Optimality of Myopic Sensing in Multi-Channel Opportunistic Access

TL;DR

This paper proves that a simple myopic sensing policy is optimal for maximizing rewards in multi-channel opportunistic communication systems under certain conditions, simplifying decision-making in complex environments.

Contribution

It establishes the optimality of the myopic policy for a class of multi-channel access problems modeled as POMDPs or restless bandits, under specific conditions.

Findings

Myopic policy is optimal under certain conditions.

The results apply to fading channels and cognitive radio networks.

Simplifies channel selection strategies in resource-constrained environments.

Abstract

We consider opportunistic communications over multiple channels where the state ("good" or "bad") of each channel evolves as independent and identically distributed Markov processes. A user, with limited sensing and access capability, chooses one channel to sense and subsequently access (based on the sensed channel state) in each time slot. A reward is obtained when the user senses and accesses a "good" channel. The objective is to design the optimal channel selection policy that maximizes the expected reward accrued over time. This problem can be generally cast as a Partially Observable Markov Decision Process (POMDP) or a restless multi-armed bandit process, to which optimal solutions are often intractable. We show in this paper that the myopic policy, with a simple and robust structure, achieves optimality under certain conditions. This result finds applications in opportunistic communications in fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and anti-jamming.