In this paper, a data-driven position-aided approach is proposed to reduce the training overhead in MIMO systems by leveraging side information and on-the-field measurements. A data tensor is constructed by collecting beam-training measurements on a subset of positions and beams, and a hybrid noisy tensor completion (HNTC) algorithm is proposed to predict the received power across the coverage area, which exploits both the spatial smoothness and the low-rank property of MIMO channels. A recommendation algorithm based on the completed tensor, beam subset selection (BSS), is proposed to achieve fast and accurate beam-training. In addition, a grouping-based BSS algorithm is proposed to combat the detrimental effect of noisy positional information. Numerical results evaluated with the Quadriga channel simulator at 60 GHz millimeter-wave channels show that the proposed BSS recommendation algorithm in combination with HNTC achieves accurate received power predictions, which enables beam-alignment with small overhead. Given power measurements on 40% of possible discretized positions, HNTC-based BSS attains a probability of correct alignment of 91%, with only 2% of trained beams, as opposed to a state-of-the-art position-aided beam-alignment scheme which achieves 54% correct alignment in the same configuration. Finally, an online HNTC method via warm-start is proposed, that alleviates the computational complexity by 50%, with no degradation in prediction accuracy.

中文翻译：

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通过噪声张量补全实现快速的位置辅助MIMO波束训练

在本文中，提出了一种数据驱动的位置辅助方法，以通过利用辅助信息和现场测量来减少MIMO系统中的训练开销。通过收集位置和波束的子集上的波束训练测量值来构建数据张量，并提出了一种混合噪声张量完成（HNTC）算法来预测覆盖范围内的接收功率，该算法同时利用了空间平滑性和低噪声性。 MIMO信道的秩属性。提出了一种基于完成张量的推荐算法-波束子集选择（BSS），以实现快速，准确的波束训练。此外，提出了一种基于分组的BSS算法来对抗嘈杂的位置信息的有害影响。用Quadriga通道模拟器在60 GHz毫米波通道上评估的数值结果表明，与HNTC结合使用的BSS推荐算法可以实现准确的接收功率预测，从而可以以较小的开销进行波束对准。在40％可能离散位置上进行功率测量的情况下，基于HNTC的BSS只能通过2％的受训练光束实现91％的正确对准，这与最新的位置辅助光束对准相反该方案在相同配置下可获得54％的正确对准。最后，提出了一种通过热启动的在线HNTC方法，该方法可将计算复杂度降低50％，而预测精度不会降低。这样就可以以较小的开销进行光束对准。在40％可能离散位置上进行功率测量的情况下，基于HNTC的BSS只能通过2％的受训练光束实现91％的正确对准，这与最新的位置辅助光束对准相反该方案在相同配置下可获得54％的正确对准。最后，提出了一种通过热启动的在线HNTC方法，该方法可将计算复杂度降低50％，而预测精度不会降低。这样就可以以较小的开销进行光束对准。在40％可能离散位置上进行功率测量的情况下，基于HNTC的BSS只能通过2％的受训练光束实现91％的正确对准，这与最新的位置辅助光束对准相反该方案在相同配置下可获得54％的正确对准。最后，提出了一种通过热启动的在线HNTC方法，该方法可将计算复杂度降低50％，而预测精度不会降低。