On Optimality of Myopic Sensing Policy with Imperfect Sensing in Multi-channel Opportunistic Access

TL;DR

This paper analyzes when a simple myopic sensing policy is optimal in multi-channel access systems with imperfect sensing, providing explicit conditions that guarantee optimality in practical scenarios.

Contribution

It derives closed-form conditions for the optimality of the myopic policy under imperfect sensing in a class of RMAB problems, extending understanding of decision-making in complex systems.

Findings

Identifies conditions where myopic policy is optimal despite sensing errors.

Provides analytical expressions for policy optimality in practical utility functions.

Results are broadly applicable across engineering domains.

Abstract

We consider the channel access problem under imperfect sensing of channel state in a multi-channel opportunistic communication system, where the state of each channel evolves as an independent and identically distributed Markov process. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem that is of fundamental importance in decision theory. It is well-known that solving the RMAB problem is PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity. A natural alternative is to consider the easily implementable myopic policy that maximizes the immediate reward but ignores the impact of the current strategy on the future reward. In this paper, we perform an analytical study on the optimality of the myopic policy under imperfect sensing for the considered RMAB problem. Specifically, for a family of generic and practically important utility functions, we establish the closed-form conditions under which the myopic policy is guaranteed to be optimal even under imperfect sensing. Despite our focus on the opportunistic channel access, the obtained results are generic in nature and are widely applicable in a wide range of engineering domains.