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.
Movable antenna (MA) is proposed as an emerging technology for future wireless networks. By leveraging the additional spatial degrees of freedom, MA can proactively reshape the wireless propagation environment, thereby enhancing network performance.However, fully unlocking the potential of MA networks necessitates the joint optimization of MA antenna positioning and beamforming. For this non-convex and highly coupled problem, existing solutions exhibit significant limitations. Therefore, this paper proposes a gradient-based meta learning (GML) optimization framework. Specifically, we first elaborate on the hardware architecture and channel characteristics of MA, based on which we analyze the primary challenges in optimizing MA wireless networks. Subsequently, we introduce the fundamental logic of the GML framework and compare it with existing methods. Furthermore, we discuss the constraint handling strategies for applying the proposed optimization framework to MA networks. A specific case is studied to show the performance of proposed framework based on numerical simulation. Finally, this paper outlines future research directions for both the GML framework and MA wireless networks.
Zhen-Dong Li, Yujie Zhao, Zhou Su et al.· 0 citations
Integrated sensing and communication (ISAC) is a key technology for future wireless networks, calling for hardware-efficient architectures to jointly support communication and sensing. In this paper, a transmissive reconfigurable intelligent surface (TRIS) transceiver is leveraged to enable an ISAC system. Under the considered system model, we investigate transmit beamforming design for the TRIS transceiver to maximize the sum-rate/beampattern gain, subject to the predefined sensing beampattern gain/communication rate thresholds and the per-unit power constraints of the TRIS transceiver. Since the objective functions and constraints are non-convex, the above two optimization problems are highly challenging. To resolve the difficult optimization problems, we combine the fractional programming (FP) method and the majorization-minimization (MM) framework to develop second-order cone programming (SOCP)-based solutions. Since the per-element power constraints introduce a large number of constraints, this increases the complexity of solving the optimization problems. By splitting the coupling constraints and applying the alternating direction method of multipliers (ADMM) framework, we propose two analytic-based algorithms for efficiently updating the beamformer configurations in the sum-rate and beampattern gain maximization problems, respectively. Simulation results demonstrate the convergence and effectiveness of the proposed algorithms, and show that the low-complexity algorithms achieve performance close to the SOCP-based benchmarks with substantially reduced computational complexity.
Yuan Guo, Wen Chen, Yang Liu et al.· 0 citations
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