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Seamless handover optimization for LTE-A communication using Kalman filtering and DQN

In LTE-A networks, achieving optimal Hard Handover (HHO) for uninterrupted connectivity and Quality of Service (QoS) adherence poses a persistent challenge, particularly in the context of Intelligent Transportation Systems (ITS) scenarios. Existing handover mechanisms often exhibit deficiencies in l...

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Main Author: Shakir, Abdullah Talaat
Format: Thesis
Language:English
English
Published: 2024
Online Access:http://eprints.utem.edu.my/id/eprint/28324/1/Seamless%20handover%20optimization%20for%20LTE-A%20communication%20using%20Kalman%20filtering%20and%20DQN.pdf
http://eprints.utem.edu.my/id/eprint/28324/2/Seamless%20handover%20optimization%20for%20LTE-A%20communication%20using%20Kalman%20filtering%20and%20DQN.pdf
http://eprints.utem.edu.my/id/eprint/28324/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124259
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spelling my.utem.eprints.283242024-12-27T15:55:36Z http://eprints.utem.edu.my/id/eprint/28324/ Seamless handover optimization for LTE-A communication using Kalman filtering and DQN Shakir, Abdullah Talaat In LTE-A networks, achieving optimal Hard Handover (HHO) for uninterrupted connectivity and Quality of Service (QoS) adherence poses a persistent challenge, particularly in the context of Intelligent Transportation Systems (ITS) scenarios. Existing handover mechanisms often exhibit deficiencies in localization improvement, integration of process models and measurement data, and determining an optimal Time to Trigger (TTT) based on past learning experiences. These limitations are compounded by deterministic rules that fail to account for the dynamic nature of mobility and the multifaceted factors influencing handover decisions. To mitigate these challenges, this thesis proposes a novel hybrid model integrating a Map Sampling-based Kalman Filter (MA-KALMAN) and a Deep Q-learning Network (DQN) for HHO decision-making. The MA-KALMAN component improves the accuracy of mobility data by merging GPS measurements with process models, while the DQN framework optimizes decisions by learning from dynamic network conditions. Comparative evaluations against traditional models, including the Kalman filter for localization, Q-learning, and static approaches for handover decision-making, were conducted, focusing on key performance metrics such as RMSE, End-to-End delay (E2Edelay), Packet Delivery Ratio (PDR), Number of Hard Handovers (No. of HHO), and Hard Handover Ping-Pong (HHO Ping-Pong) instances. Empirical findings substantiate the superior performance of the MA-KALMAN and DQN-based handover decision-making models, which minimize latency, enhance reliability, and determine an optimal TTT, ensuring seamless connectivity and QoS adherence in LTE-A networks. This research advances wireless communications by addressing critical issues in localization and TTT optimization. 2024 Thesis NonPeerReviewed text en http://eprints.utem.edu.my/id/eprint/28324/1/Seamless%20handover%20optimization%20for%20LTE-A%20communication%20using%20Kalman%20filtering%20and%20DQN.pdf text en http://eprints.utem.edu.my/id/eprint/28324/2/Seamless%20handover%20optimization%20for%20LTE-A%20communication%20using%20Kalman%20filtering%20and%20DQN.pdf Shakir, Abdullah Talaat (2024) Seamless handover optimization for LTE-A communication using Kalman filtering and DQN. Doctoral thesis, Universiti Teknikal Malaysia Melaka. https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124259
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
English
description In LTE-A networks, achieving optimal Hard Handover (HHO) for uninterrupted connectivity and Quality of Service (QoS) adherence poses a persistent challenge, particularly in the context of Intelligent Transportation Systems (ITS) scenarios. Existing handover mechanisms often exhibit deficiencies in localization improvement, integration of process models and measurement data, and determining an optimal Time to Trigger (TTT) based on past learning experiences. These limitations are compounded by deterministic rules that fail to account for the dynamic nature of mobility and the multifaceted factors influencing handover decisions. To mitigate these challenges, this thesis proposes a novel hybrid model integrating a Map Sampling-based Kalman Filter (MA-KALMAN) and a Deep Q-learning Network (DQN) for HHO decision-making. The MA-KALMAN component improves the accuracy of mobility data by merging GPS measurements with process models, while the DQN framework optimizes decisions by learning from dynamic network conditions. Comparative evaluations against traditional models, including the Kalman filter for localization, Q-learning, and static approaches for handover decision-making, were conducted, focusing on key performance metrics such as RMSE, End-to-End delay (E2Edelay), Packet Delivery Ratio (PDR), Number of Hard Handovers (No. of HHO), and Hard Handover Ping-Pong (HHO Ping-Pong) instances. Empirical findings substantiate the superior performance of the MA-KALMAN and DQN-based handover decision-making models, which minimize latency, enhance reliability, and determine an optimal TTT, ensuring seamless connectivity and QoS adherence in LTE-A networks. This research advances wireless communications by addressing critical issues in localization and TTT optimization.
format Thesis
author Shakir, Abdullah Talaat
spellingShingle Shakir, Abdullah Talaat
Seamless handover optimization for LTE-A communication using Kalman filtering and DQN
author_facet Shakir, Abdullah Talaat
author_sort Shakir, Abdullah Talaat
title Seamless handover optimization for LTE-A communication using Kalman filtering and DQN
title_short Seamless handover optimization for LTE-A communication using Kalman filtering and DQN
title_full Seamless handover optimization for LTE-A communication using Kalman filtering and DQN
title_fullStr Seamless handover optimization for LTE-A communication using Kalman filtering and DQN
title_full_unstemmed Seamless handover optimization for LTE-A communication using Kalman filtering and DQN
title_sort seamless handover optimization for lte-a communication using kalman filtering and dqn
publishDate 2024
url http://eprints.utem.edu.my/id/eprint/28324/1/Seamless%20handover%20optimization%20for%20LTE-A%20communication%20using%20Kalman%20filtering%20and%20DQN.pdf
http://eprints.utem.edu.my/id/eprint/28324/2/Seamless%20handover%20optimization%20for%20LTE-A%20communication%20using%20Kalman%20filtering%20and%20DQN.pdf
http://eprints.utem.edu.my/id/eprint/28324/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124259
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score 13.223943

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