Our new paper “Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion,” coauthored with my student Tzu-Hsuan Chou and colleagues David J. Love and James V. Krogmeier from Purdue University has been accepted in the IEEE Journal of Selected Topics in Signal Processing, Special Issue on Tensor Decomposition for Signal Processing and Machine Learning.
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.