Deep Learning-based Coordinated Beamforming for Massive MIMO-Enabled Heterogeneous Networks

Coordinated beamforming (CoBF) for multi-user massive multiple-input and multiple-output (MIMO) heterogeneous networks (HetNets) promises for capacity enhancement. However, challenges of energy efficiency (EE) and ultra-low latency are yet to be addressed due to the circuit power and calculation latency heavily depend on the number of transmit antennas. To solve these problems, a maximizing EE algorithm named coordinated beamforming based on convolutional neural networks (CoBFCNN) is proposed in which the advantages of convolutional neural networks and deep learning are fully exploited. Basing on the results of this study, an optimization problem of maximizing EE with lower complexity and lower calculation latency for the different constraints is formulated and exploited for multi-user massive MIMO HetNets. Simulation and analysis show that the proposed CoBFCNN algorithm can significantly satisfy the performance of maximizing EE for the multi-user massive MIMO HetNets with significantly lower complexity and ultra-low calculation latency, especially when the number of antennas is large.