CNN-based hybrid precoding design with geometric mean decomposition

Communications over millimeter-wave (mmWave) frequencies is a key technology for the fifth generation (5G) cellular networks due to the large bandwidth available at mmWave bands. The short wavelength of mmWave bands enables large antenna arrays to be placed on the transceivers which forms massive multiple-input multiple-output (MIMO). Massive MIMO with conventional fully-digital (FD) beamforming is difficult to be implemented due to high power consumption and hardware cost. One of the most effective solutions to this problem is hybrid beamforming which can be used to balance the beamforming gain, hardware implementation cost, and the power consumption. However, due to the non-convex constraints imposed by phase shifters, finding the global optima for the hybrid beamforming system is very challenging with high computational complexity. To address this issue, deep learning (DL)-based hybrid precoding with geometric mean decomposition (GMD) algorithm for narrowband mmWave massive MIMO system is proposed in this paper, where it can directly estimate the hybrid analog and digital precoders (combiners) from a given optimal FD precoder (combiner). Simulation results demonstrated that the proposed hybrid precoding model can more accurately approximate the FD precoding performance