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 distributed mmWave 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.

中文翻译：

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用于毫米波车联网视频缓存的深度强化学习自适应功率控制

毫米波 (mmWave) 频谱上的视频传输和缓存是一项很有前途的技术，可在许多应用中实现高数据速率和高效频率利用，包括分布式车载网络。然而，由于切换持续时间短，校准每个基站对其相关车辆的最佳功率分配和缓存分配对于它们的计算复杂性具有挑战性。迄今为止，大多数视频传输应用都基于在线或离线算法，并且它们仅限于在大规模车载网络中计算和优化低延迟内的高维目标。另一方面，深度强化学习被证明可以通过优化的策略学习阶段来学习这种规模的问题。在本文中，我们提出了基于深度确定性策略梯度的毫米波基站 (mBS) 功率控制和分布式毫米波车联网 (IoV) 网络中对 mBS 的主动缓存分配。仿真结果验证了所提出的缓存方案在各种规模的 IoV 网络中提供的视频质量和播放停顿方面的性能。