دورية أكاديمية
A novel user clustering and efficient resource allocation in non-orthogonal mutliple access for IoT networks.
| العنوان: | A novel user clustering and efficient resource allocation in non-orthogonal mutliple access for IoT networks. |
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| المؤلفون: | Hamedoon SM; Department of Electrical Engineering, School of Electrical Engineering, University of Management and Technology, Lahore, Pakistan., Chattha JN; Department of Electrical Engineering, School of Electrical Engineering, University of Management and Technology, Lahore, Pakistan., Bilal M; Center of Excellence in Intelligent Engineering Systems, Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia. |
| المصدر: | PloS one [PLoS One] 2024 Sep 09; Vol. 19 (9), pp. e0309695. Date of Electronic Publication: 2024 Sep 09 (Print Publication: 2024). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: San Francisco, CA : Public Library of Science |
| مواضيع طبية MeSH: | Resource Allocation* , Algorithms*, Cluster Analysis ; Internet of Things ; Neural Networks, Computer ; Computer Communication Networks ; Wireless Technology ; Humans |
| مستخلص: | Optimal resource allocation is crucial for 5G and beyond networks, especially when connecting numerous IoT devices. In this paper, user clustering and power allocation challenges in the downlink of a multi-carrier NOMA system are investigated, with sum rate as the optimization objective. The paper presents an iterative optimization process, starting with user clustering followed by power allocation of the users. Although the simultaneous transmission for multiple users achieves high system throughput in NOMA, it leads to more energy consumption, which is limited by the battery capacity of IoT devices. Enhancing energy efficiency by considering the QoS requirement is a primary challenge in NOMA-enabled IoT devices. Currently, fixed user clustering techniques are proposed without considering the diversity and heterogeneity of channels, leading to poor throughput performance. The proposed user clustering technique is based on the partial brute force search (P-BFS) method, which reduces complexity compared to the traditional exhaustive search method. After the user clustering, we performed optimal power allocation using the Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) optimal conditions for each user assigned to a subchannel in each cluster. Lastly, a deep neural network (DNN) based proposed P-BFS scheme is used to reduce resource allocation's complexity further. The simulation results show a significant improvement in the sum rate of the network. Competing Interests: The authors have declared that no competing interests exist. (Copyright: © 2024 Hamedoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| تواريخ الأحداث: | Date Created: 20240909 Date Completed: 20240909 Latest Revision: 20240912 |
| رمز التحديث: | 20240912 |
| مُعرف محوري في PubMed: | PMC11383250 |
| DOI: | 10.1371/journal.pone.0309695 |
| PMID: | 39250469 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0309695 |