Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion

TL;DR

This paper introduces a position-aided tensor completion approach for MIMO beam training that significantly reduces training overhead and improves accuracy by leveraging side information and channel properties.

Contribution

It proposes a hybrid noisy tensor completion algorithm and a beam subset selection method, including a grouping-based variant to handle noisy positional data, for fast and accurate beam alignment.

Findings

Achieves 91% correct beam alignment with only 2% trained beams.

Reduces computational complexity by 50% with warm-start online HNTC.

Outperforms existing position-aided schemes in accuracy and efficiency.

Abstract

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.