On Myopic Sensing for Multi-Channel Opportunistic Access: Structure, Optimality, and Performance

TL;DR

This paper analyzes a simple myopic sensing policy for multi-channel opportunistic access, showing its structure, optimality in two-channel cases, and performance benefits in multi-channel systems.

Contribution

It demonstrates that the myopic sensing policy has a simple, robust structure, is optimal for two channels, and performs well in multi-channel scenarios without needing transition probabilities.

Findings

Myopic policy reduces to a round-robin channel selection.

Optimal for two channels, conjectured for more.

Characterizes maximum throughput and scaling behavior.

Abstract

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 problem is to design a sensing policy for channel selection to maximize the average reward, which can be formulated as a multi-arm restless bandit process. 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 round-robin 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 numerical results. 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 and its scaling behavior with respect to the number of channels. These results apply to cognitive radio networks, opportunistic transmission in fading environments, and resource-constrained jamming and anti-jamming.