arXiv subjects

Christos Masouros

Publications and source records attributed to Christos Masouros.

At least 19 recordsLinked 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

OFDM-ISAC over Data Payloads: MSE Analysis, Constellation Design, and Experimentation

Orthogonal frequency division multiplexing (OFDM) is a key waveform for integrated sensing and communication (ISAC) systems due to its high spectral efficiency and inherent compatibility with modern wireless standards. However, its fundamental estimation-theoretic sensing performance under random data modulation remains largely unexplored. This paper presents a unified and explicit performance analysis of OFDM-based ISAC systems for multi-target range estimation, focusing on the distinct impacts of the modulation constellation on the sensing performance. We develop a comprehensive estimation-theoretic framework to characterize the range estimation mean-square error (MSE) for both matched filtering (MF) and reciprocal filtering (RF) sensing receiver architectures. Our theoretical analysis reveals that in multi-target and clutter-rich environments, the sensing performance of the MF receiver is fundamentally limited by the fourth-order moment (kurtosis) of the constellation, which determines the data-dependent sidelobe interference level. In contrast, the RF receiver eliminates such interference at the cost of noise enhancement, with its performance governed by the inverse second-order moment of the constellation. Building on these closed-form MSE derivations, we propose a sensing-receiver specific geometric constellation shaping (GCS) framework. By jointly optimizing the constellation geometry based on the minimum Euclidean distance (MED) and receiver-dependent sensing metrics, we enable a flexible trade-off between communication reliability and sensing precision. Our results demonstrate that the proposed constellation shaping provides significant performance gains and facilitates a tailored sensing and communication trade-off across different receiver architectures in practical over-the-air implementations.

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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 Cram\'{e}r-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

AmbSentry: Mitigating Sensing Eavesdropping in ISAC Systems by Harnessing Ambient IoT Devices

Integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for 6G networks, enabling the synergistic convergence of spectral and hardware resources to maximize system efficiency. However, the inherent openness of wireless transmission exposes ISAC systems to critical security risks, particularly regarding the privacy of the sensing information. Unauthorized sensing eavesdroppers can extract sensitive target parameters (e.g., range and velocity) by directly estimating open sensing echo channels, rendering traditional data-based protection techniques ineffective. To mitigate this threat, this paper proposes AmbSentry, an ISAC system that prevents the leakage of sensing information to sensing eavesdroppers by harnessing naturally distributed passive ambient IoT (AIoT) devices. Specifically, these AIoT devices are strategically configured to act as cooperative jammers and ghost targets, introducing controllable interference into the sensing environment. Based on the proposed system, we formulate a joint optimization problem to maximize the integrated sidelobe level at the eavesdropper under quality-of-service (QoS) constraints, thereby degrading sensing eavesdropping performance while maintaining sensing and communication performance for legitimate receivers. Since the problem is non-convex, we further develop an efficient iterative algorithm to cooperatively design the transmit beamforming at the base station and the reflection modulations of the AIoT devices based on Dinkelbach transformation and block coordinate descent methods. The detailed results also demonstrate that AmbSentry significantly enhances sensing security, allowing the legitimate sensing receiver to achieve a 14-dB SNR advantage in detection probability and a hundred times lower estimation error compared to the eavesdropper.

cs.CR↗

arXiv2026

Sub-Sampling for Positioning Privacy in ISAC: Deception by Aliasing via Sparse Arrays and Pilots

Integrated sensing and communications (ISAC) enables simultaneous communication and sensing using shared spectrum and hardware resources in wireless systems. However, securing the sensing functionality against unauthorized receivers remains a fundamental challenge. In this paper, we propose a sub-sampling based sensing-privacy framework for communication-centric (CC)-ISAC systems that jointly exploits sparse arrays and sparse pilot allocations to induce controlled aliasing in the spatial and frequency domains, respectively. By interpreting antenna arrays and pilot subcarriers as spatial and frequency sampling mechanisms, respectively, we show that spatial-frequency undersampling naturally distorts the range-angle multiple-input multiple-output (MIMO) ambiguity function (AF) observed by an unauthorized receiver. To this end, we first derive a closed-form expression for the range-angle MIMO-AF, and subsequently characterize the ghost targets that arise due to spatial and frequency-domain aliasing. Next, we establish a sufficient condition under which these ambiguities jointly translate into positioning ambiguity and show that, for sufficiently large spatial and frequency sub-sampling factors, an unauthorized receiver inevitably positions a target at incorrect ghost positions. Finally, we show that the proposed sub-sampling framework preserves the native legitimate ISAC performance without introducing additional trade-offs. Numerical results verify the analysis and show that sparse arrays and sparse pilots naturally enable sensing and positioning privacy through deception by aliasing.

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arXiv2026

Geometry-Aware Resource Allocation for Network-Level ISAC Systems

Network-level integrated sensing and communication (ISAC) is recognized as a transformative technology for next-generation mobile radio systems. By enabling collaboration among multiple transceivers, network-level ISAC can significantly enhance both communication and sensing performance through spatial diversity. However, existing resource allocation strategies typically overlook the impact of spatial geometry, where identical time-frequency resources contribute differently to sensing accuracy depending on the transceiver's location. This leaves the fundamental coupling between spatial topology and resource efficacy unclear, rendering optimal resource allocation a critical challenge for unlocking the full potential of network-level ISAC.To address this challenge, this paper investigates the optimal distribution of time-frequency resources across spatially distributed transceivers through a theoretically grounded two-stage framework. First, we analytically derive the optimal time and frequency aperture distributions for sensing, defined as the variances of the allocated symbol and subcarrier indices, respectively, under both two-transmitter and multi-transmitter scenarios. By exploiting the mathematical isomorphism between delay and Doppler estimation, we prove that the optimal resource allocation strategy follows the gradient direction of the Cramer-Rao Lower Bound (CRLB) with respect to the apertures. Second, to bridge the gap between theoretical aperture values and practical OFDMA constraints, such as the minimized communication rate of each user equipment (UE), we formulate the resource allocation as a combinatorial integer partitioning problem. To tackle the NP-hard nature of the formulated problem, a low-complexity Variance-Guided Partitioning Algorithm (VGPA) is proposed to jointly optimize the subcarrier and symbol patterns for communication and sensing.

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arXiv2026

AI Empowered Communication and Radar Modulation Recognition: A Survey

Automatic modulation recognition (AMR) is of vital importance for ensuring communication and radar reliability, efficient spectrum utilization and resistance to electronic interference. The development of artificial intelligence (AI) technology is reshaping the technological paradigm of AMR, promoting its transition from traditional modes relying on manual features to data-driven intelligent recognition. This change is not only reflected in the significant improvement of recognition accuracy, but also injects strong momentum into the intelligent evolution of both communication and radar systems through algorithm innovation, architecture optimization, and scenario expansion. In order to clarify the current development status and bottlenecks of AMR, and to find breakthrough directions, we make a comprehensive survey of recent AI-based technologies for AMR in this paper, including model-based machine learning (ML) methods and data-driven deep learning (DL) methods. We first investigate the modulation types used in current communication and radar systems. Next, we summarize the typically used features in the field of AMR, and discuss their inherent advantages and disadvantages. Then, we introduce the basic AI models for AMR and conduct a hierarchical investigation of AMR methods for communication and radar. Finally, based on existing research works, we highlight open issues and propose future research directions for AMR.

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arXiv2026

Constellation Selection and Power Allocation for Multi-Cell OFDM-ISAC: Managing Inter-Cell Interference and Sensing Sidelobes

Future integrated sensing and communication (ISAC) networks are expected to operate in dense multi-cell environments, where multiple base stations (BSs) share their time-frequency resources for communication and sensing. In such scenarios, the delay--Doppler (DD) sensing performance is strongly affected by random finite-alphabet orthogonal frequency-division multiplexing (OFDM) symbols, power allocation, receive filtering, and interference. This paper develops a modulation- and receive-filter-aware framework for the sensing-interference management in multi-cell OFDM-ISAC systems. Starting from a discrete-time OFDM sensing model, we derive closed-form signal-to-interference-plus-noise ratio (SINR) expressions for each range--Doppler bin under matched filtering (MF) and reciprocal filtering (RF). The analysis reveals distinct interference structures: MF depends on fourth-order constellation moments and power-overlap terms, whereas RF is governed by inverse-symbol-power and ratio-type interference terms. Based on these expressions, we obtain sensing-oriented power allocation structures, including a ramped water-filling solution for MF and a square-root allocation rule for RF. Furthermore, we jointly optimize the finite-alphabet constellation selection and power allocation under realistic communication and power constraints, and obtain tractable mixed-integer convex formulations for both MF and RF. Additionally, we study spectrum-overlap coordination in multi-cell scenarios and reveal the distinct MF/RF preferences for shared and orthogonalized tones. Furthermore, we extend the interference model to inter-cell propagation delays exceeding the cyclic prefix (CP), and show how the resultant delay violation redistributes the nominal interference spectrum into a delay-distorted effective spectrum...

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arXiv2026

Constellation-Independent Range Estimation in Payload-Based OFDM-ISAC

Orthogonal frequency division multiplexing (OFDM) is a key waveform for integrated sensing and communication (ISAC) due to its spectral efficiency and compatibility with modern wireless standards. In multi-target and clutter-rich environments, however, payload-based OFDM-ISAC can suffer from data-dependent sidelobes induced by non-constant-modulus modulation symbols. To overcome these limitations, this paper proposes a region-of-interest mismatched filter (ROI-MMF) that suppresses sidelobes within a prescribed delay region while preserving the mainlobe response. By leveraging the Woodbury identity, the proposed design admits an efficient closed-form implementation whose complexity scales with the ROI size rather than the number of subcarriers. We theoretically provide the ranging mean-square error (MSE) of the designed ROI-MMF, which shows the superior performance compared to conventional matched filtering (MF) and reciprocal filtering (RF) sensing receivers. Simulations across various constellations show that the proposed sensing receiver achieves a ranging MSE approaching the Cramér-Rao bound (CRB), which notably confirms that our design preserves the target ranging performance even under the non-constant-modulus constellation. Finally, the framework is experimentally validated with our over-the-air OFDM-ISAC testbed.

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arXiv2026

Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems I

This paper addresses the optimization challenges in dual-functional radar-communication (DFRC) systems with a focus on array selection and beamforming in dynamic and heterogeneous operational contexts. We propose a novel array selection criterion that integrates antenna health information into the optimization process, distinguishing our approach from traditional methods. Our methodology employs gradient dual ascent and dual proximal-gradient ascent for tackling the constrained non-convex and non-smooth nature of sparse array selection problems. A key feature of our strategy is the implementation of proportional fairness among communication users, which aligns with system resource limitations while meeting the minimum rate requirements for all users. This facet of our method not only enhances system efficiency and responsiveness but also ensures a fair distribution of resources. Through extensive simulations, the efficacy of the proposed solutions in optimizing DFRC system performance is validated, illustrating their applicability in integrated sensing and communication (ISAC) scenarios. Our findings contribute to the evolving field of DFRC systems, offering new perspectives and solutions for the challenges in array selection and beamforming optimisation.

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arXiv2026

Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems II

This study introduces an innovative beamforming design approach that incorporates the reliability of antenna array elements into the optimization process, termed "antenna health-aware selective beamforming". This method strategically focuses transmission power on more reliable antenna elements, thus enhancing system resilience and operational integrity. By integrating antenna health information and individual power constraints, our research leverages advanced optimization techniques such as the Group Proximal-Gradient Dual Ascent (GPGDA) to efficiently address nonconvex challenges in sparse array selection. Applying the proposed technique to a Dual-Functional Radar-Communication (DFRC) system, our findings highlight that increasing the sparsity promotion weight ($ρ_s$) generally boosts spectral efficiency and communication data rate, achieving perfect system reliability at higher $ρ_s$ values but also revealing a performance threshold beyond which further sparsity is detrimental. This underscores the importance of balanced sparsity in beamforming for optimizing performance, particularly in critical communication and defense applications where uninterrupted operation is crucial. Additionally, our analysis of the time complexity and power consumption associated with GPGDA underscores the need for optimizing computational resources in practical implementations.

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arXiv2026

Communication Security and Sensing Privacy in FMCW-Based ISAC Through Signal Modulation

This study proposes a novel radar-centric signaling design and architecture for secure integrated sensing and communication (ISAC) systems. The proposed framework is designed to provide robust physical layer security for data transmission while simultaneously enhancing sensing privacy. It employs index modulation and phase coding over frequency-modulated continuous-wave radar (FMCW) chirps, where index modulation (IM) provides an outer layer of data security, and we explicitly design the phase coding (PC) to perturb the resulting signal's ambiguity function (AF) to enhance sensing privacy. This design reduces the risk of unauthorized surveillance by rendering target velocity estimation practically infeasible for unauthorized passive sensing hardware (i.e., a sensing eavesdropper, S-Eve) and significantly impairing its range estimation capabilities. Furthermore, this study also presents the transmitter and receiver architectures required for effective modulation and demodulation of the proposed ISAC signaling and for performing sensing at the legitimate sensing hardware. Simulation results show that the proposed approach achieves high data throughput while enhancing communication security and sensing privacy.

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arXiv2026

FMCW-Based Integrated Sensing and Communication System: Design, Implementation, and Experimental Measurements

This study proposes a radar-centric integrated sensing and communication (ISAC) system utilizing a two-layer modulation scheme for vehicular networks. Frequency-modulated continuous wave (FMCW) chirps are jointly modulated via phase modulation (PM) and index modulation (IM) to transmit data while maintaining sensing as the primary function. To support this, a novel radar signal processing technique is developed to mitigate the impacts of IM and PM on sensing accuracy, alongside a communication receiver architecture designed to successfully demodulate IM and PM data within FMCW chirps. System performance is evaluated through simulations in the 2.4 GHz and 24 GHz bands under Doppler effects, achieving communication throughputs of 25 Mbps and 50 Mbps, respectively. Furthermore, a proof-of-concept hardware implementation is realized, and experimental measurements via a loopback cable are performed to verify the feasibility of the architecture. Finally, it evaluates the fundamental trade-off between communication throughput, sensing accuracy, and out-of-band emission, demonstrating the system's flexibility to dynamically adjust waveform parameters to meet varying operational requirements.

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arXiv2026

Geometry-Structured Channel Reconstruction for Conventional and Fluid Antenna Systems: Bayesian Inference and Fundamental Limits

Accurate channel state information (CSI) acquisition is critical for exploiting the spatial flexibility of fluid antenna systems (FASs). However, port selection and transmission optimization require CSI over a large number of candidate port positions, making direct port-wise estimation prohibitively costly in terms of pilot overhead. This paper addresses this challenge through geometry-structured channel reconstruction, which exploits the fact that the port-domain CSI can be parameterized by a small number of dominant propagation paths. We first establish fundamental mean square error (MSE) and normalized MSE (NMSE) benchmarks for both geometry-structured and unstructured channel reconstruction, providing analytical references for evaluating the intrinsic benefit of geometric modeling in conventional antenna systems and FASs. Motivated by the strong spatial correlation induced by densely distributed fluid antenna ports, we further propose a Bayesian reconstruction framework, termed geometry-structured expectation-maximization approximate message passing (GS-EM-AMP). The proposed algorithm incorporates geometric channel structure into the EM-AMP procedure and adaptively learns unknown statistical parameters from noisy observations. Numerical results demonstrate that GS-EM-AMP achieves near-bound reconstruction accuracy while maintaining strong robustness against steering-domain correlation, thereby offering an efficient and reliable solution for large-scale CSI acquisition in FASs.

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arXiv2026

Constellation Selection and Power Control for OFDM-based ISAC: From Theory to Prototype

Integrated sensing and communication (ISAC) techniques can leverage existing, wide-coverage communication networks to perform sensing tasks, enabling large-scale and low-cost target sensing. However, the inherent randomness of communication data payloads introduces undesired sidelobes in the ambiguity function that may degrade target detection and parameter estimation performance. This paper develops a communication-centric ISAC framework that is standards-compliant and compatible with existing devices. Specifically, we propose a low-complexity constellation selection scheme over a finite, off-the-shelf alphabet, achieving an efficient sensing-communication trade-off without custom waveforms or frame-structure changes. To this end, we analyze two classical sensing receivers including matched filtering (MF) and reciprocal filtering (RF) for ranging measurements, and derive closed-form sensing laws that link constellation statistics to sensing performance. Under any finite-alphabet constellation combination, MF sidelobes depend on the weighted sum of the kurtosis values of the per-subcarrier constellations, while RF noise enhancement depends on the inverse second moment of the transmit symbol, providing a tractable expression for tuning the sensing-communication trade-off. The analysis extends to multi-symbol coherent integration and achieves the expected processing gain. We prove that in flat-fading channels, any Pareto-optimal solution activates no more than three constellations. For frequency-selective channels, a bilevel algorithm with closed-form inner updates attains near-optimal performance while sharply reducing computational complexity. We validate the entire theoretical pipeline with numerical simulations as well as experimental results.

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arXiv2026

DAFT-s-AFDM Enabled ISAC Systems: Ambiguity Function Analysis and Waveform Design

Discrete affine Fourier transform spread affine frequency division multiplexing (DAFT-s-AFDM) is a promising waveform for integrated sensing and communication (ISAC) due to its low peak-to-average power ratio, robustness to Doppler shifts, and reduced multiuser interference in the uplink transmission. This paper presents a comprehensive ambiguity function (AF) analysis of DAFT-s-AFDM and derives the closed-form expression for the AF magnitude expectation. Several key insights into the impact of DAFT-s-AFDM parameters on ISAC performance are revealed, thus providing concrete guidance for the subsequent waveform design. Building on these insights, a novel probabilistic constellation shaping (PCS) framework is proposed for ISAC waveform enhancement, where the communication throughput and the sensing AF characteristics are jointly optimized by addressing a multi-objective problem. An efficient algorithm based on a closed-form bit error rate expression is developed to obtain the Pareto-optimal solutions. Extensive simulations validate the theoretical results and that the proposed PCS-enhanced DAFT-s-AFDM can significantly outperform the classical counterparts, achieving a superior and highly controllable tradeoff between the dual-functional performances.

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arXiv2026

CP-OFDM Achieves Lower Ranging CRB Than Frequency-Spread Waveforms in the Large-Sample Regime

The inherent randomness of communication symbols creates a fundamental tension in Integrated Sensing and Communications (ISAC). On the one hand, they enable data transmission while allowing sensing to fully reuse communication resources. On the other hand, their randomness induces waveform-dependent fluctuations that directly affect sensing accuracy. This paper investigates a foundational question arising from this tradeoff: \textit{How does the modulation waveform affect the ranging Cramér--Rao Bound (CRB) when sensing reuses random data symbols?} We address this question by revealing a structural factorization of the Fisher information matrix (FIM) for joint delay-amplitude estimation, which separates the deterministic Jacobian of the target geometry from the random frequency-domain signal power induced by the data symbols. This structure yields a Jensen-type universal lower bound on the CRB, which is exactly attained by CP-OFDM under PSK constellations. For QAM and broader sub-Gaussian constellations, we develop an asymptotic perturbation analysis of the inverse FIM and prove that, when the number of transmitted symbols $N$ grows large, CP-OFDM achieves a lower ranging CRB than any frequency-spread orthogonal waveform over the almost-sure event where the random FIM is invertible. This superiority is further extended to amplitude estimation and full joint delay-amplitude estimation. We also characterize the local geometry of the stochastic CRB minimization problem over the unitary group. The analysis reveals that CP-OFDM is a stationary point for finite $N$, and its Riemannian Hessian is positive semidefinite for sufficiently large $N$, establishing its asymptotic local optimality. Numerical results confirm that OFDM outperforms representative waveforms including SC, OTFS, and AFDM.

cs.IT↗

arXiv2026

ISAC for AI: A Trade-off Framework Across Data Acquisition and Transfer in Federated Learning

In this paper, we propose a resource allocation framework for federated learning (FL) in integrated sensing and communication (ISAC) systems, where we consider not only the reliability of model transfer through communication, but also the quality of data acquisition through sensing in the first place. Unlike existing works that assume training data is pre-collected or only impose a fixed sensing signal-to-noise ratio (SNR) threshold to reflect data quality, we explicitly characterize the relationship between sensing data quality (measured by sensing SNR), dataset size, and the upload reliability in FL training, and exploit this relationship to allocate resources between sensing and communication under a shared energy budget. This is non-trivial due to the intricate coupling among sensing data quality, transmission reliability, and communication resource allocation; nevertheless, it enables a principled joint optimization framework that directly enhances learning performance. Specifically, we derive a closed-form convergence upper bound that quantifies the joint impact of these factors on the FL optimality gap. Utilizing this upper bound, the original intractable optimization problem can be reformulated into a tractable resource allocation problem that jointly optimizes the sensing transmit power, number of sensing snapshots, and communication transmit power at each device subject to individual energy budget constraints. To solve the reformulated problem, we propose a two-layer optimization algorithm with linear complexity, where the outer layer employs golden section search and the inner layer solves per-device subproblems with closed-form solutions.

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