Multi-Agent Reinforcement Learning Based Opportunistic Routing and Channel Assignment for Mobile Cognitive Radio Ad Hoc Network

被引:17
作者
Barve, Sunita S. [1 ]
Kulkarni, Parag [2 ]
机构
[1] Bharati Vidyapeeth Deemed Univ, Coll Engn, Pune, Maharashtra, India
[2] Anomaly Solut Pvt Ltd, Pune, Maharashtra, India
关键词
Cognitive radio network; Routing schemes; Multi-agent; Reinforcement learning; Spectrum management; Mobility model; ALLOCATION; PROTOCOL;
D O I
10.1007/s11036-014-0551-6
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Opportunistic spectrum access using cognitive radio technology enables exploring vacant licensed spectrum bands and thereby improving the spectrum utilization. However, it will have a significant impact on upper layer performance like routing as the reliable knowledge of topology and channel statistics are not available, especially in Mobile Cognitive Radio Ad hoc Network (MCRAN). To address specific requirements of MCRAN, this paper is proposing online opportunistic routing algorithm using multi-agent reinforcement learning. The proposed routing scheme jointly addresses, link and relay selection based on transmission success probabilities. This sophisticated learning mechanism successfully explores opportunities in partially observable and non-stationary environment of MCRAN. Simulation results show the effectiveness of this algorithm.
引用
收藏
页码:720 / 730
页数:11
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