Multi-objective optimization method for spectrum allocation in cognitive heterogeneous wireless networks

In a multi-heterogeneous network dense deployment and convergence environment, the cognitive radio technology dynamically utilizes the idle spectrum resources of each primary network, which can effectively improve the data transmission rate and system capacity, and is also a key technology of the next generation wireless communication network. However, how to efficiently and reasonably select the network and allocate idle spectrum resources to meet the low cost and large traffic demand of users is a difficult problem in the case of dynamic coupling with multiple heterogeneous primary networks overlapping coverage and diversified user requirements. In this paper, with the goal of maximizing the total bandwidth and minimizing the total cost, a multi-objective optimization mathematical model for network selection and idle spectrum allocation is established in the context of comprehensive consideration of the difference of spectrum resource attributes of different primary networks in the network domain and the diversification of secondary user requirements in the user domain, and the complexity of the problem is analyzed. Based on this, two kinds of technical paths to solve the complex network selection and spectrum allocation problem are applied in this paper. The first is the simplification method. By analyzing the relationship between optimization objectives and constraints, simplification method simplifies the objective function and constraints of the mathematical model and transforms the complex allocation problem into a standard form of the 01 programming problem. The second is the intelligent optimization algorithm. By improving the traditional multi-objective optimization algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II), an improved NSGA-II(INSGA-II) method is proposed, which combines the interference constraints of the primary network and the service quality requirements of the secondary users into the objective value evaluation of NSGA-II algorithm, while making the decision selection on the optimal solution set to select a compromise solution. Finally, the performance of this two methods is compared and analyzed through experiments. The experimental results show that the simplified method has higher efficiency advantages, and the INSGA-II algorithm can often meet the user’s service requirements at a lower cost, especially the cost priority strategy.