In millimeter-wave (mmWave) integrated sensing and communication networks, users may be within the coverage of multiple access points (AP), which typically employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. Furthermore, as the antenna aperture increases, users are more likely to be situated in the near-field region of the AP antenna array. This motivates our investigation into the near-field beam training problem to achieve effective positioning services. 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 construct a near-field single-beam training codebook for the uniform planar arrays. Then, the hash functions are chosen independently to construct the multi-arm beam training codebooks for each AP. All APs traverse the predefined multi-arm beam training codeword simultaneously and the multi-AP superimposed signals at the user are recorded. Finally, the soft decision and voting methods are applied to obtain the correctly aligned beams only based on the signal powers. In addition, we logically prove that the traversal complexity is at the logarithmic level. 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. Furthermore, we verify its applicability under the far-field scenario as well.

翻译：在毫米波集成感知与通信网络中，用户可能位于多个接入点的覆盖范围内，这些接入点通常采用大规模天线阵列来缓解障碍物遮挡和路径损耗。然而，大规模阵列会产生笔形波束，这需要更多的训练波束来覆盖目标空间。此外，随着天线孔径增大，用户更可能位于接入点天线阵列的近场区域。这促使我们研究近场波束训练问题，以实现有效的定位服务。针对现有波束训练技术复杂度高、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，我们首先为均匀平面阵列构建近场单臂波束训练码本；其次，独立选择哈希函数为每个接入点构建多臂波束训练码本；然后，所有接入点同步遍历预定义的多臂波束训练码字，并记录用户处多接入点叠加信号；最后，仅基于信号功率应用软判决和投票方法获取正确对准的波束。此外，我们从逻辑上证明了遍历复杂度为对数级别。仿真结果表明，我们提出的近场哈希多臂波束训练方法能达到穷尽波束训练方法96.4%的识别精度，同时大幅降低训练开销。进一步地，我们验证了该方法在远场场景下的适用性。