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Henk Wymeersch

Publications and source records attributed to Henk Wymeersch.

At least 19 recordsLinked to original sources
arXiv2026

Pinching-Antenna Differential Spatial Modulation with RSSI-Assisted Selection and Low-Complexity Detection: Methods and Performance Analysis

Pinching antenna (PA) systems provide macroscopic spatial reconfigurability by enabling signal radiation from favorable locations along a dielectric waveguide. Exploiting this flexibility, however, typically requires channel knowledge for PA configuration, which can introduce substantial overhead in dynamic propagation environments. This paper proposes PA-assisted differential spatial modulation (PA-DSM), enabling data transfer with spatial index selection and non-coherent data detection without instantaneous channel state information (CSI) at the receiver. To exploit PA reconfigurability while preserving this CSI-efficient operation, we introduce a slow-timescale received signal strength indicator (RSSI)-assisted selection strategy that identifies a favorable subset of PAs from scalar power measurements. We further specialize a two-stage low-complexity maximum-likelihood (LC-ML) detector to phase shift keying (PSK) signaling, obtaining closed-form symbol decisions that remove the joint modulation-combination search while preserving the minimizer of exhaustive differential detection. Furthermore, a moment generating function (MGF)-based pairwise error analysis and the corresponding bit error probability (BEP) bound are developed under normalized common-K Rician fading, revealing how the difference rank and spectrum, number of receive antennas, and previous-state-dependent line-of-sight (LoS) alignment affect the error performance. Numerical results across representative indoor-office, indoor-factory, and street-canyon sixth-generation (6G) Frequency Range 3 (FR3) scenarios validate the analytical trends and quantify when PA-DSM outperforms coherent benchmark schemes whose channel estimates age between pilot transmissions.

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arXiv2026

Cross-Room Passive WiFi Tracking: Joint Estimation of Target Trajectory and Virtual Transmitter Location

Indoor bistatic WiFi sensing must often operate across rooms, where intervening walls block the direct line-of-sight (LOS) path between the transmitter (Tx) and receiver (Rx). Recovering a human trajectory from channel-state-information (CSI)-derived delay, angle-of-arrival (AoA), and Doppler estimates is difficult because the Tx position is often unknown and NLOS propagation is inconsistent with direct-path geometry. This paper presents CoTrack, a self-calibrating passive WiFi tracking scheme that does not require the Tx position. CoTrack extracts one dominant human-induced response and represents its transmitter-side propagation by a virtual Tx, which it estimates jointly with the human trajectory. Because similar measurements can be explained by different virtual-Tx--trajectory pairs, CoTrack uses multi-start nonlinear least squares and accepts an initialization only when the best-fitting candidates concentrate around a common virtual-Tx position. It then applies online alternating optimization for continuous tracking. We analyze the local identifiability of the joint estimation problem and derive the corresponding Cramér--Rao bound. Across six LOS and cross-room NLOS experiments, CoTrack achieves a median trajectory error of 1.14m(80th percentile: 1.42m) and a median virtual-Tx discrepancy of 0.47m(80th percentile: 0.54m).

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arXiv2026

Privacy-Aware ISAC for Full-Duplex Monostatic Systems Using Movable Antennas

This work investigates sensing privacy in full-duplex (FD) monostatic integrated sensing and communication (ISAC) systems with movable antennas (MAs). The proposed approach jointly optimizes beamforming and antenna trajectories to create a deceptive dummy DD-bin response at a passive sensing eavesdropper (Eve), while satisfying a true-bin sensing-quality requirement at the base station (BS). The resulting problem is highly non-convex. {To address this, a stage-wise alternating local-search framework is developed to obtain suboptimal solutions. Within this framework, we maximize the worst-case margin between dummy and true delay-Doppler (DD)-bin detector-oriented SINR surrogates over a discretized uncertainty region for Eve, incorporating detector-aligned dummy-bin refinement and true-bin preservation.} Simulation results show that the proposed MA-enabled design suppresses Eve's true-target DD-bin selection and increases dummy-bin selection probability compared with benchmark schemes, while maintaining reliable BS sensing performance.

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arXiv2026

POPT: Physics-Informed Deep Learning for Phase-Only Positioning in Distributed MIMO

This article develops a physics-informed deep learning framework for single-snapshot 3D phase-only positioning in narrowband phase-coherent distributed multiple-input multiple-output (D-MIMO) networks under two-ray propagation. Unlike prior phase-coherent D-MIMO localization methods that largely assume line-of-sight (LoS)-only channels, we consider a LoS path and a specular ground reflection. Since the narrowband single-snapshot observation cannot resolve these components in delay, their coherent superposition induces structured perturbations in the carrier phase measurements. To address this, we develop a Gaussian process (GP)-based model that learns the quasi-periodic phase distortion caused by ground reflection from carrier phase measurements collected at a small number of training locations and uses it to generate high-quality synthetic samples. Building on these samples, we propose the Phase-Only Positioning Transformer (POPT), an encoder-only transformer that captures inter-antenna point (AP) phase relationships without solving the highly non-convex maximum-likelihood (ML) problem underlying model-based estimators. We also derive the fundamental position error bound (PEB) and develop maximum-likelihood estimation (MLE) for the considered multipath phase-only D-MIMO positioning problem. Numerical results show that, with only 50 GP training locations, the proposed method achieves near-PEB accuracy. The analytical MLE is highly sensitive to relative permittivity mismatch, whereas the proposed method mitigates this dependence by learning the phase perturbation directly from measurement data. Compared with MLE, the proposed approach also reduces floating-point operation (FLOP) complexity and inference time by about 1.7 and 3.6 orders of magnitude, respectively.

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arXiv2026

Scalable Extended-Target Handover in Distributed Integrated Sensing and Communication

Distributed integrated sensing and communication (DISAC) networks require multi-target tracking methods whose per-node computation and inter-base-station communication remain bounded as both the network and target populations grow. Existing multisensor fusion methods provide a strong theoretical foundation, but scalability is seldom treated as the primary design objective. We develop a grouped-measurement belief-propagation multi-target tracking (MTT) method and combine it with a proposed event-triggered target-handover protocol. When a tracked target is predicted to become observable at another base station, the owner transfers a predicted track message while retaining its local copy. Under bounded local workload and neighbor degree, we show that all variants of the proposed handover methods have network-size-independent per-node loads. Simulations demonstrate that handover reduces boundary track loss relative to uncoordinated processing and approaches coordinated-processing accuracy with lower communication overhead.

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arXiv2026

UE-Side Location Privacy for 5G NR Uplink Positioning: Mechanisms and Trade-offs

Future 5G-Advanced and 6G networks increasingly reuse uplink communication waveforms for positioning and sensing. This raises privacy concerns, as user equipments (UEs) may unintentionally reveal precise timing information even when positioning services are not explicitly requested. While prior works show generic orthogonal frequency division multiplexing (OFDM) pilots can be manipulated to degrade time-of-arrival (ToA) estimation without compromising data links, this paper extends these concepts to a realistic 5G new radio (NR) up-link framework including Sounding Reference Signal (SRS), Demodulation Reference Signal (DMRS), Physical Uplink Shared Channel (PUSCH), and standardized 3GPP channel models. We investigate several UE-side privacy mechanisms: optimized pilot distortion, artificial noise, artificial multipath, and delay spoofing. Through 3GPP-compliant sample-level simulations, we assess their impact via localization, communication, and consistency-based detection metrics. The resulting analysis highlights the trade-offs among privacy, communication reliability, and detectability, providing key insights into waveform-level obfuscation for future integrated sensing and communication (ISAC) systems.

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arXiv2026

Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach

Accurate multi-user localization is challenging in complex urban environments, where wireless measurements can become ambiguous under noise, blockage, and multipath, while visual observations provide complementary spatial context. This paper presents a vision-wireless fusion framework for multi-user localization using pilot-indexed channel state information (CSI). Orthogonal pilot indices preserve the identities of the communicating UEs in the CSI token sequence and localization outputs. The model encodes each pilot-indexed CSI observation as a query token and uses cross-attention to retrieve user-specific information from spatial visual memory. Self-attention among CSI tokens further captures inter-user interactions, while the resulting multimodal representations are used for user-wise localization. Experiments on different datasets show consistent improvements over model-based, CSI-only, and multimodal-fusion baselines. Further experiments evaluate the model under different wireless and visual conditions.

cs.CV↗
arXiv2026

Phase-Coherent Doppler-Only Sensing for D-MIMO ISAC

This paper studies phase-coherent Doppler-only sensing for bandwidth-limited sub-6 GHz D-MIMO ISAC. We consider a multistatic system with one transmitter and multiple synchronized single-antenna receivers, and develop noncoherent and coherent maximum-likelihood estimators together with their position error bounds. The analysis shows that noncoherent sensing relies only on Doppler and cannot localize static targets, whereas phase-coherent processing exploits carrier phase to provide additional position information. Simulations confirm the bounds and show that coherent processing significantly improves localization accuracy in the considered narrowband setting.

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arXiv2026

Task-Aware Beamforming for Semantic Wireless Localization

Many semantic localization tasks require determining a contextually meaningful region or state rather than minimizing the error of a full Cartesian position estimate. In such settings, aggregate localization accuracy need not align with the accuracy of the semantic decision. This paper develops a Cramér--Rao bound (CRB)-based framework that uses the local geometry of a known semantic map to guide uplink receive beamforming. Using local boundary crossing as a surrogate for semantic misclassification, we derive normal information maximization (NIM), which minimizes the CRB of the position component along the local semantic boundary normal. Under the considered single-path line-of-sight model, an optimal receive codebook can be restricted to the subspace spanned by the matched steering vector and its angular derivative, reducing the design to an allocation of measurement resources between matched and derivative spatial modes. We derive the resulting allocation for a general smooth boundary, with geofencing and intrusion detection arising as radial and tangential limiting cases. The framework is further extended to multi-region semantic maps through a distance-normalized minimax criterion and to uncertain prior locations through worst-case and prior-weighted robust formulations. Numerical results with finite-slot implementations and observation-level Monte Carlo simulations using profile maximum-likelihood (ML) estimation show that NIM allocates measurements according to the task-relevant boundary geometry and can substantially reduce semantic error relative to the classic squared position error bound (SPEB) design in the considered scenarios. The empirical results also closely follow the local CRB-based boundary-crossing approximation in the studied operating regime.

cs.IT↗
arXiv2026

Foundation Models for Wireless Localization: Pretraining, Adaptation, and Utilization

Accurate wireless localization is a key enabler for 6G networks, yet remains challenging under diverse and rapidly changing propagation conditions. Model-based methods degrade when multipath channels are non-resolvable and model mismatches occur, while supervised deep learning demands large labeled datasets and generalizes poorly to new deployments. Inspired by foundation models (FMs) in language and vision, this article presents a unified framework for FM-based wireless localization that learns transferable channel representations from large-scale unlabeled channel state information and adapts to new environments with minimal or even no supervision. We review the fundamentals of FMs, compare the FM paradigm with existing localization approaches, and introduce a three-stage framework spanning large-scale pretraining, localization-oriented fine-tuning, and context-augmented inference, together with the location-aware applications it enables. Ray-tracing-based case studies show improved positioning accuracy and cross-environment generalization. Finally, we present an outlook on key research directions toward AI-native networks for wireless localization.

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arXiv2026

Frugal Radio SLAM: Coherent Imaging with One Subcarrier and One Snapshot in Distributed MIMO

We study uplink radio simultaneous localization and mapping (SLAM) in a pre-calibrated phase-coherent distributed MIMO (D-MIMO) system at a minimal sensing-resource operating point: a single-antenna user equipment (UE) transmits one narrowband pilot, and each single-antenna access points (APs) provides one complex observation. We formulate coherent matched-filter imaging over a spatial grid, where the UE, virtual UEs associated with planar reflectors, and point scatterers share an equivalent-source representation. A greedy detect-and-cancel procedure separates these components. Simulations show that, at the considered noise level, weak-target recovery is limited primarily by cancellation residuals and model saturation, rather than by the noise floor.

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arXiv2026

Radio Imaging and Resource Allocation in Frugal Multistatic D-MIMO ISAC Systems

Emerging integrated sensing and communication (ISAC) systems based on distributed MIMO (D-MIMO) enable radio imaging by exploiting spatial diversity across multiple access points (APs). However, joint sensing and communication introduce mutual interference between communication and sensing signals. In this paper, we propose a downlink D-MIMO ISAC framework that allocates orthogonal subcarriers to sensing and communication to eliminate inter-function interference. We consider a phase-coherent architecture in which single-antenna APs serve communication user equipment (UEs) while constructing a reflectivity image of the environment. We develop a two-timescale resource allocation framework that minimizes reconstructed-image entropy subject to a communication spectral-efficiency (SE) constraint. The proposed design follows a communication-centric policy, where imaging uses the resources left after satisfying the communication requirement. The long-timescale optimization (LTO) determines AP modes and subcarrier assignment, including the partitioning between communication and sensing and the allocation of sensing subcarriers among transmit APs, using synthetic scenarios with random UE and target distributions. The short-timescale optimization (STO) adapts the communication-sensing power-splitting factor to preserve the SE constraint in the current scenario. Numerical results show that the proposed orthogonal subcarrier allocation achieves a superior sensing-communication trade-off compared with conventional superposition, where sensing and communication share the same subcarriers. Finally, we evaluate coherent and non-coherent imaging receivers and identify the synchronization regimes in which each approach provides better imaging and localization performance.

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arXiv2026

Multi-UE Networked Sensing: A New Paradigm for 6G Perceptive Mobile Networks

Networked sensing, which jointly exploits observations from multiple distributed nodes, is essential for unlocking the full sensing potential of integrated sensing and communications (ISAC). This article introduces multi-UE sensing, a new networked sensing paradigm for future perceptive mobile networks that exploits the correlated sensing observations naturally arising from distributed user equipment devices (UEs) interacting with common targets. Representative uplink, downlink, and hybrid sensing architectures are presented, together with a multi-view signal processing framework encompassing synchronization, correlation-aware parameter estimation, and sensing fusion. Key open challenges, including correlation modelling, target association, sensing information compression, and communication-sensing co-optimization, are also discussed.

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arXiv2026

Segment-Wise Flow Matching for Vision-Aided mmWave V2I Beam Prediction

This paper proposes a vision-conditioned flow matching (FM) framework for beam prediction in millimeter-wave vehicle-to-infrastructure links. Instead of modeling discrete beam-index sequences, the proposed method learns the temporal evolution of normalized beam receive power vectors through a continuous vector field governed by an ordinary differential equation, enabling smooth dynamics and efficient sampling. By imposing FM over beam-state transitions and jointly optimizing beam prediction and flow consistency, the proposed framework provides a unified model for future beam prediction. Experimental results show that the proposed FM-based model significantly improves beam prediction performance over baselines, approaches the performance of large language model-based methods, and reduces predictor-side inference latency by about $6.9\times$ on GPU and $2.8\times10^3\times$ on CPU, respectively.

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arXiv2026

Dual-Orthogonality Waveforms for Integrated Communication and Imaging in Dynamic Multipath Channels

Dual-Orthogonality waveforms are multi-antenna signaling schemes that enforce mutual orthogonality across transmit channels and over a prescribed set of delay shifts. By relaxing strict time orthogonality to the physically admissible propagation region, they preserve full-band operation per transmit antenna while embedding communication data and maintaining stream separability. This makes them attractive for Integrated Sensing and Communications (ISAC), where reliable data transmission, high-resolution sensing, and imaging must coexist under time-varying propagation. In dynamic multipath environments, delay-Doppler dispersion across multiple paths perturbs the transmit subspaces and partially breaks the relaxed orthogonality conditions. This paper analyzes this effect and develops a multipath-aware decoding framework based on structured parameter estimation, effective-subspace reconstruction, and low-complexity linear equalization. Numerical results show communication performance comparable to OFDM-based ISAC and MIMO-OTFS baselines while improving sensing and imaging through full-band per-transmit operation. The proposed approach achieves approximately 30 cm range resolution, more than 15 dB suppression of multipath imaging artifacts with coherent SAR processing, and a favorable sensing-communication trade-off. Over-the-air experiments at 60 GHz validate multi-stream communication, the designed zero-correlation region, and accurate radar ranging. A second campaign in a highly reflective indoor environment further demonstrates multipath-aware stream equalization under strong unsuppressed reflections.

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arXiv2026

Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features

Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned with true spatial coordinates. In this work, we propose a novel method to produce CC locations in true spatial coordinates with the aid of a digital twin (DT). Our main contribution is a new framework that (i) extracts large-scale channel-state information (CSI) features from estimated CSI and the DT and (ii) matches these features with a cosine-similarity loss function. The DT-aided loss function is then combined with a conventional CC loss to learn a positioning function that provides true spatial coordinates without relying on labeled data. Our results for a simulated indoor scenario demonstrate that the proposed framework reduces the relative mean distance error by 29% compared to the state of the art. We also show that the proposed approach is robust to DT modeling mismatches and a distribution shift in the testing data.

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arXiv2026

Exploiting Phase Noise for Sensing Privacy in ISAC Systems

We investigate sensing privacy in orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) systems under the impact of phase noise (PN) arising from local oscillator (LO) imperfections. Specifically, we consider an ISAC scenario comprising a legitimate monostatic ISAC transceiver (Alice), an eavesdropper performing unauthorized bistatic sensing (Eve) and a communication user (UE), each equipped with a non-ideal LO. To characterize sensing performance in the presence of PN, we carry out a misspecified Cramér-Rao bound (MCRB) analysis of monostatic and bistatic range estimation at Alice and Eve, whose differential PN processes are self-correlated (delay-dependent) and cross-correlated (delay-independent) due to the use of a shared and an independent LO, respectively. Simulation results reveal three-way trade-offs among legitimate monostatic sensing at Alice, unauthorized bistatic sensing at Eve and communication to the UE under PN, governed by the LO quality at Alice. Through the LO asymmetry between Alice and Eve, worsening LO quality at Alice can significantly enlarge sensing privacy gap in her favor, especially for nearby targets, with only a moderate reduction in data rate in noise-limited regimes.

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arXiv2026

Goal-Oriented Semantic Communication for ISAC-Enabled Robotic Obstacle Avoidance

We investigate an integrated sensing and communication (ISAC)-enabled BS for the unmanned aerial vehicle (UAV) obstacle avoidance task, and propose a goal-oriented semantic communication (GOSC) framework for the BS to transmit sensing and command and control (C&C) signals efficiently and effectively. Our GOSC framework establishes a closed loop for sensing-C&C generation-sensing and C&C transmission: For sensing, a Kalman filter (KF) is applied to continuously predict UAV positions, mitigating the reliance of UAV position acquisition on continuous sensing signal transmission, and enhancing position estimation accuracy through sensing-prediction fusion. Based on the refined estimation position provided by the KF, we develop a Mahalanobis distance-based dynamic window approach (MD-DWA) to generate precise C&C signals under uncertainty, in which we derive the mathematical expression of the minimum Mahalanobis distance required to guarantee collision avoidance. Finally, for efficient sensing and C&C signal transmission, we propose an effectiveness-aware deep Q-network (E-DQN) to determine the transmission of sensing and C&C signals based on their value of information (VoI). The VoI of sensing signals is quantified by the reduction in uncertainty entropy of UAV's position estimation, while the VoI of C&C signals is measured by their contribution to UAV navigation improvement. Extensive simulations validate the effectiveness of our proposed GOSC framework. Compared to the conventional ISAC transmission framework that transmits sensing and C&C signals at every time slot, GOSC achieves the same 100% task success rate while reducing the number of transmitted sensing and C&C signals by 92.4% and the number of transmission time slots by 85.5%.

cs.RO↗