Chun-mei Chen, He-song Jiang, Bin Wu, Hong Jiang, and Juan Zhang
Channel selection, multi-armed bandit, upper confidence bound, fast convergence, cognitive radio sensor network
Accurate and fast convergence to the optimal channel is a challenge in a cognitive radio sensor network (CRSN) when multiple cognitive wireless channels coexist. Some traditional wireless channel selection methods can be used to study the optimal channel selection. However, their convergence speed cannot meet the requirements because of vast computation and time accumulation. In this paper, a rapid channel selection strategy based on machine learning called MAB-CQ (multi-armed bandit-channel quality) is proposed. This strategy maps the channel selection problem to the improved multi-armed bandit (MAB) model. In the model, the second users (SUs) and the channels in the CRSN correspond to the players and the arms of MAB, respectively. The optimal channel is determined based on the UCB (upper confidence bound) of MAB-CQ for each player. In addition, the UCB equation is creatively defined to balance the exploration and exploitation problem. At the same time, to reduce the computation complexity, coefficients about the factors are used to narrow down the exploratory scope of our strategy. As a result, an accuracy optimal channel and a fast convergence speed are achieved by iterative execution of MAB-CQ. Extensive experimental results demonstrate that the MAB-CQ can converge to nearly 100% within the 105 time slots. By comparison, MAB-CQ has obvious advantages in cumulative rewards, computational complexity and convergence speed.
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