In this paper, we investigate the millimeter-wave (mmWave) near-field beam training problem to find the correct beam direction. In order 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. Then, the random hash functions are chosen to construct the near-field multi-arm beam training codebook. Each multi-arm beam codeword is scanned in a time slot until all the predefined codewords are traversed. Finally, the soft decision and voting methods are applied to distinguish the signal from different base stations and obtain correctly aligned beams. Simulation results show that our proposed near-field HMB training method can reduce the beam training overhead to the logarithmic level, and achieve 96.4% identification accuracy of exhaustive beam training. Moreover, we also verify applicability under the far-field scenario.

翻译：在本文中，我们针对毫米波近场波束训练问题展开研究，旨在确定正确的波束方向。为解决现有波束训练技术复杂度高且识别精度低的问题，我们提出了一种面向近场场景的高效哈希多臂波束训练方案。具体而言，首先基于近场信道的极域稀疏性设计一组稀疏基，然后选择随机哈希函数构建近场多臂波束训练码本，每个多臂波束码字在一个时隙内完成扫描，直至遍历所有预定义码字。最后采用软判决与投票方法区分不同基站的信号，实现波束的精确对准。仿真结果表明，所提出的近场哈希多臂波束训练方法可将波束训练开销降至对数级别，并达到穷举波束训练96.4%的识别精度。此外，我们还在远场场景下验证了该方法的适用性。