[论文解读] Energy-Efficient Communication Networks via Multiple Aerial Reconfigurable Intelligent Surfaces: DRL and Optimization Approach

Pyae Sone Aung, Yu Min Park+3 more|arXiv (Cornell University)|Jul 7, 2022

Advanced Wireless Communication Technologies被引用 5

一句话总结

该论文提出了一种多无人机可重构智能表面（ARIS）系统，通过联合优化ARIS部署、反射单元开/关状态、相位偏移和功率控制，在5G/6G网络中提升能效和 spectral efficiency。采用分解方法结合连续凸逼近（SCA）、演员-评论家近端策略优化（AC-PPO）和鲸鱼优化算法（WOA），该方法相比单ARIS系统实现72%的能效提升和58%的和速率增益。

ABSTRACT

In the realm of wireless communications in 5G, 6G and beyond, deploying unmanned aerial vehicle (UAV) has been an innovative approach to extend the coverage area due to its easy deployment. Moreover, reconfigurable intelligent surface (RIS) has also emerged as a new paradigm with the goals of enhancing the average sum-rate as well as energy efficiency. By combining these attractive features, an energy-efficient RIS-mounted multiple UAVs (aerial RISs: ARISs) assisted downlink communication system is studied. Due to the obstruction, user equipments (UEs) can have a poor line of sight to communicate with the base station (BS). To solve this, multiple ARISs are implemented to assist the communication between the BS and UEs. Then, the joint optimization problem of deployment of ARIS, ARIS reflective elements on/off states, phase shift, and power control of the multiple ARISs-assisted communication system is formulated. The problem is challenging to solve since it is mixed-integer, non-convex, and NP-hard. To overcome this, it is decomposed into three sub-problems. Afterwards, successive convex approximation (SCA), actor-critic proximal policy optimization (AC-PPO), and whale optimization algorithm (WOA) are employed to solve these sub-problems alternatively. Finally, extensive simulation results have been generated to illustrate the efficacy of our proposed algorithms.

研究动机与目标

• 解决5G/6G网络中因环境遮挡导致基站（BS）与用户设备（UE）之间链路质量差的非视 Line-of-Sight（LoS）通信问题。
• 通过在无人机（UAV）上部署多个空中可重构智能表面（ARIS），提升下行链路通信的能效与频谱效率。
• 在混合整数、非凸、NP难问题设置下，联合优化ARIS部署、反射单元开/关状态、相位偏移与发射功率。
• 设计一种可扩展、高能效的通信框架，优于单ARIS系统与传统RIS系统。

提出的方法

• 将联合优化问题分解为三个子问题：ARIS部署、反射单元开/关与相位偏移的联合优化、以及功率控制。
• 采用连续凸逼近（SCA）处理ARIS部署与相位偏移子问题中的非凸性。
• 使用演员-评论家近端策略优化（AC-PPO）求解反射单元开/关状态与相位偏移的混合离散-连续优化问题。
• 采用鲸鱼优化算法（WOA）优化多ARIS间的发射功率分配。
• 以交替方式迭代求解三个子问题，逐步收敛至近似最优解。
• 应用Savitzky-Golay滤波器对仿真结果进行平滑处理，以实现更清晰的性能可视化。

实验结果

研究问题

• RQ1 与单ARIS或地面RIS系统相比，部署在无人机上的多个ARIS是否能显著提升阻塞环境下5G/6G下行链路的频谱效率与能效？
• RQ2 在混合整数、非凸设置下，ARIS部署、反射单元状态、相位偏移与功率控制的联合优化对系统性能有何影响？
• RQ3 增加ARIS数量与反射单元数量对平均和速率与能效有何影响？
• RQ4 不同学习率与发射功率水平如何影响基于AC-PPO的优化算法的收敛性与性能？
• RQ5 所提出的多ARIS系统在能效与频谱效率方面，相较于无人机中继与地面RIS系统，优势程度如何？

主要发现

• 尽管无人机中继方案因需主动信号处理而能耗更高，所提算法相比其基准仍实现72%的能效提升。
• 与单ARIS场景相比，系统在平均和速率上提升58%，证明多个ARIS可通过提供多样化传播路径显著增益。
• 与配备四个RIS的地面RIS系统相比，所提多ARIS系统在频谱效率上提升69%，主要得益于无人机移动性带来的更优LoS链路。
• 随着ARIS数量增加，能效显著提升，尤其在低ARIS部署阶段，初始部署具有极高的边际增益。
• AC-PPO算法在学习率为0.0002时收敛效果最佳，获得最高累积奖励；尽管更高学习率收敛更快，但性能下降。
• 随着反射单元数量增加，UE的平均和速率持续提升，证实更大孔径表面可有效增强频谱效率。