Dynamic Frequency Assignment for Cognitive Radio Cellular Networks Using Hysteretic Noisy Chaotic Neural Network

This paper presents a novel dynamic frequency assignment (DFA) technique for cognitive radio cellular networks (CRCNs) using hysteretic noisy chaotic neural network (HNCNN). HNCNN is a novel neural network that combines the advantages of stochastic chaotic simulated annealing and hysteretic dynamics to achieve performance improvements in dynamic frequency assignment. In DFA technique, an existing energy function is introduced, which avoids causing harmful interference to primary users (PUs) according to the real-time interference frequency table. In a CRCN, the introduced energy function also avoids causing mutual interferences among cells, considers the number of required frequencies for each cell, and simultaneously minimizes the total number of assigned frequencies to improve spectrum utilization. In the end, a typical 49-cell CRCN with 70 licensed frequencies is examined to demonstrate the validity of the proposed technique. And the results also show that HNCNN outperforms noisy chaotic neural network (NCNN) according to convergence speed and the rate of optimal solution.