Resource Allocation In Heterogeneous Networks Based On Intelligent Algorithms

With the continuous development of society and the progress of science and technology,the field of communication has undergone many tremendous changes.Nowadays,the number of communication users is increasing gradually,and the demand for communication is explosively increasing.The original cellular cell can no longer meet people's needs.A new way of networking,heterogeneous network,emerges as the times require.Heterogeneous network is a communication network which combines different types of base stations.The most typical one is the base station with other small coverage,such as home base station,under the wide coverage of macro base station.Heterogeneous network solves the problem of coverage in densely populated areas.It has low cost and high flexibility.It also avoids the energy consumption and cost problems caused by the deployment of macro base stations.It increases the system throughput and has the advantages of environmental protection and high efficiency.In this paper,the existing research results are thoroughly studied.Aiming at the problem of power allocation in heterogeneous networks,the existing research results generally have shortcomings that can not reasonably solve this high-dimensional optimization problem.The traditional algorithms,such as water injection algorithm,are no longer applicable in this situation.Firstly,this paper introduces the two-layer heterogeneous network model of Macrocell/Femtocell,introduces the important concepts in detail,deduces the formulas of signal transmission model and interference calculation,and on this basis,deduces and analyses mathematically the important indicators of network performance: throughput,energy consumption and efficiency,and establishes the single-target and multi-target scenarios respectively.Objective function.Then the teaching and learning optimization algorithm is improved and a multi-objective teaching and learning algorithm is constructed.The principle and process of the algorithm are introduced in detail.With the system throughput and system energy efficiency as the optimization objectives,the power allocation problem of heterogeneous networks is solved by single-objective method.The excellent performance of the algorithm is verified by comparing with genetic algorithm and teaching and learning algorithm.Then,taking system throughput and system energy consumption as multi-objective functions,the Pareto front-end solution of multi-objective power allocation problem is solved based on multi-objective teaching and learning algorithm.Through simulation analysis,it is proved that the solution result of the proposed algorithm is better than that of the classical multiobjective algorithm NSGA-II.Aiming at the integer programming problem of spectrum allocation,this paper improves the spider swarm optimization algorithm,and makes simulation analysis to maximize the system throughput under various circumstances.The results show that the improved spider swarm optimization algorithm can get better results than the particle swarm optimization and the spider swarm optimization.Finally,a multi-objective quantum spider swarm optimization(QPSO)algorithm is proposed.The system throughput and energy consumption are taken as multi-objective functions to solve the one-step joint resource allocation problem.The simulation results show that the proposed algorithm can solve the mixed coding problem well,and the optimization results are better than the classical multiobjective particle swarm optimization algorithm.