An Overview of Machine Learning Techniques for Radiowave Propagation Modeling
This is an incremental overview paper for researchers in radiowave propagation modeling, summarizing existing methods without presenting new results.
The paper provides an overview of machine learning techniques for radiowave propagation modeling, identifying input-output specification and model architecture as key challenges, and discusses prospects and open problems in the field.
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.