Channel modelling simulation and machine learning

In the ever-evolving landscape of modern communication systems, the ability to understand and optimise channel behaviour is crucial for achieving reliable and efficient data transmission. Channel modelling is pivotal in this endeavour by providing insights into how signals propagate through diverse...

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Main Author: Kang, Yann Jun Yan
Other Authors: Lee Yee Hui
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176822
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1768222024-05-31T15:44:50Z Channel modelling simulation and machine learning Kang, Yann Jun Yan Lee Yee Hui School of Electrical and Electronic Engineering EYHLee@ntu.edu.sg Engineering In the ever-evolving landscape of modern communication systems, the ability to understand and optimise channel behaviour is crucial for achieving reliable and efficient data transmission. Channel modelling is pivotal in this endeavour by providing insights into how signals propagate through diverse media and environments. While conventional channel modelling techniques have been effective, they often fall short of capturing the intricacies of real-world scenarios. This report delves into the intersection of channel modelling, simulation, and machine learning, offering a comprehensive overview of the synergies and advancements in these fields. A standard approach to conducting this experiment would be to gather a vast quantity of data on channel measurements before applying statistical techniques to choose the best channel models [1]. The incorporation of simulation and machine learning methodologies into channel modelling is a revolutionary strategy that enables us to address intricate communication problems with increased accuracy and flexibility. In this report, we explore the fundamental concepts of channel modelling and its significance in communication systems. We then delve into the realm of simulation, discussing how it aids in creating realistic environments for channel testing and validation. Subsequently, aiming to incorporate machine learning techniques to optimised and automated the communication systems based on data-driven insights. Bachelor's degree 2024-05-27T11:27:56Z 2024-05-27T11:27:56Z 2023 Final Year Project (FYP) Kang, Y. J. Y. (2023). Channel modelling simulation and machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176822 https://hdl.handle.net/10356/176822 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
spellingShingle Engineering
Kang, Yann Jun Yan
Channel modelling simulation and machine learning
description In the ever-evolving landscape of modern communication systems, the ability to understand and optimise channel behaviour is crucial for achieving reliable and efficient data transmission. Channel modelling is pivotal in this endeavour by providing insights into how signals propagate through diverse media and environments. While conventional channel modelling techniques have been effective, they often fall short of capturing the intricacies of real-world scenarios. This report delves into the intersection of channel modelling, simulation, and machine learning, offering a comprehensive overview of the synergies and advancements in these fields. A standard approach to conducting this experiment would be to gather a vast quantity of data on channel measurements before applying statistical techniques to choose the best channel models [1]. The incorporation of simulation and machine learning methodologies into channel modelling is a revolutionary strategy that enables us to address intricate communication problems with increased accuracy and flexibility. In this report, we explore the fundamental concepts of channel modelling and its significance in communication systems. We then delve into the realm of simulation, discussing how it aids in creating realistic environments for channel testing and validation. Subsequently, aiming to incorporate machine learning techniques to optimised and automated the communication systems based on data-driven insights.
author2 Lee Yee Hui
author_facet Lee Yee Hui
Kang, Yann Jun Yan
format Final Year Project
author Kang, Yann Jun Yan
author_sort Kang, Yann Jun Yan
title Channel modelling simulation and machine learning
title_short Channel modelling simulation and machine learning
title_full Channel modelling simulation and machine learning
title_fullStr Channel modelling simulation and machine learning
title_full_unstemmed Channel modelling simulation and machine learning
title_sort channel modelling simulation and machine learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176822
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