In cell-free multiple input multiple output (MIMO) networks, multiple base stations (BSs) collaborate to achieve high spectral efficiency. Nevertheless, high penetration loss due to large blockages in harsh propagation environments is often an issue that severely degrades communication performance. Considering that intelligent reflecting surface (IRS) is capable of constructing digitally controllable reflection links in a low-cost manner, we investigate an IRS-enhanced downlink cell-free MIMO network in this paper. We aim to maximize the sum rate of all the users by jointly optimizing the transmit beamforming at the BSs and the reflection coefficients at the IRS. To address the optimization problem, we propose a fully distributed machine learning algorithm. Different from the conventional iterative optimization algorithms that require a central processing at the central processing unit (CPU) and large amount of channel state information and signaling exchange between the BSs and the CPU, in the proposed algorithm, each BS can locally design its beamforming vectors. Meanwhile, the IRS reflection coefficients are determined by one of the BSs. Simulation results show that the deployment of IRS can significantly boost the sum user rate and that the proposed algorithm can achieve a high sum user rate with a low computational complexity.

翻译：在无蜂窝多输入多输出（MIMO）网络中，多个基站协同工作以实现高频谱效率。然而，恶劣传播环境中大型障碍物导致的严重穿透损耗常常成为严重降低通信性能的问题。考虑到智能反射面（IRS）能够以低成本方式构建数字化可控反射链路，本文研究了一种IRS增强的下行无蜂窝MIMO网络。我们的目标是通过联合优化基站的发射波束赋形和IRS的反射系数，最大化所有用户的和速率。为解决该优化问题，我们提出了一种全分布式机器学习算法。与传统需要在中央处理单元（CPU）进行中央处理、且需在基站与CPU之间交换大量信道状态信息和信令的迭代优化算法不同，在所提算法中，每个基站可本地设计其波束赋形向量，同时IRS的反射系数由其中一个基站确定。仿真结果表明，部署IRS可显著提升用户和速率，且所提算法能够以较低的计算复杂度实现较高的用户和速率。