Abstract

Artificial intelligence is employed for solving complex scientific, technical, and practical problems. Such artificial intelligence techniques as neural networks, fuzzy systems, and genetic and evolutionary algorithms are widely used for communication systems management, optimization, and prediction. Artificial intelligence approach provides optimized results in a challenging task of call admission control, handover, routing, and traffic prediction in cellular networks. 5G mobile communications are designed as heterogeneous networks, whose important requirement is accommodating great numbers of users and the quality of service satisfaction. Call admission control plays a significant role in providing the desired quality of service. An effective call admission control algorithm is needed for optimizing the cellular network system. Many call admission control schemes have been proposed. The paper proposes a methodology for developing a genetic neurofuzzy controller for call admission in 5G networks. Performance of the proposed admission control is evaluated through computer simulation.

Highlights

  • The proposed fuzzy controller is to be converted into an adaptive neurofuzzy inference system (ANFIS), suitable for operating under uncertain conditions

  • This paper considers the artificial intelligence based methodology for designing the controller for call admission in 5G networks to support their quality of service

  • To give a better solution to the optimization problem, the suggested controller is built combining a fuzzy system with an artificial neural network and genetic algorithm

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Summary

Introduction

Call admission control offers an effective way to avoid network traffic congestion providing a guaranteed quality of service. A call admission control algorithm provides a decision whether a call should be accepted into a network or dropped. Using artificial intelligence technique for call admission control was proposed in [10]. According to [11], applying the fuzzy systems provides reducing the call rejection and a higher quality of service. Its results have substantiated solving the call admission control problem with the neural network approach. In [17] genetic algorithms have been proposed to be applied for optimization of the call admission problem that led to better user’s satisfaction [18]. Using neural networks, fuzzy systems, and genetic algorithms may provide solving the call rejection problem.

Materials and Methods
E O Fuzzification
Conclusions
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