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Efficient Beam Training and Channel Estimation for Millimeter Wave Communications Under Mobility

Sun Hong Lim,Jisu Bae,Sunwoo Kim,B. Shim,J. Choi

Published 2018 in arXiv.org

ABSTRACT

In this paper, we propose an efficient beam training technique for millimeter-wave (mmWave) communications. In the presence of the mobile users under high mobility, the conventional beam training should be performed more frequently to allow the users to acquire channel state information (CSI) accurately. Since it demands high resource overhead for beam training, we introduce the dedicated beam training protocol which sends the training beams separately to a specific high mobility user (called a target user) without changing the periodicity of the conventional beam training. The dedicated beam training does not require much resource since only a small number of the training beams are sent to the target user. In order to achieve good system performance with low training overhead, we design the optimal beam selection strategy which finds the best beamforming vectors yielding the lowest channel estimation error based on the target user's probabilistic channel information. Such dedicated beam training is combined with the new greedy channel estimator which effectively estimates the mmWave channel accounting for sparse characteristics and dynamics of the target user's channel. Our numerical evaluation demonstrates that the proposed beam training scheme can maintain good channel estimation performance with significantly less training overhead than the conventional beam training protocols.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    arXiv.org

  • Publication date

    2018-04-21

  • Fields of study

    Mathematics, Computer Science, Engineering

  • Identifiers
    arXiv 1804.07973
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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