A Multi-User High Accuracy Beam Training Algorithm For MmWave Communication

In mmWave communication system, beam training is an effective way to achieve beam alignment and its objective is to find the the optimal beam which provides the highest beamforming gain. Intuitively, the sub-optimal beams with relatively high beamforming gain are more difficult to be distinguished from the optimal one than others, which indicates that more training resources should be allocated to them. In this paper, an adaptive beam training algorithm especially appropriate for multi-user mmWave systems is proposed. This algorithm works in two stages. In the first stage, it adaptively allocates training resources and progressively rejects beams with poor performance. In the second stage, it uses exhaustive search to find the optimal beam for each user from the remaining search space. Theoretical analysis and numerical simulations show that our proposed algorithm achieves lower misalignment probability compared to the conventional exhaustive-search-based algorithm, and its time overhead is less than a high-accuracy beam training method in multi-user scenario.