Fast position-aided mimo beam training via noisy tensor completion

Tzu Hsuan Chou, Nicolo Michelusi, David J. Love, James V. Krogmeier

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Article number9369831
Pages (from-to)774-788
Number of pages15
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number3
StatePublished - Apr 2021


  • Beam training
  • MIMO communication
  • millimeter wave
  • position-aided
  • sparse learning
  • tensor completion

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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