This work explores the relationship between sensing accuracy and precoding coefficients for edge artificial intelligence (AI) inference in integrated sensing, communication and computation (ISCC) networks. We start by constructing a system model of an over-the-air-empowered ISCC network for edge AI inference, involving distributed edge sensors for feature extraction and an edge server for classification. Based on this model, we introduce a discriminant gain (DG) to characterize sensing accuracy and novelly derive an explicit function of the DG about precoding coefficients, giving valuable insights into precoding design. Guided by this, we propose an effective precoding algorithm to solve a non-convex DG-maximization problem. Simulation results demonstrate that the proposed design achieves up to$15\%$and$10\%$sensing accuracy improvements on synthetic and real-world datasets, respectively, over the conventional scheme at low SNR, thereby validating its effectiveness and superiority for edge AI inference in ISCC networks.

This paper proposes a cooperative cell-free integrated sensing and communication network (CoCF-ISACNet) adopting hybrid beamforming (HBF) architecture, which improves both radar sensing and communication performance. The main contributions of this work are three-fold. First, we introduce a CoCF-ISACNet with energy-efficient HBF architecture. To show the benefits of proposed CoCF-ISACNet, we propose to jointly design the HBF to maximize the network communication capacity while satisfying the constraint of beampattern similarity for radar sensing, which results in a highly dimensional and non-convex problem. Second, to facilitate the joint design, we propose a novel distributed optimization framework called Proximal grAdieNt Decentralized Alternating direction method of multipliers (PANDA). Third, we further adopt the proposed PANDA framework to solve the joint HBF design problem for the CoCF-ISACNet. By using the proposed PANDA framework, all access points (APs) optimize the HBF in parallel, where each AP only requires local channel state information and limited message exchange among the APs. Such framework reduces significantly the computational complexity and thus has pronounced benefits in practical scenarios. Simulation results verify the effectiveness of the proposed algorithm compared with the conventional centralized algorithm and show the remarkable performance improvement of radar sensing and communication by deploying CoCF-ISACNet.

In recent years, security has emerged as a critical aspect of integrated sensing and communication (ISAC) systems. While significant research has focused on secure communications, particularly in ensuring physical layer security, the issue of sensing security has received comparatively less attention. This paper addresses the sensing security problem in ISAC, particularly under the threat of multi-intercept adversaries. We consider a realistic scenario in which the sensing target is an advanced electronic reconnaissance aircraft capable of employing multiple signal interception techniques, such as power detection (PD) and cyclostationary analysis (CA). To evaluate sensing security under such sophisticated threats, we analyze two critical features of the transmitted signal: (i) power distribution and (ii) cyclic spectrum. Further, we introduce a novel ergodic cyclic spectrum metric which leverages the intrinsic mathematical structure of cyclostationary signals to more comprehensively characterize their behavior. Building on this analysis, we formulate a new ISAC design problem that explicitly considers sensing security, and we develop a low-complexity, efficient optimization approach to solve it. Simulation results demonstrate that the proposed metric is both effective and insightful, and that our ISAC design significantly enhances sensing security performance in the presence of multi-intercept threats.

This paper deals with the security problem of covert transmission in integrated sensing and communication (ISAC) systems, which aims to probe a target while simultaneously transmitting confidential signals to a covert communication user in the presence of multiple public communication users. Specifically, the target angular location is unknown and random, and among the public users, there is a warden hostile to the covert user. To realize covert transmission and target localization, we propose a covert beamforming strategy by maximizing the covert rate subject to public communication quality-of-service, transmission covertness, direction-of-angle (DoA) estimation, and power constraints. To tackle the formulated non-convex design problem, we devise a covert beamforming algorithm based on the alternating direction method of multipliers. Simulation results verify the effectiveness and feasibility of the proposed ISAC design in transmission security and DoA estimation.

This work focuses on the synergy of physical layer covert transmission and millimeter wave (mmWave) integrated sensing and communication (ISAC) to improve the performance, and enable secure internet of things (IoT) systems. Specifically, we employ a physical layer covert transmission as a prism, which can achieve simultaneously transmitting confidential signals to a covert communication user equipment (UE) in the presence of a warden and regular communication UEs. We design the transmit beamforming to guarantee information transmission security, communication quality-of-service (QoS) and sensing accuracy. By considering two different beamforming architectures, i.e., fully digital beamforming (FDBF) and hybrid beamforming (HBF), an optimal design method and a low-cost beamforming scheme are proposed to address the corresponding problems, respectively. Furthermore, building on the previously derived algorithm, two robust variants are proposed to address the more challenging case where the warden’s CSI is imperfect. Numerical simulations validate the effectiveness and superiority of the proposed FDBF/HBF algorithms compared with traditional algorithms in terms of information transmission security, communication QoS and target detection performance.

This work focuses on the use of reconfigurable intelligent surface (RIS) in dual-function radar-communication (DFRC) systems to improve communication capacity and sensing precision, and enhance coverage for both functions. In contrast to most of the existing RIS aided DFRC works where the RIS is modeled as a diagonal phase shift matrix and can only reflect signals to half space, we propose a novel beyond diagonal RIS (BD-RIS) aided DFRC system. Specifically, the proposed BD-RIS supports the hybrid reflecting and transmitting mode, and is compatible with flexible architectures, enabling the system to realize full-space coverage and to achieve enhanced performance. To achieve the expected benefits, we jointly optimize the transmit waveform, the BD-RIS matrices, and sensing receive filters, by maximizing the minimum signal-to-clutter-plus-noise ratio for fair target detection, subject to the constraints of the communication quality of service, different BD-RIS architectures and power budget. To solve the non-convex and non-smooth max-min problem, a general solution based on the alternating direction method of multipliers is provided. Numerical simulations validate the efficacy of the proposed algorithm and show the superiority of the BD-RIS aided DFRC system in terms of both communication and sensing compared to conventional RIS aided DFRC.

The over-the-horizon (OTH) radar systems are seriously affected by sea clutter. The precise estimation of sea clutter covariance (SCC) plays an important role in suppressing the sea clutter, especially in the case of limited samples. Towards this end, this letter proposes a robust dictionary learning (DL) based on the SCC reconstruction method to suppress sea clutter with a single observation sample. The DL procedure is formulated as a non-convex optimization problem, which is known to be NP-hard. To address this problem and reconstruct the SCC effectively, we propose a novel alternating proximal method designed for solving ℓ0-norm based problems. Extensive analyses demonstrate that our proposed algorithm exhibits global convergence with a sub-linear convergence rate. Through simulation results, we verify the effectiveness of our proposed algorithm, demonstrating a notable performance improvement compared to conventional methods using single samples. Additionally, we conduct experiments using real sea clutter datasets, further demonstrating the practical applicability of our method.

In the symbol-level precoding (SLP) based wireless systems, the reconfigurable intelligent surface (RIS) is usually configured on a block level, which causes a mismatch to the SLP design in terms of update rate. Although it is expected that updating both the RIS and precoding on the symbol level could boost the system performance, the requirements for synchronization and system overhead will become demanding inevitably. In this paper, we consider the RIS-aided dual-function radar-communication (DFRC) system and investigate the benefit of increasing the RIS updating frequency. We jointly design the SLP and RIS to maximize the target illumination power while satisfying the power budget and the multiuser multiple input single output (MU-MISO) communication quality of service (QoS) requirements, where the RIS is updated multiple times in a block (at a sub-block level). Through the simulation results, we demonstrate the optimized trade-off between system performance and RIS update rate.

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this work, in the presence of extended target and clutters, a HBF design based on the subarray connection architecture is proposed for a multiple-input multiple-output (MIMO) DFRC system. In this HBF, the double-phase-shifter (DPS) structure is embedded to further increase the design flexibility. We derive the communication spectral efficiency (SE) and radar signal-to-interference-plus-noise-ratio (SINR) with respect to the transmit HBF and radar receiver, and formulate the HBF design problem as the SE maximization subjecting to the radar SINR and power constraints. To solve the formulated nonconvex problem, the joinT Hybrid bEamforming and Radar rEceiver OptimizatioN (THEREON) is proposed, in which the radar receiver is optimized via the generalized eigenvalue decomposition, and the transmit HBF is updated with low complexity in a parallel manner using the consensus alternating direction method of multipliers (consensus-ADMM). Furthermore, we extend the proposed method to the multi-user multiple-input single-output (MU-MISO) scenario. Numerical simulations demonstrate the efficacy of the proposed algorithm and show that the solution provides a good trade-off between number of phase shifters and performance gain of the DPS HBF.

This paper deals with the problem of the hybrid beamforming design of wideband orthogonal frequency division multiplexing (OFDM) dual-function radar-communication (DFRC) system, which is expected to achieve a satisfactory user's spectral efficiency and form excellent space-frequency spectrum behavior as well as spatial nulls on the directions of strong signal-dependent interference (such as clutters) simultaneously. For such purpose, we formulate our problem by maximizing the communication spectral efficiency subject to the constraints of radar integrated sidelobe to mainlobe ratio (ISMR) and spatial spectrum nulling (SSN). Due to the fact that the analog beamformer for all subcarriers and digital beamformer for each subcarrier are simultaneously optimized in the wideband OFDM system, the resultant problem is difficult to solve. Towards that end, an efficient algorithm is devised based on the consensus alternating direction method of multipliers (CADMM) framework. Numerical simulation results demonstrate the superiority of the proposed hybrid beamforming algorithm.