This paper proposes a distributed learning-based framework to tackle the sum ergodic rate maximization problem in cell-free massive multiple-input multiple-output (MIMO) systems by utilizing the graph neural network (GNN). Different from centralized schemes, which gather all the channel state information (CSI) at the central processing unit (CPU) for calculating the resource allocation, the local resource of access points (APs) is exploited in the proposed distributed GNN-based framework to allocate transmit powers. Specifically, APs can use a unique GNN model to allocate their power based on the local CSI. The GNN model is trained at the CPU using the local CSI of one AP, with partially exchanged information from other APs to calculate the loss function to reflect system characteristics, capturing comprehensive network information while avoiding computation burden. Numerical results show that the proposed distributed learning-based approach achieves a sum ergodic rate close to that of centralized learning while outperforming the model-based optimization.

翻译：本文提出一种基于分布式学习的框架，通过利用图神经网络来解决无蜂窝大规模多输入多输出系统中的和遍历速率最大化问题。与集中式方案将所有信道状态信息汇集于中央处理单元以计算资源分配不同，所提出的基于分布式GNN的框架利用接入点的本地资源来分配发射功率。具体而言，各接入点可采用统一的GNN模型，基于本地CSI进行功率分配。GNN模型在中央处理单元使用单个接入点的本地CSI进行训练，并通过部分交换其他接入点的信息来计算损失函数以反映系统特性，从而在捕获完整网络信息的同时避免计算负担。数值结果表明，所提出的基于分布式学习的方法实现了接近集中式学习的和遍历速率，同时优于基于模型的优化方案。