For vehicle-to-network communications, handover (HO) management enables vehicles to maintain the connection with the network while transiting through coverage areas of different base stations (BSs). However, the high mobility of vehicles means shorter connection periods with each BS that leads to frequent HOs, hence raises the necessity for optimal HO decision making for high quality infotainment services. Machine learning is capable of capturing underlying pattern via data driven methods to find optimal solutions to complex problems, and much learning-based HO optimization research has been conducted focusing on specific network setups. However, attention still needs to be paid to the actual deployment aspect and standardized datasets or simulation environments for evaluation. This paper proposes a deep reinforcement learning-based HO algorithm using the input parameters that are configurable in the existing measurement report of cellular networks. The performance of the proposed algorithm is evaluated using the well-known network simulator ns-3 with its official LTE module. A realistic network setup in the city center of Glasgow (U.K.) is configured with vehicle trajectories generated by the routes mobility model using Google Maps Directions API. Evaluation results reveal that the proposed algorithm significantly outperforms the A3 RSRP baseline with an average of 25.72% packet loss reduction per HO, suggesting significant improvement in quality of service of phone call and video streaming, etc. The proposed algorithm also has a small implementation cost compared to some state-of-the-art and should be deployed by a software update to a local BS controller.

中文翻译：

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双深度 Q-Learning 车网通信智能切换算法


对于车辆到网络通信，切换（HO）管理使车辆在经过不同基站（BS）的覆盖区域时能够保持与网络的连接。然而，车辆的高移动性意味着与每个基站的连接周期较短，从而导致频繁的 HO，因此提出了为高质量信息娱乐服务制定最佳 HO 决策的必要性。机器学习能够通过数据驱动的方法捕获底层模式，以找到复杂问题的最佳解决方案，并且许多基于学习的 HO 优化研究都集中在特定的网络设置上。但仍需关注实际部署方面以及标准化数据集或模拟环境进行评估。本文提出了一种基于深度强化学习的 HO 算法，该算法使用可在蜂窝网络现有测量报告中配置的输入参数。使用著名的网络模拟器 ns-3 及其官方 LTE 模块来评估所提出算法的性能。格拉斯哥（英国）市中心的真实网络设置配置了使用 Google 地图方向 API 的路线移动模型生成的车辆轨迹。评估结果表明，所提出的算法显着优于A3 RSRP基线，平均每个HO丢包减少25.72%，这表明电话和视频流等的服务质量得到显着改善。所提出的算法的实施成本也很小与一些最先进的技术相比，应该通过软件更新来部署到本地 BS 控制器。