# On The Optimality of Myopic Sensing in Multi-State Channels

arXiv: https://arxiv.org/abs/1305.6993 · HTML: https://mlsanity.com/paper/1305.6993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2013.

## Built on by (most cited first, 1 of 1)

- [Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits](https://mlsanity.com/paper/2108.00892/) (2021)

## Abstract (arXiv)

We consider the channel sensing problem arising in opportunistic scheduling over fading channels, cognitive radio networks, and resource constrained jamming. The communication system consists of N channels. Each channel is modeled as a multi-state Markov chain (M.C.). At each time instant a user selects one channel to sense and uses it to transmit information. A reward depending on the state of the selected channel is obtained for each transmission. The objective is to design a channel sensing policy that maximizes the expected total reward collected over a finite or infinite horizon. This problem can be viewed as an instance of a restless bandit problem, for which the form of optimal policies is unknown in general. We discover sets of conditions sufficient to guarantee the optimality of a myopic sensing policy; we show that under one particular set of conditions the myopic policy coincides with the Gittins index rule.