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Dohyun Kwon,Joongheon Kim,David A. Mohaisen,Wonjun Lee 한국통신학회 2020 Journal of communications and networks Vol.22 No.4
Video delivery and caching over the millimeter-wave(mmWave) spectrum is a promising technology for high data rateand efficient frequency utilization in many applications, includingdistributed vehicular networks. However, due to the short handoffduration, calibrating both optimal power allocation of each basestation toward its associated vehicles and cache allocation are challenging for their computational complexity. Heretofore, most videodelivery applications were based on on-line or off-line algorithms,and they were limited to compute and optimize high dimensionalobjectives within low-delay in large scale vehicular networks. Onthe other hand, deep reinforcement learning is shown for learningsuch scale of a problem with an optimized policy learning phase. In this paper, we propose deep deterministic policy gradient-basedpower control of mmWave base station (mBS) and proactive cacheallocation toward mBSs in distributed mmWave Internet-of-vehicle(IoV) networks. Simulation results validate the performance of theproposed caching scheme in terms of quality of the provisionedvideo and playback stall in various scales of IoV networks.