Research on Dynamic Spectrum Allocation Algorithm Based on Cyclic Neural Network

Abstract

Due to the wide application of cognitive wireless network, the network structure is becoming more and more complex. It is difficult to establish the corresponding mathematical model to simulate the high complexity network environment. The algorithm based on recurrent neural network in deep reinforcement learning can effectively solve this problem. In addition, with the rise of deep learning in recent years, the combination of reinforcement learning and deep learning shows excellent ability in dealing with complex problems and data operation. This paper is aimed at studying dynamic spectrum allocation based on cyclic neural network. This paper briefly introduces MATLAB, sets up the network environment of algorithm simulation, then analyzes the overall performance of the improved genetic algorithm, and explores the influence of genetic algorithm-related parameters and network environment-related parameters on the performance of the algorithm. The results show the improved genetic algorithm. The network efficiency can be improved by about 2%, but the spectrum switching frequency can be reduced by 69%. When the number of primary users in the network is large, the network benefit of improving the genetic algorithm is superior to the other two algorithms. In addition, when the crossover probability is 0.6 and 0.1, the fitness value is higher than the crossover probability of 0.9 and 0.5; the interference of authorized users in the network initially has less impact on the secondary user.