A Fairness-Enhanced Intelligent MAC Scheme Using Q-Learning-Based Bidirectional Backoff for Distributed Vehicular Communication Networks  

作　　者：Ping Wang Shuai Wang 

机构地区：[1]College of Information Science and Technology,Donghua University,Shanghai 201620,China

出　　处：《Tsinghua Science and Technology》2023年第2期258-268,共11页清华大学学报（自然科学版（英文版）

摘　　要：With the increasing attention to front-edge vehicular communication applications,distributed resource allocation is beneficial to the direct communications between vehicle nodes.However,in highly dynamic distributed vehicular networks,quality of service(QoS)of the systems would degrade dramatically because of serious packet collisions in the absence of sufficient link knowledge.Focusing on the fairness optimization,a Q-learning-based collision avoidance(QCA)scheme,which is characterized by an ingenious bidirectional backoff reward model RQCA corresponding to arbitrary backoff stage transitions,has been proposed in an intelligent distributed media access control protocol.In QCA,an intelligent bidirectional backoff agent based on the Markov decision process model can actively motivate each vehicle agent to update itself toward an optimal backoff sub-intervel BSIopt through either positive or negative bidirectional transition individually,resulting in the distinct fair communication with a proper balance of the resource allocation.According to the reinforcement learning theory,the problem of goodness evaluation on the backoff stage self-selection policy is equal to the problem of maximizing Q function of the vehicle in the current environment.The final decision on BSI_(opt) related to an optimal contention window range was solved through maximizing the Q value or Q_(max).The ε-greedy algorithm was used to keep a reasonable convergence of the Q_(max) solution.For the fairness evaluation of QCA,four kinds of dynamic impacts on the vehicular networks were investigated:mobility,density,payload size,and data rate with a network simulator NS2.Consequently,QCA can achieve fair communication efficiently and robustly,with advantages of superior Jain’s fairness index,relatively high packet delivery ratio,and low time delay.

关 键 词：media access control(MAC) FAIRNESS Q-learning-based collision avoidance(QCA) bidirectional backoff agent vehicular communication networks 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]