Adaptive Spectrum Management of Cognitive Radio in Intelligent Transportation System
With the rapid development of urban rail transit,the demand of the public in rail transportation to take real-time,reliable and efficient wireless access services,has become the focus of mobile broadband communications.Wireless cognitive radio (CR) over urban rail transit is a newly emerging paradigm that attempts to opportunistically transmit in licensed frequencies,without affecting the pre-assigned users of these bands.To enable this functionality,such a radio must predict its operational parameters,such as transmit power and spectrum.These tasks,collectively called spectrum management,is difficult to achieve in a dynamic distributed environment,in which CR users may only take local decisions,and react to the environmental changes.In this paper,we propose a reinforcement learning based approach for spectrum management.Our approach uses value functions to evaluate the desirability of choosing different transmission parameters,and enables efficient assignment of spectrums and transmit powers by maximizing long-term reward.We then investigate various real-world scenarios,and compare the communication performance using different sets of learning parameters.The results proves our reinforcement learning based spectrum management can significantly reduce interference to licensed users,while maintaining a high probability of successful transmissions in a cognitive radio ad hoc network.
Reinforcement learning,Cognitive Radio,Spectrum Management,Intelligent Transportation System
Cheng Wu Yiming Wang Xiang Qiang Zhaoyang Zhang
School of Urban Rail Transportation, Soochow University, Suzhou 215011, China
国际会议
重庆
英文
765-773
2015-03-21（万方平台首次上网日期，不代表论文的发表时间）