School of Space Information, Space Engineering University, Beijing 101416, China.
College of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, China.
Sensors (Basel). 2023 Mar 11;23(6):3034. doi: 10.3390/s23063034.
In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.
在本文中，我们考虑了基于可重构智能表面 (RIS) 的集成卫星高空平台地面网络 (IS-HAP-TN)，通过利用 HAP 的稳定性和 RIS 的反射来提高网络性能。具体来说，将反射器 RIS 安装在 HAP 的一侧，以将信号从多个地面用户设备 (UE) 反射到卫星。为了最大化系统和速率，我们联合优化地面 UE 和 RIS 相移矩阵的发射波束赋形矩阵。由于 RIS 反射元件约束的单位模约束，组合优化问题难以通过传统的求解方法有效地解决。基于此，本文研究了深度强化学习 (DRL) 算法，以实现该联合优化问题的在线决策。此外，通过仿真实验验证了所提出的 DRL 算法在系统性能、执行时间和计算速度方面均优于标准方案，从而使实时决策真正成为可能。
