1 research outputs found
Machine Learning for Wireless Link Quality Estimation: A Survey
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 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 LQE models from two perspectives. 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