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.

翻译：在毫米波通信中，常采用大规模天线阵列以减轻障碍物遮挡和路径损耗的影响。然而，大规模阵列产生的笔形波束需要更多训练波束来覆盖目标空间，导致沉重的波束训练开销。此外，随着天线孔径增大，用户更可能位于基站（BS）天线阵列的近场区域。这促使我们研究近场区域的波束训练问题以实现高效波束对准。针对现有波束训练技术复杂度高且识别精度低的问题，我们提出了一种用于近场场景的高效哈希多臂波束（HMB）训练方案。具体而言，首先基于近场信道的极域稀疏性设计稀疏基，并构建近场单波束训练码本；随后选择哈希函数构建近场多臂波束训练码本，每个多臂波束训练码字在时隙中使用直至遍历预定义码本；最后应用软判决与投票方法区分不同基站的信号，获得正确对准的波束。此外，我们给出了计算复杂度的逻辑严谨证明。仿真结果表明，所提近场HMB训练方法能达到穷举波束训练方法96.4%的识别精度，并将训练开销大幅降低至对数级别。同时，我们验证了该方法在远场场景下的适用性。