无人机支持的移动边缘计算系统中移动天线辅助的能量最小化
Movable-Antenna Assisted Energy Minimization in UAV-Enabled Mobile Edge Computing Systems
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中文总结 AI 辅助
研究无人机支持的移动边缘计算系统中移动天线辅助的能量最小化问题，通过联合优化计算资源分配等参数，采用基于块坐标下降法的交替优化算法，实现显著节能。
中文摘要 AI 辅助
受对延迟敏感应用呈指数级增长的推动，移动边缘计算（MEC）已成为关键范式，但其高能耗问题仍需解决。本文探索了一种无人机支持的MEC系统中移动天线辅助的能量最小化方案，其中配备移动天线阵列的无人机作为边缘服务器处理地面消费电子设备卸载的任务。为使系统总能耗最小，联合优化计算资源分配、消费电子设备发射功率、接收波束成形和移动天线位置。针对耦合变量产生的非凸问题，开发了基于块坐标下降法的鲁棒交替优化算法，将问题迭代分解为三个子问题。通过二次变换技术重新制定和优化发射功率与接收波束成形子问题，利用粒子群优化算法优化移动天线阵列位置。数值模拟验证了该方案比传统基准有显著节能效果。
英文摘要
Driven by the exponential growth of latency-sensitive applications, mobile edge computing (MEC) has emerged as a pivotal paradigm, yet mitigating its substantial energy consumption remains critical. This paper explores a movable-antenna (MA) assisted energy minimization scheme in an uncrewed aerial vehicle (UAV)-enabled MEC system, where a UAV equipped with an MA array serves as an edge server to process tasks offloaded from terrestrial consumer electronics (CE) devices. To minimize the total system energy consumption, we jointly optimize computation resource allocation, CE transmit power, receive beamforming, and MA positions. To tackle the resulting non-convex problem with coupled variables, a robust alternating optimization algorithm based on the block coordinate descent method is developed. The problem is iteratively decomposed into three subproblems. In particular, the subproblem of transmit power and receive beamforming is reformulated and optimized using the quadratic transform technique, while the MA array positions are optimized via the particle swarm optimization algorithm. Numerical simulations verify that the proposed scheme achieves substantial energy savings over conventional benchmarks.
