A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer
It synthesizes existing research for researchers and practitioners in wireless networks, but is incremental as a survey.
This paper provides a systematic survey reviewing machine learning-based approaches to improve performance in wireless networks across PHY, MAC, and network layers, categorizing works into radio analysis, MAC analysis, and network prediction.
This paper provides 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 for non-machine learning experts to 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-of-service (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.