C-V2X通信中资源分配与功率控制联合优化

doi:10.19678/j.issn.1000-3428.0059152

摘要/Abstract

摘要： 在C-V2X通信中，Mode 4资源分配方式使用基于感知的半持续调度（SB-SPS）算法进行资源分配，但该算法以最大功率传输安全消息，在高密度交通流状态下会导致系统的可靠性下降。为对SB-SPS算法进行优化，提出一种基于深度强化学习的联合资源分配与功率控制算法。车辆在感知到信道后，为安全消息选择干扰最小的子信道，并根据信道状态自适应调整传输功率，通过与环境交互学习的方式求解最优的子信道选择方案和功率控制方案。仿真结果表明，与SB-SPS优化算法相比，该算法在高密度公路场景下分组接收率提高5%，有效提升了车间通信的可靠性。

关键词: C-V2X通信, 资源分配, SB-SPS算法, 功率控制, 深度强化学习

Abstract: In C-V2X communications, Mode 4 uses the Sensing Based Semi-Persistent Scheduling(SB-SPS) algorithm for resource allocation.This algorithm transmits messages with the maximum power, which will reduce the reliability of the system in the high-density traffic flow state.To optimize the SB-SPS algorithm, a joint resource allocation and power control algorithm based on Deep Reinforcement Learning(DRL) is proposed.After sensing the channel, the vehicle selects the sub-channel with the least interference and adjusts the transmission power adaptively according to the channel state.Then, it solves the optimal sub-channel selection scheme and power control scheme by interactive learning with the environment.The simulation results show that compared with the existing SB-SPS optimization algorithms, the proposed algorithm can improve the packet reception ratio by 5% in high-density highway scenarios, effectively improving the reliability of vehicle-to-vehicle communication.

Key words: C-V2X communications, resource allocation, SB-SPS algorithm, power control, Deep Reinforcement Learning(DRL)

中图分类号:

TP393

金久一, 邱恭安. C-V2X通信中资源分配与功率控制联合优化[J]. 计算机工程, 2021, 47(10): 147-152.

JIN Jiuyi, QIU Gongan. Joint Optimization of Resource Allocation and Power Control in C-V2X Communications[J]. Computer Engineering, 2021, 47(10): 147-152.

https://www.ecice06.com/CN/Y2021/V47/I10/147

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