Interleaved training has been studied for single-user and multi-user massive MIMO downlink with either fully-digital or hybrid beamforming. However, the impact of channel correlation on its average training overhead is rarely addressed. In this paper, we explore the channel correlation to improve the interleaved training for single-user massive MIMO downlink. For the beam-domain interleaved training, we propose a modified scheme by optimizing the beam training codebook. The basic antenna-domain interleaved training is also improved by dynamically adjusting the training order of the base station (BS) antennas during the training process based on the values of the already trained channels. Exact and simplified approximate expressions of the average training length are derived in closed-form for the basic and modified beam-domain schemes and the basic antenna-domain scheme in correlated channels. For the modified antenna-domain scheme, a deep neural network (DNN)-based approximation is provided for fast performance evaluation. Analytical results and simulations verify the accuracy of our derived training length expressions and explicitly reveal the impact of system parameters on the average training length. In addition, the modified beam/antenna-domain schemes are shown to have a shorter average training length compared to the basic schemes.

翻译：针对采用全数字或混合波束赋形的单用户及多用户大规模MIMO下行链路，已有研究探讨了交错训练方法。然而，信道相关性对其平均训练开销的影响鲜有涉及。本文通过挖掘信道相关性来改进单用户大规模MIMO下行链路的交错训练。针对波束域交错训练，我们提出了一种通过优化波束训练码本的改进方案。基于已训练信道值动态调整基站天线训练顺序的改进方案，也提升了基本天线域交错训练的性能。针对相关信道下的基本与改进波束域方案以及基本天线域方案，我们推导了平均训练长度的精确与简化近似闭式表达式。针对改进天线域方案，我们提供了基于深度神经网络的快速性能评估近似方法。分析结果与仿真验证了所推导训练长度表达式的准确性，并明确揭示了系统参数对平均训练长度的影响。此外，改进的波束/天线域方案相比基本方案具有更短的平均训练长度。