Adaptive channel selection and slot length configuration in cognitive radio

This paper investigates the channel selection and slot time configuration in a cognitive radio network with a number of potential channels. Each channel alternates between ON state (i.e., the primary user is using the channel) and OFF state (i.e., the primary user does not use the channel), and the state evolution process is modeled as a continuous-time Markov process. The traffic parameters (the transition rates) of the Markov process also evolve with time, modeled as a discrete-time Markov process. A secondary user adopts a slotted structure with dynamic slot length. At each slot, the secondary user needs to determine which channel to sense and, if the channel is sensed idle, how long the slot length should be. Considering both the amount of data that the secondary user can transmit and the duration when the secondary user interferes with primary activities, a reward definition is given. Based on the reward definition, an adaptive channel selection and slot length configuration method is proposed, which includes a reward maximization procedure to maximize the achieved reward and an update procedure for the channel state belief vector and traffic parameter state belief vector. Numerical results are given to demonstrate the effectiveness and features of the proposed method.