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dc.contributor.advisorSabuj, Saifur Rahman
dc.contributor.advisorBhuian, Mohammed Belal Hossain
dc.contributor.authorAdhikari, Rudra Lal
dc.date.accessioned2023-09-19T04:00:54Z
dc.date.available2023-09-19T04:00:54Z
dc.date.copyright2023
dc.date.issued2023-04
dc.identifier.otherID 22271003
dc.identifier.urihttp://hdl.handle.net/10361/20991
dc.descriptionThis project report is submitted in partial fulfilment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, 2023.en_US
dc.descriptionCataloged from PDF version of the project report.
dc.descriptionIncludes bibliographical references (pages 60-82).
dc.description.abstractThe 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.en_US
dc.description.statementofresponsibilityRudra Lal Adhikari
dc.format.extent82 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac 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.
dc.subjectListen Before Talken_US
dc.subject5G NRen_US
dc.subjectLTE-LAAen_US
dc.subjectJains Fairness indexen_US
dc.subjectReceiver operating characteren_US
dc.subject.lcshMachine learning
dc.titleA comparative study of listen before talk categories using machine learningen_US
dc.typeProject reporten_US
dc.contributor.departmentDepartment of Electrical and Electronic Engineering, BRAC University
dc.description.degreeM. Electrical and Electronic Engineering


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