发表机构
School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对6G近场信道估计的高开销问题，提出可移动天线辅助近场系统的分层波束训练策略及对应码本，仿真显示其性能优于传统固定天线与远场方法。
AI 中文摘要
随着第六代（6G）通信系统向更高频段和更大阵列孔径发展，近场范围迅速扩大，使得近场信道估计变得愈发重要且具有挑战性。波束训练已被公认为获取信道状态信息（CSI）的有效方法，但由于球面波的传播特性，波束训练需要在角度域和距离域进行联合搜索，这会导致难以承受的波束训练开销。通过灵活重构天线位置，可移动天线（MA）技术能够充分利用无线信道的空间变化，实现更精准的波束聚焦，从而为高效波束训练设计提供额外灵活性。因此，本文基于MA辅助近场系统，开发了一种兼顾低训练开销与高波束增益的分层波束训练策略，并设计了对应的分层码本。该码本在角度-距离联合域形成聚焦波束，最大化目标区域内的波束增益，同时抑制能量向非目标区域泄漏。仿真结果证实，与传统固定位置天线（FPA）系统及远场波束训练方法相比，该方法具有显著的性能提升。
英文摘要
As sixth-generation (6G) communication systems evolve toward higher frequency bands and larger array apertures, the near-field range expands rapidly, making near-field channel estimation increasingly important and challenging. Beam training has been recognized as an effective approach for channel state information (CSI) acquisition. However, because of the propagation characteristics of spherical waves, beam training needs to perform a joint search in the angle and distance domains, which results in unaffordable beam training overhead. By flexibly reconfiguring antenna positions, movable antenna (MA) technology can fully exploit the spatial variations of wireless channels and achieve more accurate beam focusing, thereby providing additional flexibility for efficient beam training design. Therefore, based on MA-assisted near-field systems, we develop a hierarchical beam training strategy that combines reduced training overhead with high beam gain and design a corresponding hierarchical codebook. This codebook forms focused beams over the joint angle-distance domain, maximizing beam gain within the target region while suppressing energy leakage into non-target regions. Simulation results confirm substantial performance gains of the method over the conventional fixed-position antenna (FPA) system and the far-field beam training method.
Journal refThe 25th International Symposium on Communications and Information Technologies (ISCIT 2026)
