An Agile Adaptation Method for Multi-mode Vehicle Communication Networks

TL;DR

This paper introduces an adaptive reinforcement learning-based method for optimizing multi-mode vehicle communication networks, enabling quick adaptation to dynamic environments and improving communication efficiency.

Contribution

It presents a novel reinforcement learning framework using Q-learning for agile mode adaptation in vehicle networks, addressing delay measurement issues in unstable scenarios.

Findings

Fast adaptation to dynamic environments

High concurrency and communication efficiency achieved

Effective handling of unstable communication scenarios

Abstract

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.