This paper considers a downlink cell-free multiple-input multiple-output (MIMO) network in which multiple multi-antenna base stations (BSs) serve multiple users via coherent joint transmission. In order to reduce the energy consumption by radio frequency components, each BS selects a subset of antennas for downlink data transmission after estimating the channel state information (CSI). We aim to maximize the sum spectral efficiency by jointly optimizing the antenna selection and precoding design. To alleviate the fronthaul overhead and enable real-time network operation, we propose a distributed scalable machine learning algorithm. In particular, at each BS, we deploy a convolutional neural network (CNN) for antenna selection and a graph neural network (GNN) for precoding design. Different from conventional centralized solutions that require a large amount of CSI and signaling exchange among the BSs, the proposed distributed machine learning algorithm takes only locally estimated CSI as input. With well-trained learning models, it is shown that the proposed algorithm significantly outperforms the distributed baseline schemes and achieves a sum spectral efficiency comparable to its centralized counterpart.

翻译：本文研究了下行无小区多输入多输出（MIMO）网络，其中多个多天线基站（BS）通过相干联合传输服务多个用户。为降低射频组件的能耗，各基站在估计信道状态信息（CSI）后选择部分天线进行下行数据传输。我们旨在通过联合优化天线选择与预编码设计来最大化总频谱效率。为减轻前传开销并实现实时网络操作，我们提出了一种分布式可扩展机器学习算法。具体而言，在每个基站部署卷积神经网络（CNN）进行天线选择，并部署图神经网络（GNN）进行预编码设计。与传统需要大量CSI及基站间信令交换的集中式方案不同，该分布式机器学习算法仅以本地估计的CSI作为输入。仿真表明，经过良好训练的模型使所提算法显著优于分布式基线方案，且可实现与集中式方案相当的总频谱效率。