Self-Adaptive Power Control with Deep Reinforcement Learning for Millimeter-Wave Internet-of-Vehicles Video Caching

Abstract

Video delivery and caching over the millimeter-wave (mmWave) spectrum is a promising technology for high data rate and efficient frequency utilization in many applications, including distributed vehicular networks. However, due to the short handoff duration, calibrating both optimal power allocation of each base station toward its associated vehicles and cache allocation are challenging for their computational complexity. Heretofore, most video delivery applications were based on on-line or off-line algorithms, and they were limited to compute and optimize high dimensional objectives within low-delay in large scale vehicular networks. On the other hand, deep reinforcement learning is shown for learning such scale of a problem with an optimized policy learning phase. In this paper, we propose deep deterministic policy gradient-based power control of mmWave base station (mBS) and proactive cache allocation toward mBSs in distributedmmWave Internet-of-vehicle (IoV) networks. Simulation results validate the performance of the proposed caching scheme in terms of quality of the provisioned video and playback stall in various scales of IoV networks.