论文标题
极度大规模MIMO的分层梁训练：从远场到近场
Hierarchical Beam Training for Extremely Large-Scale MIMO: From Far-Field to Near-Field
论文作者
论文摘要
极大的MIMO（XL-MIMO）是一种未来6G通信的有前途的技术。天线数量的急剧增加会导致电磁传播从远场变为近场。由于近场效应，各个角度和距离的详尽近场训练需要很高的头顶。改进的基于时间延迟结构的快速近场训练方案可以减少开销，但遭受了非常高的硬件成本和由时间延迟电路引起的能源消耗。在本文中，我们提出了一个近场二维（2D）分层梁训练方案，以减少开销，而无需额外的硬件电路。具体而言，我们首先制定了涵盖不同角度和距离覆盖率的多分辨率近场代码字设计问题。接下来，受到数字全息成像技术的相位检索问题的启发，我们提出了基于Gerchberg-Saxton（GS）的算法，以考虑理想的完全数字体系结构来获取理论代码字。基于理论代码字，提出了交替的优化算法来通过考虑混合数字分析体系结构来获取实用的代码字。最后，在多分辨率代码簿的帮助下，我们提出了一个近场二维分层梁训练方案，以大大减少训练开销，这通过广泛的模拟结果得到了验证。
Extremely large-scale MIMO (XL-MIMO) is a promising technique for future 6G communications. The sharp increase in the number of antennas causes electromagnetic propagation to change from far-field to near-field. Due to the near-field effect, the exhaustive near-field beam training at all angles and distances requires very high overhead. The improved fast near-field beam training scheme based on time-delay structure can reduce the overhead, but it suffers from very high hardware costs and energy consumption caused by time-delay circuits. In this paper, we propose a near-field two dimension (2D) hierarchical beam training scheme to reduce the overhead without the need for extra hardware circuits. Specifically, we first formulate the multi-resolution near-field codewords design problem covering different angle and distance coverages. Next, inspired by phase retrieval problems in digital holography imaging technology, we propose a Gerchberg-Saxton (GS)-based algorithm to acquire the theoretical codeword by considering the ideal fully digital architecture. Based on the theoretical codeword, an alternating optimization algorithm is then proposed to acquire the practical codeword by considering the hybrid digital-analog architecture. Finally, with the help of multi-resolution codebooks, we propose a near-field 2D hierarchical beam training scheme to significantly reduce the training overhead, which is verified by extensive simulation results.
