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...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/176822 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-176822 |
---|---|
record_format |
dspace |
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 |
_version_ |
1806059844628643840 |