一种面向多模式车辆通信网络的敏捷自适应方法
An Agile Adaptation Method for Multi-mode Vehicle Communication Networks
AI总结：
该研究结合马尔可夫决策过程与 Q-learning，为多模式车辆通信设备构建按场景和业务需求自适应选模的机制，提升动态车联网中的并发性与通信效率。
摘要：
本文聚焦于揭示车辆通信网络中通信模式分配对通信效率的影响。具体而言，采用马尔可夫决策过程与强化学习，根据驾驶场景和业务需求，为多模式通信设备建立敏捷自适应机制。随后，利用 Q-learning 训练该敏捷自适应强化学习模型并输出训练后的模型。通过学习在不同状态下应采取的最佳动作以最大化累积奖励，避免不稳定通信场景中因时延测量不准确而导致自适应效果较差的问题。实验表明，所提方案能够快速适应动态车联网环境，同时实现高并发性与通信效率。
英文摘要：
This paper focuses on discovering the impact of communication mode allocation on communication efficiency in the vehicle communication networks. To be specific, Markov decision process and reinforcement learning are applied to establish an agile adaptation mechanism for multi-mode communication devices according to the driving scenarios and business requirements. Then, Q-learning is used to train the agile adaptation reinforcement learning model and output the trained model. By learning the best actions to take in different states to maximize the cumulative reward, and avoiding the problem of poor adaptation effect caused by inaccurate delay measurement in unstable communication scenarios. The experiments show that the proposed scheme can quickly adapt to dynamic vehicle networking environment, while achieving high concurrency and communication efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
