Almost Optimal Dynamically-Ordered Channel Sensing and Accessing for Cognitive Networks

For cognitive wireless networks, one challenge is that the status and statistics of the channels' availability are difficult to predict. Numerous learning based online channel sensing and accessing strategies have been proposed to address such challenge. In this work, we propose a novel channel sensing and accessing strategy that carefully balances the channel statistics exploration and multichannel diversity exploitation. Unlike traditional MAB-based approaches, in our scheme, a secondary cognitive radio user will sequentially sense the status of multiple channels in a carefully designed order. We formulate the online sequential channel sensing and accessing problem as a sequencing multi-armed bandit problem, and propose a novel policy whose regret is in optimal logarithmic rate in time and polynomial in the number of channels. We conduct extensive simulations to compare the performance of our method with traditional MAB-based approach. Simulation results show that the proposed scheme improves the throughput by more than 30% and speeds up the learning process by more than 100%.