A Hopfield Neural Network for Channel Assignment Problem in Cellular Radio Networks

  •  Omid Moradi    


In wireless mobile communication system, radio spectrum is limited resource. However, efficient use of available channels has been shown to improve the system capacity. The role of a channel assignment scheme is to allocate channels to calls or mobiles in such a way as to minimize call blocking or call dropping probabilities, and also to maximize the quality of service. Channel assignment is known to be an NP-hard optimization problem. In this paper, a new channel-assignment algorithm using a modified Hopfield neural network is proposed. The channel-assignment problem is formulated as an energy-minimization problem that is implemented by a modified discrete Hopfield network. 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. The algorithm will be tested by solving seven benchmark problems, where the total number of frequencies varied from 73 to 533. This new algorithm, together with the proposed regular interval initialization and new interconnection weights, has better performance results than the existing algorithms in all of the seven problems.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

WJCI (2021): 0.557

Impact Factor 2021 (by WJCI):  0.304

h-index (December 2022): 40

i10-index (December 2022): 179

h5-index (December 2022): N/A

h5-median(December 2022): N/A

( The data was calculated based on Google Scholar Citations. Click Here to Learn More. )