On Myopic Sensing for Multi-Channel Opportunistic Access

We consider a multi-channel opportunistic communication system where the states of these channels evolve as independent and statistically identical Markov chains (the Gilbert-Elliot channel model). A user chooses one channel to sense and access in each slot and collects a reward determined by the state of the chosen channel. The objective is to design a sensing policy for channel selection to maximize the average reward. In this paper, we study the structure, optimality, and performance of the myopic sensing policy. We show that the myopic sensing policy has a simple robust structure that reduces channel selection to a counting procedure and obviates the need for knowing the channel transition probabilities. The optimality of this simple policy is established for the two-channel case and conjectured for the general case based on extensive simulations. The performance of the myopic sensing policy is analyzed, which, based on the optimality of myopic sensing, characterizes the maximum throughput of a multi-channel opportunistic communication system in terms of the number of channels. These results apply to cognitive radio networks for spectrum overlay, opportunistic transmission in fading environments, and resource-constrained jamming and anti-jamming.