Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

Abstract

This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.

Demand-Side Management Optimization Using Genetic Algorithms: A Case Study

“…Computational intelligence approaches have also been applied in [19][20][21][22][23] with a focus on cost reduction, peak energy demand, energy saving, and DSM. Aiming at solving optimization problems, several studies have proposed genetic algorithms, including [24][25][26][27][28][29][30]. The results justify the applicability of the genetic algorithms for power utilization reduction, minimizing the electricity cost and peak-to-average ratio considering shifting and nonshifting loads via appliance scheduling.…”