Research On Cognitive Radio Spectrum Allocation Algorithm Based On Artificial Physics Optimization

With the rapid development of wireless communication services,there is a growing demand for spectrum,and the limited spectrum resources have become severely inadequate.Spectrum allocation of Cognitive Radio is an effective way to solve this problem;however,it is still difficult to meet the spectrum requirements of users.A large number of research results show that the problem of spectrum resource scarcity is largely due to the irrational spectrum allocation of different wireless access technologies.It is very important to improve the quality of spectrum resource allocation by optimizing the technical model of spectrum allocation and studying the algorithm.In order to improve the quality of spectrum resource allocation,network benefit and user spectrum allocation fairness,this paper applies pseudo physics optimization algorithm combined with graph theory model to spectrum allocation.Aiming at the problems existing in the traditional pseudo physics optimization algorithm,the paper improves it from three aspects,and applies it to spectrum allocation,as follows:(1)The traditional Artificial Physics Optimization(APO)does not consider the fairness of spectrum allocation,but pays more attention to the overall benefits of the network.In order to solve this problem,this paper introduces the spectrum availability matrix P in the graph theory model in the particle correction scheme.When two cognitive users compete for spectrum interference at the same time,they do not know how to allocate the right to use the spectrum.At this time,the degree of demand for the spectrum is judged,and the spectrum is allocated to the user with high availability.The simulation results show that the fairness of the algorithm is improved by about 10%compared with the traditional algorithm.(2)Artificial physics optimization(APO)algorithm used in traditional fixed force calculation method,it is not conducive to the convergence to the global optimal solution,slow convergence speed and poor stability.To solve this problem,the paper increases the distance parameter in the original force rules,adjusting the force between particles by distance parameters to improve the global search ability.Finally,through the simulation analysis with four functions,it is proved that when the distance parameter is set to 0.01,the convergence speed is the fastest and the stability is the best.(3)The traditional Artificial Physics Optimization(APO)has the poor initial population diversity,and the parameters of gravity are chosen as a fixed value,causes the algorithm to fall into a local optimum and has limited network performance.This paper combines chaotic search technique with Artificial Physics Optimization;the Tent mapping is used to initialize the physical individuals to improve the ergodic uniformity of the initialized population.The gravitational parameter determines the direction of the movement of the population,therefore,by analyzing its convergence,an adaptive gravitational parameter is designed by combining gravitational parameters with population size and inertia weight coefficient,so that 'the population is initialized by Tent mapping.Gravity parameters can dynamically adjust the motion of particles.The simulation results show that the network income of the improved algorithm and the average maximization of the network benefit are improved compared with the traditional algorithm.