A stochastic game framework for joint frequency and power allocation in dynamic decentralized cognitive radio networks

Authors: Xiu Liu, Guoru Ding, Yang Yang, Qihui Wu, Jinlong Wang

Abstract:
Cognitive radio networks (CRNs) have been recognized as a promising solution to improve the radio spectrum utilization. This article investigates a novel issue of joint frequency and power allocation in decentralized CRNs with dynamic or time-varying spectrum resources. We firstly model the interactions between decentralized cognitive radio links as a stochastic game and then proposed a strategy learning algorithm which effectively integrates multi-agent frequency strategy learning and power pricing. The convergence of the proposed algorithm to Nash equilibrium is proofed theoretically. Simulation results demonstrate that the throughput performance of the proposed algorithm is very close to that of the centralized optimal learning algorithm, while the proposed algorithm could be implemented distributively and reduce information exchanges significantly.

Keywords:
Cognitive radio networks
Frequency allocation
Power control
Stochastic game
Multi-agent learning

Published in: AEÜ-International Journal of Electronics and Communications (Volume 67, Issue 10, Pages 817-904, October 2013)

Publisher: Elsevier

ISSN Information: 1434-8411

A stochastic game framework for joint frequency and power allocation in dynamic decentralized cognitive radio networks

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