DRL Enabled Coverage and Capacity Optimization in STAR-RIS-Assisted Networks
DRL Enabled Coverage and Capacity Optimization in STAR-RIS-Assisted Networks
复制标题
DOI：
10.1109/tcomm.2023.3296753
复制
发表时间：
2022-09
影响因子：
8.3
通讯作者：
Xinyu Gao;Wenqiang Yi;Yuanwei Liu;Jianhua Zhang;Ping Zhang
中科院分区：
文献类型：
--
作者：
Xinyu Gao;Wenqiang Yi;Yuanwei Liu;Jianhua Zhang;Ping Zhang
同时发射和反射可重构智能表面(STAR-RISS)是一种很有前途的无源器件，它通过同时发射和反射入射信号来实现全空间覆盖。作为无线通信中的一种新范式，如何分析STAR-RISS的覆盖和容量性能变得至关重要但也是具有挑战性的。为了解决星形RIS辅助网络中的覆盖和容量优化(CCO)问题，提出了一种处理长期影响的多目标近端策略优化(MO-PPO)算法。为了在各个目标之间取得平衡，MO-PPO算法提供了一组逼近Pareto前沿的最优解，其中近似Pareto前沿上的解被视为最优结果。此外，为了提高MO-PPO算法的性能，研究了基于动作值的更新策略(AVUS)和基于损失函数的更新策略(LFUS)。对于AVUS，改进的要点是对覆盖和容量的动作值进行积分，然后更新损失函数。对于LFUS，改进的点是只为覆盖和容量的损失函数分配动态权重，而权重在每次更新时由最小范数求解器计算。数值结果表明，在不同的样本网格个数、不同的STAR-RISS个数、不同的STAR-RISS单元个数和不同的STAR-RISS大小的情况下，所研究的更新策略都优于固定权重MO优化算法。此外，STAR-RIS辅助网络比没有STAR-RISS的传统无线网络获得了更好的性能。此外，在相同的带宽下，毫米波能够提供比低于6 GHz的容量更高的容量，但代价是覆盖范围更小。
Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) is a promising passive device that contributes to full-space coverage via transmitting and reflecting the incident signal simultaneously. As a new paradigm in wireless communications, how to analyze the coverage and capacity performance of STAR-RISs becomes essential but challenging. To solve the coverage and capacity optimization (CCO) problem in STAR-RIS-assisted networks, a multi-objective proximal policy optimization (MO-PPO) algorithm is proposed to handle long-term effects. To strike a balance between each objective, the MO-PPO algorithm provides a set of optimal solutions to approach a Pareto front (PF), where the solution on the approximate PF is regarded as an optimal result. Moreover, in order to improve the performance of the MO-PPO algorithm, two update strategies, i.e., action-value-based update strategy (AVUS) and loss function-based update strategy (LFUS), are investigated. For the AVUS, the improved point is to integrate the action values of both coverage and capacity and then update the loss function. For the LFUS, the improved point is only to assign dynamic weights for both loss functions of coverage and capacity, while the weights are calculated by a min-norm solver at every update. The numerical results demonstrated that the investigated update strategies outperform the fixed weights MO optimization algorithms in different cases, which include a different number of sample grids, the number of STAR-RISs, the number of elements in the STAR-RISs, and the size of STAR-RISs. Additionally, the STAR-RIS-assisted networks achieve better performance than conventional wireless networks without STAR-RISs. Moreover, with the same bandwidth, a millimetre wave is able to provide higher capacity than sub-6 GHz, but at a cost of smaller coverage.