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Simulation results demonstrate that compared with benchmark schemes, the proposed algorithm can better guarantee the secrecy rate and effectively track the moving trajectory of the eavesdropper.
An alternating optimization (AO) framework is proposed, 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.
The key advantages of MA-enabled WPNs in enhancing wireless power transfer efficiency, realizing flexible and adaptive beamforming, and improving system robustness and interference resilience are highlighted.
This paper elaborates on the hardware architecture and channel characteristics of MA and introduces the fundamental logic of the GML framework and compares it with existing methods, and discusses the constraint handling strategies for applying the proposed optimization framework to MA networks.
This paper investigates over-the-air (OTA) computation enabled online federated learning (FL) in low-Earth orbit (LEO) satellite networks and develops a proximal policy optimization (PPO)-based deep reinforcement learning framework that jointly optimizes adaptive beam hopping and power control.
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...
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.
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