Research Of Genetic Algorithm Based Satellite Beamforming And Hotspot Staring

Spatial information network has become the focus of theoretical research and commercial application based on satellite communication because of its wide coverage area and large communication capacity.However,there are a lot of problems in spatial information network,such as dynamic time-varying problem,sparse network nodes problem and the capability of power computing firstly problem.In order to improve antenna gain and capacity of systems,the satellite multi-beam antenna technology has been paid attention by many scholars at home and broad.With the development of multi-beam antenna technology,the corresponding results obtained has been successfully used in various satellite systems.However,the satellite multi-beam antenna technology is still in initial stage and the relevant study is in progress.In the mean time,many problems have not solved yet.Especially for the sparse nodes network,using dynamic staring beam to research on hotspot by means of active multi-beam,which is still blank at home and abroad.This paper analyzes the basic theory of antenna array and genetic algorithm theory,at the same time it combines genetic algorithm with beamforming and deduces the formula of ground coverage angle change in satellite staring process.Then,the paper does research and simulation the waveform design of linear array and planar array by using genetic algorithm on Matlab,which embodies mainly the control of directional beam.Later,by using the phase of the array as the initial population,the improved genetic algorithm is used to study the fast convergence range based on LEO satellite model.In the course of the study,the improved genetic algorithm is compared with the traditional genetic algorithm,which embodies mainly the image curve of the fitness value changes with the algebraic function for two methods.We observe two methods to achieve convergence of the algebra,then make a comparison and draw conclusions.Then,the improved genetic algorithm uses the final population with 45 degree direction as the initial population generated by genetic algorithm,and uses 1 degree as an interval,then the iterative genetic algorithm is used to complete the 60 degree directional graph for 15 times in the process of changing from 45 degree to 60 degree.By comparing the fitness curve with algebra of genetic algorithm and traditional genetic algorithm,the convergence speed through two kinds of genetic algorithm are basically the same,but the improved genetic algorithm run many times,the complexity of the algorithm is greatly reduced.At the same time,the improvedgenetic algorithm generates a set of phase information of the staring beam in the iterative process,and takes the hotspot region as the target,which realizes the dynamic staring beamforming of the satellite.Finally,the public control channel and the random access channel need to guarantee the consistency of the coverage intensity in all directions,and the satellite needs public channel shaping.In this paper,the directional pattern of satellite array antenna is controlled in all directions,and the antenna gain is almost the same in each direction.