Quality of service (QoS) for LTE network based on adaptive neuro fuzzy inference system

Abstract

Abstract The main objective of this paper is to design an Adaptive Neuro Fuzzy Inference System model to calculate the quality of service for LTE HetNet applications. The quality of service parameters considered are delay, loss rate, throughput, and jitter. The adaptive neuro fuzzy inference system is an integration of fuzzy logic and neural network. The advantage of neural network in adaptive neuro fuzzy inference system is to train the neural network algorithm on the parameter values of membership function for fuzzy logic to construct fuzzy decision. So, adaptive neuro fuzzy inference system gives better performance than fuzzy logic alone for LTE network applications (e.g. VOIP, HTTP, VIDEO, and EMAIL). The results based on adaptive neuro fuzzy inference system model produce high quality of service for LTE network applications as compared with fuzzy logic alone. It is also found that adaptive neuro fuzzy inference system results in EMAIL and quality of service outperform fuzzy logic alone by about 28.7% at medium delay, low loss rate, jitter, and high throughput.