Research On Resource Allocation Based On Energy Harvesting In CR

In recent years,with the rapid development of the mobile communication internet industry,a variety of wireless devices will occupy a large amount of spectrum resources when they are connected to the network.However,the shortage of spectrum resources has a long history and it is becoming increasingly severe.In addition,with the increase of the number of communication equipments,the problem of energy consumption has become more and more negligible.The huge consumption of traditional energy by a large number of equipments is inconsistent with the development concept of Green China.Therefore,improving the allocation efficiency of spectrum resources and reducing the energy consumption of network communication equipment s are the important research directions at the present time.In this paper,we study how to dynamically allocate channels based on cognitive radio(CR)related technologies,so that primary user(PU)and secondary user(SU)can use the same band of resources,thereby the efficiency of spectrum utilization can be improved.At the same time,energy harvesting is introduced into the cognitive radio network in this paper.Wireless equipments can provide energy for themselves by harvesting energy from the environment,which can reduce the consumption of traditional energy.The main work of this article as follows:(1)The cognitive radio network model based on energy harvesting is analyzed and established,and the corresponding mathematical model is established for the maximum throughput of secondary user in the cognitive radio network based on the relevant knowledge of graph theory coloring.In this paper,the genetic algorithm(GA)is used to study the channel allocation problem.In view of the shortcomings of basic genetic algorithm,such as the GA is easy to get a local range optimal value and easy to converge prematurely,this paper introduces the Fibonacci spiral curve to the adaptive genetic algorithm,the crossover probability and mutation probability of the algorithm are continuously dynamically adjusted(larger in the early period,stable in the middle period,and smaller in the later period),which can achieve the goal of global optimization and accelerated convergence.(2)A time slot structure model of energy harvesting,spectrum sensing,channel allocation and data transmission is anal yzed and established.Based on this model,a mathematical model for the time resources consumed of cognitive radio's secondary user is given in each time slot.In this paper,particle swarm optimization(PSO)is used to solve the optimal value of time resources allocation.Aiming at the problem that the basic PSO algorithm is easy to obtain a local optimal solution,an improved PSO algorithm with adaptive weighting factor is proposed which using a curve impairment strategy instead of a linear impairment strategy,so that the improved weighting factor can smoothly transition from a larger value in the early stage to a smaller value in the later stage in the algorithm simulation,so that we can obtain a better optimization ability.