Deep Learning and Compressed Sensing Based Fast Beam Training for Cell-Free Millimeter Wave System

In millimeter wave (mmWave) systems with the typical two-stage hybrid precoding structure, it is necessary to determine the optimal transmit (TX)-and-receive (RX) beam pairs through beam training. However, existing beam training methods are time-consuming and costly, making them unsuitable for cell-free mmWave systems with a large number of TX-RX pairs. In this paper, we propose a fast and efficient beam training method that adopts a hierarchical codebook design and requires only a small number of wide-sweeping beams to achieve the optimal beam pairs based on deep learning and compressed sensing. Additionally, it simultaneously supports the user-centric cell-free access point (AP) clustering and the corresponding AP beam training process. The experimental results demonstrate that our approach can accurately predict the effective beams with high accuracy while significantly reducing the beam training time and overhead.