Large scale survey for radio propagation in developing machine learning model for path losses in communication systems

Abstract

Several orthodox approaches, such as empirical methods and deterministic methods, had earlier been used for the prediction of path loss in wireless communication systems. These approaches are either inefficient or complex. Robustness and performance motivated the adoption of machine learning for modeling path loss in wireless communication systems in place of traditional modeling schemes. Surveys on modeling path loss in communication systems exist in the literature; however, emerging deep learning architectures in-depth analysis, machine learning taxonomies related to path loss, and in-depth analysis on feature engineering in modeling path loss are missing in the already published surveys. To fill this existing gap, a survey of machine learning modeling for path loss in wireless communication systems is conducted to resolve the outlined issues, hence making this survey unique. Synthesis and analysis of deep learning architectures to solve path loss problems in communication systems are hereby presented. New taxonomy – deep learning, nature-inspired meta-heuristic algorithms, and shallow algorithms approach to path loss modeling have been created. Analysis of feature engineering in path loss modeling is exploited. Lastly, challenges militating against the full potential of modeling path loss based on machine learning are highlighted and discussed. Alternative approaches for resolving the outlined challenges are also presented in the survey to help in designing more practical applications in the future.