Complex-BP-Neural-Network-based Hybrid Precoding for Millimeter Wave Multiuser Massive MIMO Systems
arXiv:1908.00404
The paper proposes a hybrid precoding method for millimeter‑wave massive MIMO that uses a complex‑valued neural network trained to mimic full‑digital zero‑forcing precoding, aiming to reduce RF chain count and energy consumption while maintaining performance.
Abstract
The high energy consumption of massive multi-input multi-out (MIMO) system has become a prominent problem in the millimeter wave(mm-Wave) communication scenario. The hybrid precoding technology greatly reduces the number of radio frequency(RF) chains by handing over part of the coding work to the phase shifting network, which can effectively improve energy efficiency. However, conventional hybrid precoding algorithms based on mathematical means often suffer from performance loss and high computational complexity. In this paper, a novel BP-neural-network-enabled hybrid precoding algorithm is proposed, in which the full-digital zero-forcing(ZF) precoding is set as the training target. Considering that signals at the base station are complex, we choose the complex neural network that has a richer representational capacity. Besides, we present the activation function of the complex neural network and the gradient derivation of the back propagation process. Simulation results demonstrate that the performance of the proposed hybrid precoding algorithm can optimally approximate the ZF precoding.