  High-speed trains (HSTs) are being widely deployed around the world. To meet
the high-rate data transmission requirements on HSTs, millimeter wave (mmWave)
HST communications have drawn increasingly attentions. To realize sufficient
link margin, mmWave HST systems employ directional beamforming with large
antenna arrays, which results in that the channel estimation is rather
time-consuming. In HST scenarios, channel conditions vary quickly and channel
estimations should be performed frequently. Since the period of each
transmission time interval (TTI) is too short to allocate enough time for
accurate channel estimation, the key challenge is how to design an efficient
beam searching scheme to leave more time for data transmission. Motivated by
the successful applications of machine learning, this paper tries to exploit
the similarities between current and historical wireless propagation
environments. Using the knowledge of reinforcement learning, the beam searching
problem of mmWave HST communications is formulated as a multi-armed bandit
(MAB) problem and a bandit inspired beam searching scheme is proposed to reduce
the number of measurements as many as possible. Unlike the popular deep
learning methods, the proposed scheme does not need to collect and store a
massive amount of training data in advance, which can save a huge amount of
resources such as storage space, computing time, and power energy. Moreover,
the performance of the proposed scheme is analyzed in terms of regret. The
regret analysis indicates that the proposed schemes can approach the
theoretical limit very quickly, which is further verified by simulation
results.
