A comparative study of listen before talk categories using machine learning

This project report is submitted in partial fulfilment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, 2023.

Bibliografiske detaljer
Hovedforfatter:	Adhikari, Rudra Lal
Andre forfattere:	Sabuj, Saifur Rahman
Format:	Project report
Sprog:	English
Udgivet:	Brac University 2023
Fag:	Listen Before Talk 5G NR LTE-LAA Jains Fairness index Receiver operating character Machine learning
Online adgang:	http://hdl.handle.net/10361/20991

id	10361-20991
record_format	dspace
spelling	10361-209912023-09-19T21:02:23Z A comparative study of listen before talk categories using machine learning Adhikari, Rudra Lal Sabuj, Saifur Rahman Bhuian, Mohammed Belal Hossain Department of Electrical and Electronic Engineering, BRAC University Listen Before Talk 5G NR LTE-LAA Jains Fairness index Receiver operating character Machine learning This project report is submitted in partial fulfilment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, 2023. Cataloged from PDF version of the project report. Includes bibliographical references (pages 60-82). The cellular industry is seeking solutions to efficiently utilize the available spectrum band due to the rapid growth of wireless traffic and technological advancements. One potential solution that has gained attention is implementing Long Term Evolution (LTE) with unlicensed spectrum (LTE-U) using a Listen before Talk (LBT) approach, as prescribed by international regulators. To ensure fair channel access for co-located networks, it is crucial to establish a coexistence strategy that incorporates expected traffic requirements for both present and future needs. Machine learning has been recognized for its ability to automate critical wireless communication network activities, gather data from multiple sources, and employ various algorithms. Thus, this project emphasizes the significance of researching LTE and Wi-Fi coexistence in unlicensed spectrum using machine learning. It provides an overview of existing LTE-U and Wi-Fi technologies, and reviews the studies that have been conducted on their coexistence. The project also discusses LBT mechanisms and their categories as defined by the 3GPP standard, as well as previous research conducted in various categories, providing a basis for future research. The study evaluates the performance of each priority class of LBT Cat 4 using the Jains Fairness with machine learning approach to determine the best coexistence priority class of LBT Cat 4 that will enhance future network performance when coexisting with Wi-Fi. Thus in wireless communication systems, machine learning can be used to optimize the LBT protocol by learning the patterns and characteristics of the communication channel. By training the large amounts of data collected from the communication channel, the network can learn to predict when the channel will be free and when it will be busy. This can help to reduce the waiting time for devices and increase the efficiency of the communication system. Moreover it help us to understand the channel sharing fairness and signal detection probability better for each of the priority class of LBT Cat4. Rudra Lal Adhikari M. Electrical and Electronic Engineering 2023-09-19T04:00:54Z 2023-09-19T04:00:54Z 2023 2023-04 Project report ID 22271003 http://hdl.handle.net/10361/20991 en Brac University project reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 82 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Listen Before Talk 5G NR LTE-LAA Jains Fairness index Receiver operating character Machine learning
spellingShingle	Listen Before Talk 5G NR LTE-LAA Jains Fairness index Receiver operating character Machine learning Adhikari, Rudra Lal A comparative study of listen before talk categories using machine learning
description	This project report is submitted in partial fulfilment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, 2023.
author2	Sabuj, Saifur Rahman
author_facet	Sabuj, Saifur Rahman Adhikari, Rudra Lal
format	Project report
author	Adhikari, Rudra Lal
author_sort	Adhikari, Rudra Lal
title	A comparative study of listen before talk categories using machine learning
title_short	A comparative study of listen before talk categories using machine learning
title_full	A comparative study of listen before talk categories using machine learning
title_fullStr	A comparative study of listen before talk categories using machine learning
title_full_unstemmed	A comparative study of listen before talk categories using machine learning
title_sort	comparative study of listen before talk categories using machine learning
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/20991
work_keys_str_mv	AT adhikarirudralal acomparativestudyoflistenbeforetalkcategoriesusingmachinelearning AT adhikarirudralal comparativestudyoflistenbeforetalkcategoriesusingmachinelearning
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A comparative study of listen before talk categories using machine learning

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