An Overview of Machine Learning Techniques for Radiowave Propagation Modeling

TL;DR

This paper reviews recent machine learning approaches for modeling radiowave propagation, highlighting challenges, categorizing methods, and discussing future prospects and open problems in this rapidly evolving field.

Contribution

It provides a comprehensive overview of machine learning techniques applied to radiowave propagation modeling, identifying key challenges and categorizing existing approaches.

Findings

Identification of main challenges in ML-based propagation models

Categorization of recent research approaches

Discussion of open problems and future directions

Abstract

We give an overview of recent developments in the modeling of radiowave propagation, based on machine learning algorithms. We identify the input and output specification and the architecture of the model as the main challenges associated with machine learning-driven propagation models. Relevant papers are discussed and categorized based on their approach to each of these challenges. Emphasis is given on presenting the prospects and open problems in this promising and rapidly evolving area.