A Quick Primer on Machine Learning in Wireless Communications

TL;DR

This paper introduces a Python-based prototype for simulating MIMO-OFDM wireless systems, demonstrating AI/ML applications and providing tools for reproducible research on accessible hardware.

Contribution

It presents a new Python library, deepwireless, for implementing AI/ML in wireless communication simulations, tailored for educational and research purposes.

Findings

Demonstrates AI/ML use cases in wireless systems

Provides a reproducible simulation framework

Introduces the deepwireless library

Abstract

This is our final issue of the quick primer on the use of Python to build a wireless communications prototype. This prototype simulates multiple-input and multiple-output (MIMO) systems for a single orthogonal frequency division multiplexing (OFDM) symbol. Further, it shows several artificial intelligence (AI) and machine learning (ML) use cases and introduces the deepwireless library for code implementation. The intent of this primer is to empower the reader with the means to efficiently create reproducible simulations related to AI and ML in wireless communications on inexpensive computing devices. This primer has sprung from a draft aligned with the syllabus of a graduate course (EESC 7v86) -- which we created to be first taught in Fall 2022 -- and has since evolved to where it stands today.