An Overview of Machine Learning Techniques for Radiowave Propagation Modeling

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.

15 pages, 9 figures, 2 small tables and 1 1-page table

References in corpus (3)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Conditional Generative Adversarial Nets
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Cited by in corpus (3)

• Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements
• DeepAlloc: CNN-Based Approach to Efficient Spectrum Allocation in Shared Spectrum Systems
• Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling