-based expectation maximization (EM) classifier for binary time-series classification. The evaluation is conducted on two scalar linear Gaussian state space models differing only in their noise statistics, where the Kalman filter likelihood ratio test with true parameters serves as a reference for the best achievable classification performance.Through Monte Carlo simulations, the classifiers are evaluated across three axes: task difficulty, controlled by the separation in process or measurement noise between the two models; sequence length; and training
dataset size. The results show that the EM classifier, which exploits the known model structure, performs strongly when the data conform to the assumed model class. The LSTM classifier requires a larger separation in noise statistics to achieve reliable classification, and its performance saturates below the reference classifier when the models differ only in measurement noise, regardless of sequence length or training
dataset size.
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Systems and Control · Cross-listed from cs.NI
UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning
Wenhao Zhuang, Yuyi Mao, Ivan Wang-Hei Ho et al. (+1 more)
The low-altitude economy (LAE) is reshaping the industrial landscape by deploying unmanned aerial vehicles (
UAVs) to facilitate a wide range of applications demanding flexible aerial mobility. Integrating edge artificial intelligence (AI) into LAE platforms creates a compelling paradigm where
UAVs provide real-time AI-driven analysis while simultaneously executing their primary aerial mission duties. However, realizing this paradigm remains challenging due to the strict mission constraints imposed by these primary duties and the throughput bottlenecks of wireless links. To bridge this gap, we propose a
UAV-assisted cooperative edge inference framework where
UAVs execute mission-critical LAE duties, quantified by trajectory deviations from reference paths, while concurrently supporting ground devices via intermediate feature offloading. Within this framework,
UAV trajectories, inference task offloading decisions, and feature compression ratios are jointly optimized to maximize the system performance. We cast this joint optimization task into a constrained partially observable Markov decision process (POMDP) framework. To efficiently solve it, we propose HDRL-MoE, a novel hierarchical deep reinforcement learning framework that decouples the optimization of slow-varying inference decisions from rapidly changing
UAV trajectory control. Furthermore, HDRL-MoE integrates a mixture-of-experts (MoE) architecture, where a router network orchestrates discrete offloading decisions while expert networks independently optimize the feature compression ratios. Extensive simulations show that HDRL-MoE achieves significant inference accuracy gains over baselines and exhibits high scalability and efficiency through its MoE design.
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All authors Wenhao Zhuang, Yuyi Mao, Ivan Wang-Hei Ho, Xianghao Yu
Systems and Control
ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract)
Guanwen Xie, Jingzehua Xu, Yiyuan Yang et al. (+2 more)
We propose ERFSL, an efficient reward function searcher using large
language models (LLMs) for custom-environment, multi-objective learning-based methods (LB). ERFSL generates reward components based on explicit user requirements, rectifies them using a reward critic, and iteratively optimizes the weights of these components based on textual context generated by the training log analyzer. Applied to a simulation-based
benchmark task, the reward critic corrects reward codes with only one feedback iteration per requirement, and the reward weight initializer acquires diverse reward functions within the Pareto set. Even when a weight is off by a factor of 500, an average of only 5.2 iterations is needed to meet user requirements. The approach works adequately with GPT-4o mini and does not require advanced understanding capabilities.
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All authors Guanwen Xie, Jingzehua Xu, Yiyuan Yang, Yimian Ding, Shuai Zhang
Systems and Control · Cross-listed from cs.CR
Detecting and Mitigating Backdoor Attacks in OTA-FL Systems: A Two-Stage Robust Aggregation Scheme
Xiaoyan Ma, Seohyun Lee, Taejoon Kim et al. (+1 more)
Over-the-air
federated learning (OTA-FL) improves communication efficiency by exploiting the superposition property of wireless channels, but this same property also creates a critical security vulnerability: the parameter server (PS) cannot access individual local updates, making it difficult to identify and exclude poisoned gradients. The challenge is further exacerbated under non-independent and identically distributed (Non-IID) training data, where benign gradient drift can closely resemble malicious updates. In this paper, we propose a two-stage robust aggregation framework for defending against
backdoor attacks in OTA-FL. Under our scheme, each client is first assigned a modality-aware multi-indicator trust score, where the specific indicators are selected according to the data modality (e.g., waveform, text, image) and model architecture to capture the most discriminative footprint of
backdoor updates. Based on this score, the PS then performs trust-based multiple access (TBMA) to separate clients into trusted, suspicious, and malicious categories. Suspicious clients are further examined through PS-side layer-wise inspection and a longitudinal reputation mechanism. Experimental results on several
datasets demonstrate that the proposed methodology effectively suppresses stealthy
backdoor attacks, including bounded-scaling attacks, Euclidean-constrained attacks, Cosine-constrained attacks, and Neurotoxin, while maintaining competitive main-task accuracy.
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All authors Xiaoyan Ma, Seohyun Lee, Taejoon Kim, Christopher G. Brinton