Counter-intuitive channel allocation improvement in distributed cognitive radio networks by adding optimal interference in user's utility

Abstract

Cognitive Radio Network (CRN) is a promising wireless communication solution whereby radio resources are automatically varied to optimize performance in the changing wireless environment. Our work focuses on distributed CRN with rational and independent users. The eventual state often reached in such a system is Nash Equilibrium (NE); multiple NEs with different performance exists. In this paper we segregate the data; individual user's-throughput and interference received, at maximum total throughput and different grades of NE. Based on the characteristics of these metrics we develop policies for individual users which are implemented through their utility functions. Counter-intuitive policy is inferred and implemented; adding optimal amount of interference received by a user in its utility enhances the CRN performance measured in the form of normalized cumulative total throughput. At the optimal addition the CRN performance increases by 23% which is illustrated by the simulation results obtained from an average of 1,000 random scenarios.