Abstract

In a cognitive radio network (CRN), when the secondary users (SUs) are battery-powered devices, the concept of energy-efficient design is very important. The sensing time and the number of cooperative SUs in the cooperative spectrum sensing could greatly affect energy consumption and throughput of the system. In this paper, we propose an energy utility function by formulating the achievable data rate of a cooperative CRN in terms of the energy consumption by the CRN. The maximization of the energy utility function is obtained by jointly designing the sensing time, sensing threshold, and number of cooperative SUs with the constraint of sufficient protection for primary user (PU). Numerical results show that the CRN can achieve almost the maximum achievable data rate with significant energy saving through the joint optimization.

Highlights

  • With the rapid growth of wireless services, scarcity of spectrum resources has become the bottleneck of its development

  • We focus on the energy efficiency because of its importance in a practical cognitive radio network (CRN) which is powered by batteries

  • Taking the maximization of the energy utility as the design objective, the optimization problem is formulated as a function of three variables, which are sensing time, sensing threshold, and the number of cooperative secondary users (SUs), subject to the target detection probability

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Summary

Introduction

With the rapid growth of wireless services, scarcity of spectrum resources has become the bottleneck of its development. Under the current fixed spectrum allocation policy, the utilization of the licensed spectrum varies from 15% to 85%, whereas only 2% of the spectrum would be used in the US at any given moment [1]. This motivates the advent of cognitive radio network (CRN) which was first introduced in 1999 by J. In [3], the use of multipath and single-path quality of service (QoS)-aware routing algorithms under harsh smart grid (SG) environmental conditions is investigated in order to evaluate their service differentiation capabilities in reliability and timeliness domains. In [4], a novel approach is proposed to set different reliability values for image packets for image transmission in wireless

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