arXiv subjects

Christos Masouros

Publications and source records attributed to Christos Masouros.

At least 37 records · Page 2Linked to original sources

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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arXiv2026

Tri-Hybrid Beamforming Design for ISAC Systems with Reconfigurable Antennas

Integrated Sensing and Communication (ISAC) systems require efficient beamforming architectures to jointly support communication and sensing functionalities. To reduce hardware overhead, Hybrid Beamforming (HBF) has been widely studied and shown to achieve performance close to fully digital beamforming under practical hardware constraints. As a promising evolution, Reconfigurable Antenna (RA) technologies have recently emerged to further enhance beamforming Degrees of Freedom (DoFs) by dynamically reconfiguring antenna Electromagnetic(EM) characteristics, yet their integration into ISAC systems remains largely unexplored. In this paper, we investigate an RA-assisted ISAC system and develop a decoupled Triple-Hybrid Beamforming (Tri-HBF) framework that alternatively optimizes digital, analog, and EM beamformers to maximize the communication rate and sensing Signal-to-Clutter-plus-NoiseRatio (SCNR). For both Single-user Single-target (SUST) and Multiple-user Multiple-target (MUMT) scenarios, we first transform the original fractional objectives into fraction-free ones via methods tailored to their respective structures. The resulting problems are then solved via alternating optimization over different variable blocks. Closed-form updates are derived for all variables except the EM beamforming subproblem in the MUMT scenario. To further reduce the complexity introduced by Semidefinite Relaxation (SDR) in EM beamforming, we propose a low-complexity iterative approach across antennas with closed-form updates. Simulation results demonstrate that the proposed scheme significantly outperforms benchmark designs with conventional omnidirectional and directional antennas, achievingalmost 100% improvement in spectrum efficiency and 62.5% reduction in antenna overhead, thereby unveiling the

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arXiv2026

Cramer-Rao Bounds for Activity Detection in Conventional and Fluid Antenna Systems

In this letter, we develop a unified Cramér-Rao bound (CRB) framework to characterize the fundamental performance limits of transmission activity detection in fluid antenna systems (FASs) and conventional multiple fixed-position antenna (FPA) systems. To facilitate CRB analysis applicable to activity indicators, we relax the binary activity states to continuous parameters, thereby aligning the bound-based evaluation with practical threshold-based detection decisions. Closed-form CRB expressions are derived for two representative detection formulations, namely covariance-oriented and coherent models. Moreover, for single-antenna FASs, we obtain a closed-form coherent CRB by leveraging random matrix theory. The results demonstrate that CRB-based analysis provides a tractable and informative benchmark for evaluating activity detection across architectures and detection schemes, and further reveal that FASs can deliver strong spatial-diversity gains with significantly reduced complexity.

cs.IT↗

arXiv2026

Ultra-Massive MIMO with Orthogonal Chirp Division Multiplexing for Near-Field Sensing and Communication Integration

This paper integrates the emerging ultra-massive multiple-input multiple-output (UM-MIMO) technique with orthogonal chirp division multiplexing (OCDM) waveform to tackle the challenging near-field integrated sensing and communication (ISAC) problem. Specifically, we conceive a comprehensive ISAC architecture, where an UM-MIMO base station adopts OCDM waveform for communications and a co-located sensing receiver adopts the frequency-modulated continuous wave (FMCW) detection principle to simplify the associated hardware. For sensing tasks, several OCDM subcarriers, namely, dedicated sensing subcarriers (DSSs), are each transmitted through a dedicated sensing antenna (DSA) within the transmit antenna array. By judiciously designing the DSS selection scheme and optimizing receiver parameters, the FMCW-based sensing receiver can decouple the echo signals from different DSAs with significantly reduced hardware complexity. This setup enables the estimation of ranges and velocities of near-field targets in an antenna-pairwise manner. Moreover, by leveraging the spatial diversity of UM-MIMO, we introduce the concept of virtual bistatic sensing (VIBS), which incorporates the estimates from multiple antenna pairs to achieve high-accuracy target positioning and three-dimensional velocity measurement. The VIBS paradigm is immune to hostile channel environments characterized by spatial non-stationarity and uncorrelated multipath environment. Furthermore, the channel estimation of UM-MIMO OCDM systems enhanced by the sensing results is investigated. Simulation results demonstrate that the proposed ISAC scheme enhances sensing accuracy, and also benefits communication performance.

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arXiv2026

Closed-Loop Integrated Sensing, Communication, and Control for Efficient Drone Flight

Low-altitude wireless networks (LAWN) require drones to follow specific trajectories controlled by ground base stations (GBSs). However, given complex low-altitude channel conditions and limited spectrum and power resources, sensing errors and wireless link unreliability cannot be ignored, leading to trajectory deviations that threaten flight safety. To address this issue, this paper proposes an integrated sensing-communication-control (ISCC) closed-loop trajectory tracking approach, aiming to reveal the coupling mechanisms among communication, sensing, and control during drone flight. In detail, we incorporate sensing errors in trajectory state estimation, packet losses in control command transmission, and finite blocklength transmission effects into the closed-loop dynamics. First, through theoretical analysis, we identify the dominant role of the time-frequency resources allocated to control in ensuring system stability and derive a lower bound on the resources required to guarantee stable operation. Second, to minimize tracking error, we formulate a time-frequency resource allocation optimization problem for the sensing, communication, and control components, subject to constraints on communication rate and closed-loop stability. Accordingly, a solution algorithm based on successive convex approximation is proposed. Third, simulation results indicate that once stability is ensured, system performance is primarily determined by sensing accuracy, with the trajectory tracking error exhibiting an approximately linear dependence on the position error bound. Finally, it is shown that the proposed ISCC scheme avoids trajectory divergence under FBL transmission compared with ISCC designs ignoring control packet loss, and could achieve decimeter-level average tracking accuracy, reducing the error to only 17.37% of that observed in the baseline global navigation satellite system scheme.

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arXiv2026

A Unified Cloud-Edge-Terminal Framework for Multimodal Integrated Sensing and Communication

The transition to 6G calls for tightly integrated sensing and communication to support mission-critical services such as autonomous driving, embodied AI, and high-precision telemedicine. However, most existing ISAC designs rely on a single sensing modality (often RF), which limits environmental understanding and becomes a bottleneck in complex and dynamic scenes. This motivates a shift from single-modal to multimodal ISAC, where heterogeneous sensors (e.g., radar, LiDAR, and cameras) complement each other to improve robustness and semantic awareness. In this article, we first summarize key challenges for multimodal ISAC, including heterogeneous fusion, communication overhead, and scalable system design. We then highlight three enabling technologies: large AI models, semantic communications, and multi-agent systems, and discuss how their combination can enable task-oriented multimodal perception. Building on these insights, we propose a unified cloud-edge-terminal (CET) framework that hierarchically distributes intelligence and supports three adaptive operation modes: global fusion mode (GFM), cooperative relay mode (CRM), and peer interaction mode (PIM). A case study evaluates the framework across three modes, demonstrating that GFM achieves the highest accuracy, PIM minimizes latency, and CRM strikes an optimal balance between performance and efficiency. Finally, we conclude with open research issues and future directions.

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arXiv2026

Uplink Networked Sensing via Multiuser Correlation Exploitation

In this correspondence, we investigate networked sensing in perceptive mobile networks under a bistatic multi-transmitter single-receiver uplink topology, where multiple user equipments (UEs) transmit signals over orthogonal frequency-division multiple access (OFDMA) resources and a single base station performs joint sensing. Uplink clock asynchronism introduces offsets that destroy inter-packet coherence and hinder high-resolution sensing, while multi-user observations exhibit exploitable cross-user correlation. We therefore formulate an asynchronous multi-user uplink OFDMA sensing model and exploit common delay-cluster sparsity across UEs. A line-of-sight (LoS)-referenced calibration first suppresses the offsets, after which a shared-private delay-domain sparse Bayesian learning (SBL) model is used for delay support recovery and user grouping. Doppler and angle of arrival are then estimated from temporal and spatial phase differences. Simulation results show that the proposed scheme outperforms per-user processing, particularly under limited subcarrier budgets and in low signal-to-noise ratio (SNR) regimes.

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arXiv2026

On Discrete Ambiguity Functions of Random Communication Waveforms

This paper provides a fundamental characterization of the discrete ambiguity functions (AFs) of random communication waveforms under arbitrary orthonormal modulation with random constellation symbols, which serve as a key metric for evaluating the delay-Doppler sensing performance in future ISAC applications. A unified analytical framework is developed for two types of AFs, namely the discrete periodic AF (DP-AF) and the fast-slow time AF (FST-AF), where the latter may be seen as a small-Doppler approximation of the DP-AF. By analyzing the expectation of squared AFs, we derive exact closed-form expressions for both the expected sidelobe level (ESL) and the expected integrated sidelobe level (EISL) under the DP-AF and FST-AF formulations. For the DP-AF, we prove that the normalized EISL is identical for all orthogonal waveforms. To gain structural insights, we introduce a matrix representation based on the finite Weyl-Heisenberg (WH) group, where each delay-Doppler shift corresponds to a WH operator acting on the ISAC signal. This WH-group viewpoint yields sharp geometric constraints on the lowest sidelobes: The minimum ESL can only occur along a one-dimensional cut or over a set of widely dispersed delay-Doppler bins. Consequently, no waveform can attain the minimum ESL over any compact two-dimensional region, leading to a no-optimality (no-go) result under the DP-AF framework. For the FST-AF, the closed-form ESL and EISL expressions reveal a constellation-dependent regime governed by its kurtosis: The OFDM modulation achieves the minimum ESL for sub-Gaussian constellations, whereas the OTFS waveform becomes optimal for super-Gaussian constellations. Finally, four representative waveforms, namely, SC, OFDM, OTFS, and AFDM, are examined under both frameworks, and all theoretical results are verified through numerical examples.

cs.IT↗

arXiv2026

Integrated Sensing and Communications Over the Years: An Evolution Perspective

Integrated Sensing and Communications (ISAC) enables efficient spectrum utilization and reduces hardware costs for beyond 5G (B5G) and 6G networks, facilitating intelligent applications that require both high-performance communication and precise sensing capabilities. This survey provides a comprehensive review of the evolution of ISAC over the years. We examine the expansion of the spectrum across RF and optical ISAC, highlighting the role of advanced technologies, along with key challenges and synergies. We further discuss the advancements in network architecture from single-cell to multi-cell systems, emphasizing the integration of collaborative sensing and interference mitigation strategies. Moreover, we analyze the progress from single-modal to multi-modal sensing, with a focus on the integration of edge intelligence to enable real-time data processing, reduce latency, and enhance decision-making. Finally, we extensively review standardization efforts by 3GPP, IEEE, and ITU, examining the transition of ISAC-related technologies and their implications for the deployment of 6G networks.

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