Research Of Swarm Optimization Algorithm For FSO Communication System Based On Adaptive Optics

In recent years,free space optical communication(FSO)has a rapid development with the developing space industry.Many countries have a growing increase in FSO communication system technology research because FSO has great development potential.With advantages of a high bandwidth,low cost and license-free,FSO has been applied in deep space,middle orbit and ground stations.Generally we called the laser communication between satellites "space optics communication" and the laser communication between ground stations "atmosphere laser communication".But there are still many problems in FSO communication: the laser propagation in the atmosphere is influenced by atmospheric scattering,atmospheric absorption,the speckle and scintillation caused by the atmosphere turbulence,atmospheric multiple effects.The result is optical power attenuation,inter-symbol interference,received power attenuation,spot center offset.These phenomenon leads to the decline in the quality of FSO communications.At present,one of the most important problems in the application of FSO communication is atmosphere turbulence.Aiming to solve this problem,People have made plenty of research both theoretically and experimentally.In recent years,people pay more and more attention to solve this problem by adaptive optics(AO).Many classical algorithms,such as stochastic parallel gradient descent algorithm(SPGD),simulated annealing algorithm(SA),hill climbing algorithm(HC),genetic algorithm(GA)and other algorithms are proposed for FSO communication based on AO.But few people apply swam intelligence optimization algorithms to FSO communication system.In this paper,we will analyze the influence of atmosphere turbulence on FSO communication system firstly.Then the FSO experiment system will be introduced and at last three swam intelligence optimization algorithms,particle swarm optimization algorithm(PSO),shuffled frog leaping algorithm(SFLA)and modified shuffled frog leaping algorithm(MSFL),will be applied in our experiment system.This paper has mainly studied correction of high order difference by swarm intelligence optimization algorithm.In order to evaluate the performance of swarm intelligence optimization algorithm,we also apply SPGD in the experiment system as contrast experiment.In the course of the experiment,a new evaluation indicator used for evaluating correction effect is put forward.The new evaluation indicator J is proportional to the Strehl rate(SR).Moreover,it is convenient to calculate and stable in the experiment.The effect of individual number on PSO,SFLA and MSFL are studied in the experiment.Experiments show that individual number influences the overall search ability of PSO while it has little influence on SFLA and MSFL.And the increase of individual number can speed up the convergence on all of the three algorithms.Compared with SPGD,swam intelligence optimization algorithms with appropriate individual number can converge much faster.With regard to stability,the convergence rate of SPGD varies greatly with different phase difference and SPGD may converge at a local optimum.But swam intelligence optimization algorithms do not occur under the appropriate individual number.Comparing the performance of PSO,SFLA and MSFL,it is found that all of them can converge at a global optimum with appropriate individual numbers.The MSFL converge faster than PSO and SFLA under the same individual number.Comparing with SFLA,updating the "elite frog" up,MSFL solve the problem that the SFLA is easy to converge at a local optimum too much.