  In this paper, we propose an efficient beam training technique for
millimeter-wave (mmWave) communications. When some mobile users are under high
mobility, the beam training should be performed frequently to ensure the
accurate acquisition of the channel state information. In order to reduce the
resource overhead caused by frequent beam training, we introduce a dedicated
beam training strategy 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 requires small
amount of resources since the training beams can be optimized for the target
user. In order to satisfy the performance requirement with low training
overhead, we propose the optimal training 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 greedy channel estimation algorithm that accounts
for sparse characteristics and temporal dynamics of the target user's channel.
Our numerical evaluation demonstrates that the proposed scheme can maintain
good channel estimation performance with significantly less training overhead
compared to the conventional beam training protocols.
