A maximum channel reuse scheme with Hopfield Neural Network based static cellular radio channel allocation systems

Abstract:The channel allocation problem in cellular radio systems is NP-complete, and thus its general solution is not known for even the 2-channel case. It is well known that the link gain system matrix (or received-signal power system matrix) of the radio network is (and may be highly) asymmetric, and that as the Hopfield neural network is applied to optimization problems, its weight matrix should be symmetric. The main contribution of this paper is as follows: turning the channel allocation problem into a maxCut graph partitioning problem, we propose a simple and effective continuous-time Hopfield neural network-based solution by determining its symmetric weight matrix from the asymmetric received-signal-power-system matrix. Computer simulations confirm the effectiveness and superiority of the proposed solution as compared to standard algorithms for various illustrative cellular radio scenarios for the 2-channel case.

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A maximum channel reuse scheme with Hopfield Neural Network based static cellular radio channel allocation systems

Cited by 2 publications

References 13 publications

Channel Assignment Problem in Cellular Mobile Network: A Distributed Constraint Satisfaction Approach

Channel Assignment Problem in Cellular Mobile Network: A Distributed Constraint Satisfaction Approach

Continuous-time Hopfield neural network-based optimized solution to 2-channel allocation problem

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