Research on Cross-layer Congestion Control Strategy Based on Multi-agent Reinforcement Learning in Ad hoc Network
Shao Fei①; Wu Chun①; Wang Li-feng②
①The School of Telecommunications Engineering, Xidian University, Xi’an 710071, China; ②Institute of China Electronic System Engineering Corporation, Beijing 100141, China
Abstract：In the paper, the existence of an Nash equilibrium in the network congestion mode induced by MAC layer competition is proved firstly; Secondly, a cross-layer congestion-control mechanism named WCS is proposed based on WOLF-PHC learning strategy. WCS selects a couple of decoupled node as next-hop nodes at routing layer; Meanwhile, source’s traffic is spitted and forwarded at MAC layer, which improves the space reusing efficiency of link. Simulation result shows that: without any exchanging information, optimum split-flow point of source node will be sought by WOLF-PHC in order to maximize the network throughput; Furthermore, WOLF-PHC will discover new optimum split-flow point in order to adapt to new network environment.
邵 飞; 伍 春; 汪李峰. 基于多Agent强化学习的Ad hoc网络跨层拥塞控制策略[J]. 电子与信息学报, 2010, 32(6): 1520-1524 .
Shao Fei①; Wu Chun①; Wang Li-feng②. Research on Cross-layer Congestion Control Strategy Based on Multi-agent Reinforcement Learning in Ad hoc Network. , 2010, 32(6): 1520-1524 .
2010, Vol. 32 
