论文标题
通过噪声张量完成
Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion
论文作者
论文摘要
在本文中，提出了一种数据驱动的位置辅助方法，以减少MIMO系统中的训练开销，利用侧面信息和现场测量。通过在位置和梁的子集上收集束训练测量值，并提出了混合噪声张量完成（HNTC）算法来构建数据张量，并提出了混合噪声张量的完成（HNTC）算法来预测整个覆盖面积的接收功率，从而利用了空间平滑度和MIMO频道的低级别性能。提出了基于完整的张量梁子集选择（BSS）的推荐算法，以实现快速准确的光束训练。此外，提出了一种基于分组的BSS算法来应对嘈杂位置信息的有害效果。 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 achieve accurate received power predictions, enabling 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梁与最先进的位置辅助梁对准方案相反，该方案在同一配置中实现了54％的正确对准。最后，提出了通过温暖启动的在线HNTC方法，可以减轻计算复杂性50％，而预测准确性没有降解。
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. Besides, 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 achieve accurate received power predictions, enabling 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.
