Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently. Most of the relevant literature is focused on the single cell setting where a single IRS is deployed and perfect channel state information (CSI) is assumed. In this work, we develop a novel methodology for multi-IRS-assisted multi-cell networks in the uplink. We consider the scenario in which (i) channels are dynamic and (ii) only partial CSI is available at each base station (BS); specifically, scalar effective channel powers from only a subset of user equipments (UE). We formulate the sum-rate maximization problem aiming to jointly optimize the IRS reflect beamformers, BS combiners, and UE transmit powers. In casting this as a sequential decision making problem, we propose a multi-agent deep reinforcement learning algorithm to solve it, where each BS acts as an independent agent in charge of tuning the local UE transmit powers, the local IRS reflect beamformer, and its combiners. We introduce an efficient information-sharing scheme that requires limited information exchange among neighboring BSs to cope with the non-stationarity caused by the coupling of actions taken by multiple BSs. Our numerical results show that our method obtains substantial improvement in average data rate compared to baseline approaches, e.g., fixed UE transmit power and maximum ratio combining.
翻译:在无线网络中,智能反射表面的应用最近引起极大关注,大多数相关文献侧重于单一细胞设置,即部署单一IRS,并假定有完美的频道状态信息;在这项工作中,我们为上行链路中多IRS协助的多细胞网络开发了新方法;我们考虑了以下假设情况:(一) 频道是动态的,而且(二) 每个基地站只有局部的CSI;具体而言,只有一组用户设备(UE)才有规模化的有效频道权力。我们制定了总和率最大化问题,目的是联合优化IRS反射光源、BS组合器和UE传输权力。在提出这一顺序决策问题时,我们建议采用多剂深度强化学习算法加以解决,即每个BS作为独立代理机构,负责调控当地UE传输能力、当地IRS反映的信号信号,及其组合。我们引入了高效的信息共享计划,要求邻近的BS系统之间进行有限的信息交流,以便共同优化IRS反射光镜、BS组合和UE传输能力。我们通过采用不固定的组合方法,通过多层次方法,以最大速度显示我们的平均数据传输率,从而取得最大比例的压率。