Low-Resolution PSs Based Hybrid Precoding for Multiuser Communication Systems

Large antenna array systems are favored in next-generation wireless communications, as it can offer multiplexing and array gains that enhance the system sum-rate. However, the large antenna array systems often necessitate the use of high-cost and power-hungry radio frequency (RF) devices. To reduce the hardware complexity and avoid the explicit high-dimensional channel estimation, we propose a joint iterative training based hybrid precoding using low-resolution phase shifters (PSs). Different from the existing works based on the predefined codebook, the iterative training is applied for the hybrid architectures. The iterative training converges to the dominant steering vectors that align with the direction of the largest channel gain, thus it can harvest more array gains than the predefined codebook method. In addition, the performance loss induced by the finite phase quantization is analytically investigated for multiple RF chains. Simulation results show that the developed joint iterative training method having a fast convergence can achieve similar array gain compared with the systems equipped with the continuous PSs. Furthermore, the proposed hybrid precoding utilizing low-resolution PSs can offer a sum-rate comparable to the fixed-rank fully-digital multiple-input multiple-output systems, but with limited hardware cost and energy consumption.