Intelligent Distributed Beam Selection for Cell-Free Massive MIMO Hybrid Precoding

Hybrid precoding in cell-free massive MIMO (CF-mMIMO) faces many challenges, e.g., large cascaded beamspace and high signaling overhead. In this letter, we propose a two-stage distributed beam selection for CF- mMIMO hybrid precoding. Each base station (BS) first learns an efficient beamspace compression with a deep neural network (DNN) from the local received signal strength indicator (RSSI), based on which good beam-selection labels are obtained with a proposed potential game algorithm. By learning the mapping from local RSSI to the corresponding label, a DenseNet-based online local beam selection can be realized at each BS. Simulations verify the effectiveness of our proposed scheme.