Research on cross-layer congestion control strategy based on multi-agent reinforcement learning in Ad hoc network

被引：0

作者：

Shao F. ^{[1
]}

Wu C. ^{[1
]}

Wang L.-F. ^{[2
]}

机构：

[1] The School of Telecommunications Engineering, Xidian University

[2] Institute of China Electronic System Engineering Corporation

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2010年 / 32卷 / 06期

关键词：

Ad hoc; Congestion control; Cross-layer design; Game theory; Win-Or-Lose-Fast Policy Hill Climbing(WOLF-PHC);

D O I：

10.3724/SP.J.1146.2009.01092

中图分类号：

学科分类号：

摘要：

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.

引用

页码：1520 / 1524

页数：4

共 5 条

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[4] (2009)
[5] Thulasiraman P., Shen X., Decoupled optimization of interference aware routing and scheduling for throughput maximization in wireless relay mesh networks, 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad hoc Communications and Networks Workshops, pp. 1-6, (2009)

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