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In this paper, we propose an efficient beam training technique formillimeter-wave (mmWave) communications. When some mobile users are under highmobility, the beam training should be performed frequently to ensure theaccurate acquisition of the channel state information. In order to reduce theresource overhead caused by frequent beam training, we introduce a dedicatedbeam training strategy which sends the training beams separately to a specifichigh mobility user (called a target user) without changing the periodicity ofthe conventional beam training. The dedicated beam training requires smallamount of resources since the training beams can be optimized for the targetuser. In order to satisfy the performance requirement with low trainingoverhead, we propose the optimal training beam selection strategy which findsthe best beamforming vectors yielding the lowest channel estimation error basedon the target user's probabilistic channel information. Such dedicated beamtraining is combined with the greedy channel estimation algorithm that accountsfor sparse characteristics and temporal dynamics of the target user's channel.Our numerical evaluation demonstrates that the proposed scheme can maintaingood channel estimation performance with significantly less training overheadcompared to the conventional beam training protocols.

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