As millimeter-wave (mmWave) MIMO systems adopt larger antenna arrays, near-field propagation becomes increasingly prominent, especially for users close to the transmitter. Traditional far-field beam training methods become inadequate, while near-field training faces the challenge of large codebooks due to the need to resolve both angular and distance domains. To reduce in-band training overhead, prior work has proposed to leverage the spatial-temporal congruence between sub-6 GHz (sub-6G) and mmWave channels to predict the best mmWave beam within a near-field codebook from sub-6G channel estimates. To cope with the uncertainty caused by sub-6G/mmWave differences, we introduce a novel Sub-6G Channel Aided Near-field BEam SelecTion (SCAN-BEST) framework that wraps around any beam predictor to produce candidate beam subset with formal suboptimality guarantees. The proposed SCAN-BEST builds on conformal risk control (CRC), and is calibrated offline using limited calibration data. Its performance guarantees apply even in the presence of statistical shifts between calibration and deployment. Numerical results validate the theoretical properties and efficiency of SCAN-BEST.

翻译：随着毫米波大规模MIMO系统采用更大的天线阵列，近场传播效应日益显著，尤其对于靠近发射机的用户。传统的远场波束训练方法变得不再适用，而近场训练则因需同时分辨角度域和距离域而面临码本规模过大的挑战。为降低带内训练开销，已有研究提出利用Sub-6 GHz与毫米波信道间的空时一致性，通过Sub-6 GHz信道估计来预测近场码本中的最优毫米波波束。为应对Sub-6 GHz/毫米波差异带来的不确定性，我们提出了一种新颖的Sub-6GHz信道辅助近场波束选择框架，该框架可封装任意波束预测器，以生成具有形式化次优性保证的候选波束子集。所提出的SCAN-BEST方法基于保形风险控制理论，并利用有限的校准数据进行离线校准。即使在校准与部署阶段存在统计偏移的情况下，其性能保证依然成立。数值结果验证了SCAN-BEST的理论特性与有效性。