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Bandit Inspired Beam Searching Scheme for mmWave High-Speed Train Communications. (arXiv:1810.06150v1 [cs.IT])
来源于:arXiv
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 查看全文>>