This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination. To determine the transmission policy of distributed APs, it is essential to develop a network-wide collaborative optimization mechanism. To address this challenge, we design a cooperative learning (CL) framework which manages computation and coordination strategies of the CP and APs using dedicated deep neural network (DNN) modules. To build a versatile learning structure, the proposed CL is carefully designed such that its forward pass calculations are independent of the number of APs. To this end, we adopt a parameter reuse concept which installs an identical DNN module at all APs. Consequently, the proposed CL trained at a particular configuration can be readily applied to arbitrary AP populations. Numerical results validate the advantages of the proposed CL over conventional non-cooperative approaches.

翻译：本文研究基于学习的去中心化功率控制方法，应用于无蜂窝大规模多输入多输出（MIMO）系统，其中中央处理器（CP）通过前传协调控制接入点（AP）。为了确定分布式AP的传输策略，必须开发一种网络范围的协同优化机制。针对这一挑战，我们设计了一个协作学习（CL）框架，该框架利用专用深度神经网络（DNN）模块管理CP和AP的计算与协调策略。为了构建通用的学习结构，所提出的CL经过精心设计，使其前向传播计算与AP数量无关。为此，我们采用了参数复用概念，在所有AP上安装相同的DNN模块。因此，在特定配置下训练的CL可轻松应用于任意AP规模。数值结果验证了所提出的CL相较于传统非协作方法的优越性。