Effective Power Allocation with Uniform Pricing Model Based on Game Theory in Nano-Computing Environment of Cognitive Radio Network

Abstract

Efficient resource allocation in cognitive radio network (CRN) remains a challenge due to dynamic nature of available spectrum in working band and implementation in nano-computing environment. Adoption of game theory in power allocation based on pricing model requires the formulation of strategy of the players as profit/loss function which may lead to Nash equilibrium. Earlier research focuses on the impact of game models such as Cournot, and Bertrand in formulating the utility function under the constraint power and service quality. In Cournot and Bertrand game the users play simultaneously that may not be acquainted with the other user’s action. It suits for common regimes such as all user are to be unlicensed (SU) and they may simultaneously try to access a set of available channels. In this paper, we formulate the condition for optimal power allocation in CRN using Stackelberg game where the previous user decides its output and then the erstwhile user does so, knowing the output characteristics by the former user. Based on the profit of PU (as a leader) and SU (as a follower), the optimized solution is formulated for power and interference price and it is modelled as a convex function of transmission power achieved by Nash equilibrium which involves backward induction. By means of a uniform pricing scheme every PU aims to maximize its profit under channel data rate and interference power constraint. The proposed Resource Allocation using Stackelberg Game (RASG) algorithm tries to optimize uniform pricing and power allocation among SUs such that maximizing throughput and fairness. The simulation results show a significant enhancement in throughput and fairness compared to power optimization based on Bertrand and Cournot game theory.