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

Omid Moradi | Zendy

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

Author(s) -

Omid Moradi

Publication year - 2011

Publication title -

computer and information science

Language(s) - English

Resource type - Journals

eISSN - 1913-8997

pISSN - 1913-8989

DOI - 10.5539/cis.v4n1p116

Subject(s) - computer science , channel (broadcasting) , assignment problem , initialization , hopfield network , generalized assignment problem , benchmark (surveying) , optimization problem , quality of service , computer network , artificial neural network , algorithm , mathematical optimization , artificial intelligence , mathematics , geodesy , programming language , geography

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.

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