A novel sense-then-train (STT) scheme is proposed for beam training in near-field multiple-input multiple-output (MIMO) systems. Compared to conventional codebook-based schemes, the proposed STT scheme is capable of not only addressing the complex spherical-wave propagation but also effectively exploiting the additional degrees-of-freedoms (DoFs). The STT scheme is tailored for both single-beam and multi-beam cases. 1) For the single-beam case, the STT scheme first utilizes a sensing phase to estimate a low-dimensional representation of the near-field MIMO channel in the wavenumber domain. Then, in the subsequent training phase, an online learning algorithm is proposed to obtain the optimal beam pair without predefined codebooks or training datasets. 2) For the multi-beam case, based on the single-beam STT, a Gram-Schmidt method is further utilized to guarantee the orthogonality between beams in the training phase. Numerical results unveil that 1) the proposed STT scheme can significantly enhance the beam training performance in the near field compared to the conventional far-field codebook-based schemes, and 2) the proposed STT scheme can perform fast and low-complexity beam training, while achieving a near-optimal performance without full channel state information in both cases.

翻译：本文针对近场多输入多输出（MIMO）系统中的波束训练问题，提出了一种新颖的“感知-训练”（sense-then-train, STT）方案。与传统基于码本的方案相比，所提STT方案不仅能处理复杂的球面波传播问题，还能有效利用额外的自由度。该方案针对单波束和多波束两种场景进行了定制化设计：1）对于单波束场景，STT方案首先利用感知阶段在波数域中估计近场MIMO信道的低维表征；随后在后续训练阶段，提出一种在线学习算法，无需预定义码本或训练数据集即可获得最优波束对。2）对于多波束场景，基于单波束STT方案，进一步采用Gram-Schmidt方法在训练阶段确保波束间的正交性。数值结果表明：1）与传统远场基于码本的方案相比，所提STT方案能显著提升近场波束训练性能；2）所提STT方案在两种场景下均能实现快速低复杂度的波束训练，同时在不依赖完整信道状态信息的情况下接近最优性能。