Cellular radio channel assignment using a modified Hopfield network

The channel-assignment problem is important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal channel-assignment problem has become increasingly important. A new channel-assignment algorithm using a modified Hopfield (1985, 1986) neural network is proposed. The channel-assignment problem is formulated as an energy-minimization problem that is implemented by a modified discrete Hopfield network. Also, a new technique to escape the local minima is introduced. In this algorithm, an energy function is derived, and the appropriate interconnection weights between the neurons are specified. The interconnection weights between the neurons are designed in such a way that each neuron receives inhibitory support if the constraint conditions are violated and receives excitatory support if the constraint conditions are satisfied. To escape the local minima, if the number of assigned channels are less than the required channel numbers (RCNs), one or more channels are assigned in addition to already assigned channels such that the total number of assigned channels is the same as the required number of channels in the cell even though the energy is increased. Various initialization techniques, which use the specific characteristics of frequency-assignment problems in cellular radio networks, such as cosite constraint (CSC), adjacent channel constraint (ACC), and cochannel constraint (CCC), and updating methods are investigated. In the previously proposed neural-network approach, some frequencies are fixed to accelerate the convergence time. In our algorithms, no frequency is fixed before the frequency-assignment procedure. This new algorithm, together with the proposed initialization and updating techniques and without fixing frequencies in any cells, has better performance results than the results reported previously utilizing fixed frequencies in certain cells.