Resource Allocation Algorithms Based On Genetic Theory In Heterogeneous Networks

With the popularization of broadband wireless applications, wireless resources become much scarcer than before. The problem that must be solved in heterogeneous networks is how to achieve complementary advantages and coordinated management of heterogeneous resource through designing rational resource optimization algorithms, thus maximizing resource utilization and providing services with quality of service (QoS) guarantee to users. The thesis focuses on the key problem about how to maximize resource utilization as well as provide services with QoS guarantee to users, and deeply studies the resource allocation algorithms based on genetic theory in heterogeneous networks, in order to obtain the most optimal resource allocation schemes.The main work of this thesis is as follows.(1) The key technologies of resource optimization management in heterogeneous networks are described, the research status of resource optimization algorithms is discussed, and the characteristics and challenges of resource allocation algorithms in heterogeneous networks are analyzed.(2) Two resource allocation algorithms based on genetic operation in heterogeneous networks are proposed. The first algorithm aims to maximize resource utilization as well as guarantee the QoS of different services. Considering the different services supporting abilities of the radio access technologies (RATs), the second algorithm introduces a service priority mechanism and aims to maximize network utility and system throughput. The simulation results show that after considering services priority, not only the resource utilization is improved, but also the traffic blocking rate is reduced. Compared with the existing resource allocation algorithms, the simulation results show that the proposed algorithm achieves better performance than existing resource allocation algorithm.(3) To solve the problem of premature convergence of resource allocation algorithm based on genetic operation, the resource allocation algorithm based on ant colony optimization in heterogeneous networks is proposed. The algorithm mainly uses the local positive feedback information to search for the optimal solution, which can help the algorithm avoid falling into local optimal solution. Simulation results show that resource allocation algorithm based on ant colony optimization has higher efficiency ratio in seeking exact solution than that of resource allocation algorithm based on genetic operation, but slower speed of convergence than that of resource allocation algorithm based on genetic operation. (4) The resource allocation algorithm based on hybrid optimization in heterogeneous networks is proposed. In the first stage, the fast global search capability of genetic algorithm is used to obtain the initial results of the resource allocation, and then the results are translated into the initial pheromone distribution of ant colony optimization. In the second stage, positive feedback and parallelism of ant colony algorithm are used to get the global optimal solution. The simulation results show that the proposed resource allocation algorithm based on hybrid optimization has faster speed of solving the problem than that of resource allocation algorithm based on ant colony optimization, higher efficiency ratio in seeking exact solution than that of resource allocation algorithm based on genetic optimization.(5) The research work in this thesis is summarized and the prospects of future research work are given.