Research On Location Optimization Of LTE Base Station Based On Hybrid Immune Algorithm

In recent years,with the rapid development of China's mobile communications industry,mobile communications technology has widely penetrated into all walks of life.At the same time,with the continuous development of 4G networks and services,the reliance on mobile communications is gradually deepened,and the requirements for speed,quality,and security in the communication process are also constantly increasing.Therefore,it is urgent to build a large number of base stations to meet people's needs.According to the related network planning and construction,the construction cost of the base station accounts for about two-thirds of the network construction cost.The huge network construction cost has become an obstacle to the development of operators.Therefore,scientific and rational base station planning has become one of the research contents of network planning and construction.Based on TD-LTE base station planning is a large-scale,highly nonlinear combinatorial optimization problem,it is a typical NP-hard problem.Therefore,artificial intelligence algorithms are often used for optimization.Since the artificial clone immune algorithm is an intelligent algorithm widely used in scientific research and engineering applications in recent years,this thesis uses the artificial clone immune algorithm as a basis for research.However,the artificial cloning immune algorithm is not perfect either.When the problem space is large,it is easy to fall into a local optimum and leads to premature convergence when the large-scale complex problem occurs.When the algorithm enters the middle and late stages,it will follow certain rules in the antibody mutation and crossover process.The random variation and crossover of the probability make the solution after the mutation crossover uncertain.Not only the efficiency of the solution is reduced,but also the local convergence phenomenon may be formed,so that the solution accuracy is not high.Therefore,in this thesis,the artificial immune algorithm is improved accordingly,and the optimized algorithm is used in the TD-LTE base station site selection.The specific work of this thesis is as follows:1.In order to overcome the shortcomings of the traditional artificial cloning immune algorithm,this thesis proposes a hybrid immune algorithm,which proposes an existing prior solution as heuristic information as a guide and anti-learning method during the initialization phase.The combined initialization scheme is used to solve large-scale complex problem solving.In the mutation and crossover stages,the differential mutation and binary crossover algorithm is integrated into the artificial immune algorithm to solve the late phase of the algorithm,resulting in uncertainties due to antibody variation and crossover process.Finally,Perform corresponding performance tests with standard test functions.The results prove that the proposed improved immune algorithm has better performance.2.Studying some domestic and international TD-LTE network base station selection literature,analysis and summary of the current deficiencies in the base station site selection optimization algorithm,according to the principle of base station site selection,the construction of TD-LTE network base station location problem.The mathematical model and Monte Carlo simulation of base station site optimization using improved hybrid immune algorithm are compared with the convergence speed and convergence accuracy of the base station site optimization algorithm proposed in other related papers,and the result proves that this thesis proposes.The improved immune algorithm has better performance and is more suitable for base station site optimization.