Optimization And Application Of Ray Tracing Model In Transhorizon Propagation And Urban Environment

Based on the law of electromagnetic wave propagation in the atmosphere,thesis analyzes the calculation method of the transmission path of electromagnetic waves in the groundionospheric waveguide,and studies the propagation characteristics of radio waves under trans-horizon.Aiming at the problem that the micro-cell radio wave propagation prediction algorithm is not highly adaptable to environmental changes,thesis uses the genetic algorithm combined with the measured power data to invert the optimal electrical parameters of a given environment,which improves the simulation accuracy of the micro-cell radio wave propagation prediction algorithm and the Environmental adaptability.Using the optimized micro-cell radio wave propagation prediction algorithm,combined with the intelligent optimization algorithm,thesis studies the method of obtaining the optimal base station location in the micro-cell based on the theory of network planning optimization.The main research results of this article are as follows:1.Based on the Snell law of spherical layering,the propagation law of electromagnetic wave in the ground-ionospheric waveguide is studied.Combining the measured data provided by ITU-R and related statistical models,refined modeling the ground-ionospheric waveguide environment.The ray tracing algorithm is used to compare and analyze the electromagnetic wave propagation over the horizon,especially the influence of the different launch angles of the rays,the different latitude and longitude of the transmitting and receiving antenna,and the influence of different time and places,etc.,this work provides a theoretical basis for studying the problem of radio wave propagation under the beyond horizon.By comparing the prediction results with the actual measurement results,it can be seen that the prediction algorithm has high accuracy at low altitudes and can be used to accurately predict the propagation path of low-altitude radio waves.In addition,the algorithm maintains a high degree of credibility and prediction accuracy in various scenarios such as transpolar regions,day-night interfaces,and ground-sea interfaces.2.Based on the micro-cell radio wave propagation prediction algorithm,aims to solve the problem of different simulation environments correspond to different relative permittivity and conductivity,thesis organically combines genetic algorithm and micro-cell radio wave propagation prediction algorithm,optimizes the accuracy and adaptability of the micro-cell radio wave propagation prediction algorithm based on the measured power data in a specific environment.Finally,the influence of different measured data formats,objective function settings and fitness function settings on the optimization results is analyzed and discussed,an optimization theory to improve the accuracy and adaptability of the micro-cell radio wave propagation prediction algorithm is given.The research results show that by optimizing the environmental electrical parameters,the error between the algorithm simulation results and the measured results can be effectively reduced.The optimization effect obtained by using the measured data of different frequency points,different environments,and different parameter settings is different,the optimization effect is obvious when the frequency is low and the environment is relatively single.3.Based on the precision optimized micro-cell radio wave propagation prediction algorithm,the micro-cell base station location optimization method based on the intelligent optimization algorithm is researched.Using the theory of network planning and optimization in mobile communications,this article focuses on the coverage of communication base stations and the interference between several base stations.In this paper,the coverage problem of a single base station in the micro cell is extended to the coverage and interference problems of multiple base stations,the application range of the algorithm is improved.Adopt particle swarm optimization algorithm and genetic algorithm,the effects of different parameter settings and function settings on the optimization results are compared.The calculation results show that the genetic algorithm has better global convergence ability,while the particle swarm algorithm has the characteristics of fast calculation speed.The optimization results of the two optimization algorithms all can be controlled by the objective function parameter settings to achieve the desired index.