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| 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 |
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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.
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Received: 17 August 2009
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Corresponding Authors:
Shao Fei
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2010, Vol. 32 
