  Since the emergence of wireless communication networks, a plethora of
research papers focus their attention on the quality aspects of wireless links.
  The analysis of the rich body of existing literature on link quality
estimation using models developed from data traces indicates that the
techniques used for modeling link quality estimation are becoming increasingly
sophisticated. A number of recent estimators leverage machine learning (ML)
techniques that require a sophisticated design and development process, each of
which has a great potential to significantly affect the overall model
performance.
  In this paper, we provide a comprehensive survey on link quality estimators
developed from empirical data and then focus on the subset that use ML
algorithms. We analyze ML-based link quality estimation (LQE) models from two
perspectives using performance data. Firstly, we focus on how they address
quality requirements that are important from the perspective of the
applications they serve. Secondly, we analyze how they approach the standard
design steps commonly used in the ML community. Having analyzed the scientific
body of the survey, we review existing open source datasets suitable for LQE
research. Finally, we round up our survey with the lessons learned and design
guidelines for ML-based LQE development and dataset collection.
