论文标题
DDPG学习用于航空RIS辅助MU-MISO通信
DDPG Learning for Aerial RIS-Assisted MU-MISO Communications
论文作者
论文摘要
本文定义了优化下行链路多用户多输入，单个输出（MU-MISO）总和率的问题，可用于空中可重构智能表面（ARIS）服务的地面用户，该表面充当了地面基地的继电器。提出了深层确定性策略梯度（DDPG），以计算基站的最佳主动波束成形矩阵以及ARIS处反射元素的相移以最大化数据速率。模拟结果表明，与深Q学习（DQL）和基线方法相比，所提出的方案的优越性。
This paper defines the problem of optimizing the downlink multi-user multiple input, single output (MU-MISO) sum-rate for ground users served by an aerial reconfigurable intelligent surface (ARIS) that acts as a relay to the terrestrial base station. The deep deterministic policy gradient (DDPG) is proposed to calculate the optimal active beamforming matrix at the base station and the phase shifts of the reflecting elements at the ARIS to maximize the data rate. Simulation results show the superiority of the proposed scheme when compared to deep Q-learning (DQL) and baseline approaches.
