arXiv subjects

Henk Wymeersch

Publications and source records attributed to Henk Wymeersch.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2026

Multi-User Localization via Active Sensing with Electromagnetically Reconfigurable Antennas

This paper investigates multi-user localization in uplink wireless systems assisted by electromagnetically reconfigurable antennas (ERAs). Unlike traditional localization schemes, we formulate an active sensing problem where a base station (BS) exploits historical pilot observations accumulated over previous sensing stages to adapt the shared ERA configuration and progressively refine position estimates. To capture both theoretical flexibility and practical hardware constraints, we establish a unified wideband geometric signal model accommodating two complementary ERA paradigms: a synthesis-based model utilizing spherical-harmonic basis functions, and a finite-state model based on measured radiation codebooks. Because analytically solving the resulting joint design problem is highly intractable due to the high-dimensional observation and the shared-aperture coupling among multiple users, we develop a learning-based active sensing framework. Specifically, pilot-matched wideband observations are compressed into compact user-wise features and sequentially accumulated by a long short-term memory (LSTM) module. These temporal features are then processed by a graph neural network (GNN) to capture multi-user shared-aperture coupling. Model-specific output heads generate either continuous synthesis coefficients or finite-state ERA selections, while a localization head produces stage-wise position estimates. Numerical results under a specific channel distribution show that the proposed ERA-assisted active sensing framework achieves progressive localization refinement across sensing stages and obtains better performance than conventional non-reconfigurable arrays and representative ablation baselines.

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arXiv2026

On-Site Beam Calibration for RIS-Aided Positioning Systems

High precision positioning is a key enabler for next-generation communication applications such as smart transportation and augmented reality. Reconfigurable intelligent surface (RIS) technology can enhance positioning by providing additional angular information and improving coverage under obstructed propagation conditions. However, true RIS beams can differ significantly from the simplified or ideal beam response models commonly used in RIS-aided positioning, leading to beam model mismatch and an elevated positioning error floor. This paper proposes an on-site RIS beam calibration framework that reduces this error floor by estimating a realistic 3D RIS beam response model from on-site measurements. The proposed calibration algorithm first extracts the RIS-reflected channel response from signals received by a calibration agent sampling the angular range of interest, using delay-domain sparse recovery, and then estimates the beam model parameters with a gradient-based estimator. To validate the proposed framework, 3D beam patterns under 66 phase modulations were measured and incorporated into simulations. With an angular sampling step of 1 deg, the calibrated model achieves an average beam response similarity of 88.5% with respect to the ground truth, compared with 43.7% for the ideal model. The probability that the absolute lower bound of the positioning error is below 0.5m increases from 0.52 without calibration to 0.74 after calibration, showing that on-site RIS beam calibration effectively reduces the positioning error floor caused by true beam model mismatch.

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arXiv2026

HoloTrace: a Location Privacy-Preserving Framework for mmWave MIMO-OFDM Systems

The technological innovation towards 6G cellular networks introduces unprecedented capabilities for user equipment (UE) localization, but it also raises serious concerns about physical layer location privacy. This paper introduces HoloTrace, a signal-level privacy preservation framework that relies on user-side spoofing of localization-relevant features to prevent the extraction of precise location information from the signals received by a base station (BS) in a mmWave MIMO-OFDM system. Spoofing is performed by the user on location parameters such as angle of arrival (AoA), angle of departure (AoD), and time difference of arrival (TDoA). Without requiring any protocol modification nor network-side support, our method strategically perturbs pilot transmissions to prevent a BS from performing non-consensual UE localization. The methodology allows the UE to spoof its position, keeping the precoder unchanged. We formulate spoofing as a unified rank-constrained projection problem, and provide closed-form solutions under varying levels of channel state information (CSI) at the UE, including scenarios with and without CSI knowledge. Simulation results confirm that the proposed approach enables the UE to deceive the BS, inducing significant localization errors, while the impact on link capacity varies depending on the spoofed position. Our findings establish HoloTrace as a practical and robust privacy-preserving solution for future 6G networks.

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arXiv2026

JEPA for AI-Native 6G: Predictive Representations and Open Challenges

Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core. This transition requires learning from limited labels, heterogeneous wireless and network data, partial observations, non-stationary propagation, and latency-constrained control loops. Joint-embedding predictive architecture (JEPA) is a promising self-supervised paradigm for this setting because it predicts missing or future representations in latent space instead of reconstructing raw measurements or using contrastive negative samples. This article presents a wireless-oriented tutorial on JEPA for 6G intelligence. We define the JEPA training mechanism, describe how CSI, beam measurements, KPIs, topology graphs, and sensing observations can be tokenized and masked, and position the learned encoder as a predictive representation layer for RAN, O-RAN, edge, and core functions, with task-specific heads or controllers producing final decisions. Then we present an illustrative, beam-management case study suggesting that a wireless-aware target, specifically an auxiliary future beam-energy target during self-supervised pretraining, can improve label efficiency and robustness across shifted deployment conditions relative to a supervised source domain. Finally, we outline open challenges in multi-timescale prediction, action-conditioned modeling, distributed training, trustworthiness, efficient deployment, benchmarking, and standardization.

cs.LG↗

arXiv2026

Amplitude-Independent Robust Snapshot 6-D Radio SLAM via a Uniffed Angle-Delay Formulation

This paper addresses bistatic snapshot radio SLAM, in which a user equipment (UE) with unknown 6-D pose and clock bias is localized and environmental landmarks are reconstructed from a single multipath channel snapshot. Under mixed line-of-sight (LoS)/non-line-of-sight (NLoS) propagation, existing robust snapshot SLAM methods are mainly developed or validated in planar/2-D settings and often use path-amplitude or path-loss information for LoS handling, which makes them sensitive to calibration errors and propagation-model mismatch. We propose an amplitude-independent robust radio SLAM method built on a uniffed angle-delay formulation for LoS and single-bounce NLoS inlier paths. In the coarse stage, the method estimates the UE state and selects geometrically consistent inliers directly from angle-delay measurements, without amplitudebased LoS preclassiffcation or path-wise latent variables; the formulation is further extended to general 3-D/6-D pose estimation through twist-swing two-stage traversal initialization and local reffnement on SO(3). A subsequent Jacobian-row-equilibrated iteratively reweighted least-squares (IRLS) reffnement, combined with quasi-Akaike information criterion (QAIC) model comparison, detects the LoS path and jointly reffnes the UE state and scattering points. We also analyze formulation-speciffc local-rank properties and their minimal-set implications under unknown path identity. Simulations show that the proposed method remains competitive with calibrated amplitude-dependent baselines and is more robust to path-loss-model mismatch.

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arXiv2026

Exploiting Structural Sparsity and Delay-Doppler Decoupling for Low-Complexity OTFS-ISAC Receivers

In this work, the problems of channel estimation, radar sensing, and data detection are addressed for monostatic integrated sensing and communications (ISAC) applications within orthogonal time frequency space (OTFS) systems operating with a reduced cyclic prefix (RCP). Specifically, the delay-Doppler (DD) input-output relationship is formulated in a discrete representation that enables signal-independent disjoint parameter estimation by encapsulating fractional delay and Doppler effects through distinct, structurally sparse matrices. This exact algebraic separability is directly exploited to develop a low-complexity parameter estimation framework for the communication channel, which is seamlessly adapted for monostatic radar sensing on backscattered data frames. To enhance path detection robustly and safeguard estimation accuracy under low signal-to-noise ratio (SNR) regimes where traditional stopping criterionc(SC)-based methods fail, a deep learning (DL) architecture is integrated to perform model order selection via multi-class classification. Furthermore, a path-wise variant of the iterative Landweber method, designated as iterative matched filtering and combining (IMFC), is introduced for low-complexity data detection by leveraging the identical structural sparsity unlocked by the decoupled framework. Simulation results indicate the proposed estimation scheme achieves lower normalized mean squared error (NMSE) than conventional channel estimation algorithms and sensing performance close to the Cramer-Rao lower bound (CRLB). Finally, the IMFC equalizer is shown to deliver bit error rate (BER) performance comparable to the traditional linear minimum mean squared error (LMMSE) benchmark while dramatically reducing the computational load.

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arXiv2026

Differential LEO Navigation under Asynchronous Satellite Clocks: Architecture and Performance Bounds

Low Earth Orbit (LEO) communication satellites have emerged as a promising source of signals of opportunity for resilient positioning, navigation, and timing (PNT) in GNSS-challenged environments. Unlike GNSS constellations, however, commercial LEO systems are not globally synchronized, and independent satellite clock biases and drifts severely degrade kinematic estimation and induce statistical inconsistency when not explicitly managed. This paper proposes a base-station-aided differential navigation architecture that explicitly mitigates independent LEO satellite clock biases and drifts without inflating the state vector of the mobile rover. A fixed base station continuously tracks time-varying per-satellite clock states and transmits them as measurement-domain corrections, along with their rigorously propagated uncertainties, to a compact, 8-state rover Extended Kalman Filter (EKF). To support this architecture, we introduce a robust visibility management scheme to seamlessly handle the frequent entry and exit of LEO satellites. Furthermore, we derive the Recursive Bayesian Cramer-Rao Bound (RBCRB) directly linked to the channel-domain signal model to establish fundamental theoretical performance limits. The proposed methodology is validated using real-world urban vehicular data combined with high-fidelity LEO constellation simulations. Results demonstrate that the differential framework completely eliminates the statistical inconsistency endemic to single-receiver baselines. Crucially, the rover's posterior estimation uncertainty perfectly tracks the theoretical RBCRB limits across all kinematic and clock states, ensuring highly reliable PNT even during periods of severe geometric degradation.

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arXiv2026

Goal-Oriented Access Optimization for ISAC-Enabled Digital Twins

Digital twins (DTs) of physical systems enable real-time remote tracking, control, and learning, but require to be updated with environmental sensory data to maintain alignment with their physical counterparts. In a network context, integrated sensing and communication (ISAC) capabilities can expand the DT's environmental awareness by linking received updates to the location where wireless sensors acquired them. Integrating localization services, however, increases the complexity of the communication system, and can only be supported through smart access optimization. To tackle this problem, we design a two-step goal-oriented approach: firstly, sensors with a high Value of Information (VoI) inform the network of their resource demands through a push-based random access; then, pull-based scheduled transmissions of the actual sensory data are optimized to satisfy ISAC performance constraints. This design allows to maximize the VoI of the information delivered to the DT while locating the transmitting nodes, significantly outperforming existing schemes.

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arXiv2026

RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models

Wireless localization is a fundamental capability of sixth-generation (6G) networks. Conventional model-based methods require accurate modeling of the propagation environment and degrade in complex multipath and non-line-of-sight scenarios, while learning-based methods couple model parameters tightly to the training scene, requiring costly retraining whenever the base station (BS) configuration or propagation environment changes. In this paper, we propose RA-LWLM, a retrieval-augmented in-context localization framework that achieves training-free cross-scene adaptation by externalizing scene-specific information into a per-scene fingerprint database rather than encoding it in model weights. The framework consists of three components: a frozen wireless foundation model (FM) encoder that maps raw channel state information into a scene-agnostic representation; a retrieval module that selects the most informative references from the per-scene database via similarity search in the representation space; and a transformer-based in-context learning (ICL) module that fuses the query with the retrieved references to predict the user equipment (UE) position. To accommodate varying retrieval quality and propagation complexity across queries, the ICL module adopts a mixture-of-experts design in which experts specialize in different context sizes and are softly combined by a learnable selector. Extensive ray-tracing-based experiments across heterogeneous scenes with diverse BS configurations show that RA-LWLM achieves nearly identical accuracy on seen and unseen scenes without any per-scene retraining, substantially outperforming end-to-end and FM-based baselines. These results validate the proposed retrieval-augmented in-context paradigm as a scalable solution for cross-scene localization in 6G networks.

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arXiv2026

Artificial-Intelligence-Assisted Multi-Modal Terahertz Sensing and Environment Reconstruction

Multi-modal sensing is an important enabler for future environment-aware wireless systems, since a single sensing modality is generally insufficient to provide accurate metric geometry, material awareness, and semantic interpretability in complex environments. This paper presents a measurement-based multi-modal THz sensing and vision framework for indoor environment reconstruction. A three-dimensional monostatic THz channel sounding system operating at 290-310 GHz is integrated with an omnidirectional fisheye camera to acquire radio-frequency and visual observations from a common sensing viewpoint. From the measured THz data, a signal processing pipeline extracts multipath components and infers geometryand material-consistent structural primitives through trajectory tracking-assisted parameter estimation, graph-based structure discovery, planar reconstruction, and reflection-loss analysis. In parallel, AI-based visual perception modules extract object-level semantic masks and depth priors from panoramic images. To associate these heterogeneous representations, an agentic-AI-based task-driven THz-agent module is developed to select appropriate integration tools according to the attributes of the modality-specific outputs. Through angular alignment and consistency analysis, THz-derived metric geometry and material information are associated with vision-derived semantic regions and depth priors, enabling geometry-consistent and semantically interpretable environment reconstruction directly from measurements. Experimental validation in the indoor L-shaped hallway demonstrates that the proposed framework reconstructs dominant structural elements with centimeter-level accuracy while identifying semantic categories and material attributes of representative indoor objects.

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arXiv2026

On Spatial Degree-of-Freedom Analysis of Near-Field Multipath Channels for Ultra-massive MIMO Systems

The transition to near-field (NF) communications in ultra-massive multiple-input multiple-output (UM-MIMO) systems fundamentally alters the spatial degrees of freedom (DoF) of wireless channels. While the NF DoF of line-of-sight (LoS) transmission channels is well-characterized in the literature, the DoF in NF multipath scenarios remains underexplored. This paper investigates the spatial DoF of NF UM-MIMO channels under practical multipath conditions. A generic DoF metric is derived by modeling multipath propagation and analyzing the resulting eigenvalue distribution based on the Green' s function representation of the channel. The DoF contribution of each path is determined by the product of the effective electrical aperture and the subtended solid angle, and the total DoF is obtained through the effective union of spatially resolvable path contributions. A mapping between the eigenvalue distribution and multipath powers is further established. Numerical simulations and real-world NF channel measurements at 28-30 GHz with 720 array elements are conducted for validation in both LoS multipath and non-LoS scenarios. The results show that multipath propagation can significantly increase the spatial DoF and that the proposed metric accurately predicts the DoF of practical NF channels. The proposed framework provides a practical tool for DoF prediction and supports capacity analysis and spatial multiplexing design in future NF UM-MIMO systems.

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arXiv2026

Multi-Domain Security for 6G ISAC: Challenges and Opportunities in Transportation

Integrated sensing and communication (ISAC) will be central to 6G-enabled transportation, providing both seamless connectivity and high-precision sensing. However, this tight integration exposes attack points not encountered in pure sensing and communication systems. In this article, we identify unique ISAC-induced security challenges and opportunities in three interrelated domains: cyber-physical (where manipulation of sensors and actuators can mislead perception and control), physical-layer (where over-the-air signals are vulnerable to spoofing and jamming) and protocol (where complex cryptographic protocols cannot detect lower-layer attacks). Building on these insights, we put forward a multi-domain security vision for 6G transportation and propose an integrated security framework that unifies protection across domains by leveraging existing ISAC measurements for lightweight cross-checks.

cs.CR↗