Optimal Power Allocation for Green Cognitive Radio using Differential Evolution Approach

Abstract

Abstract-In this study, the problem of determining the power allocation that maximizes the energy efficiency of cognitive radio network is investigated using differential evolution algorithm with constraint handling technique. The energy-efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non-linear nonconvex optimization problem. Nature inspired algorithms like Differential Evolution (DE) can describe and resolve complex relationships from intrinsically very simple initial conditions with little or no knowledge of the search space. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithm is investigated.Index Terms-Green cognitive radio, power allocation, energy efficiency, differential evolution 1 INTRODUCTION Energy efficiency plays a key role in designing wireless communication networks. The energy-efficient wireless networks help in saving the battery life and reduction in global warming. Massive growth and demand of high data rate wireless devices and applications cause significant increase in the greenhouse gas emissions and crowdedness in available frequency spectrum. The main goal of green communication is to develop wireless networks, protocols and devices that jointly maximize the high data rate and minimize the greenhouse gas emissions, that is, minimize the transmit power. The maximum data rate transfer with minimum transmit power is the key of green communication.