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High-speed trains (HSTs) are being widely deployed around the world. To meetthe high-rate data transmission requirements on HSTs, millimeter wave (mmWave)HST communications have drawn increasingly attentions. To realize sufficientlink margin, mmWave HST systems employ directional beamforming with largeantenna arrays, which results in that the channel estimation is rathertime-consuming. In HST scenarios, channel conditions vary quickly and channelestimations should be performed frequently. Since the period of eachtransmission time interval (TTI) is too short to allocate enough time foraccurate channel estimation, the key challenge is how to design an efficientbeam searching scheme to leave more time for data transmission. Motivated bythe successful applications of machine learning, this paper tries to exploitthe similarities between current and historical wireless propagationenvironments. Using the knowledge of reinforcement learning, the beam searchingproblem of mmWave HST communications is formulated as a multi-armed bandit(MAB) problem and a bandit inspired beam searching scheme is proposed to reducethe number of measurements as many as possible. Unlike the popular deeplearning methods, the proposed scheme does not need to collect and store amassive amount of training data in advance, which can save a huge amount ofresources such as storage space, computing time, and power energy. Moreover,the performance of the proposed scheme is analyzed in terms of regret. Theregret analysis indicates that the proposed schemes can approach thetheoretical limit very quickly, which is further verified by simulationresults.

Bandit Inspired Beam Searching Scheme for mmWave High-Speed Train Communications