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Predicting VoLTE Quality using Random Neural Network

Duy-Huy Nguyen, Hang Nguyen, Eric Renault. Published in Networks

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Duy-Huy Nguyen, Hang Nguyen, Eric Renault
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  1. Duy-Huy Nguyen, Hang Nguyen and Eric Renault. Predicting VoLTE Quality using Random Neural Network. International Journal of Applied Information Systems 11(3):1-5, August 2016. URL, DOI BibTeX

    	author = "Duy-Huy Nguyen and Hang Nguyen and Eric Renault",
    	title = "Predicting VoLTE Quality using Random Neural Network",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "August 2016",
    	volume = 11,
    	number = 3,
    	month = "Aug",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "1-5",
    	numpages = 5,
    	url = "",
    	doi = "10.5120/ijais2016451587",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


Long Term Evolution (LTE) was initially designed for a high data rates network. However, voice service is always a main service that drives huge profits benefit for mobile phone operators. Hence the deployment of Voice over LTE (VoLTE) is very essential. LTE network is a fully All-IP network, thus, the deployment of VoLTE is quite complex, specially for guaranteeing of Quality of Service (QoS) for meeting quality of experience of mobile users. The key purpose of this paper is to present an object, non-intrusive prediction model for VoLTE quality based on Random Neural Network (RNN). In order to simulate an experiment, a three-layer feedforward RNN architecture with gradient descent training algorithm is applied. The inputs of this model are object network impairments such as Packet Loss Rate (PLR), Delay and Jitter. The VoLTE quality was predicted in term of the Mean Opinion Score (MOS). The simulation results show that this model offers MOS values which are quite close to well-known method is WB-PESQ (Wideband Perceptual Evaluation of Speech Quality) model. The results also show that the proposed model is very suitable for predicting voice quality over LTE network.


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Voice quality, VoLTE, MOS, WB-PESQ, RNN