Cell-free massive multiple-input-multiple-output is promising to meet the stringent quality-of-experience (QoE) requirements of railway wireless communications by coordinating many successional access points (APs) to serve the onboard users coherently. A key challenge is how to deliver the desired contents timely due to the radical changing propagation environment caused by the growing train speed. In this paper, we propose to proactively cache the likely-requesting contents at the upcoming APs which perform the coherent transmission to reduce end-to-end delay. A long-term QoE-maximization problem is formulated and two cache placement algorithms are proposed. One is based on heuristic convex optimization (HCO) and the other exploits deep reinforcement learning (DRL) with soft actor-critic (SAC). Compared to the conventional benchmark, numerical results show the advantage of our proposed algorithms on QoE and hit probability. With the advanced DRL model, SAC outperforms HCO on QoE by predicting the user requests accurately.
翻译:通过协调许多连续接入点,为机上用户一致服务,实现铁路无线通信的严格的经验质量要求(QoE),无细胞型的大规模多投入-多输出产出大都大有希望。一个关键的挑战是如何及时提供理想内容,因为火车速度的提高导致传播环境的急剧变化。在本文中,我们提议在即将到来的为减少终端到终端延迟而进行连贯传输的AP中,积极主动地将可能要求的内容存储在即将到来的AP中。制定了长期的QoE-最大化问题,并提出了两个缓存定位算法。一个基于超光速二次曲线优化(HCO),其他则利用软动作-critic(SAC)进行深度强化学习(DRL)。与常规基准相比,数字结果显示了我们提议的QoE和撞击概率算法的优势。与先进的DRL模型相比,SAC通过准确预测用户请求,在QoE上优于HCO。