A Fuzzy Neural Network Model for Balancing Loads on a Wireless Network

Abstract:

Over the years, there has been a growing demand for mobile communications which has become essential in the day to day activities of humans. This calls for a robust and dynamic networks that with stand the rigour of the ever increasing demand for mobile communication. Radio frequency channels have to be utilized as much as possible and are a precious resource. Various techniques have been used to enhance the efficiency and flexibility of mobile communication networks to deal with the need of increased traffic and user needs and characteristics. Load balancing is a critical self-organizing operation that ensures an equitable distribution of network users. Several channel allocation schemes, including fixed channel assignment (FCA) and dynamic channel allocation (DCA) were proposed to assign frequencies to cells in order to maximize frequency resumption (HCA).This work presents Fuzzy-Neural Network model in the balancing of loads on a wireless network. Firstly, fuzzy rules are applied at level 1. Secondly, an adaptive fuzzy-neural network is designed and simulated. Adaptive Neuro-Fuzzy Inference System (ANFIS) is enhanced by expert knowledge of the fuzzy deduction method and neural network capacities.

Keywords: Fixed channel assignment (FCA), dynamic channel allocation (DCA), Adaptive Neuro Fuzzy Inference System (ANFIS)
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