Energy-Efficient Sensing for Delay-Constrained Cognitive Radio Systems Via Convex Optimization

Abstract

Spectrum sensing is a critical issue in cognitive radio (CR) systems. When the energy of the CR system is constrained, using the energy efficiently is an important problem, which must be considered. In this paper, the energy efficiency is defined as the ratio of average spectrum efficiency of the CR system over average power consumed by the CR system. We consider a joint optimization of sensing time and power allocation to maximize the energy efficiency under the condition of sufficient protection to primary user and the delay constraint. It is demonstrated that convex optimization plays an essential role in solving the problem. An efficient iterative algorithm is proposed to obtain the optimal values. Then, we propose a block sensing scheme in which several adjacent frames are bundled as a block. Our results show that significant improvement in the energy efficiency is obtained via joint optimization of sensing time and power allocation. The energy efficiency and the delay performance can be further improved by using the proposed block sensing scheme.

Intelligent resource allocation scheme for the cognitive radio network in the presence of primary user emulation attack

“…10 b illustrates EE and ST as a function of sensing time for and . It is well known that both ST and EE are a concave function of [26]. Furthermore, EE for is more because per each SU is less.…”

A novel approach for energy‐efficient resource allocation in double threshold‐based cognitive radio network

“…In Scheme III, Particle Swarm Optimization algorithm is used for evaluating the optimal power allocation as in a previous study . In Scheme IV, the method of optimizing both sensing time and transmission power allocation is obtained by employing bisection search method as discussed in a previous study . In the proposed scheme, both IDM and EPA algorithms are used.…”

“…EE, energy efficiency FIGURE 6 Variation of throughput against signal-to-noise ratio (SNR) for δ = 0 and δ = 0.5 FIGURE 7 Variation of energy efficiency (EE) and system throughput against the interference threshold Ith for signal-to-noise ratio (SNR) = −30 dB and −15 dB The impact of R min on the maximum energy efficiency (EE) and on the sum of transmission power for Ith = −160 dBm and Ith = −150 dBm FIGURE 9 The impact of outage threshold O th on maximum energy efficiency (EE) and throughput for Ith = −160 dBm and Ith = −150 dBm search method as discussed in a previous study. 13 In the proposed scheme, both IDM and EPA algorithms are used. It is clearly observed that our proposed scheme outperforms the other existing schemes and double threshold method performs better than the single threshold method.…”

“…where P s is sensing power and is assumed to be same for all the SUs. Then, EE (bits/s/joule) is defined as the ratio of the average system throughput (13) and the total power required by the SUs (17) which is expressed as…”

“…Here, the optimization problem was formulated as convex optimization via its equivalent hypograph form, where the computational complexity was reduced by employing fast barrier algorithm . In, EE maximization problem was formulated in single PU and single SU CRN scenario, where the optimal sensing time and power were obtained by using Bi‐Section method under the constraints of interference to the PU and transmission delay. EE was evaluated in a CR system considering the Rayleigh fading both in the sensing and reporting channels .…”