A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

Abstract

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.

Multi-Antenna Data-Driven Eavesdropping Attacks and Symbol-Level Precoding Countermeasures

“…AI has been envisioned by several researchers as the most prominent feature of 6G [21], since it is an efficient tool for several contemporary complex scenarios. For instance, ML techniques can be categorized into two distinct objectives related to extracting patterns from data: first, performance improvement, in which ML is used to optimize the operating parameters at the lower layers; second, information processing of the huge data generated by wireless devices at the application layer [22].…”