Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning

The recent years have seen a proven impact of the reinforcement learning use in many applications which showed tremendous success in solving many decision-making paradigms in machine learning. Most of the successful applications involves the existence of more than one agent, which makes it fall into...

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Main Authors: Alatabani L.E., Saeed R.A., Ali E.S., Mokhtar R.A., Khalifa O.O., Hayder G.
Other Authors: 57224509526
Format: Conference Paper
Published: Springer Nature 2024
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-345632024-10-14T11:20:41Z Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning Alatabani L.E. Saeed R.A. Ali E.S. Mokhtar R.A. Khalifa O.O. Hayder G. 57224509526 16022855100 57221716104 16022551600 9942198800 56239664100 DDPG Hybrid NOMA MARL Random allocation Reinforcement Learning Spectrum allocation V2V communications The recent years have seen a proven impact of the reinforcement learning use in many applications which showed tremendous success in solving many decision-making paradigms in machine learning. Most of the successful applications involves the existence of more than one agent, which makes it fall into the multi-agent category, taking autonomous driving as an example of these applications. We know that today�s Internet of Vehicles (IoVs) consists of multi-communication patterns which work efficiently in keeping all the IoV network components connected. With regards to sharing the frequency spectrum, applying Non-Orthogonal Multiple Access (NOMA) communication built over deep deterministic policies gradients (DDPG) scheme to cope with the rabid erratic channels conditions due to fast mobility nature of vehicles network has proven promising results. In this paper the framework of NOMA communication-based DDPG and multiple agent reinforcement learning approach (MARL) are discussed in brief, and then, the performance evaluation of DDPG scheme compared with MARL and random spectrum allocation approaches for vehicular network spectrum and resources allocation is analysed. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. Final 2024-10-14T03:20:41Z 2024-10-14T03:20:41Z 2023 Conference Paper 10.1007/978-3-031-26580-8_23 2-s2.0-85161558042 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161558042&doi=10.1007%2f978-3-031-26580-8_23&partnerID=40&md5=e1bedc6c43f20b2b97ce40f8179aafd5 https://irepository.uniten.edu.my/handle/123456789/34563 151 158 Springer Nature Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic DDPG
Hybrid NOMA
MARL
Random allocation
Reinforcement Learning
Spectrum allocation
V2V communications
spellingShingle DDPG
Hybrid NOMA
MARL
Random allocation
Reinforcement Learning
Spectrum allocation
V2V communications
Alatabani L.E.
Saeed R.A.
Ali E.S.
Mokhtar R.A.
Khalifa O.O.
Hayder G.
Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning
description The recent years have seen a proven impact of the reinforcement learning use in many applications which showed tremendous success in solving many decision-making paradigms in machine learning. Most of the successful applications involves the existence of more than one agent, which makes it fall into the multi-agent category, taking autonomous driving as an example of these applications. We know that today�s Internet of Vehicles (IoVs) consists of multi-communication patterns which work efficiently in keeping all the IoV network components connected. With regards to sharing the frequency spectrum, applying Non-Orthogonal Multiple Access (NOMA) communication built over deep deterministic policies gradients (DDPG) scheme to cope with the rabid erratic channels conditions due to fast mobility nature of vehicles network has proven promising results. In this paper the framework of NOMA communication-based DDPG and multiple agent reinforcement learning approach (MARL) are discussed in brief, and then, the performance evaluation of DDPG scheme compared with MARL and random spectrum allocation approaches for vehicular network spectrum and resources allocation is analysed. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
author2 57224509526
author_facet 57224509526
Alatabani L.E.
Saeed R.A.
Ali E.S.
Mokhtar R.A.
Khalifa O.O.
Hayder G.
format Conference Paper
author Alatabani L.E.
Saeed R.A.
Ali E.S.
Mokhtar R.A.
Khalifa O.O.
Hayder G.
author_sort Alatabani L.E.
title Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning
title_short Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning
title_full Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning
title_fullStr Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning
title_full_unstemmed Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning
title_sort vehicular network spectrum allocation using hybrid noma and multi-agent reinforcement learning
publisher Springer Nature
publishDate 2024
_version_ 1814061185253769216