Channel assignment for cellular networks based on a local modified Hopfield neural network

In this paper, we propose a modified discrete Hopfield neural networks algorithm for the channel assignment problem. In opposition to previous work, we tried to apply the optimization locally on a per cell basis in order to reduce the CPU processing time and decrease the designed system complexity while obtaining a near-optimum solution. In addition, the research is extended to study the algorithm performance in a more realistic cellular system where the number of requested channels is continuously changing with time. In this paper, the channel assignment problem is formulated as an energy function which is at its minimum when all the defined compatibility constraints are satisfied and the assigned channel number (ACN) is equal to the requested channel number (RCN) in each cell.