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Electrical Engineering and Systems Science Signal Processing

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Sunday 11 October

Signal Processing

Load Disaggregation under Incomplete Inventory with Structured Unclaimed Demand

Saman Mostafavi and Robert W. Cox

An incomplete inventory creates a specific attribution ambiguity in load disaggregation: unclaimed demand can offset changes in claimed loads without altering meter reconstruction. We study how the allowed target and nuisance trajectories govern this ambiguity. A bounded ordered Markov chain represents unclaimed demand, while an unweighted acceptor removes commissioning-inconsistent paths from one target without reweighting surviving paths; exact factorial MAP jointly decodes all paths. At two fully instrumented commercial buildings, restricting exterior-lighting support improves agreement on the count of rooftop compressors although no compressor chain is constrained; the gain depends on the nuisance representation. A synthetic experiment identifies the boundary: the effect persists under support-preserving changes but reverses when the true path violates commissioned support or the commissioned level is miscalibrated. Attribution therefore depends on joint target-nuisance path geometry, not reconstruction error alone.
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Saturday 10 October

Signal Processing

CSI-Aging-Aware Functional Split Optimization and KAN-LSTM Prediction for Open-RAN NTN

Alexandar Tsvetanov Dimitrov, Yosefine Triwidyastuti, Eray Guven et al. (+2 more)

In this paper, we study functional-split selection for an Open radio access network (RAN) deployed as a non-terrestrial network (NTN), in which a low-Earth-orbit (LEO) satellite hosts the radio unit, so that each candidate split (7.2x, 6, 2) places the RAN at a distinct point of the transparent-to-regenerative axis. We capture the space-ground geometry exactly through an instantaneous link plane, which yields closed-form slant range, elevation, delay, and Doppler for an inclined orbit and a rotating Earth. On this geometry we derive the received-SNR density of the elevation-driven LOS/NLOS mixture channel and, through a split-dependent channel state information (CSI) aging mechanism, an uncoded bit-error-rate (BER) law for each split, establishing that what separates the splits is the freshness of the CSI each brings to its adaptation loops: a loop closed on the ground pays the feeder round-trip and incurs an aging-induced error floor. We formulate split selection as a prefer-centralized feasibility problem over reliability, aging, and fronthaul-capacity constraints, and we show that its exact solution is a closed-form region rule in four state coordinates, validated against exhaustive enumeration without a single mismatch. Because this solution is reactive whereas a split change must be initiated ahead of time, we propose a compact Kolmogorov-Arnold network (KAN)-LSTM, sized for an edge xApp/rApp, that predicts the split schedule seconds ahead; we show that it attains high schedule accuracy at a sub-percent BER-violation probability and delivers nearly all of the ground-truth policy's goodput, outperforming naive and reactive baselines.
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All authors Alexandar Tsvetanov Dimitrov, Yosefine Triwidyastuti, Eray Guven, Tri Nhu Do, Gunes Karabulut Kurt

Signal Processing

Physics-Informed Unsupervised Learning for Reliable Beamforming in Cluttered MIMO-ISAC

Yosefine Triwidyastuti, Nghia Thinh Nguyen and Tri Nhu Do

In this paper, we consider joint beamforming and power allocation for a mobile multiuser MIMO integrated sensing and communication (ISAC) network with clutter, residual self-interference, and outdated channel state information, under per-user rate and sensing signal-to-interference-plus-noise ratio (SINR) constraints that must hold on every realization. The design must be recomputed every block, and distant or weak targets can make the sensing constraint unmeetable. We propose a physics-informed unsupervised learning (PI-UnSL) network that corrects the analytical design, meets the power and norm constraints exactly by construction, and is conditioned on closed-form interference statistics, spanning the whole tradeoff in one pass. We derive a closed-form sensing SINR at the interference-aware combiner, yielding a necessary range-radar-cross-section-power condition and a priority-independent analytic violation floor, so the problem is intermittently infeasible. We then show that a dual targeting below the attainable floor has no fixed point, and calibrate the label-free objective against the measured floor. Numerical results confirm the floor and the bounded calibrated dual, and show that PI-UnSL substantially raises per-realization joint quality of service over the closed-form baseline, delivers more rate than time-division ISAC at matched detection over most of the tradeoff, and exceeds the reliability of an uncalibrated alternating-optimization solver at a fraction of its latency. More broadly, the physical knowledge given to the network, not its architecture, makes such problems learnable without labels: five architectures fail alike from scratch and perform alike when started from the closed-form design and conditioned on its interference statistics.
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Signal Processing

UAV-Mounted Hybrid RIS-Assisted ISAC-RSMA: Performance Analysis with Imperfect CSI and SIC

Chandan Kumar Singh, Arthur Sousa de Sena, Smriti Uniyal et al. (+3 more)

In integrated sensing and communication (ISAC) systems, strict sensing requirements can substantially constrain the transmit power available for communication. A hybrid reconfigurable intelligent surface (HRIS) with passive reflection and active amplification capabilities mounted on an unmanned aerial vehicle (UAV) can enhance coverage and strengthen communication links in such power-limited regimes. Meanwhile, rate-splitting multiple access (RSMA) provides an effective transmission strategy for managing inter-user interference, particularly in dense multiuser wireless scenarios. Motivated by this, we analyze the performance of a UAV-mounted HRIS-assisted monostatic ISAC system employing RSMA scheme. We consider the imperfections in the channel state information and successive interference cancellation in the RSMA, which are inevitable in practical system implementations. We then derive highly accurate expressions for outage probability, ergodic rate, and throughput to evaluate communication performance over Nakagami-$m$ fading channels. These expressions reveal key insights into the diversity order and coding gain of the system in the high signal-to-noise ratio regime. To characterize the sensing performance, we derive expressions for the probability of detection, the probability of false alarm, and the Cramér-Rao lower bound. Numerical results validate the theoretical findings and highlight the performance gains of the proposed system over conventional multiple-access schemes and RIS-based systems.
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All authors Chandan Kumar Singh, Arthur Sousa de Sena, Smriti Uniyal, Deepak Kumar, Prabhat K. Upadhyay, Matti Latva-aho

Signal Processing

CKM-Based Predictive Beamforming for Energy-Efficient Mobile Tracking in ISCAP System

Maryam Asadi-Ahmadabadi, S. Mohammad Razavizadeh and Vahid Jamali

This paper proposes a channel-knowledge-map (CKM)-based predictive beamforming framework for integrated sensing, communication, and powering (ISCAP) of a mobile energy-constrained node in non-line-of-sight (NLoS) multipath environments. The CKM provides location-dependent channel information for both predictive beamforming and EKF-based tracking, while resolved path parameters estimated from the sensing echoes are used as observations. Based on the predicted node location, the transmit covariance and power-splitting ratio are jointly optimized to maximize wireless power-transfer efficiency subject to communication, sensing, transmit-power, and tracking-quality constraints. A finite-battery model and an adaptive inter-frame energy-management policy are further introduced to balance decoding energy consumption and battery preservation. The resulting non-convex fractional problem is solved using Dinkelbach's method, alternating optimization, and semidefinite relaxation. Numerical results show that the proposed framework maintains reliable tracking, satisfies the prescribed position-uncertainty requirement, achieves near-oracle wireless power-transfer performance after the initial tracking transient, and preserves more battery energy while meeting the required communication quality of service.
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Signal Processing

Optimally Setting RMS Energy Bands for Detecting Oscillations

Chetan Mishra, Luigi Vanfretti, Bikal Pudasaini et al. (+2 more)

Oscillation monitors have root-mean-square (RMS) energy detectors at their core. These detect oscillation in a predefined frequency band by comparing the signal power in that band with a threshold (usually set empirically). A key design decision is the choice of frequency bands. Traditionally, for monitoring the stability of wide-area electromechanical modes, finer bands were set below 1 Hz, while higher frequencies were covered by wider bands. However, this approach can yield poor detection in modern power systems with fast and largely unknown local dynamics. This letter proposes an algorithm to set frequency bands from the ambient noise spectrum to maximize the average detection performance.
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All authors Chetan Mishra, Luigi Vanfretti, Bikal Pudasaini, Jaime Delaree Jr., Kevin D. Jones

Signal Processing · Cross-listed from cs.CV

Towards Privacy-Preserving Thermal Human Perception from Dataset to Deployment

Kailai Sun, Yue Cui, Xinwei Wang et al. (+8 more)

Indoor human perception is the foundation for smart homes and healthcare. However, RGB cameras raise privacy concerns in private indoor environments, while low-resolution thermal cameras provide limited spatial and semantic information. Large-scale indoor thermal datasets and real-world deployments remain limited. Here, we introduce Indoor RGB-T, a large-scale paired RGB–thermal dataset for indoor human perception. The dataset covers 20 indoor rooms and contains 90,576 80x62 thermal frames, 87,016 temporally matched RGB–thermal pairs, and 115,557 annotated human bounding boxes. We establish benchmarks for robust thermal occupant-state detection with 0.75 mAP and occupancy estimation with an accuracy of 0.847 in unseen rooms. We further evaluate RGB-to-thermal and thermal-to-RGB translation, achieving a temperature MAE of 0.654 C and an R2 of 0.923. Finally, we deploy the thermal perception model on a commercial sleep-monitoring system in the real world. Field testing with 100 participants and deployment of approximately 1,000 devices further demonstrate its practical feasibility. We identify practical challenges that motivate future research directions, including environmental thermal interference, sensing range and multi-person occlusion. The code, trained models and partial dataset are available at https://github.com/kailaisun/RGBT and https://huggingface.co/skl24/RGBT.
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All authors Kailai Sun, Yue Cui, Xinwei Wang, Zijie Gao, Baoshen Guo, Zhiqing Hong, Irfan Qaisar, Xu Yang, Yueting Li, Dian Fan, Qianchuan Zhao

Signal Processing

Point-Supervised Visual Learning for GNSS Measurement Quality Assessment

Weiwei Zhai, Penggao Yan, Liang Wang et al. (+1 more)

Multipath and non-line-of-sight (NLOS) propagation degrade global navigation satellite system (GNSS) positioning accuracy in urban environments. Although fisheye sky segmentation identifies obstructions along satellite directions, binary visibility does not fully characterize observation quality. This study proposes a point-supervised visual framework that predicts GNSS observation faults from fisheye images using supervision derived from GNSS observations. During offline labeling, a vision-assisted differential GNSS procedure selects multiple reference satellites based on sky visibility and elevation constraints. Double-difference pseudorange residuals automatically generate fault labels and sample importance weights at satellite projection locations. A residual-weighted loss is computed only at valid labeled points, enabling learning from sparse supervision without manual pixelwise fault annotations. The trained network generates dense fault probability maps, which are sampled at satellite projection locations to weight observations in weighted least squares positioning, reducing the influence of potentially faulty observations. The framework is evaluated using U-Net, DeepLabv3+, DINOv3, and SAM3 on urban vehicle data collected on a different day from the training data. All four models reduce horizontal root mean square error (RMSE) by 29.51% to 36.07% compared with SAM binary sky segmentation, with U-Net achieving the lowest RMSE of 0.78 m. The framework also provides positioning solutions during some intervals when the segmentation baseline fails. These results demonstrate the framework's generalization ability and show that GNSS-derived point supervision enables fisheye images to provide observation quality information beyond binary sky visibility, supporting GNSS fault detection.
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All authors Weiwei Zhai, Penggao Yan, Liang Wang, Li-Ta Hsu

Signal Processing

A QoE-Driven Framework for O-RAN Network Slicing Based on Empirical Multi-User VR Traffic Modelling

Ahmed Chyad, David Naseh, Maryam Hafeez et al. (+1 more)

Virtual Reality (VR) requires strict Quality-of-Experience (QoE) guarantees, making radio resource management harder. Static slice allocations are wasteful, while dynamic approaches often rely on heuristic penalty functions lacking an empirical basis. We introduce a QoE-driven network-slicing framework for Open RAN in which resource allocation is guided by empirically derived multi-user VR traffic statistics. Closed-form n-fold convolutions model the aggregate data rate and inter-arrival time of n concurrent users, validated empirically for up to four. The fitted data-rate cumulative distribution function is checked against the measured throughput to give a percentile-based satisfaction indicator; the inter-arrival law sets the slice targets. A near-real-time RIC xApp closes a loop over the E2SM-KPM and E2SM-RC interfaces to apply slice-level Physical Resource Block (PRB) quotas. On an over-the-air testbed it reclaims up to 41% of the cell's committed PRBs from a greedy neighbouring slice while never reducing the protected VR slice. An analytical study then estimates the PRB demand and per-user QoE across user counts and channel qualities, giving the admission boundary beyond which the target QoE is unattainable. A controlled comparison further shows that a fixed-rate synthetic load overstates the VR slice's PRB footprint by 68%, showing the necessity of data-driven slice dimensioning.
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All authors Ahmed Chyad, David Naseh, Maryam Hafeez, Syed Ali Raza Zaidi

Signal Processing · Cross-listed from cs.RO

Global Optimality of Pose Graph Optimization

Shuan Ding, Ruishuang Chen, Mengfan Cao et al. (+1 more)

Pose Graph Optimization (PGO) is a classical and widely applied problem in state estimation for robotics, playing a fundamental role in localization, mapping, and navigation. Yet, it is nonconvex in nature and may have many local minimizers, thus difficult to analyze. In this paper, we investigate the optimization landscape and establish a sufficient condition of global optimality for PGO. The ultimate goal of this paper is to theoretically confirm that the optimization landscape is benign and attractive around the global minimizer, if noise is smaller than an explicit and a priori bound. To reveal such an insight, this paper develops three main technical results. First, we identify a proper basin of attraction (BOA) containing global minimizer, and characterize its geometric property by showing that its radius is controlled by noise in an explicit way. Then, by exploring the first order optimality conditions of PGO, we prove that a local minimizer is globally optimal if a noise-controlled criterion is satisfied. Finally, by combining above two results, we prove that one and only one local minimizer exists in the BOA and this minimizer is globally optimal, when the noise is smaller than an explicit bound that can be verified a priori. Numerical simulations validate the proposed theory under various random cases, showing that the obtained conditions provide an effective criterion for certifying the global optimality of PGO.
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All authors Shuan Ding, Ruishuang Chen, Mengfan Cao, Zaiyue Yang

Signal Processing

UL-Assisted Parametric CSI Feedback and Prediction for FDD Systems

Hao Chang, Mingcheng Nie, Shuangyang Li et al. (+4 more)

In frequency-division duplex (FDD) systems, the uplink (UL) and downlink (DL) channels are not fully reciprocal. Therefore, DL channel state information (CSI) estimated by each user equipment (UE) must be fed back to the base station (BS). However, because the channel varies between CSI acquisition and utilization, the feedback CSI may become outdated before it is used at the BS. To address this challenge, this paper proposes an UL-assisted parametric CSI feedback and prediction scheme for multi-user FDD systems with time-varying channel parameters. By exploiting the partial reciprocity between the UL and DL channel parameters, the BS first estimates the UL channel parameters from the sounding reference signals (SRSs) transmitted by the UEs. The BS then broadcasts DL pilots, allowing each UE to obtain observations containing information about its current DL channel. Instead of explicitly estimating the DL channel from these observations, each UE directly encodes them into feedback symbols and transmits the symbols to the BS. The BS decoder combines the feedback symbols with the partially reciprocal UL parameters to predict the future DL channel parameters. Finally, the corresponding DL channel matrix is synthesized from the predicted parameters using an explicit physical channel model. Simulation results on Sionna ray tracing (RT) dataset show that the proposed scheme outperforms the considered baselines in terms of channel prediction performance over different signal-to-noise ratios and prediction lengths.
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All authors Hao Chang, Mingcheng Nie, Shuangyang Li, Chang Liu, Branka Vucetic, Yonghui Li, Wibowo Hardjawana

Signal Processing

Adaptive DOA Estimation With KL-Based Kernel Bandwidth Adaptation Under Impulsive Noise

Mojtaba Hajiabadi, Saeid Pakravan, Ghosheh Abed Hodtani et al. (+1 more)

Direction-of-arrival (DOA) estimation can degrade markedly in impulsive noise, where methods relying on Gaussian or second-order statistical assumptions are particularly susceptible to large-amplitude outliers. This paper develops an adaptive DOA estimator that employs Gaussian kernel weighting to attenuate the influence of large prediction errors while adjusting the kernel bandwidth online according to a Kullback–Leibler (KL) divergence criterion. A sample-based approximation of the KL criterion yields a recursive bandwidth update that adapts the degree of outlier suppression to the observed error statistics rather than prescribing a fixed bandwidth a priori. The spatial-filter coefficients and kernel bandwidth are updated recursively, and the DOAs are recovered from the resulting spatial spectrum. Mean and mean-square analyses are developed under standard adaptive-filtering approximations to characterize the coefficient convergence and steady-state behavior. Simulation results under Gaussian-mixture impulsive noise demonstrate stable coefficient adaptation and improved DOA estimation accuracy over representative competing methods in several challenging operating regimes, with the most pronounced gains occurring at low-to-moderate signal-to-noise ratios. The results also indicate reduced sensitivity to the initial kernel bandwidth through online bandwidth adaptation.
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All authors Mojtaba Hajiabadi, Saeid Pakravan, Ghosheh Abed Hodtani, Ming Zeng

Signal Processing

Efficient Multimodal Beam Prediction for Real-World 6G UAV Communications via Image-Position-Synthetic Fusion

Rifat Bin Reza, Md Ahadul Haque Suvo, Jia Uddin et al. (+1 more)

Camera-based beam selection can reduce the need for exhaustive channel measurement in highly mobile millimeter-wave UAV links, but the complementary value of visual and position-derived representations and their behavior under modality degradation require further investigation. This paper develops a staged multimodal framework for current-beam prediction using real-world UAV measurements. The proposed 32-beam classifier combines an ImageNet-pretrained EfficientNet-B3 visual encoder with two deterministic position-derived representations: an explicit Geometric-Kinematic vector and a nonlinear synthetic feature basis constructed from position. The image and synthetic representations are fused first, followed by late fusion with the geometry representation. Experiments on DeepSense 6G Scenario 23 show that Image-Synthetic fusion achieves 86.92% Top-1, 99.03% Top-3, and 99.56% Top-5 accuracy, improving Top-1 performance by 2.64 percentage points over image-only inference and outperforming the 60.71% Top-1 accuracy of synthetic-only inference. Adding the Geometric-Kinematic branch yields the highest measured performance, reaching 87.14% Top-1, 99.12% Top-3, and 99.60% Top-5 accuracy. Controlled input interventions further demonstrate complementary behavior under visual degradation and strong sensitivity to severe synthetic-input corruption. Position-derived nonlinear features provide complementary information to visual context and improve current-beam prediction in the evaluated real-world UAV setting.
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All authors Rifat Bin Reza, Md Ahadul Haque Suvo, Jia Uddin, Saifur Rahman Sabuj

Signal Processing

Performance Evaluation of Mobile Target Sensing and Tracking Across Multiple Sensing Modes

Necati Kagan Erkek, Marco Di Renzo, Arman Shojaeifard et al. (+3 more)

Moving extended-target sensing and tracking are studied in a beamformed PRS/OFDM ISAC framework. The target is modeled as a rigid cluster of scatterers whose Tx–target–Rx echoes coherently combine in a MIMO scattering channel. Delay, azimuth/elevation, and Doppler are estimated using PRS matched filtering with delay centroiding, single-cluster 2D-MUSIC, and FFT-based Doppler estimation, then used for independent localization and EKF-based tracking. The framework is evaluated in monostatic and bistatic modes under target-directed, receiver-directed, and omnidirectional beamforming. Simulation results show that EKF tracking reduces the aggregate 3D RMSE from 1.61 m to 1.38 m relative to independent localization. Target-directed beamforming substantially improves tracking accuracy, achieving 1.38 m RMSE compared with 5.91 m and 8.38 m for omnidirectional and receiver-directed beamforming, respectively. The results highlight the importance of target-aware beam management, delay-angle-Doppler fusion, and sensing geometry for mobile-target tracking in ISAC networks.
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All authors Necati Kagan Erkek, Marco Di Renzo, Arman Shojaeifard, Kunjan Shah, Mohammad Heggo, Yasser Mestrah

Signal Processing

UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems

Keying Zhu, Le Liang, Jie Yang et al. (+2 more)

Unmanned aerial vehicle-enabled synthetic aperture radar (SAR) offers significant potential for providing communication services to ground users while sensing the surrounding environment in low-altitude integrated sensing and communication (ISAC) systems. However, its practical deployment is challenged by known non-ULM trajectories and unknown position errors, which violate conventional SAR assumptions and cause severe image defocusing. We address these non-ideal impacts by formulating the ISAC SAR imaging as an exact linear inverse problem to accurately model arbitrary trajectories with multiple-input multiple-output orthogonal frequency division multiplexing waveforms. Based on this formulation, we develop a diffusion posterior sampling (DPS)-based super-resolution SAR framework, where a pre-trained diffusion model provides a data-driven image prior and the exact physical model enforces measurement consistency during posterior inference. The inference process is further accelerated for practical implementation. For unknown position errors, we develop a joint iterative DPS and motion compensation (MoCo) optimization algorithm with discrete cosine transform reparameterization and coarse-to-fine spectral scheduling. Simulations demonstrate that our framework achieves high-accuracy reconstruction across diverse trajectories and remarkable super-resolution, maintaining greater than 21.3 dB peak signal-to-noise ratio (PSNR) even under half physical resolution truncation. Under unknown motion errors, the joint DPS and MoCo algorithm achieves 23.65 dB PSNR with a residual position-error estimation error of 0.19 cm.
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All authors Keying Zhu, Le Liang, Jie Yang, Fan Liu, Shi Jin

Signal Processing

Mutual Coupling and Visibility Effects in 2D Holographic Curvature-Reconfigurable Apertures

Hang Lin and Shu Sun

Holographic multiple-input multiple-output arrays provide high spatial resolution and flexible wavefront control, but curved or conformal deployments jointly introduce curvature, mutual coupling (MC), and direction-dependent visibility. This paper studies a two-dimensional (2D) holographic curvaturereconfigurable aperture (HoloCuRA). A geometry-based model characterizes element positions, steering, and spatial correlation, while an impedance-based coupling matrix and a visibility vector capture electromagnetic interactions and shadowing. Relative directivity and spectral efficiency are evaluated versus curvature. Results show that MC mainly reshapes local directivity fluctuations, whereas visibility dominates the global pattern and spectral-efficiency variation under both isotropic scattering and CDL-A channels.
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Friday 9 October

Signal Processing · Cross-listed from cs.IT

Sequential Change Point Detection for PSK Modulation Switching

Gopal Kataria, Davin Sequeira and Arti Yardi

Transmitters in adaptive communication systems may switch their modulation scheme at an unknown time, and a receiver must detect this switch quickly without raising frequent false alarms. Existing modulation classification methods label a finite block of received data and do not provide stopping rules with controlled false-alarm and delay performance. We study the sequential detection of a switch from one phase shift keying (PSK) scheme to another over an additive white Gaussian noise channel. We build cumulative-sum (CUSUM) and moving-sum (MOSUM) detectors from the log-likelihood ratio and its low-SNR approximation. For a switch from quadrature PSK to binary PSK, we derive the moment-generating function and probability density function of the approximate statistic. We use them to relate the CUSUM threshold to the average run length to false alarm and the worst-case detection delay. For a switch between any two PSK orders, we approximate the vector of overlapping MOSUM statistics as multivariate Gaussian and obtain bounds on both measures. Monte Carlo simulations show that our derived expressions for ARL2FA and WADD closely match the empirical results across thresholds, validating the accuracy of the proposed analytical approximations and theoretical bounds.
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Signal Processing · Cross-listed from cs.NI

Semantic Edge-Cloud Communication for Real-Time Urban Traffic Digital Twins

Murat Arda Onsu, Arman Dogru, Poonam Lohan et al. (+2 more)

Urban traffic systems are becoming increasingly complex, demanding real-time traffic perception, proactive collision prediction, and intelligent management to improve safety and reduce congestion. Although AI-driven traffic management has shown strong promise, its success relies on diverse, high-quality data spanning critical scenarios such as collisions, near-collisions, and traffic rule violations. Digital twins offer a powerful solution by enabling synchronized virtual replicas of physical traffic environments for real-time monitoring, collision prediction, traffic-flow optimization, and diverse realistic scenario generation. However, continuously synchronizing a high-fidelity urban traffic digital twin through edge surveillance cameras requires substantial edge-cloud communication, and transmitting raw video streams leads to severe bandwidth and latency constraints. To overcome this challenge, this paper proposes a semantic communication-based edge-cloud framework for real-time urban traffic digital twin applications. By leveraging edge intelligence to extract and transmit only task-relevant semantic traffic information instead of full video feeds, the proposed framework significantly reduces communication overhead while preserving the essential dynamics needed for accurate real-time digital twin reconstruction. Experimental results demonstrate more than 99% transmission savings and an average trajectory reconstruction error of only 0.12 m, confirming the proposed framework as an effective, scalable, and low-latency solution for AI-enabled urban traffic digital twin applications.
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All authors Murat Arda Onsu, Arman Dogru, Poonam Lohan, Burak Kantarci, Aisha Syed

Signal Processing · Cross-listed from eess.AS

OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation

Fu-An Chao, Bi-Cheng Yan and Berlin Chen

Recent advances in automatic pronunciation assessment (APA) have driven a shift from scripted to free-form assessment. However, existing methods often rely on multi-stage pipelines with forced alignment and separate feature extractors, limiting their practicality across response settings. In light of this, we propose OpenPA, a novel APA framework that leverages Whisper's cross-attention mechanism to jointly predict word- and utterance-level pronunciation scores without forced alignment. Through mixed-transcript adaptation and adaptive substitution weighting, OpenPA accommodates heterogeneous transcripts across closed- and open-response settings within a single model. Trained solely on speechocean762, OpenPA achieves the strongest overall performance among evaluated systems under in-domain closed and simulated open-response conditions, while maintaining its performance zero-shot on MultiPA. An additional cross-task evaluation on oral-cancer speech suggests its potential applicability beyond non-native speech.
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Signal Processing

OpenH-RF: A Large-Scale Ultrasound Raw Channel Dataset Initiative

T.S.W. Stevens, S.W. Penninga, W.L. van Nierop et al. (+183 more)

Medical ultrasound is a widely accessible, real-time imaging modality, yet progress in data-driven ultrasound research is constrained by the scarcity of large, open collections of raw channel data. Existing public datasets are predominantly based on reconstructed images, while the smaller number of open raw channel datasets is generally specialized to individual reconstruction or imaging tasks, limiting the development of general-purpose methods and foundation models that operate directly on ultrasound signals. While interpretive tasks can be performed on reconstructed images, many of the key technical challenges in ultrasound imaging, such as aberration correction and reverberation reduction, require the raw channel data itself. We introduce OpenH-RF, a community-driven open dataset initiative comprising approximately 40 TB of medical ultrasound raw channel data in 19,332 acquisitions, contributed by 38 institutions across academia and industry, spanning in-vivo, phantom, and simulated data. Each dataset is annotated with a number of supported tasks, ranging from generalized reconstruction, speed-of-sound estimation, and aberration correction to blood flow imaging, motion estimation, quantitative ultrasound, and image interpretation. OpenH-RF provides a unified, self-contained data format that captures raw channel data together with the acquisition, probe, metadata, and reconstruction information required for reproducible processing across scanners and acquisition schemes. The corpus is publicly hosted under a permissive CC BY 4.0 license and can be streamed programmatically and selectively at scale. By bringing heterogeneous ultrasound data into a common representation, backed by modern data infrastructure, OpenH-RF lowers the barrier to working with raw channel data and aims to open medical ultrasound research to new communities. See https://open-h.github.io/OpenH-RF.
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All authors T.S.W. Stevens, S.W. Penninga, W.L. van Nierop, O. Nolan, F. Duelmer, B.N. Frey, B. Federici, V. van Hal, L.D. van Harten, H. de Hoop, R. Lopata, B. Luijten, M. van Sambeek, V. van der Schaft, H.-M. Schwab, M.F. Azampour, N. Navab, R. Ali, A. Andrzejek, J. Baek, B. Boitnott, T. Brevett, H. Hashemi, D. Hyun, J.H. Park, A.V. Telichko, C. Thomas, L. Zhuang, Y. Aklilu, H. Arora, J. Barker, A.K. Mackanic, P. McDowall, L. McHugh, I. Pirozzi, S. Simon-Trench, A.M. Bronstein, O. Senouf, S. Vedula, D. Zadok, H. Rivaz, M. Sharifzadeh, S. Chen, G.P. Luke, S. Wang, Yujia Wu, J. Verde, R. Göbl, O. Zettinig, E. Boctor, Yixuan Wu, H.-M. Bae, M.-G. Kim, Y.-M. Kim, H. Lee, S.-H. Oh, P. Pinto, M. Berrada-Sounni, M. Carley, R.J. Fedewa, S. Gavin, N. Goldsmith, R. Kassayan, C. Low, R. Mehranfar, G. Bardoscia, M. Boccardo, A. Botter, M. Carbonaro, G.L. Cerone, E. Cesti, K. Meiburger, S. Seoni, F. Truscello, B.J. Raiteri, L. Caron, M. Gasse, N. Ghigo, O. Gulenko, A. Leconte, S.A. Lee, A. Malescot, E. Martineau, J. Porée, J. Provost, G. Ramos-Palacios, B. Rauby, R.L. Rungta, A.F. Sadikot, A. Wu, P. Xing, A. Chavignon, C. Ferlay, V. Hingot, M. Reberol, A. Waelbroeck, H.-S. Kwon, J. Bucholz, I. Guracar, R. Loftman, C. Williams, T. Grutman, T. Ilovitsh, T.A. Aleef, P.B. Chen, W. Chen, Y. Chen, M. Frew, M. Honarvar, Y.C. Lee, Z. Li, H. Moradi, R. Rohling, S.E. Salcudean, Z. Wu, Q. Zeng, P.C. Cattin, S. El Hadramy, N. Bottenus, A.S. Perez, S. Feng, Y. Feng, P. Jin, Y. Lin, O. Ostras, G. Pinton, F. Santibanez, M. Sode, B. Turkbey, H. Wang, B.J. Wood, L. Zhang, S. Luo, J. Wiskin, A. Austeng, Y. Karabiyik, S.P. Näsholm, O.M.H. Rindal, D. Bustamante, M. Mehrmohammadi, H. Raeis, A. Hameddine, G. Lajoinie, H. de Roo, M. Versluis, R. Zorgdrager, J.S. Au, A.J.Y. Chee, J. Cohen, T. Gu, J.Y.-H. Hsu, S. Ling, M. Mourtzakis, H. Nahas, P. de la Torre, D. Xiao, A.C.H. Yu, E. Zafiris, P. Jarosik, P. Karwat, Z. Klimonda, M. Lewandowski, B. Byram, C. Khan, Z. Marshall, Y.-C. Pan, S. Liu, T. Sultan, C. Hoerig, J. Mamou, D. Auerbach, Y.C. Eldar, A. Wegerhoff, G.S. Fischer, S. Katayose, X. Ma, R. Murakami, Y. Tang, R. Tsumura, Y. Wang, H.K. Zhang, M. Azizian, S. Huver, J.J. Dahl, R.J.G. van Sloun, W. Simson

Signal Processing

HF Single-Station Localization via Multimode Ray Tracing and Time-Frequency Information Fusion

Qinglin Li, Wen Liu, Yun Ning et al. (+4 more)

High-frequency (HF) single-station localization estimates an emitter location from measured directions of arrival (DOAs) and an ionospheric propagation model. When the propagation mode composition cannot be identified from conventional direction-finding (DF) measurements, the same DF observation may produce multiple physically valid ground landing points under different propagation modes, making the location inversion ambiguous. This paper proposes a multimode time-frequency information fusion localization method (MTFIF-L).Rather than assuming a propagation mode a priori, MTFIF-L retains valid landing points generated under different propagation modes and uses repeated observations across time windows and, when available, multiple operating frequencies to progressively constrain the resulting candidate locations. Experiments on four measured HF links examine the respective roles of multimode candidate retention, cross-window processing, and cross-frequency processing. All four links yield unique location decisions. Links A–C achieve relative errors of $1.40\%$, $0.78\%$, and $3.41\%$, respectively.Link D, the only single-frequency case, also yields a unique decision from repeated temporal observations, although its post- localization error is substantially larger. Propagation-path analysis further reveals link-dependent differences in reflection layer, hop count, magnetoionic mode, and elevation angle among the ray-tracing paths associated with the measured links.Overall, the results show that repeated temporal observations can support localization within an individual frequency, while multifrequency information provides additional robustness against unreliable frequency-specific outcomes, enabling HF single-station localization under unknown and potentially coexisting propagation modes while retaining interpretable propagation information for post-localization analysis.
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All authors Qinglin Li, Wen Liu, Yun Ning, Zhongxin Deng, Junkang He, Mingshuo Liu, Chuangjian Xia

Signal Processing · Cross-listed from math.PR

Zeros of the Spectrogram of White Noise with Hermite Windows

Charaf Bellourak, Sylvain Meignen, Kévin Polisano et al. (+1 more)

We study the zeros of the spectrogram of complex white noise analyzed with Hermite windows of order n, extending the classical Gaussian-window correspondence with Gaussian Analytic Functions to the polyanalytic setting. Using the polyanalytic Bargmann transform, these zeros split into two families according to the sign of the local Jacobian, giving rise to a natural notion of charge, and we show that these zeros are almost surely simple, isolated, and form a translation and rotation-invariant point process. We derive closed-form expressions for the one-point intensity, decomposed by charge, and also for the unsigned, signed, and hybrid two-point intensities. Besides, we prove that unlike the total repulsion observed with a Gaussian window, the Hermite window of order n generates a pair correlation function that converges at the origin to a strictly positive constant depending on n. Finally, we propose a numerical method for detecting spectrogram zeros via the winding number around candidate local minima.
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All authors Charaf Bellourak, Sylvain Meignen, Kévin Polisano, Mickaël Nahon

Signal Processing

Suppressing Multiple GNSS Interferers with Local-Gradient Adaptive Notch Filtering

Burak Soner and Felix Ott

Adaptive notch filters (ANFs) based on the normalized least mean squares (NLMS) algorithm are an established technique for suppressing narrowband interference in GNSS receivers. The traditional solution for multiple interferers is simply cascading the NLMS-ANF into multiple stages but this solution degrades performance when multiple chirp jammers of similar power are present. We propose a low-complexity modification to the cascaded NLMS-ANF update rule to resolve this: a local gradient (LG) formulation: LG-ANF. The LG-ANF forms a locally bandlimited gradient signal by translating the input to baseband around the current frequency estimate, low-pass filtering it, and translating it back, thereby attenuating competing interferers before adaptation. The modification generalizes the traditional NLMS-ANF to handle the similar-power multiple interferer problem, using just one additional parameter. We characterize the gradient-averaging failure analytically and visually, demonstrate independent chirp tracking in the worst-case two-chirp scenarios, and evaluate statistical suppression performance by sweeping amplitude ratio against a standard NLMS-ANF, a frequency-locked-loop (FLL)-based ANF and a per-sample oracle upper bound. We further characterize the LG advantage over various ANF parameters and chirp conditions. Simulations show that the LG-ANF reduces to the standard NLMS-ANF outside the pathological multi-chirp regime, while recovering up to 4dB of in-band suppression around pathological amplitude ratios under per-condition optimization of the local bandwidth, and up to 13dB for selected edge-case parameter combinations. These optimized results represent an upper bound on the achievable LG gain. In a hardware experiment using a fixed alpha_lp=0.5, the LG-ANF extends position-lock survival by 24s relative to the standard ANF.
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Signal Processing

Blind and Asynchronous Initial Beam Acquisition and Clock-Assisted Re-Acquisition in Standalone Mobile MIMO-OFDM Systems

Ali Abdi, Firooz B. Saghezchi and Haris Gacanin

Building upon our preliminary study, this paper proposes a standalone beam-management framework for mobile MIMO-OFDM systems that integrates Blind Initial Beam Acquisition (BIBA) with Clock-Assisted Re-Acquisition (CARA). BIBA is a blind and asynchronous protocol that requires neither prior synchronization nor an out-of-band feedback channel, so the Base Station (BS) and User Equipment (UE) can independently identify the beam pair maximizing received signal strength. To handle subsequent link drops, the newly introduced CARA protocol exploits the clock synchronization established after BIBA to reduce the re-acquisition time by approximately 55% compared with BIBA. The operational reliability of both protocols is bounded by a probabilistic feasibility analysis under random UE mobility trajectories. Protocols are validated on a 28 GHz mmWave testbed under practical hardware constraints, including beam-switching and Time Division Duplex (TDD) latencies, and measured Software-Defined Radio (SDR) software processing delays. Finally, we formulate a unified Finite State Machine (FSM) model for both BIBA and CARA to ensure robust integration into practical wireless systems
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Signal Processing

CNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation

Lifeng Wang, Xintong Li and Aifei Liu

This paper addresses the grid quantization error of deep-learning (DL)-based direction-of-arrival (DOA) estimation. Classification- based DOA methods discretize the continuous angular space into a grid, so that with a peak-based read-out, the accuracy and resolution are limited by the grid step and by the width of the label peak. To address this issue, we propose a sigmoid label that encodes the sources' DOAs as the sum of angle-shifted monotonic sigmoid functions. Differentiating the output of a CNN-Transformer network trained with the sigmoid label produces sharp spectral peaks at the source DOAs. Combined with a moderately refined grid step of 0.25 degrees, the CNN-Transformer with the proposed sigmoid label attains sub-degree accuracy and a 1-degree resolution, breaking the limit of the grid quantization error. Under the unified CNN-Transformer backbone, we compare the proposed Sigmoid label with the Gaussian label, the 0-1 hard label, and the binary cross-entropy (BCE) baseline. Numerical results demonstrate that the Sigmoid label attains the highest accuracy and superior resolution compared to other labels.
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Signal Processing

One Thing Leads to Another: The Essentials of Time Series Causal Discovery

Kurt Butler, Damian Machlanski, Michael P.J. Camilleri et al. (+5 more)

Time series causal discovery — the process of detecting causation between time series — has its roots in signal processing research from over a half century ago. It has since received active and widespread interest, evolving into different forms across domains such as statistics, dynamical systems and machine learning. Entering this fragmented and constantly evolving research landscape can be daunting. This tutorial provides a unified overview of time series causal discovery across research communities. We introduce the key principles invoked by modern discovery methods, and we discuss their limitations and assumptions. We also discuss how discovery methods are evaluated, benchmarked and fine-tuned. We further discuss how causal discovery for time series is becoming crucial for downstream tasks in agriculture and Earth science, in healthcare, and in artificial intelligence systems. We believe that this tutorial will serve as a valuable resource in navigating the deluge of recent causality research, as well as an invitation to signal processing audiences to contribute to causal problems. To further help readers, we host a companion repository to link to useful code packages and resources at https://github.com/CHAI-UK/Awesome-Time-Series-Causal-Discovery.
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All authors Kurt Butler, Damian Machlanski, Michael P.J. Camilleri, Zhihua Liu, Panagiotis Dimitrakopoulos, Feng Chen, Ricardo Silva, Sotirios A. Tsaftaris

Signal Processing

Tri-Hybrid SWIPT with Reconfigurable Parasitic Antennas

Thien Duc Hua, Mohammadali Mohammadi, Hien Quoc Ngo et al. (+1 more)

We propose a coupling-aware tri-hybrid precoding architecture for simultaneous wireless information and power transfer (SWIPT) using reconfigurable parasitic antennas. By integrating multiport network theory and information theory, we develop a third precoding layer that explicitly accounts for mutual coupling and reactive energy storage within the antenna domain. To efficiently optimize this architecture, we propose a low-complexity tri-hybrid solution comprising coordinate descent (CD) for parasitic reactive load optimization, a singular value decomposition (SVD)-based analog precoder, and a protective partial zero-forcing (PPZF) digital precoder. Numerical results demonstrate that the transmit array introduces impedance mismatch that reduces the real power accepted by the antenna array. Moreover, the proposed tri-hybrid architecture significantly outperforms hybrid precoding by constructively exploiting the coupling effects, yielding substantial gains in spectral efficiency (SE) and sum non-linear harvested energy (NLHE) at a fraction of the hardware cost.
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All authors Thien Duc Hua, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou

Signal Processing · Cross-listed from cs.CR

Secrets in Preamble: Practical and Robust PHY Information Hiding for Wi-Fi

Rui Pan, Guanxiong Shen, Linning Peng et al. (+2 more)

Physical layer (PHY) information hiding refers to the concealment of secrets within PHY waveforms without compromising the integrity of the primary communication link, thereby enabling critical applications such as covert communication, PHY steganography, and watermarking. The widespread deployment of Wi-Fi has motivated substantial research interest in hiding secrets within Wi-Fi waveforms. However, existing Wi-Fi information hiding approaches suffer from two limitations: restricted steganographic capacity and insufficient robustness against environmental variations, both of which severely constrain practical deployment. This work proposes to embed secrets within the Wi-Fi legacy short training field (L-STF). The designed method preserves the autocorrelation properties of Wi-Fi L-STF, ensuring that packet detection and coarse frequency offset compensation functionalities remain uncompromised. The secret embedding and extraction processes employ a pair of neural networks designed to learn optimal embedding rules and maximize steganographic capacity. We developed a prototype system and conducted comprehensive experiments across both indoor and outdoor environments. Experimental results show that our method can reliably embed 46 secret bits within Wi-Fi packets across diverse channel conditions, achieving significantly enhanced steganographic capacity and environmental robustness. The hardware prototype study validates the feasibility of the proposed Wi-Fi information hiding scheme.
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All authors Rui Pan, Guanxiong Shen, Linning Peng, Xinyu Qi, Junqing Zhang

Signal Processing

ECGWmark: Deep Watermarking for Electrocardiogram Signals

Kuan-Yu Chen, Kuan-Chen Wang, Yu Tsao et al. (+1 more)

Secure transmission of electrocardiogram (ECG) signals remains a major challenge in telecardiology. While traditional mathematical watermarking can embed patient metadata directly into ECG signals, it is highly fragile against temporal desynchronization such as circular phase shifts. In contrast, existing deep watermarking approaches for temporal signals, such as audio, typically rely on lossy quantization, which compromises the high-frequency micro-morphological details critical for clinical diagnosis. To bridge this gap, we propose ECGWmark, a fully convolutional and quantization-free deep watermarking framework for medical time-series. ECGWmark operates in a continuous, full-resolution latent space and utilizes a localized frame-level mechanism to improve the robustness to severe desynchronization. Crucially, we introduce a differentiable Physiological Loss guided by a clinical classifier to preserve diagnostic semantics. Experiments on the benchmark MIT-BIH database show that ECGWmark maintains channel-bit extraction accuracy above 90% under the evaluated circular temporal shifts. Furthermore, under a rigorous inter-patient diagnostic evaluation, Class-V sensitivity changes from 71.97% to 76.50%, whereas Macro-F1 changes from 35.25% to 34.71% and Class-S sensitivity from 8.89% to 1.39%; these results indicate mixed class-level effects.
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All authors Kuan-Yu Chen, Kuan-Chen Wang, Yu Tsao, Jian-Jiun Ding

Signal Processing

Scalable Quantum Clustering of Renewable Profiles via a Hybrid Qubit-efficient Strategy

Yifeng Cao, Chengjun Liu, Yijun Xu et al. (+4 more)

Large-scale renewable energy profile clustering is a computationally challenging NP-hard problem. Recent quantum computing techniques offer a potential remedy, yet their scalability is severely constrained by the limited qubit count of current quantum hardware. To address this issue, we first cast the problem into a QUBO form and then we propose a consensus-based variable reduction (CBVR) method to drastically cut qubit usage. More specifically, we tailor the one-hot constraint to eliminate assignment variables and leverage consensus analysis to convert stable clustering relations from initial anneals into equality constraints for further reduction. Experiments on real quantum hardware confirm that CBVR compresses oversized QUBO models into feasible sizes while retaining high clustering quality and efficiency.
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All authors Yifeng Cao, Chengjun Liu, Yijun Xu, Kai Wen, Wei Gu, Lamine Mili, Shuai Lu