论文标题
在车辆网络中的低延迟服务的深度强化学习横梁跟踪
Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks
论文作者
论文摘要
考虑到不断调整光束方向的必要性，在毫米波频段的车辆网络中的超级可靠和低延迟通信（URLLC）服务提出了重大挑战。常规方法主要基于经典控制理论，例如Kalman滤波器及其变体，主要涉及固定情况。因此，存在严重的应用局限性，尤其是在复杂的动态车辆到所有（V2X）通道中。本文通过首先修改经典方法，例如扩展的卡尔曼滤波器（EKF）和粒子滤清器（PF），对该主题进行了详尽的研究，以实现非平稳场景，然后提出基于加固的方法（RL）基于强化的方法，可以在典型的相互作用场景中实现URLLC的需求。基于商业射线追踪模拟器的仿真结果表明，增强的EKF和PF方法可以通过从培训数据中提取上下文信息来使$ 10 $ MS的数据包延迟延迟超过$ 10 $ MS。
Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constantly adjusting the beam directions. Conventional methods are mostly based on classical control theory, e.g., Kalman filter and its variations, which mainly deal with stationary scenarios. Therefore, severe application limitations exist, especially with complicated, dynamic Vehicle-to-Everything (V2X) channels. This paper gives a thorough study of this subject, by first modifying the classical approaches, e.g., Extended Kalman Filter (EKF) and Particle Filter (PF), for non-stationary scenarios, and then proposing a Reinforcement Learning (RL)-based approach that can achieve the URLLC requirements in a typical intersection scenario. Simulation results based on a commercial ray-tracing simulator show that enhanced EKF and PF methods achieve packet delay more than $10$ ms, whereas the proposed deep RL-based method can reduce the latency to about $6$ ms, by extracting context information from the training data.
