Channel Assignment using Chaotic Simulated Annealing Enhanced Hopfield Neural Network

Channel assignment problem in cellular communication is a difficult combinatorial optimization problem. There is no exact polynomial-time solution for it and searching the whole solution space is infeasible for large problems. By defining the problem´s cost function as the energy function of a chaotic Hopfield neural network, we devise a framework for finding competitive suboptimal or even optimal solutions for combinatorial optimization problem in general, and channel assignment problem in particular. In our architecture, we inject chaotic noise in order to help the network escape from local minima of the energy function while we enforce problem constraints by external inputs of neurons. Experimental results show the superiority of our method to other methods.