  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.
