[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120979-en":3,"doc-seo-120979-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120979,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",6,"Technology","A Quick Primer on Machine Learning in Wireless Communications","This primer equips readers to build reproducible AI and ML simulations for wireless communications using Python on inexpensive computing devices. It demonstrates how to simulate multiple-input multiple-output (MIMO) systems for a single OFDM symbol, while introducing practical AI/ML use cases and the deepwireless library for code implementation. The material develops from a graduate-course aligned draft and evolves into a foundation for prototyping AI-based air interfaces and exploring scenarios reproducibly.","arXiv :2312 . 17713v6 [ cs .NI] 20 May 2025  \nA Quick Primer on Machine Learning in Wireless Communications  \nFaris B. Mismar  \nAbstract  \nThis 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.  \nIndex Terms  \nPython, MIMO, OFDM, deepwireless, supervised learning, artificial intelligence, deep learning, convolution, time series, unsupervised learning, reinforcement learning.  \nI. INTRODUCTION  \nThe research domain of wireless communications and machine learning has been supplied with an abundance of publications, yielding numerous source codes implementing countless assumptions and configurations. These source codes—especially when proprietary or released under restrictive license agreements—can make the idea of reproducibility extremely challenging. Because of this, and keeping simplicity in mind, we share this quick primer on machine learning in wireless communications in addition to the source code related to it. We call the essential library that implements this primer “deepwireless.” The objective of this primer and  \nThe author is an adjunct associate professor of electrical and computer engineering at The University of Texas at Dallas (email: [fbm090020@utdallas.edu](fbm090020@utdallas.edu)) .  \nthe deepwireless library is to fast-track the reader into the ability to build their simulations efficiently using inexpensive computing environments and open-source tools—especially ones with machine learning (ML) and artificial intelligence (AI) applications. Because the wireless communications system of our choice supports multiple-input and multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM), this source code can be used for the prototyping of AI-based air interfaces (AI-AI) for 4G LTE and 5G air interfaces alike. We hope that AI-AI would empower next-generation wireless networks beyond 5G and thus extend the longevity of these libraries and related source code. The deepwireless library and the related source code are written in Python and are available online. Specifically, the deepwireless library is available on GitHub [1] or can be installed using pip and a well-documented source code implementing all the scenarios shown in this primer using deepwireless is available on GitHub. It is no surprise that we have chosen Python to implement deepwireless as Python has become the quintessential programming language for building AI and ML applications due to its simplicity and abundance of supporting libraries. Moreover, it is free to use.  \nAlso, considering the large number of acronyms and abbreviations in this primer, we build atable that can be referenced. These abbreviations can be found in Table I.  \nII. PROTOCOL STACK  \nThe wireless protocol stack enables communication between devices in cellular networks, facilitating seamless data transmission over the air. The stack is divided into two planes: the user plane and the control plane. The user plane protocol is responsible for handling the actual user data: the content transmitted between end devices (e.g., voice, video, and Internet traffic) .  \nREVISION DATE: 5/15/2025 2  \nTABLE I  \nABBREVIATIONS  \nAI  \nBER  \nBLER  \nBS  \nCDL  \nCNN  \nCRC  \nCSI  \nCoMP  \nDFT  \nDNN  \nDQN  \nFEC  \nFFT  \nGAN  \nArtificial Intelligenc","cbCaiu97ktIRsY1N","https://ap.wps.com/l/cbCaiu97ktIRsY1N","pdf",687959,1,34,"English","en",105,"# Introduction\n## Protocol objective and reproducibility\n# Protocol stack\n## User plane and control plane\n## PHY layer sublayers\n## Medium access control (MAC)\n# Abbreviations\n## Key AI/ML and wireless terms","[{\"question\":\"What is the main goal of this primer?\",\"answer\":\"Enable readers to create efficient, reproducible AI and ML simulations for wireless communications using Python and open-source tools.\"},{\"question\":\"What wireless system setup does the primer simulate?\",\"answer\":\"It simulates MIMO systems for a single orthogonal frequency division multiplexing (OFDM) symbol.\"},{\"question\":\"What is the deepwireless library used for?\",\"answer\":\"It provides code implementation and scenarios that fast-track readers in building their wireless AI/ML simulations.\"}]","A Quick Primer on Machine Learning in Wireless Communications | 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