arXiv subjects

Sofie Pollin

Publications and source records attributed to Sofie Pollin.

At least 19 recordsLinked to original sources

arXiv2026

Structured Agentic Learning for Persistent Spatial Acquisition in RDT-TAIS-Assisted Communications

Persistent TAIS operation can reduce repeated radio-information acquisition by reusing environmental knowledge across control intervals. This knowledge changes with radio-environment evolution and current communication task. This paper studies persistent spatial knowledge maintenance for TAIS control and formulates it as a sequential information-acquisition problem that maximizes effective communication performance while accounting for physical-acquisition overhead under an outage-reliability constraint. We develop structured agentic spatial intelligence (ASI), which realizes a communication-oriented agent that maintains persistent spatial knowledge, forms beliefs from radio and context observations, and adapts physical information acquisition through closed-loop interaction with the radio environment. By jointly exploiting radio-consistent context inference, temporal knowledge-sufficiency assessment, graph-structured regional reasoning, and predictive outage-risk feedback, structured ASI determines when additional acquisition is needed, where it is directed, and how much physical information is sufficient before stopping or escalating to full radio-environment reacquisition. Numerical results show that structured ASI improves the effective rate by 2.59% while reducing acquisition overhead by 25.0% at six structural changes per 100 control intervals, and achieves a 2.82% effective-rate gain with a 41.8% overhead reduction over a heterogeneous 400-interval mission.

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arXiv2026

Approximation of the Narrowband Velocity Ambiguity Function in Near-Field Sensing Systems

This work investigates the velocity ambiguity function in the radiative near-field of a uniform linear antenna array. First, the general ambiguity function is formulated and the velocity cross-section at a target position is considered. The velocity vector is decomposed into radial and tangential components, which are analyzed separately. Next, the closed-form approximations for the velocity ambiguity function are derived and shown to be accurate despite relying on far-field approximations. The results show that the near-field regime does not improve radial velocity estimation, but enables estimation of the tangential velocity. Finally, the velocity ambiguity functions and the achievable resolution are analyzed for different array configurations. For tangential velocity estimation, MIMO and a collection of SISO radars offer improved ambiguity function and resolution as compared to the SIMO/MISO configuration.

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arXiv2026

RT-VSS: In-House Ray Tracing for Vital Signs Sensing in FR3 Band

Vital Signs Sensing, enabled by the evolution of wireless communications, is inherently challenging as the chest's micro-Doppler signatures can be corrupted by multipath, body movement and noise. Additionally, the prospects of the Frequency Range (FR) 3 band, spanning 7-24 GHz, are compelling due to centimeter-scale wavelength improving phase sensitivity to chest motion, while its abundant bandwidth and rich multipath enhance temporal and spatial diversity. In this paper, we validate the feasibility of using cellular communications in the FR3 band to monitor a human's breathing and heartbeat in an indoor environment. Using Blender to model physiologically accurate vital-signs signals as ground truth and Sionna Ray Tracing simulations, we find that it is feasible to extract breathing and heartbeat waveforms at FR3 frequencies. Furthermore, we perform a comparative analysis of ISAC performance against representative sub-6 GHz (FR1) and mmWave (FR2) frequencies, indicating the utility of the study across the frequency bands.

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arXiv2026

Comparative Analysis and Behavioral Modelling of Power Amplifiers in FR3

Power amplifier (PA) nonlinearity is a critical transmitter impairment in the upper mid-band (7 GHz to 24 GHz), designated as Frequency Range 3 (FR3) in emerging 6G standardisation. The 3GPP PA model baseline, TR 38.803 Annex A, was derived from 2.1 GHz and 28 GHz measurements and has not been revisited yet for FR3 standards work. This paper addresses that gap with characterisation measurements of multiple PA devices-under-test (DUTs), driven into gain compression in the 7 GHz and 15 GHz bands with 100 MHz and 200 MHz bandwidth. Memoryless polynomial and Generalized Memory Polynomial (GMP) behavioral models are extracted by least-squares regression and compared with 3GPP models spanning FR1 and FR2. The GaN PA DUT is modelled best at a higher nonlinear order and shows markedly stronger phase distortion than the wideband PA. The memory terms of the GMP model measurably improve the Normalized Mean Square Error (NMSE) at all operating points. An orthogonal frequency-division multiplexing (OFDM) ambiguity function analysis from the Integrated Sensing and Communication (ISAC) literature, using the extracted models, illustrates how PA compression raises the sidelobe floor of the sensing response. These results provide a reference for PA impairment modelling in FR3, directly applicable to ISAC system design. All model coefficients and measurement data are publicly available [1].

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arXiv2026

SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing

Future 6G networks aim to expose sensing as a native service by reusing communication infrastructure. We study respiration sensing on a cell-free massive multiple-input multiple-output (MIMO) base station, where a 64-antenna channel must be fused into a breathing waveform. The state-of-the-art hand-crafted fusion is near-optimal in benign conditions. It collapses, however, under strong in-band motion interference, whose frequency falls inside the respiration band. We show that a learned complex-weight beamformer recovers respiration by spatial nulling, and that the remaining gap to a per-recording oracle can be closed at deployment by label-free test-time adaptation. Crucially, we identify which label-free signal makes this work. Frequency- and variance-based criteria cannot separate an in-band interferer from breathing. Our spatial-redundancy test-time adaptation (SR-TTA), which maximizes consistency across random antenna subsets under an out-of-band spectral veto, preserves benign performance in our tests. The respiration-rate error drops from 5.8 to 0.8 breaths per minute (bpm) under simulated in-band interference, and the pipeline maps onto the Open Radio Access Network (O-RAN) architecture as O-RAN distributed-unit (O-DU) range-gating, an adaptation xApp, and a calibration rApp. On real testbed recordings, a one-time cross-subject calibration plus SR-TTA reduces failures from 47% to 7%, drawing level with the hand-crafted combiner using label-free test-time adaptation.

cs.LG↗

arXiv2026

Analysis and Approximation of Spatially Wideband Array Factor of Thinned Antenna Arrays

This work investigates the spatially wideband (SWB) array factor (AF) of linear thinned antenna arrays. The SWB AF is formulated as a matched filter and expressed as a spatially variant convolution of a spatially narrowband (SNB) AF and a SWB kernel. Originally derived for uniform arrays, the SWB AF approximation is revisited and extended to thinned arrays. The approximation is shown to remain accurate across a wide range of parameters. Representing the SWB AF as a convolution offers an intuitive interpretation of how the bandwidth-aperture product modifies the SNB AF. Specifically, the SWB kernel acts as an averaging window for the SNB AF, suppressing the sidelobe levels (SL) of the SNB AF. In thinned arrays, the sidelobes away from the mainlobe can be treated as random, noise-like, making averaging and suppression particularly effective. Next, the analytical formulas for the expected SL and peak-to-sidelobe level (PSL) in SNB thinned arrays are first introduced and subsequently extended to the SWB regime. Finally, simple closed-form expressions for the expected SL and PSL levels of the uniformly thinned arrays with a uniform spectrum are provided. The expected SWB SL and PSL are formulated as functions of the bandwidth-aperture product and are shown to steadily decrease with distance from the mainlobe.

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arXiv2026

Packet-Level Complex CIR Tracking for Communication-Native mmWave Sensing: An 802.11ad Testbed

Commercial millimetre-wave (mmWave) Wi-Fi platforms based on IEEE 802.11ad/ay have demonstrated sensing capabilities, but they provide limited visibility into the packet-level measurements and synchronisation mechanisms that determine temporal coherence. We present a programmable 60~GHz IEEE 802.11ad testbed that exposes the complete complex 128-tap channel impulse response (CIR) for every detected packet. This access exposes packet-to-packet delay shifts and common phase drift that commercial-transceiver studies generally do not characterise. We develop a delay-profile alignment method and define a static-reference relative CIR phase that cancels common drift while preserving target-induced variation. We use 30~s respiration sensing to evaluate long-term phase stability for sub-wavelength motion tracking. Across 39 recordings, median absolute waveform correlation increases from 0.41 (timestamp regularised) to 0.52 (delay aligned) and 0.61 (relative-phase calibrated); breathing-rate mean absolute error decreases from 5.1 to 4.1 and 3.6~breaths/min. The results demonstrate controlled characterisation and stabilisation of communication-native WiGig CIR measurements on a fully programmable physical layer.

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arXiv2026

First Galileo SAS Authenticated Time Solution

Spoofing attacks against civilian GNSS receivers have grown more common, especially near conflict zones where they now disrupt civil aviation, maritime operations, and critical infrastructure on a daily basis. Spoofing is possible because legacy civil GNSS signals are largely predictable in both their navigation data and ranging codes, allowing an attacker to forge a signal that imposes a false position and time on an unsuspecting receiver. Cryptographic authentication schemes such as Galileo's Open Service Navigation Message Authentication (OSNMA) mitigate this threat by verifying the authenticity of the navigation data. The ranging code itself, however, remains unprotected. To close this gap, Galileo is introducing a Signal Authentication Service (SAS) in the E6-C signal, which directly authenticates ranging measurements. SAS is currently transmitted by only two satellites in an elliptical orbital plane, of which at most one is visible at a time, meaning a full position solution is not yet possible; however, a georeferenced receiver can still obtain an authenticated time solution. This paper presents, to the authors' knowledge, for the first time, a timing solution computed from an authenticated civil GNSS signal. We develop a snapshot software receiver implementing a simplified version of the Galileo SAS protocol to compute the receiver clock bias from an authenticated pseudorange, using radio-frequency data recorded with an engineering prototype software-defined radio receiver from Septentrio. We evaluate the resulting timing solution using recordings from both SAS-capable satellites collected at different locations, demonstrating the feasibility of authenticated timing ahead of full SAS operational deployment.

cs.CR↗

arXiv2026

Analysis and Approximation of a Spatially Wideband Antenna Array Factor

This work investigates the spatially wideband (SWB) antenna array factor (AF) of uniform linear arrays. First, the SWB AF approximation is derived for narrowband (NB) signals and a large aperture with element spacing satisfying the Nyquist criterion. The derivation accounts for different spatial and spectral windows. Next, an approximation of the SWB AF for a wideband (WB) signal is developed under uniform spatial and spectral weighting. The analysis shows that for fully populated arrays, increasing the bandwidth effectively suppresses the AF sidelobes. Finally, a universal SWB AF approximation is introduced, which is based on recognizing the SWB AF as a spatially variant convolution. In this formulation, the SWB AF is expressed as a convolution of the spatially narrowband (SNB) AF and the SWB kernel, providing insight into how the bandwidth and the spectral weighting affect the resulting SWB AF. The proposed approximation is shown to be accurate for a wide range of bandwidths and element spacings, including sparse arrays. In particular, for sparse arrays, the bandwidth enables suppression of grating-lobe amplitudes by spreading their energy over a wider angular range. An approximation of the grating lobe envelope as a function of the bandwidth-aperture product is provided.

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arXiv2026

Near-Field Velocity Estimation and Doppler-Aware Localization in OFDM Massive MIMO

In Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) near-field (NF) sensing, target motion induces an antenna-dependent bistatic Doppler variation across the array aperture. Ignoring this spatial Doppler variation leads to a model mismatch that degrades NF localization. In this paper, we propose a low-complexity recursive framework for joint radial/transverse velocity estimation and Doppler-aware localization. Initialized by a constant-Doppler coarse localization, the method alternates between closed-form Least Squares Estimator (LSE)-based velocity estimation and antenna-dependent Doppler-aware localization refinement. Simulation and measurement results demonstrate the effectiveness of the proposed framework against two benchmark methods. Compared with a low-complexity constant-Doppler baseline method, the proposed algorithm improves range, angle, and radial velocity estimation results, while also enabling transverse velocity estimation. In the measurement results, the overall localization error decreases from 0.268 m to 0.064 m. The radial and transverse velocity estimation errors are 0.032 m/s and 0.069 m/s, respectively. Compared with a high-complexity exhaustive four-dimensional (4D) Maximum Likelihood Estimator (MLE), the proposed method achieves comparable velocity estimation results while yielding a more accurate localization result when the 4D MLE has a practical finite search grid.

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arXiv2026

Depthwise Separable CNN for D-MIMO Indoor Localization with Data Reduction

Indoor localization using Distributed Multiple-Input Multiple-Output (D-MIMO) and machine learning (ML) achieves sub-centimeter accuracy but faces midhaul capacity bottlenecks when transmitting raw Channel State Information (CSI) in Open Radio Access Networks (O-RAN) architectures. To address this, we propose a lightweight, distributed ML framework that shifts initial processing to the network edge. By deploying localized models as dApps on Distributed Units (DUs), each requiring just 1.39 MB of memory and 1.96 MFLOPs, the system performs CSI feature extraction and reduction on the edge. The reduced low-dimensional features are transmitted to the Central Unit (CU), where another dApp is deployed for location estimation. Evaluated on a high-density dataset, this framework reduces midhaul traffic by 100x while maintaining an average error of 8.5 mm, even with half the deployed Radio Units (RUs), providing a scalable blueprint for practical D-MIMO localization.

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arXiv2026

Aliased Time-Modulated Array OFDM Transmit System

The time-modulated array is a simple array architecture in which each antenna is connected to an RF switch that serves as a modulator. The phase shift is achieved by digitally controlling the relative delay between the periodic modulating sequences of the antennas. Two factors limit the practical use of this architecture for communication and sensing. First, the switching frequency is high, as it must be a multiple of the sampling frequency. Second, the discrete modulating sequence introduces undesired harmonic replicas of the signal, which are out-of-band interference. This paper proposes the OFDM modulation with an appropriate precoder to facilitate the aliasing of the harmonic components to simultaneously reduce sideband radiation and switching frequency. The transmit signal has a repeated block structure in the frequency domain to facilitate coherent combining of the aliased signal blocks. As a result, a factor $A$ reduction in switching frequency is achieved at the cost of a factor $A$ reduction in communication capacity. Doubling $A$ reduces sideband radiation by around 2.9 dB. The feasibility of the proposed method is experimentally validated for wideband signals. Full-wave simulations are performed to validate the beamforming performance based on the experimental results.

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arXiv2026

Low Complexity Neural Network Digital Predistortion of Wideband Power Amplifiers through Feature Selection

Due to the continuous increase in communication bandwidth and the use of highly efficient yet nonlinear power amplifiers, Digital Predistortion (DPD) algorithms are becoming increasingly complex. In particular, neural network (NN) based DPD approaches using Phase-Normalized NN architectures often incur substantially higher computational costs than widely deployed polynomial-based methods, such as the Memory Polynomial (MP) and Generalized Memory Polynomial (GMP) models. To bridge this gap between research performance and practical implementation, we propose a low-complexity Feature Selection NN DPD architecture. The proposed method employs an offline feature-engineering pipeline based on the Least Absolute Shrinkage and Selection Operator (LASSO) and the Minimum Redundancy Maximum Relevance (MRMR) algorithm to construct a compact and informative input representation. Using measured wideband FR3 power amplifier datasets that are publicly released with this work, we demonstrate up to 30% reduction in computational complexity while maintaining comparable linearization performance.

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arXiv2026

Multi-/Uni-Cast Non-Orthogonal Multiple Access-Based INAC

With the rapid development of satellite communication and navigation, there is an urgent need to integrate both technologies to achieve reliable communication and precise navigation services within the same satellite system. By combining multi-/uni-cast (MUC) and non-orthogonal multiple access (NOMA) technologies, we propose a novel MUC-NOMA-based integrated navigation and communication (INAC) signal structure, in which the navigation and communication signals share a common pseudo noise (PN) sequence, thereby integrating satellite communication and navigation at the signal level. According to different power allocation strategies, two scenarios are defined: multi-cast-oriented (MO-) INAC and uni-cast-oriented (UO-) INAC, where a greater portion of power is assigned to either the multi-cast or the uni-cast signal, respectively. To mitigate co-channel interference, we employ successive interference cancellation (SIC) at the receiver and design a signal processing algorithm for the proposed INAC signal. Then, closed-form expressions are subsequently derived for the bit error rates (BER) of both the navigation and communication signals, along with the positioning accuracy of the navigation signal. To gain further insights, the impacts of power allocation factors and communication rates are evaluated. Our analysis results show that: i) In the MO-INAC scenario, the positioning and BER performance of navigation signal are excellent when more power is assigned to the multi-cast signal; ii) In the UO-INAC scenario, interference in the shared resources is reduced when more power is assigned to the uni-cast signal; iii) The ranging accuracy decreases as the communication data rate increases. Numerical results confirm the superior BER and positioning accuracy of the MO-INAC scenario for MEO satellites.

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arXiv2026

Joint Mobile User Positioning and Passive Target Sensing using Optimized Sequential Beamforming

Integrated sensing and communication (ISAC) relies on monostatic sensing (MS) and bistatic positioning (BP) to enable comprehensive environmental awareness and user localization. However, existing frameworks predominantly assume static geometries and optimize these modalities independently, neglecting user mobility and sequential information sharing. In this paper, we propose a velocity-aware sequential beamforming framework that dynamically couples MS and BP in time. We derive the Cramer-Rao bounds (CRBs) in the position domain to formulate a non-convex resource allocation problem. Instead of relying on static weighted-sum tradeoffs, we introduce a sequential Bayesian optimization strategy where MS is executed first to construct a reliable structural prior on the UE and passive targets (PTs). This covariance prior is subsequently passed to the UE to regularize the BP estimation stage. We demonstrate that optimizing a single shared beamformer globally across both phases yields superior synergistic gains compared to a two-stage greedy approach. Simulation results validate that the shared sequential design efficiently balances limited symbol resources, achieving centimeter-level positioning accuracy for both the UE and PTs, robust velocity estimation, and a significantly reduced computational runtime.

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arXiv2026

Disaggregated multi-domain interference classification for O-RAN

Spectrum sharing and dynamic spectrum reuse are becoming increasingly critical in modern wireless networks to address spectrum scarcity. However, these techniques inevitably increase Cross-Technology Interference (CTI). In this context, the Open Radio Access Network (O-RAN), as a modern and disaggregated network architecture, necessitates accurate, low-latency, and computationally efficient CTI classification and mitigation to support real-time control and maintain Quality of Service (QoS). Unfortunately, existing solutions predominantly rely on high-complexity, monolithic deep learning-based solutions that, while achieving high classification accuracy, incur significant latency and computational overhead This paper exploits the O-RAN functional split to leverage multi-domain raw signal representations (time, frequency, and Channel State Information (CSI)) directly from the same data stream. Each domain is processed locally, naturally interleaving CTI within the distributed, disaggregated O-RAN architecture. This distributed strategy enables a cost-aware, multi-domain fusion architecture that balances classification accuracy with computational overhead and latency. Our proposed multi-domain distributed architecture achieves a 400 $μs$ inference latency on standard CPUs. Compared to a state-of-the-art monolithic frequency-domain classifier, this represents an average 9x reduction in latency and an 11-fold decrease in computational cost, while sacrificing only 4% in classification performance and maintaining >90% accuracy in high-interference conditions.

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arXiv2026

Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning

This paper addresses the problem of dual-technology scheduling in hybrid Internet-of-Things (IoT) networks that integrate Optical Wireless Communication (OWC) with Radio Frequency (RF). We first present an optimization formulation that jointly maximizes throughput and minimizes delivery-based Age of Information (AoI) between access points and IoT nodes under energy and link availability constraints. However, solving such NP-hard problems at scale is computationally intractable and typically assumes full channel observability, which is impractical in real deployments. To address this challenge, we propose the Dual-Graph Embedding with Transformer (DGET) framework, a supervised multi-task learning architecture that combines a two-stage Graph Neural Network (GNN) with a Transformer encoder. The first stage employs a transductive GNN to encode the known graph topology together with initial node and link states, such as energy levels, available links, and queued transmissions. The second stage introduces an inductive GNN for temporal refinement, enabling the model to generalize these embeddings to evolving network states while capturing variations in energy and queue dynamics over time through a consistency loss. The resulting embeddings are then processed by a Transformer-based classifier that models cross-link dependencies using multi-head self-attention. Simulation results show that hybrid RF-OWC networks outperform standalone RF systems by supporting higher traffic loads and reducing AoI by up to 20% while maintaining comparable energy consumption. Compared with optimization-based methods, the proposed DGET framework achieves near-optimal scheduling with over 90% classification accuracy, lower computational complexity, and improved robustness under partial channel observability.

cs.NI↗

arXiv2026

Data Augmentation and Attention for massive MIMO-based Indoor Localization in Changing Environments

The demand for high-precision indoor localization has grown significantly with the rise of smart environments, industrial automation, and location-aware applications. While massive Multiple-Input and Multiple-Output (MIMO) systems enable millimeter-level accuracy by leveraging rich Channel State Information (CSI), most existing solutions are optimized for static environments, where users or devices remain fixed during data collection and inference. Real-world applications, however, often require real-time localization in changing environments, where rapid movement, unpredictable blockages, and dynamic channel conditions pose significant challenges. To address these challenges, we introduce two data augmentation techniques designed to resemble blocked antennas, enhancing the generalizability of localization models to dynamic scenarios. Additionally, we enhance an existing Deep Learning (DL) model by incorporating attention modules, improving its ability to focus on relevant channel features and antennas. We train our model on data from a static scenario, augmented with the proposed techniques, and evaluate it on a dataset collected in changing scenarios. We investigate the performance enhancements achieved by the data augmentation techniques and the Attention modules, and observe a localization accuracy improvement from a mean error of 286 mm, when trained without Attention and without data augmentations, to 66 mm, when trained with Attention and data augmentation. This shows that high localization accuracy can be maintained in changing environments, even without training data from those scenarios.

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