An Agile Adaptation Method for Multi-mode Vehicle Communication Networks

TL;DR

The paper addresses low-latency, reliable data delivery in multi-mode vehicle communication networks (VCNs) under dynamic driving scenarios. It tackles this by formulating mode selection as a Markov decision process and training an agile adaptation reinforcement learning model (AARLM) via Q-learning to minimize per-link delay constraints $D(i,j)$ and the overall delay $\tau$. Key contributions include an ILP-based problem formulation, a full RL framework with explicit state, action, and reward definitions (including $R_i(s_k,a)$ and $T_{max}$), and a policy evaluation/optimization algorithm that yields a $\pi^*$; numerical results demonstrate rapid adaptation, low latency, and high task completion across varying network sizes and mode counts. The work offers a scalable approach to cross-mode scheduling in VCNs, enabling concurrent multi-user transmissions with guaranteed low latency in CCAD settings.

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.

Figures (6)

• Figure 1: Multi-mode vehicle communication network.
• Figure 2: Agile adaptation reinforcement learning model.
• Figure 3: Transmission delay under 100 nodes.
• Figure 4: Transmission delay under 5 modes.
• Figure 5: Transmission task completion rate under 5 modes.