Adaptive Spectrum Management of Cognitive Radio in Intelligent Transportation System

Abstract

With the rapid development of urban rail transit, the demand of the public in rail transportationto take real-time, reliable and efficient wireless access services, has become the focus of mobilebroadband communications. Wireless cognitive radio (CR) over urban rail transit is a newly emergingparadigm that attempts to opportunistically transmit in licensed frequencies, without affecting the preassignedusers of these bands. To enable this functionality, such a radio must predict its operationalparameters, 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 takelocal decisions, and react to the environmental changes. In this paper, we propose a reinforcementlearning based approach for spectrum management. Our approach uses value functions to evaluate thedesirability of choosing different transmission parameters, and enables efficient assignment of spectrumsand transmit powers by maximizing long-term reward. We then investigate various real-worldscenarios, and compare the communication performance using different sets of learning parameters.The results proves our reinforcement learning based spectrum management can significantly reduceinterference to licensed users, while maintaining a high probability of successful transmissions in acognitive radio ad hoc network.