Structure and Optimality of Myopic Sensing for Opportunistic Spectrum Access

We consider opportunistic spectrum access for secondary users over multiple channels whose occupancy by primary users is modeled as discrete-time Markov processes. Due to hardware limitations and energy constraints, a secondary user can choose, in each slot, one channel to sense and decide whether to access based on the sensing outcome. The design of sensing strategies that govern channel selections in each slot for optimal throughput performance of the secondary user can be formulated as a partially observable Markov decision process (POMDP). We exploit the structure of this problem when channels are independently and identically distributed. We reveal that the myopic sensing policy has a simple structure: channel selection is reduced to a counting process with little complexity. Further, for the two-channel case, we prove that the myopic sensing policy is in fact the optimal policy. Numerical results have also demonstrated the optimality of the myopic sensing policy when there are more than two channels.