Spectrum Decision and Transmission Algorithm Based on Reinforcement Learning

In cognitive radio(CR) communications,each channel may have different properties such as bandwidth,interference strength and PU conflict probabilities,etc.It's one of key problems how to select the best channel and transmission strategy according to their own service types.An online learning algorithm based on Q-Learning was proposed to solve the problem of channel selection and adaptive transmission for multi-user and multi-channel CR system.The scheme,although did not know the channel state and PU traffic characteristics,could achieve the best spectrum selection and adaptive transmission strategy through learning interactive experience with environment.In order to verify the effectiveness of the scheme,it was compared with a random spectrum selection and minimum interference selection algorithm.The simulation results show that the online learning scheme realizes the adaptive control of CR,and increases the communication performances of CR.