Power allocation with non-orthogonal multiple access for 5G heterogeneous network

The fifth generation of mobile network technology (5G) through heterogeneous networks (HetNet) demands increasing the capacity of cellular wireless communications networks. In addition to the HetNet, a Non-Orthogonal Multiple Access (NOMA) can enhance its performance techniques to support a large nu...

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Main Authors: Johari, Muhammad Amirul Aiman, Anwar Apandi, Nur Ilyana, Muhammad, Nor Aishah
Format: Conference or Workshop Item
Language:English
Published: 2023
Online Access:http://eprints.utem.edu.my/id/eprint/28129/1/Power%20allocation%20with%20non-orthogonal%20multiple%20access%20for%205G%20heterogeneous%20network.pdf
http://eprints.utem.edu.my/id/eprint/28129/
https://ieeexplore.ieee.org/document/10419786
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Institution: Universiti Teknikal Malaysia Melaka
Language: English
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spelling my.utem.eprints.281292024-10-17T16:32:40Z http://eprints.utem.edu.my/id/eprint/28129/ Power allocation with non-orthogonal multiple access for 5G heterogeneous network Johari, Muhammad Amirul Aiman Anwar Apandi, Nur Ilyana Muhammad, Nor Aishah The fifth generation of mobile network technology (5G) through heterogeneous networks (HetNet) demands increasing the capacity of cellular wireless communications networks. In addition to the HetNet, a Non-Orthogonal Multiple Access (NOMA) can enhance its performance techniques to support a large number of users. Coordinated multi-point communication (CoMP) has also been identified as a desirable technology for reducing interference in HetNet. However, existing techniques cannot fulfil the high demands of the next 5G, which require direct interference between users in such an orthogonal architecture and good system-level performance. This study aimed to determine the optimal resource allocation for the downlink HetNet 5G NOMA system. The system models were derived to analyze the power performance based on the power allocation factor. An algorithm is constructed based on the deep learning coordinated multi-point to simulate the performance of the downlink 5G. Deep learning can be implemented to improve resource allocation in the context of CoMP by instructing a neural network to predict the optimal power allocation for a specific set of users to resemble the NOMA with HetNet system. Simulation validates the proposed algorithm in terms of the power allocation factor and the impact of the SNR. The results indicate that the power allocation factor ratio for various user signal strengths is capable of allocating dynamic resources to meet the need for efficient resource management. 2023 Conference or Workshop Item PeerReviewed text en http://eprints.utem.edu.my/id/eprint/28129/1/Power%20allocation%20with%20non-orthogonal%20multiple%20access%20for%205G%20heterogeneous%20network.pdf Johari, Muhammad Amirul Aiman and Anwar Apandi, Nur Ilyana and Muhammad, Nor Aishah (2023) Power allocation with non-orthogonal multiple access for 5G heterogeneous network. In: 16th IEEE Malaysia International Conference on Communication, MICC 2023, 10 December 2023 through 12 December 2023, Kuala Lumpur. https://ieeexplore.ieee.org/document/10419786
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
description The fifth generation of mobile network technology (5G) through heterogeneous networks (HetNet) demands increasing the capacity of cellular wireless communications networks. In addition to the HetNet, a Non-Orthogonal Multiple Access (NOMA) can enhance its performance techniques to support a large number of users. Coordinated multi-point communication (CoMP) has also been identified as a desirable technology for reducing interference in HetNet. However, existing techniques cannot fulfil the high demands of the next 5G, which require direct interference between users in such an orthogonal architecture and good system-level performance. This study aimed to determine the optimal resource allocation for the downlink HetNet 5G NOMA system. The system models were derived to analyze the power performance based on the power allocation factor. An algorithm is constructed based on the deep learning coordinated multi-point to simulate the performance of the downlink 5G. Deep learning can be implemented to improve resource allocation in the context of CoMP by instructing a neural network to predict the optimal power allocation for a specific set of users to resemble the NOMA with HetNet system. Simulation validates the proposed algorithm in terms of the power allocation factor and the impact of the SNR. The results indicate that the power allocation factor ratio for various user signal strengths is capable of allocating dynamic resources to meet the need for efficient resource management.
format Conference or Workshop Item
author Johari, Muhammad Amirul Aiman
Anwar Apandi, Nur Ilyana
Muhammad, Nor Aishah
spellingShingle Johari, Muhammad Amirul Aiman
Anwar Apandi, Nur Ilyana
Muhammad, Nor Aishah
Power allocation with non-orthogonal multiple access for 5G heterogeneous network
author_facet Johari, Muhammad Amirul Aiman
Anwar Apandi, Nur Ilyana
Muhammad, Nor Aishah
author_sort Johari, Muhammad Amirul Aiman
title Power allocation with non-orthogonal multiple access for 5G heterogeneous network
title_short Power allocation with non-orthogonal multiple access for 5G heterogeneous network
title_full Power allocation with non-orthogonal multiple access for 5G heterogeneous network
title_fullStr Power allocation with non-orthogonal multiple access for 5G heterogeneous network
title_full_unstemmed Power allocation with non-orthogonal multiple access for 5G heterogeneous network
title_sort power allocation with non-orthogonal multiple access for 5g heterogeneous network
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/28129/1/Power%20allocation%20with%20non-orthogonal%20multiple%20access%20for%205G%20heterogeneous%20network.pdf
http://eprints.utem.edu.my/id/eprint/28129/
https://ieeexplore.ieee.org/document/10419786
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