An expanded maximum neural network algorithm for a channel assignment problem in cellular radio networks

In this paper, we propose a neural network algorithm that uses the expanded maximum neuron model to solve the channel assignment problem of cellular r adio networks, which is an NP-complete combinatorial optimization problem. The channel assignment problem demands minimizing the total interference be tween the assigned channels needed to satisfy all of the communication need s. The proposed expanded maximum neuron model selects multiple neurons in d escending order from the neuron inputs in each neuron group. As a result, t he constraints will always be satisfied for the channel assignment problem. To improve the accuracy of the solution, neuron fixing, which is a heurist ic technique used in the binary neuron model, a hill-climbing term, a shaki ng term, and an Omega function are introduced. The effectiveness of these a dditions to the expanded maximum neuron model algorithm is demonstrated. Si mulations of benchmark problems demonstrate the superior performance of the proposed algorithm over conventional algorithms in finding the solution. ( C) 2000 Scripta Technica.