基于深度强化学习的无线自组网拥塞控制性能提升方法  

Method of improving performance of congestion control in wirelessAd hoc network based on deep reinforcement learning

作　　者：陈世河 徐彦彦[1] 潘少明[1] Chen Shihe;Xu Yanyan;Pan Shaoming(State Key Laboratory of Information Engineering for Surveying,Mapping&Remote Sensing,Wuhan University,Wuhan 430079,China)

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,武汉430079

出　　处：《计算机应用研究》2023年第7期2138-2145,共8页Application Research of Computers

基　　金：国家自然科学基金资助项目(41871312,42271425);国家重点研发计划资助项目(2022YFB3903404,2022YFB3902804)。

摘　　要：针对现有传统拥塞控制算法难以适应高度动态变化的无线自组网链路环境的问题,提出了一种基于深度强化学习的拥塞控制性能提升方法Enhanced-CC(enhanced congestion control)。通过利用传统拥塞控制算法对拥塞窗口进行初步探测,在此基础上,利用深度强化技术学习链路实时最佳拥塞窗口区间,在传统拥塞控制算法计算的拥塞窗口过大或过小时,对拥塞窗口进行调整,从而使发送速率能够与高度动态变化的链路带宽相匹配,提升传统拥塞控制算法的传输性能。实验结果表明,Enhanced-CC能够大幅度提升BBR、CUBIC、Westwood、Reno等传统拥塞控制算法的性能,同时也优于PCC、PCC Vivace等完全基于学习的拥塞控制算法以及Orca、DeepCC等结合深度强化学习与传统拥塞控制算法方案的性能。Most existing traditional congestion control algorithms are difficult to adapt to the highly dynamic link environment of wireless Ad hoc network.In order to solve the above problem,this paper proposed a method of improving the performance of congestion control based on deep reinforcement learning,Enhanced-CC.It conducted a preliminary detection of the congestion window by using the traditional congestion control algorithm.On this basis,the method used deep reinforcement technology to learn the real-time optimal congestion window range of the link.When the congestion window calculated by the traditional congestion control algorithm was too large or too small,the method adjusted the congestion window,so that the sending rate could match the highly dynamic link bandwidth,and the method could improve the transmission performance of the traditional congestion control algorithm.The experimental results show that Enhanced-CC can significantly improve the performance of traditional congestion control algorithms such as BBR,CUBIC,Westwood,Reno,and is superior to the performance of fully lear-ning based congestion control algorithms such as PCC and PCC Vivace,and the combination of deep reinforcement learning and traditional congestion control algorithms such as Orca and DeepCC.

关 键 词：无线自组网 拥塞控制 深度强化学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]