USE OF DISTANCE INFORMATION TO IMPROVE PATHLOSS ESTIMATION PERFORMANCE AT SUB-6 GHZ AND MMWAVE FREQUENCIES WITH DEEP LEARNING

The rapid development of technology and the increasing need for community connectivity demands very fast and stable internet network access. In recent years, mobile operators have been racing to launch 5G services, which requires accurate channel models to estimate path loss, as an important comp...

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Main Author: Nur Afifah, Renata
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/84840
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:84840
spelling id-itb.:848402024-08-18T23:31:00ZUSE OF DISTANCE INFORMATION TO IMPROVE PATHLOSS ESTIMATION PERFORMANCE AT SUB-6 GHZ AND MMWAVE FREQUENCIES WITH DEEP LEARNING Nur Afifah, Renata Indonesia Theses CNN, satellite imagery, deep learning, distance features, empirical model, pathloss, VGG, 5G. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/84840 The rapid development of technology and the increasing need for community connectivity demands very fast and stable internet network access. In recent years, mobile operators have been racing to launch 5G services, which requires accurate channel models to estimate path loss, as an important component in the design of 5G systems and future mobile communications systems. Path loss refers to the loss of signal strength transmitted over long distances, so more detailed and accurate path loss estimates are needed. Various models have been proposed, including conventional empirical models, but these methods are often too simple and only provide one-shot solutions that are less adaptive. Alternatively, machine learning, especially deep learning, has been proven to provide faster and more accurate results in wireless communications. Previous research using the VGG architecture without additional features showed low correlation: 0.091 at 2.6 GHz, 0.156 at 28 GHz, and 0.116 at 38 GHz, and was unable to capture changes in path loss with distance well. To overcome these limitations, we propose three new architectures: VGG+D, VGG+DFC, and VGG+EM, which are developed for sub-6 GHz and mmWave frequencies. The results show that the VGG+EM architecture, which integrates empirical data with CNN output, shows the best performance with correlations of 0.617 at 2.6 GHz, 0.593 at 28 GHz, and 0.621 at 38 GHz, far surpassing other architectures. This research confirms that the VGG+EM architecture is not only superior in terms of correlation and prediction accuracy compared to deep learning models, but also more effective than traditional empirical models. Therefore, this architecture offers a more reliable and adaptive solution for planning and optimizing 5G networks, especially at mmWave frequencies. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description The rapid development of technology and the increasing need for community connectivity demands very fast and stable internet network access. In recent years, mobile operators have been racing to launch 5G services, which requires accurate channel models to estimate path loss, as an important component in the design of 5G systems and future mobile communications systems. Path loss refers to the loss of signal strength transmitted over long distances, so more detailed and accurate path loss estimates are needed. Various models have been proposed, including conventional empirical models, but these methods are often too simple and only provide one-shot solutions that are less adaptive. Alternatively, machine learning, especially deep learning, has been proven to provide faster and more accurate results in wireless communications. Previous research using the VGG architecture without additional features showed low correlation: 0.091 at 2.6 GHz, 0.156 at 28 GHz, and 0.116 at 38 GHz, and was unable to capture changes in path loss with distance well. To overcome these limitations, we propose three new architectures: VGG+D, VGG+DFC, and VGG+EM, which are developed for sub-6 GHz and mmWave frequencies. The results show that the VGG+EM architecture, which integrates empirical data with CNN output, shows the best performance with correlations of 0.617 at 2.6 GHz, 0.593 at 28 GHz, and 0.621 at 38 GHz, far surpassing other architectures. This research confirms that the VGG+EM architecture is not only superior in terms of correlation and prediction accuracy compared to deep learning models, but also more effective than traditional empirical models. Therefore, this architecture offers a more reliable and adaptive solution for planning and optimizing 5G networks, especially at mmWave frequencies.
format Theses
author Nur Afifah, Renata
spellingShingle Nur Afifah, Renata
USE OF DISTANCE INFORMATION TO IMPROVE PATHLOSS ESTIMATION PERFORMANCE AT SUB-6 GHZ AND MMWAVE FREQUENCIES WITH DEEP LEARNING
author_facet Nur Afifah, Renata
author_sort Nur Afifah, Renata
title USE OF DISTANCE INFORMATION TO IMPROVE PATHLOSS ESTIMATION PERFORMANCE AT SUB-6 GHZ AND MMWAVE FREQUENCIES WITH DEEP LEARNING
title_short USE OF DISTANCE INFORMATION TO IMPROVE PATHLOSS ESTIMATION PERFORMANCE AT SUB-6 GHZ AND MMWAVE FREQUENCIES WITH DEEP LEARNING
title_full USE OF DISTANCE INFORMATION TO IMPROVE PATHLOSS ESTIMATION PERFORMANCE AT SUB-6 GHZ AND MMWAVE FREQUENCIES WITH DEEP LEARNING
title_fullStr USE OF DISTANCE INFORMATION TO IMPROVE PATHLOSS ESTIMATION PERFORMANCE AT SUB-6 GHZ AND MMWAVE FREQUENCIES WITH DEEP LEARNING
title_full_unstemmed USE OF DISTANCE INFORMATION TO IMPROVE PATHLOSS ESTIMATION PERFORMANCE AT SUB-6 GHZ AND MMWAVE FREQUENCIES WITH DEEP LEARNING
title_sort use of distance information to improve pathloss estimation performance at sub-6 ghz and mmwave frequencies with deep learning
url https://digilib.itb.ac.id/gdl/view/84840
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