摘要
针对车联网动态不确定特性、业务类型多元化以及无线通信资源稀缺性,研究了蜂窝车联网车与网络(vehicle-to-network,V2N)和车与车(vehicle-to-vehicle,V2V)链路共存且共享频谱场景下保证业务多指标需求和无线资源有效利用的问题.首先建立多目标优化问题模型来表示蜂窝车联网信道选择和功率控制的决策过程,该问题考虑了网络环境动态变化的影响,旨在实现优化目标V2V链路的性能(即信息年龄、延迟以及传输速率)和V2N链路传输速率之间的权衡.在此基础上,提出了基于多目标深度强化学习的车车通信无线资源分配算法进行神经网络训练和问题求解.通过训练好的神经网络模型可以得到多目标优化问题的帕累托前沿.仿真实验表明,所提出算法能够有效地权衡不同通信链路可实现的性能.与4种有代表性的算法比较,V2V链路信息年龄降低12.0%~17.2%,V2N链路传输速率提升11.4%~21.6%,V2V链路传输成功率提高4.6~13.91个百分点,决策延迟时间降低10.6%~20.3%.
Due to the dynamic uncertainty,diversified service types and scarcity of wireless communication resources in the context of vehicle-to-everything,we explore the challenge of ensuring the requirement for multiple quality of service and the effective utilization of wireless resources in the scenario where V2N(vehicle-to-network)and V2V(vehicle-to-vehicle)links coexist and share spectrum in C-V2X(cellular vehicle-to-everything)networks.First,a multi-objective optimization problem is presented to model the decision-making process of channel selection and power control in C-V2X.The problem considers the impact of dynamic changes in the network environment,aiming to make a balance between the performance of the V2V link(i.e.,age of information,delay,and capacity)and the capacity of the V2N link.On this basis,V2V wireless resource allocation algorithm based on multi-objective deep reinforcement learning is also proposed for training neural networks to solve the problem.Through the trained neural network model,the Pareto frontier of the multi-objective optimization problem can be obtained.Simulation results demonstrate that the proposed algorithm can achieve the near-optimal performance for different communication links.Compared with four representative algorithms,the age of information for V2V link is reduced by 12.0%to 17.2%,the V2N link capacity is increased by 11.4%to 21.6%,the V2V link transmission success rate is increased by 4.6%to 13.9%,and the decision delay time is reduced by 10.6%to 20.3%.
作者
李可
马赛
戴朋林
任婧
范平志
Li Ke;Ma Sai;Dai Penglin;Ren Jing;Fan Pingzhi(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756;School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731;School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756)
出处
《计算机研究与发展》
EI
CSCD
北大核心
2024年第9期2229-2245,共17页
Journal of Computer Research and Development
基金
国家重点研发计划项目(2020YFB1807800)
国家自然科学基金项目(62202392,62172342,U20A20156)
网络与数据安全四川省重点实验室项目(NDS2022-1)
四川省自然科学基金项目(2023NSFSC0459,2022NSFSC0944)
河北省优秀青年基金项目(F2022105003)。
关键词
蜂窝车联网
无线资源分配
信息年龄
深度强化学习
多目标优化
cellular vehicle-to-everything
wireless resource allocation
age of information
deep reinforcement learning
multi-objective optimization
作者简介
李可,1983年生.博士,硕士生导师.CCF会员.主要研究方向为机器学习、分布式系统、车联网,keli@swjtu.edu.cn;马赛,2000年生.硕士研究生.主要研究方向为机器学习、车联网;戴朋林,1990年生.博士,副教授.CCF会员.主要研究方向为车联网、边缘计算、智能交通系统;任婧,1982年生.博士,助理研究员.主要研究方向为网络架构、协议设计、网络建模与优化、网络安全;范平志,1955年生.博士,教授.IEEE会士.主要研究方向为高移动无线通信、大规模随机接入技术、信号设计与编码.
