A survey on machine learning-based performance improvement of wireless networks
PHY, MAC and network layer
Merima Kulin (Universiteit Gent)
Tarik Kazaz (TU Delft - Signal Processing Systems)
Eli De Poorter (Universiteit Gent)
Ingrid Moerman (Universiteit Gent)
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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.
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