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 v...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.展开更多
在无线接入网络中,上行TCP流会极大地压制下行TCP流,导致严重的上下行信道TCP流不公平问题.本文指出TCP流的ACK包在接入节点下行缓存中的侵占性是上下行TCP不公平问题的直接原因,从限制缓存大小的新角度提出了MBA(Maximum Buffer for AC...在无线接入网络中,上行TCP流会极大地压制下行TCP流,导致严重的上下行信道TCP流不公平问题.本文指出TCP流的ACK包在接入节点下行缓存中的侵占性是上下行TCP不公平问题的直接原因,从限制缓存大小的新角度提出了MBA(Maximum Buffer for ACKs)算法.MBA算法基于上下行TCP流的不公平比例和缓存大小的关系,自适应地调节ACK包的最大缓存空间.理论分析和仿真实验结果表明MBA算法不但能通过限制ACK包的缓存空间实现上下行TCP流公平,还能通过减少无线信道ACK包传输概率提高网络总有效吞吐率.展开更多
文摘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.
文摘在无线接入网络中,上行TCP流会极大地压制下行TCP流,导致严重的上下行信道TCP流不公平问题.本文指出TCP流的ACK包在接入节点下行缓存中的侵占性是上下行TCP不公平问题的直接原因,从限制缓存大小的新角度提出了MBA(Maximum Buffer for ACKs)算法.MBA算法基于上下行TCP流的不公平比例和缓存大小的关系,自适应地调节ACK包的最大缓存空间.理论分析和仿真实验结果表明MBA算法不但能通过限制ACK包的缓存空间实现上下行TCP流公平,还能通过减少无线信道ACK包传输概率提高网络总有效吞吐率.
