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
This paper investigates over-the-air (OTA) computation enabled online federated learning (FL) in low-Earth orbit (LEO) satellite networks. Specifically, we consider a dual-layer OTA aggregation architecture, where ground devices upload analog model updates to serving satellites via uplink OTA aggregation, and satellites forward the aggregated signals to a data processing center through the second round OTA aggregation. Then, we formulate a long-term data-utilization maximization problem in which devices continuously collect new data and untrained samples gradually lose freshness. The problem is subject to the satellite beam budget, transmit-power limit, and global mean squared error (MSE) constraint that governs end-to-end aggregation distortion. This yields a coupled mixed-integer nonlinear programming (MINLP) problem, involving tightly coupled discrete beam-hopping decisions and continuous power control. Due to the combinatorial action space and nonconvex constraints, the problem is NP-hard and computationally intractable. Furthermore, the time-varying satellite topology and dynamic data generation render it a sequential decision-making problem, necessitating adaptive online scheduling. To address these issues, we cast the problem as a Markov decision process and develop a proximal policy optimization (PPO)-based deep reinforcement learning framework that jointly optimizes adaptive beam hopping and power control, using an MSE-aware reward to balance data utilization and aggregation accuracy. Numerical simulation results verify that the proposed algorithm consistently outperforms other benchmark schemes, achieving superior long-term data utilization and faster FL convergence while satisfying the MSE requirement.
Zhen-Dong Li, Shao-Jie Wang, Zhou Su et al.· 0 citations
In this paper, we propose a robust beamforming algorithm for reconfigurable holographic surface (RHS)-enhanced uplink covert satellite communication systems. Specifically, the jamming signal is utilized to confuse multiple eavesdropping satellites. We derive the minimum detection error probability (DEP) and the optimal noise parameters to maximize the ability of noise to mask covert transmission. Under this paradigm, we formulate a covert sum rate maximization problem by jointly optimizing the digital beamforming and holographic radiation coefficient while satisfying the transmit power budget, covertness constraints, and radiation coefficient constraints. The problem is inherently non-convex and presents challenges due to the imperfect channel state information (CSI) and the high coupling among variables. Hence, we transform the original non-convex problem into a series of convex approximations by utilizing the Lagrangian duality, quadratic transformation, and alternating optimization. We then propose a robust joint covert beamforming algorithm (RJCB) to achieve near-optimal solutions for holographic and digital beamforming vectors. Finally, simulation results demonstrate that the proposed algorithm exhibits superior robustness and significantly enhanced covert communication capacity compared to benchmark schemes.
Ce Guo, Ying Wang, Zhendong Li et al.· IEEE Transactions on Communi...· 0 citations
This article outlines the fundamental principles of the dual-layer OTA model and introduces the adaptive BH mechanism designed for time-varying topologies, aiming to provide insights for the evolution of ubiquitous non-terrestrial intelligence.
Zhendong Li, Shao-Jie Wang, Zhou Su et al.· 0 citations
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