Research on Distributed Communication Congestion Control in C-V2X Based on Action-Restricted Deep Reinforcement Learning

ZHU Xiaokang; OUYANG Min; WANG Yinan; WANG Tong; GAO Shan; XIAO Pei

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Research on Distributed Communication Congestion Control in C-V2X Based on Action-Restricted Deep Reinforcement Learning

更新时间：2026-03-03

- Research on Distributed Communication Congestion Control in C-V2X Based on Action-Restricted Deep Reinforcement Learning
- Chinese Journal on Internet of Things (2026)
- 作者机构：
  
  1.哈尔滨工程大学，黑龙江哈尔滨150001
  2.黑龙江省智能感知与群体智能计算创新研究中心，黑龙江哈尔滨150001
  3.哈尔滨市公安局南岗分局网络安全保卫大队，黑龙江哈尔滨150001
  4.英国萨里大学通信系统研究所，英格兰萨里郡GU2 7XH
- 作者简介：
  
  Wang Tong, wangtong@hrbeu.edu.cn
- 基金信息：
  
  The National Natural Science Foundation of China(62372131);The Key Research and Development Program of Heilongjiang Province(2023ZX01A20)
- DOI：
  CLC：
- Received：03 December 2025，
  
  Revised：2026-02-16，
  
  Accepted：25 February 2026，
- 稿件说明：
移动端阅览
ZHU Xiaokang, OUYANG Min, WANG Yinan, et al. Research on Distributed Communication Congestion Control in C-V2X Based on Action-Restricted Deep Reinforcement Learning[J/OL]. Chinese Journal on Internet of Things, 2026.
DOI：

ZHU Xiaokang, OUYANG Min, WANG Yinan, et al. Research on Distributed Communication Congestion Control in C-V2X Based on Action-Restricted Deep Reinforcement Learning[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

对车联网领域蜂窝车联网（C-V2X

Cellular Vehicle-to-Everything）分布式拥塞控制（DCC

Distributed congestion control）在动态需求下的适应性问题进行了研究，提出了基于行动限域深度Q网络（AC-DQN

Action-confined deep Q-network）的单层和双层架构的自适应DCC算法，实现动态优化车对车(V2V

Vehicle-to-Vehicle)通信参数以提高网络数据包传输率（PDR

Packet Delivery Ratio）。该算法通过在行动域中选定范围限定的子行动域，并在子行动域中确定具体行动，实现自主调整通信参数以适应不同环境，从而缓解信道拥塞。采用集中训练、分布式部署模式，且适用于高维状态动作空间的深度强化学习(DRL

Deep reinforcement learning)算法。仿真结果表明，与现有 C-V2X DCC算法相比，所提算法在PDR 和吞吐量方面表现更优。

Abstract

This study investigates the adaptability issue of distributed congestion control (DCC) for cellular vehicle-to-everything (C-V2X) in vehicular networks under dynamic demands. It proposes adaptive DCC algorithms based on action-confined deep Q-network (AC-DQN) with single-layer and dual-layer architectures

achieving dynamic optimization of vehicle-to-vehicle (V2V) communication parameters to improve the packet delivery ratio (PDR). The algorithm autonomously adjusts communication parameters to adapt to different environments by selecting a range-limited sub-action domain within the action domain and determining specific actions within the sub-action domain

thereby alleviating channel congestion. It employs a centralized training and distributed deployment mode and is suitable for deep reinforcement learning (DRL) algorithms with high-dimensional state-action spaces. Simulation results show that compared to existing C-V2X DCC algorithms

the proposed algorithm performs better in terms of PDR and throughput.

关键词

Keywords

references

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