Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach
Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach
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DOI：
10.1109/iccworkshops50388.2021.9473585
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Junghoon Kim;Seyyedali Hosseinalipour;Taejoon Kim;D. Love;Christopher G. Brinton
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作者：
Junghoon Kim;Seyyedali Hosseinalipour;Taejoon Kim;D. Love;Christopher G. Brinton
智能反射面（IRSs）在无线网络中的应用近年来备受关注。大多数相关文献都集中在部署单个IRS并假设完美通道状态信息（CSI）的单cell设置上。在这项工作中，我们为上行链路中的多irs辅助多小区网络开发了一种新的方法。我们考虑的场景是(i)频道是动态的，（ii）每个基站（BS）只有部分CSI可用；具体来说，仅来自用户设备子集（UE）的标量有效信道功率。针对IRS反射波束形成器、BS合并器和UE发射功率的共同优化，提出了和速率最大化问题。在将其作为一个顺序决策问题时，我们提出了一个多智能体深度强化学习算法来解决它，其中每个BS作为一个独立的智能体，负责调整本地UE发射功率，本地IRS反射波束形成器及其组合器。我们引入了一种有效的信息共享方案，该方案要求相邻的BSs之间进行有限的信息交换，以应对多个BSs所采取的行动耦合引起的非平定性。我们的数值结果表明，与基线方法相比，我们的方法在平均数据速率方面有了实质性的提高，例如固定UE发射功率和最大比组合。
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.