Fast Beam Training for Near-Field Communication Systems

In millimeter-wave communications, large-scale antenna arrays are commonly employed to mitigate obstacle occlusion and path loss. However, these large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. This results in the heavy beam training overhead. Furthermore, as the antenna aperture increases, users are more likely to be situated in the near-field region of the base station (BS) antenna array. This motivates our investigation into the beam training problem in the near-field region to achieve efficient beam alignment. To address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first design a set of sparse bases based on the polar domain sparsity of the near-field channel and construct a near-field single-beam training codebook. Then, the hash functions are chosen to construct the near-field multi-arm beam training codebook. Each multi-arm beam training codeword is used in a time slot until the predefined codebook is traversed. Finally, the soft decision and voting methods are applied to distinguish the signal from different BS and obtain the correctly aligned beams. In addition, we provide the logically rigorous proof of computational complexity. Simulation results show that our proposed near-field HMB training method can achieve 96.4% identification accuracy of the exhaustive beam training method and greatly reduce the training overhead to the logarithmic level. Furthermore, we verify its applicability under the far-field scenario as well.