Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The proposed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam selection decisions of the joint designs with low computational complexities. Since the training of ML algorithms is performed off-line, we propose a well-constructed joint design that combines multiple initializations, iterations, and selection features, as well as beam conflict control, i.e., the same beam cannot be used for multiple users. The numerical results indicate that ML algorithms can retain 99-100% of the original sum-rate results achieved by the proposed well-constructed designs.
翻译:无细胞大型IMIM系统由许多分布式接入点组成,其中含有为用户共同服务的简单组件。在毫米波段中,只能支持有限的一组预定光束。在整合这些技术的网络中,下链接模拟光束选择对于网络和速率最大化来说是一项艰巨的任务。低成本的数字过滤器可以进一步改进网络和速率。在这项工作中,我们提出模拟光束选择和数字过滤器的低成本联合设计。拟议的联合设计比不协调的设计基准要高得多。监督机器学习算法可以有效地接近联合设计中计算复杂度低的波段选择决定的输入-输出映射功能。由于ML算法的培训是离线进行的,我们提出一个精心构建的联合设计,将多个初始化、迭代和选择特性结合起来,以及波段冲突控制,即无法对多个用户使用同样的波段。数字算法显示,ML算法可以保留最初设计结果的99-100%。