In this paper, beam training and beam tracking are investigated for extremely large-scale multiple-input-multiple-output communication systems with partially-connected hybrid combining structures. Firstly, we propose a two-stage hybrid-field beam training scheme for both the near field and the far field. In the first stage, each subarray independently uses multiple far-field channel steering vectors to approximate near-field ones for analog combining. To find the codeword best fitting for the channel, digital combiners in the second stage are designed to combine the outputs of the analog combiners from the first stage. Then, based on the principle of stationary phase and the time-frequency duality, the expressions of subarray signals after analog combining are analytically derived and a beam refinement based on phase shifts of subarrays~(BRPSS) scheme with closed-form solutions is proposed for high-resolution channel parameter estimation. Moreover, a low-complexity near-field beam tracking scheme is developed, where the kinematic model is adopted to characterize the channel variations and the extended Kalman filter is exploited for beam tracking. Simulation results verify the effectiveness of the proposed schemes.

翻译：本文研究了部分连接混合合并结构下的超大规模多输入多输出通信系统中的波束训练与波束跟踪问题。首先，我们提出了一种针对近场和远场的两阶段混合域波束训练方案。在第一阶段，每个子阵列独立使用多个远场信道导向矢量来近似近场导向矢量，以进行模拟合并。为了找到最匹配信道的码字，第二阶段设计的数字合并器用于合并第一阶段模拟合并器的输出。然后，基于平稳相位原理和时频对偶性，推导了模拟合并后子阵列信号的解析表达式，并提出了基于子阵列相移的波束细化方案（BRPSS），该方案具有闭式解，用于高分辨率信道参数估计。此外，还开发了一种低复杂度的近场波束跟踪方案，其中采用运动学模型表征信道变化，并利用扩展卡尔曼滤波器进行波束跟踪。仿真结果验证了所提方案的有效性。