Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO
This work addresses the critical challenge of energy consumption in Mobile Edge Computing (MEC), a burgeoning field that extends cloud computing capabilities to the edge of cellular networks. Given the exponential growth of mobile devices and the resultant surge in energy demands, there is an urgent...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10855410/ |
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| author | Qusay Alghazali Husam Al-Amaireh Tibor Cinkler |
| author_facet | Qusay Alghazali Husam Al-Amaireh Tibor Cinkler |
| author_sort | Qusay Alghazali |
| collection | DOAJ |
| description | This work addresses the critical challenge of energy consumption in Mobile Edge Computing (MEC), a burgeoning field that extends cloud computing capabilities to the edge of cellular networks. Given the exponential growth of mobile devices and the resultant surge in energy demands, there is an urgent need for efficient energy management strategies to ensure sustainable development and operation of MEC infrastructures. This paper introduces a comprehensive framework for reducing energy consumption in MEC environments by leveraging advanced optimization techniques and energy-efficient resource allocation algorithms. We propose a novel approach that dynamically adjusts the computational resources based on the current network load and the type of services requested, thus minimizing unnecessary energy consumption. We derive and propose an optimized energy consumption for local processing. Then, we study the two network scenarios: Non-Orthogonal Multiple Access (NOMA) and Massive Multiple-Input Multiple-Output (mMIMO). We propose an optimized energy consumption algorithm in NOMA based on the derived processing resource requirements. Then, in mMIMO, we derive optimized power allocation algorithms. Simulations validate the effectiveness of our proposed framework, demonstrating significant energy savings. |
| format | Article |
| id | doaj-art-8fd7549c3bb744ebbde7c913adf8ae17 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8fd7549c3bb744ebbde7c913adf8ae172025-02-05T00:01:09ZengIEEEIEEE Access2169-35362025-01-0113214562147010.1109/ACCESS.2025.353523310855410Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMOQusay Alghazali0https://orcid.org/0000-0001-9564-0438Husam Al-Amaireh1Tibor Cinkler2Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, HungarySchool of Engineering, Luminus Technical University College, Amman, JordanDepartment of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, HungaryThis work addresses the critical challenge of energy consumption in Mobile Edge Computing (MEC), a burgeoning field that extends cloud computing capabilities to the edge of cellular networks. Given the exponential growth of mobile devices and the resultant surge in energy demands, there is an urgent need for efficient energy management strategies to ensure sustainable development and operation of MEC infrastructures. This paper introduces a comprehensive framework for reducing energy consumption in MEC environments by leveraging advanced optimization techniques and energy-efficient resource allocation algorithms. We propose a novel approach that dynamically adjusts the computational resources based on the current network load and the type of services requested, thus minimizing unnecessary energy consumption. We derive and propose an optimized energy consumption for local processing. Then, we study the two network scenarios: Non-Orthogonal Multiple Access (NOMA) and Massive Multiple-Input Multiple-Output (mMIMO). We propose an optimized energy consumption algorithm in NOMA based on the derived processing resource requirements. Then, in mMIMO, we derive optimized power allocation algorithms. Simulations validate the effectiveness of our proposed framework, demonstrating significant energy savings.https://ieeexplore.ieee.org/document/10855410/Mobile edge computingnon orthogonal multiple accesscapacitymobile energymassive multiple input multiple output |
| spellingShingle | Qusay Alghazali Husam Al-Amaireh Tibor Cinkler Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO IEEE Access Mobile edge computing non orthogonal multiple access capacity mobile energy massive multiple input multiple output |
| title | Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO |
| title_full | Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO |
| title_fullStr | Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO |
| title_full_unstemmed | Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO |
| title_short | Energy-Efficient Resource Allocation in Mobile Edge Computing Using NOMA and Massive MIMO |
| title_sort | energy efficient resource allocation in mobile edge computing using noma and massive mimo |
| topic | Mobile edge computing non orthogonal multiple access capacity mobile energy massive multiple input multiple output |
| url | https://ieeexplore.ieee.org/document/10855410/ |
| work_keys_str_mv | AT qusayalghazali energyefficientresourceallocationinmobileedgecomputingusingnomaandmassivemimo AT husamalamaireh energyefficientresourceallocationinmobileedgecomputingusingnomaandmassivemimo AT tiborcinkler energyefficientresourceallocationinmobileedgecomputingusingnomaandmassivemimo |