发表机构
Hanyang University; University of Southern California（汉阳大学; 南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动天线系统在移动延迟和长期能量预算下的在线吞吐量最大化问题，提出基于李雅普诺夫框架和近端最小化-最大化算法的在线重定位策略，实现预算满足与吞吐量增益。
AI 中文摘要
移动天线（MA）系统通过重新定位天线来改善信道，但每次重新定位都会中断数据传输，中断时间由最大位移决定，并消耗执行器能量，该能量在所有位移中累积。现有设计在单个传输块内优化位置，而移动能量在长期内受到限制，且一个时隙中选择的位置约束了下一个时隙中可达到的位置。我们研究了在移动延迟和长期能量预算下的在线吞吐量最大化问题。利用李雅普诺夫漂移加惩罚框架，我们将长期问题转化为每时隙问题，并通过一种近端最小化-最大化算法求解每个时隙问题，该算法不需要信道统计信息，并且每当天线重新定位不值得消耗能量时，就保持天线静止。我们证明了对于每个信道实现，预算都能得到满足，并且该算法在可控的惩罚内，实现了随时间重新分配预算的吞吐量增益。仿真表明，当重新定位需求在时间上集中时，这种重新分配比逐时隙施加预算能提高吞吐量，而后者（由同一求解器支持）在信道连续变化时更可取，但代价是天线的执行动作数量增加一个数量级。
英文摘要
Movable antenna (MA) systems improve the channel by repositioning antennas, but each repositioning interrupts data transmission for a time set by the largest displacement and consumes actuator energy that accumulates over all displacements. Existing designs optimize the positions within a single transmission block, whereas the movement energy is limited over the long term and the positions chosen in one slot constrain those reachable in the next. We study online throughput maximization under the movement delay and a long-term energy budget. Using the Lyapunov drift-plus-penalty framework, we convert the long-term problem into per-slot problems and solve each by a proximal minorization-maximization algorithm that requires no channel statistics and keeps an antenna stationary whenever its relocation is not worth the energy. We prove that the budget is met for every channel realization and that the algorithm attains, within a controllable penalty, the throughput gain of reallocating the budget over time. Simulations show that this reallocation improves the throughput over imposing the budget slot by slot when the demand for repositioning is concentrated in time, whereas the latter, supported by the same solver, is preferable when the channel varies continuously, at the cost of an order of magnitude more antenna actuation.
CommentsSubmitted to IEEE Transactions on Communications
