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Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion

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Document pages: 13 pages

Abstract: In this paper, a data-driven position-aided approach is proposed to reducethe training overhead in MIMO systems, by leveraging side information andon-the-field measurements. A data tensor is constructed by collectingbeam-training measurements on a subset of positions and beams, and a hybridnoisy tensor completion (HNTC) algorithm is proposed to predict the receivedpower across the coverage area, which exploits both the spatial smoothness andthe low-rank property of MIMO channels. A recommendation algorithm based on thecompleted tensor, beam subset selection (BSS), is proposed to achieve fast andaccurate beam-training. Besides, a grouping-based BSS algorithm is proposed tocombat the detrimental effect of noisy positional information. Numericalresults evaluated with the Quadriga channel simulator at 60 GHz millimeter-wavechannels show that the proposed BSS recommendation algorithm in combinationwith HNTC achieve accurate received power predictions, enabling beam-alignmentwith small overhead: given power measurements on 40 of possible discretizedpositions, 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-aidedbeam-alignment scheme which achieves 54 correct alignment in the sameconfiguration. Finally, an online HNTC method via warm-start is proposed, thatalleviates the computational complexity by 50 , with no degradation inprediction accuracy.

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