Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


January Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the January 2023 Edition of the journal. The last date of research paper submission is December 15, 2022.

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
Download full text
  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.


  1. 3GPP.
  2. Charlie C Chen. An overview of Quality of service measurement and optimization for voice over Internet. 8(1):2053–2063, 2015.
  3. ZTI Communications. http://www.
  4. Erol Gelenbe. Random neural networks with negative and positive signals and product form solution. Neural computation, 1(4):502–510, 1989.
  5. Tarik Ghalut and Hadi Larijani. Non-intrusive method for video quality prediction over lte using random neural networks (rnn). In Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2014 9th International Symposium on, pages 519–524. IEEE, 2014.
  6. Jonghwan Hyun, Jian Li, ChaeTae Im, Jae-Hyoung Yoo, and James Won-Ki Hong. A volte traffic classification method in lte network. In Network Operations and Management Symposium (APNOMS), 2014 16th Asia-Pacific, pages 1–6. IEEE, 2014.
  7. GL Communications Inc. products.html.
  8. ITU-T.
  9. ITU-T. en.
  10. ITU-T. en.
  11. ITU-T. genaudio/AudioForm-g.aspx?val=1000050.
  12. Jitsi.
  13. Kailash Chandra Mishra and Padma Charan Das. Measuring quality of service of voip based on artificial neural network approach. International Journal, 5(3), 2015.
  14. Samir Mohamed, Gerardo Rubino, and Martin Varela. Performance evaluation of real-time speech through a packet network: a random neural networks-based approach. Performance evaluation, 57(2):141–161, 2004.
  15. Kapilan Radhakrishnan and Hadi Larijani. A study on qos of voip networks: a random neural network (rnn) approach. In Proceedings of the 2010 Spring Simulation Multiconference, page 114. Society for Computer Simulation International, 2010.
  16. Kapilan Radhakrishnan and Hadi Larijani. Evaluating perceived voice quality on packet networks using different random neural network architectures. Performance Evaluation, 68(4):347–360, 2011.
  17. Stelios Timotheou. The random neural network: a survey. The computer journal, 53(3):251–267, 2010.
  18. WANem.
  19. Wikipedia. Jitsi — wikipedia, the free encyclopedia, 2015. [Online; accessed 21-October-2015].


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