A Machine Learning Based Energy-Efficient Non-Orthogonal Multiple Access Scheme
Электронный архив ТПУ
Информация об архиве | Просмотр оригиналаПоле | Значение | |
Заглавие |
A Machine Learning Based Energy-Efficient Non-Orthogonal Multiple Access Scheme
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Автор |
Khan, Rabia
Dzhayakodi (Jayakody) Arachshiladzh, Dushanta Nalin Kumara Vishal Sharma Vinay Kumar Kuljeet Kaur Zheng Chang |
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Тематика |
BEEM-NOMA
cooperative communication EE GLMA ITS ITS ML NOMA point-to-point communication RFEH множественный доступ машинное обучение искусственный интеллект беспроводная связь передача данных надежность энергоэффективность энергоэффективные системы |
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Описание |
Applicability of Artificial Intelligent (AI) and NonOrthogonal Multiple Access (NOMA) have drawn remarkable attraction towards the implementation of 5th Generation (5G) wireless communication systems. 5G demands significant improvements in terms of data rate, throughput, reliability, Quality of Service (QoS), fairness, Symbol Error Rate (SER), Outage, reliability and latency as compared to the current standards. The aforementioned parameters have a critical impact when applied to the Internet of Thing (IoT). Considering the demand of high power and energy, we have optimized Energy-Efficiency (EE) and Radio Frequency Energy Harvesting (RFEH) using Machine Learning based Genetic Algorithm (MLGA). For the system integration, we proposed to Built-in Energy Efficient Modulation based NOMA (BEEM-NOMA). BEEM NOMA is an energy efficient system that has the capability to prevent the waste of energy. Combination of BEEM-NOMA with MLGA further enhances the performance of the system, as proved with the simulation results in this paper.
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Дата |
2020-01-23T09:13:06Z
2020-01-23T09:13:06Z 2019 |
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Тип |
Conference Paper
Published version (info:eu-repo/semantics/publishedVersion) Conference paper (info:eu-repo/semantics/conferencePaper) |
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Идентификатор |
A Machine Learning Based Energy-Efficient Non-Orthogonal Multiple Access Scheme / R. Khan [et al.] // 14th International Forum on Strategic Technology (IFOST-2019), October 14-17, 2019, Tomsk, Russia : [proceedings]. — Tomsk : TPU Publishing House, 2019. — [С. 330-335].
http://earchive.tpu.ru/handle/11683/57475 |
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Язык |
en
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Связанные ресурсы |
14th International Forum on Strategic Technology (IFOST-2019), October 14-17, 2019, Tomsk, Russia : [proceedings]. — Tomsk, 2019.
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Права |
Open access (info:eu-repo/semantics/openAccess)
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