Link quality prediction in wireless community networks using deep recurrent neural networks

Mohamed Abdel-Nasser*, Karar Mahmoud, Osama A. Omer, Matti Lehtonen, Domenec Puig

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Downloads (Pure)

Abstract

Wireless community networks (WCNs) are large, heterogeneous, dynamic, and decentralized networks. Such complex characteristics raise different challenges, such as the effect of wireless communications on the performance of networks and routing protocols. The prediction approaches of link quality (LQ) can improve the performance of routing algorithms of WCNs while avoiding weak links. The prediction of LQ in WCNs can be a complex task because of the fluctuated nature of LQ measurements due to the dynamic wireless environment. In this paper, a deep learning based approach is proposed to accurately predict LQ in WCNs. Specifically, we propose the use of two variants of deep recurrent neural network (RNN): long short-term memory recurrent neural networks (LSTM-RNN) and gated recurrent unit (GRU). The positive feature of the proposed variants is that they can handle the fluctuating nature of LQ due to their ability to learn and exploit the context in LQ time-series. The experimental results on data collected from a real-world WCN show that the proposed LSTM-RNN and GRU models accurately predict LQ in WCNs compared to related methods. The proposed approach could be a helpful tool for accurately predicting LQ, thereby improving the performance of routing protocols of WCNs.
Original languageEnglish
Pages (from-to)3531-3543
Number of pages13
JournalAlexandria Engineering Journal
Volume59
Issue number5
Early online date26 Jun 2020
DOIs
Publication statusPublished - Oct 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Link quality prediction
  • Time-series analysis
  • Deep learning
  • RNN
  • LSTM
  • GRU

Fingerprint

Dive into the research topics of 'Link quality prediction in wireless community networks using deep recurrent neural networks'. Together they form a unique fingerprint.

Cite this