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The Scientific World Journal
Volume 2014 (2014), Article ID 960584, 22 pages
Research Article

Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

1Department of Computer Science and Networked Systems, Sunway University, No. 5 Jalan Universiti, Bandar Sunway, 46150 Petaling Jaya, Selangor, Malaysia
2Wireless Network and Protocol Research Lab, MIMOS Berhad, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia

Received 13 April 2014; Accepted 31 May 2014; Published 16 July 2014

Academic Editor: Su Fong Chien

Copyright © 2014 Hasan A. A. Al-Rawi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs.