A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer

Merima Kulin, Tarik Kazaz, Eli De Poorter, Ingrid Moerman

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
3 Downloads (Pure)

Abstract

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY,MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-ofservice (QoS) and quality-of-experience (QoE).We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

Original languageEnglish
Article number318
Pages (from-to)1-64
Number of pages64
JournalElectronics (Switzerland)
Volume10
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • AI
  • Data science
  • Deep learning
  • MAC
  • Machine learning
  • Performance optimization
  • PHY
  • Protocol layers

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