Competition: Channel Exploration/Exploitation Based on a Thompson Sampling Approach in a Radio Cognitive Environment

Machine learning approaches have been extensively applied in interference mitigation and cognitive radio devices. In this work, we model the spectrum selection process as a multi-arm bandit problem and apply Thompson sampling, a fast and efficient algorithm, to find the best channel in the shortest time interval. The learning algorithm will work on top of a network layer to efficiently route the event information to the sink.