A decision tree cognitive routing scheme for cognitive radio mesh networks

• Published in: Wireless communications and mobile computing Vol. 15; no. 10; pp. 1405 - 1417
• Main Authors: Soltani, Soroor, Mutka, Matt W.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.07.2015; Hindawi Limited
• Subjects: Cognitive radio; Computer networks; decision theory; decision tree; Decision trees; Dynamics; Mathematical analysis; Mathematical models; Networks; routing; Routing (telecommunications)
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1002/wcm.2418

Routing in a cognitive radio network operating in a dynamic environment is a complex decision problem. Diversity in the number of available spectrum bands and their stability, in addition to the secondary users' heterogeneities, affect the consequence of the routing decision. We use a decision theory framework to model the problem of routing under uncertainties involved in a cognitive radio network. A utility function is designed to capture the effect of spectrum measurement, fluctuation of bandwidth availability, and path quality. A node cognitively decides its best candidate among its neighbors by utilizing a decision tree. Each branch of the tree is quantified by the utility function and a posterior probability distribution that predicts the suitability of available neighbors. In decision tree cognitive routing (DTCR), nodes learn their operational environment and adapt their decision‐making accordingly. We compared our scheme with the optimal performance in a highly dynamic environment and local coordination‐based routing and spectrum assignment protocol [1]. Our results show that the DTCR tends to perform near optimum. It easily adapts to environmental dynamics. Copyright © 2013 John Wiley & Sons, Ltd. Routing in a cognitive radio network operating in a dynamic environment is a complex decision problem. Diversity in the number of available spectrum bands and their stability, in addition to the secondary users' heterogeneities, affect the consequence of the routing decision. We use a decision theory framework to model the problem of routing under uncertainties involved in a cognitive radio network. A utility function is designed to capture the effect of spectrum measurement, fluctuation of bandwidth availability, and path quality.