Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
Long-Term Evolution (LTE) technology is utilized efficiently for wireless broadband communication for mobile devices. It provides flexible bandwidth and frequency with high speed and peak data rates. Optimizing resource allocation is vital for improving the performance of the Long-Term Evolution (LT...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/12/9/179 |
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| author | Falah Y. H. Ahmed Amal Abulgasim Masli Bashar Khassawneh Jabar H. Yousif Dilovan Asaad Zebari |
| author_facet | Falah Y. H. Ahmed Amal Abulgasim Masli Bashar Khassawneh Jabar H. Yousif Dilovan Asaad Zebari |
| author_sort | Falah Y. H. Ahmed |
| collection | DOAJ |
| description | Long-Term Evolution (LTE) technology is utilized efficiently for wireless broadband communication for mobile devices. It provides flexible bandwidth and frequency with high speed and peak data rates. Optimizing resource allocation is vital for improving the performance of the Long-Term Evolution (LTE) system and meeting the user’s quality of service (QoS) needs. The resource distribution in video streaming affects the LTE network performance, reducing network fairness and causing increased delay and lower data throughput. This study proposes a novel approach utilizing an artificial neural network (ANN) based on normalized radial basis function NN (RBFNN) and generalized regression NN (GRNN) techniques. The 3rd Generation Partnership Project (3GPP) is proposed to derive accurate and reliable data output using the LTE downlink scheduling algorithms. The performance of the proposed methods is compared based on their packet loss rate, throughput, delay, spectrum efficiency, and fairness factors. The results of the proposed algorithm significantly improve the efficiency of real-time streaming compared to the LTE-DL algorithms. These improvements are also shown in the form of lower computational complexity. |
| first_indexed | 2024-03-10T22:54:13Z |
| format | Article |
| id | doaj.art-6c90c3625b3c406cb7cf00c384a2c0dd |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-10T22:54:13Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-6c90c3625b3c406cb7cf00c384a2c0dd2023-11-19T10:07:28ZengMDPI AGComputers2073-431X2023-09-0112917910.3390/computers12090179Optimized Downlink Scheduling over LTE Network Based on Artificial Neural NetworkFalah Y. H. Ahmed0Amal Abulgasim Masli1Bashar Khassawneh2Jabar H. Yousif3Dilovan Asaad Zebari4Faculty of Computing and Information Technology, Sohar University, Sohar 311, OmanFaculty of Education, Computer Science Department, Misurata University, Misrata 9329+V25, LibyaDepartment of Computer Science, Irbid National University, Irbid 2600, JordanFaculty of Computing and Information Technology, Sohar University, Sohar 311, OmanDepartment of Computer Science, College of Science, Nawroz University, Duhok 42001, IraqLong-Term Evolution (LTE) technology is utilized efficiently for wireless broadband communication for mobile devices. It provides flexible bandwidth and frequency with high speed and peak data rates. Optimizing resource allocation is vital for improving the performance of the Long-Term Evolution (LTE) system and meeting the user’s quality of service (QoS) needs. The resource distribution in video streaming affects the LTE network performance, reducing network fairness and causing increased delay and lower data throughput. This study proposes a novel approach utilizing an artificial neural network (ANN) based on normalized radial basis function NN (RBFNN) and generalized regression NN (GRNN) techniques. The 3rd Generation Partnership Project (3GPP) is proposed to derive accurate and reliable data output using the LTE downlink scheduling algorithms. The performance of the proposed methods is compared based on their packet loss rate, throughput, delay, spectrum efficiency, and fairness factors. The results of the proposed algorithm significantly improve the efficiency of real-time streaming compared to the LTE-DL algorithms. These improvements are also shown in the form of lower computational complexity.https://www.mdpi.com/2073-431X/12/9/179LTE networkresource allocationANNnormalized model |
| spellingShingle | Falah Y. H. Ahmed Amal Abulgasim Masli Bashar Khassawneh Jabar H. Yousif Dilovan Asaad Zebari Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network Computers LTE network resource allocation ANN normalized model |
| title | Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network |
| title_full | Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network |
| title_fullStr | Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network |
| title_full_unstemmed | Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network |
| title_short | Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network |
| title_sort | optimized downlink scheduling over lte network based on artificial neural network |
| topic | LTE network resource allocation ANN normalized model |
| url | https://www.mdpi.com/2073-431X/12/9/179 |
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