Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments
Device-to-device (D2D) communication in heterogeneous networks (HetNets) poses significant challenges in resource allocation and interference management, especially within 5G networks where spectrum sharing between cellular users (CUEs) and D2D user equipment (DUEs) is critical. This study develope...
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College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria
2025-05-01
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| Series: | ABUAD Journal of Engineering Research and Development |
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| Online Access: | https://www.journals.abuad.edu.ng/index.php/ajerd/article/view/1229 |
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| author | Ashraf Adam Ahmad Usman Bem Abubakar Fatai Olatunde Adunola Amina Jibril Kulu Ahmad Amalo |
| author_facet | Ashraf Adam Ahmad Usman Bem Abubakar Fatai Olatunde Adunola Amina Jibril Kulu Ahmad Amalo |
| author_sort | Ashraf Adam Ahmad |
| collection | DOAJ |
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Device-to-device (D2D) communication in heterogeneous networks (HetNets) poses significant challenges in resource allocation and interference management, especially within 5G networks where spectrum sharing between cellular users (CUEs) and D2D user equipment (DUEs) is critical. This study developed an adaptive resource allocation framework using Long Short-Term Reinforcement Learning (LSRL), which integrated Long Short-Term Memory (LSTM) networks with Deep Reinforcement Learning (DRL) technique. The proposed approach addressed the dynamic nature of interference in urban microcell environments by leveraging a Hierarchical Data Format (HDF5) dataset generated from network simulations. These simulations incorporate diverse scenarios, including varying user densities, transmission power levels, and interference conditions. The LSRL-based scheme was evaluated against conventional DRL methods, demonstrating notable improvements in network performance. Specifically, the proposed framework achieved up to a 6.67% increase in sum throughput and an 8.2% enhancement in power efficiency, even under dense user conditions. Additionally, the LSRL model proved resilient to variations in D2D pair distances, maintaining robust spectral efficiency and quality of service (QoS). These findings underscore the potential of the LSRL-based adaptive approach for improving resource management in 5G HetNets, particularly in dense urban deployments, and provide valuable insights for optimizing next-generation wireless communication systems.
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| format | Article |
| id | doaj-art-c6738d3a5bf540bc97e2a63f6fb0e3a8 |
| institution | Kabale University |
| issn | 2756-6811 2645-2685 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| record_format | Article |
| series | ABUAD Journal of Engineering Research and Development |
| spelling | doaj-art-c6738d3a5bf540bc97e2a63f6fb0e3a82025-08-20T03:49:40ZengCollege of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, NigeriaABUAD Journal of Engineering Research and Development2756-68112645-26852025-05-018210.53982/ajerd.2025.0802.06-jDevelopment of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell EnvironmentsAshraf Adam Ahmad0Usman Bem Abubakar1Fatai Olatunde Adunola2Amina Jibril3Kulu Ahmad Amalo4Department of Electrical/Electronic Engineering, Faculty of Engineering and Technology, Nigerian Defence Academy, Kaduna, NigeriaDepartment of Electrical/Electronic Engineering, Faculty of Engineering and Technology, Nigerian Defence Academy, Kaduna, Nigeria & Playout Center, Engineering Unit, Nigerian Television Authority, Abuja, NigeriaDepartment of Electrical/Electronic Engineering, Faculty of Engineering and Technology, Nigerian Defence Academy, Kaduna, NigeriaDepartment of Electrical/Electronic Engineering, Faculty of Engineering and Technology, Nigerian Defence Academy, Kaduna, NigeriaElectrical Technology Education Department, College of Technical Education, Kaduna Polytechnic, Kaduna, Nigeria Device-to-device (D2D) communication in heterogeneous networks (HetNets) poses significant challenges in resource allocation and interference management, especially within 5G networks where spectrum sharing between cellular users (CUEs) and D2D user equipment (DUEs) is critical. This study developed an adaptive resource allocation framework using Long Short-Term Reinforcement Learning (LSRL), which integrated Long Short-Term Memory (LSTM) networks with Deep Reinforcement Learning (DRL) technique. The proposed approach addressed the dynamic nature of interference in urban microcell environments by leveraging a Hierarchical Data Format (HDF5) dataset generated from network simulations. These simulations incorporate diverse scenarios, including varying user densities, transmission power levels, and interference conditions. The LSRL-based scheme was evaluated against conventional DRL methods, demonstrating notable improvements in network performance. Specifically, the proposed framework achieved up to a 6.67% increase in sum throughput and an 8.2% enhancement in power efficiency, even under dense user conditions. Additionally, the LSRL model proved resilient to variations in D2D pair distances, maintaining robust spectral efficiency and quality of service (QoS). These findings underscore the potential of the LSRL-based adaptive approach for improving resource management in 5G HetNets, particularly in dense urban deployments, and provide valuable insights for optimizing next-generation wireless communication systems. https://www.journals.abuad.edu.ng/index.php/ajerd/article/view/1229Long Short-Term Reinforcement Learning (LSRL)Deep Reinforcement Learning (DRL)Long Short-Term Memory (LSTM)Device-to-Device (D2D) CommunicationHeterogeneous Networks (HetNets) |
| spellingShingle | Ashraf Adam Ahmad Usman Bem Abubakar Fatai Olatunde Adunola Amina Jibril Kulu Ahmad Amalo Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments ABUAD Journal of Engineering Research and Development Long Short-Term Reinforcement Learning (LSRL) Deep Reinforcement Learning (DRL) Long Short-Term Memory (LSTM) Device-to-Device (D2D) Communication Heterogeneous Networks (HetNets) |
| title | Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments |
| title_full | Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments |
| title_fullStr | Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments |
| title_full_unstemmed | Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments |
| title_short | Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments |
| title_sort | development of adaptive resource allocation and interference mitigation for spectrum sharing in d2d enabled 5g heterogeneous networks a case study of urban microcell environments |
| topic | Long Short-Term Reinforcement Learning (LSRL) Deep Reinforcement Learning (DRL) Long Short-Term Memory (LSTM) Device-to-Device (D2D) Communication Heterogeneous Networks (HetNets) |
| url | https://www.journals.abuad.edu.ng/index.php/ajerd/article/view/1229 |
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