Fluid Antennas Empowered RIS-Aided Integrated Sensing and Communication Systems

Movable Antenna Enabled RIS-Aided ISAC Systems With Self-Interference Suppression

In this paper, a movable antenna enabled reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, where the monostatic BS attempts to detect a single target while providing communication services for multiple users by utilizing a RIS. To characterize the inherent performance trade-off between sensing and communication, an optimization problem of maximizing the weighted sum of mutual information of sensing target and achievable rate of all users is formulated. Suffering from the self-interference from transmit to receive antennas of BS, it requires a joint design of transceiver beamforming, RIS phase shift and transceiver antenna positions. To address this highly non-convex problem with multiple coupled variables, an effective fractional programming-alternating optimization (FP-AO) framework is proposed, where FP is adopted to transform the original problem into several subproblems, and then an AO-based algorithm is proposed to iteratively solve them. Specifically, closed-form solutions of transceiver beamforming are derived through utilizing the Karush–Kuhn–Tucker conditions, and the transceiver antenna positions are optimized by the proposed two-stage method integrating coarse-grained search and projected gradient ascent. At 40 dBm BS transmit power, the proposed FP-AO framework outperforms the random phase shifts, direct gradient ascent, and fixed-position antenna by 4.2%, 7.4%, and 11.3% in ISAC performance, respectively.

Performance Analysis and Joint Beamforming for Hybrid RIS-Aided Massive MIMO ISAC

In integrated sensing and communication (ISAC) systems, stringent sensing performance constraints can severely limit the power available for communication. Hybrid reconfigurable intelligent surfaces (HRISs) with capabilities of both passive reflection and active signal amplification can significantly improve communication performance in the power-limited regime. This motivates us to analyze and optimize the performance of an HRIS-aided multiple-input-multiple-output (mMIMO) ISAC system. We first estimate the effective uplink/downlink channels using the minimum mean square error method. We then derive closed-form expressions for the communication sum-rate and sensing Cram\'er-Rao lower bound (CRLB). It is shown that under the equal power allocation strategy, the CRLB remains independent of the HRIS coefficients. Then, we formulate a joint optimization problem of power allocation and HRIS beamforming to maximize the communication sum-rate while ensuring specified sensing CRLB constraints. To solve the formulated non-convex problem, we propose an alternating optimization algorithm based on fractional programming and successive convex approximation. Extensive simulations validate our analysis and proposed algorithm, showing significant improvements in both communication and sensing performances enabled by the HRIS. For example, an HRIS with only $4$ active elements offers $97.30\%$ improvement in the communication sum-rate, while ensuring a sensing CRLB constraint of $-30$ dB.

Joint Beamforming and Phase Shifts Design for RIS-Enabled RSMA-ISAC Systems

This letter investigates the sensing-centric design of reconfigurable intelligent surface (RIS)-enabled rate-splitting multiple access-integrated sensing and communication (RSMA-ISAC) systems. Specifically, we propose a new beam-gain approximation method to enhance the sensing beam gain while satisfying communication quality-of-service (QoS) constraints. Since the joint optimization of the beamforming vectors and RIS phase shifts is highly coupled and non-convex, existing methods typically rely on generic optimization solvers involving substantial computational complexity. To address this issue, we propose an efficient constraints-separation-based alternating optimization algorithm (CS-AO). Our proposed algorithm effectively decouples the optimization variables and yields closed-form solutions for all subproblems, thereby significantly reducing the computational burden. Simulation results show that the proposed algorithm achieves sensing beam-gain performance comparable to successive convex approximation (SCA) and semidefinite relaxation (SDR) benchmarks, while achieving more than 120-fold and 50-fold runtime reductions. In addition, compared with conventional space-division multiple access (SDMA) schemes, the proposed design exhibits substantial sensing beam gain.

Discrete Antenna Positioning and Beamforming Design for RIS-Assisted MA Secure ISAC Systems

This paper investigates a reconfigurable intelligent surface (RIS)-assisted movable antenna (MA) secure integrated sensing and communication (ISAC) system. In this architecture, the RIS establishes indirect transmission links to provide communication services for multiple legitimate users, while the high spatial diversity gain of MA is leveraged to enhance system security. Then, we formulate an optimization problem to maximize the system total secrecy rate by jointly optimizing the MA position selection, active beamforming design for base station and passive beamforming design for RIS. The problem also accounts for practical constraints including transmit power budget, sensing beampattern mean square error (MSE), RIS unit-modulus constraint. However, it is challenging to solve this problem due to its non-convexity and strong coupling of the optimization variables. Consequently, we propose an alternating optimization (AO) framework, employing techniques including discrete binary particle swarm optimization (BPSO), successive convex approximation (SCA) and difference-of-convex (DC) programming to transform the optimization problem into convex subproblems. Based on the solution above, the convex sub-problems are solved iteratively until convergence is achieved. Numerical results demonstrate that the proposed algorithm outperforms other baseline algorithms in terms of secure communication performance.

Full-Duplex Fluid-Antennas for Integrated Sensing and Backscatter Communication: A DRL-Based Fairness Design

This letter investigates the efficacy of full-duplex fluid-antennas for a multi-target, multi-tag integrated sensing and backscatter communication system (ISABC) which exploits the extended degrees-of-freedom provided by fluid antennas at both the transmitter and the monostatic receiver. We formulate a max-min fairness-based joint sensing mutual information (MI) and communication rate maximization problem and develop a deep reinforcement learning (DRL)-based solution to effectively solve it. The proposed framework jointly optimizes the transmit precoder, radar signal, receive combiners, and the position vectors of the transmit and receive antennas. Simulation results show that the proposed scheme outperforms the benchmark system in both minimum communication rate and sensing MI, and approaches the performance of the alternating-optimization (AO) benchmark while offering a substantially lower runtime and better scalability.

Robust beamforming design for NOMA system assisted by STAR-RIS