Optimal Energy-Delay Tradeoff for Opportunistic Spectrum Access in Cognitive Radio Networks

TL;DR

This paper develops an optimal opportunistic spectrum access strategy in cognitive radio networks that balances energy consumption and packet delay, using a POMDP framework with online learning for practical implementation.

Contribution

It introduces a POMDP-based model for spectrum access considering energy and delay, deriving threshold strategies and proposing online learning algorithms for real-time decision making.

Findings

Optimal threshold strategies exist for the OSA problem.

The proposed online learning algorithms effectively estimate PU activity.

Numerical results validate the theoretical optimality and practicality.

Abstract

Cognitive radio (CR) has been considered as a promising technology to enhance spectrum efficiency via opportunistic transmission at link level. Basic CR features allow SUs to transmit only when the licensed primary channel is not occupied by PUs. However, waiting for idle time slot may include large packet delay and high energy consumption. We further consider that the SU may decide, at any moment, to use another dedicated way of communication (3G) in order to transmit its packets. Thus, we consider an Opportunistic Spectrum Access (OSA) mechanism that takes into account packet delay and energy consumption. We formulate the OSA problem as a Partially Observable Markov Decision Process (POMDP) by explicitly considering the energy consumption as well as packets' delay, which are often ignored in existing OSA solutions. Specifically, we consider a POMDP with an average reward criterion. We derive structural properties of the value function and we show the existence of optimal strategies in the class of the threshold strategies. For implementation purposes, we propose online learning mechanisms that estimate the PU activity and determine the appropriate threshold strategy on the fly. In particular, numerical illustrations validate our theoretical findings.