Static and dynamic channel assignment using neural networks

In this paper, we examine the problem of assigning calls in a cellular mobile network to channels in the frequency domain. Such assignments must be made so that interference between calls is minimized, while demands for channels are satisfied. A new nonlinear integer programming representation of the static channel assignment (SCA) problem is formulated. We then propose two different neural networks for solving this problem. The first is an improved Hopfield neural network which resolves the issues of infeasibility and poor solution quality which have plagued the reputation of the Hopfield network The second approach is a new self-organizing neural network which is able to solve the SCA problem and many other practical optimization problems due to its generalizing ability. A variety of test problems are used to compare the performances of the neural techniques against more traditional heuristic approaches. Finally, extensions to the dynamic channel assignment problem are considered.