Low-Complexity Beam Training for 5G Millimeter-Wave Massive MIMO Systems

We consider a 5G millimeter-wave (mmWave) massive MIMO system with subconnected hybrid structure, where each transceiver unit (TXRU) is connected to a subarray at the transmitter (TX) or the receiver (RX). Beam training is the first step for codebook-based beamforming in the absence of ideal channel state information (CSI). The complexity of the optimal exhaustive search beam training is exponentially increased with the number of TXRUs (or subarrays) at the TX and the RX, which is infeasible in practice. The conventional beam training schemes are mainly based on the idea of mathematical search algorithms and novel beam codebook designs. In this study, we explore a novel low-complexity beam training scheme by fully exploiting the sparse nature of the mmWave channel and the strong correlations of large-scale antenna arrays. Specifically, we propose a training scheme to reduce beam search space for 5G mmWave system, which consists of the following two key stages: 1) a beam subset optimization method from the perspective of array gain for capturing the energy of dominant channel propagation paths, and 2) a class of linear iterative beam search algorithms within the optimized beam subset utilizing the idea of limiting the degrees of freedom of the subarray indices during beam training. The range of the optimized beam subset size and linear search condition are also given. The performances of the proposed two algorithms, beam subset optimization-single subarray linear search and beam subset optimization-subarray pair linear search, are evaluated in a 5G mmWave system. Analysis has shown that the complexity of the proposed scheme is linear with the number of channel propagation paths, which can considerably reduce the complexity of conventional schemes. Simulation results have verified the near-optimal performance of the proposed schemes when compared with exhaustive search as well as other existing training methods.