Towards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid heuristic-based deep learning
Abstract Fifth generation (5G) networks are desired to offer improved data rates employed for enhancing innovations of device-to-device (D2D) communication, small base stations densification, and multi-tier heterogeneous networks. In relay-assisted D2D communication, relays are employed to minimize...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08290-x |
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| author | C. H. Ramesh Babu S. Nandakumar |
| author_facet | C. H. Ramesh Babu S. Nandakumar |
| author_sort | C. H. Ramesh Babu |
| collection | DOAJ |
| description | Abstract Fifth generation (5G) networks are desired to offer improved data rates employed for enhancing innovations of device-to-device (D2D) communication, small base stations densification, and multi-tier heterogeneous networks. In relay-assisted D2D communication, relays are employed to minimize data rate degradation when D2D users are distant from one another. However, resource sharing between relay-based and cellular D2D connections often results in mutual interferences, reducing the system sum rate. Moreover, traditional relay nodes consume their own energy to support D2D communication without gaining any benefit, affecting network sustainability. To address these challenges, this work proposes an efficient relay selection and resource allocation using the novel hybrid manta ray foraging with chef-based optimization (HMRFCO). The relay selection process considers parameters like spectral efficiency, energy efficiency, throughput, delay, and network capacity to attain effectual performance. Then, the data provided as the input to the adaptive residual gated recurrent unit (AResGRU) model for the automatic prediction of an optimal number of relays and allocation of resources. Here, the AResGRU technique’s parameters are optimized by the same HMRFCO for improving the prediction task. Finally, the designed AResGRU model offered the predicted outcome. |
| format | Article |
| id | doaj-art-da9c3107026c4e028df5a256b2024d35 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-da9c3107026c4e028df5a256b2024d352025-08-20T03:42:31ZengNature PortfolioScientific Reports2045-23222025-07-0115112810.1038/s41598-025-08290-xTowards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid heuristic-based deep learningC. H. Ramesh Babu0S. Nandakumar1School of Electronics Engineering, Vellore Institute of TechnologySchool of Electronics Engineering, Vellore Institute of TechnologyAbstract Fifth generation (5G) networks are desired to offer improved data rates employed for enhancing innovations of device-to-device (D2D) communication, small base stations densification, and multi-tier heterogeneous networks. In relay-assisted D2D communication, relays are employed to minimize data rate degradation when D2D users are distant from one another. However, resource sharing between relay-based and cellular D2D connections often results in mutual interferences, reducing the system sum rate. Moreover, traditional relay nodes consume their own energy to support D2D communication without gaining any benefit, affecting network sustainability. To address these challenges, this work proposes an efficient relay selection and resource allocation using the novel hybrid manta ray foraging with chef-based optimization (HMRFCO). The relay selection process considers parameters like spectral efficiency, energy efficiency, throughput, delay, and network capacity to attain effectual performance. Then, the data provided as the input to the adaptive residual gated recurrent unit (AResGRU) model for the automatic prediction of an optimal number of relays and allocation of resources. Here, the AResGRU technique’s parameters are optimized by the same HMRFCO for improving the prediction task. Finally, the designed AResGRU model offered the predicted outcome.https://doi.org/10.1038/s41598-025-08290-xAdaptive residual gated recurrent unitDevice-to-device communicationHybrid manta-ray foraging with chef based optimizationJoint relay selection and resource allocation |
| spellingShingle | C. H. Ramesh Babu S. Nandakumar Towards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid heuristic-based deep learning Scientific Reports Adaptive residual gated recurrent unit Device-to-device communication Hybrid manta-ray foraging with chef based optimization Joint relay selection and resource allocation |
| title | Towards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid heuristic-based deep learning |
| title_full | Towards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid heuristic-based deep learning |
| title_fullStr | Towards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid heuristic-based deep learning |
| title_full_unstemmed | Towards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid heuristic-based deep learning |
| title_short | Towards energy-efficient joint relay selection and resource allocation for D2D communication using hybrid heuristic-based deep learning |
| title_sort | towards energy efficient joint relay selection and resource allocation for d2d communication using hybrid heuristic based deep learning |
| topic | Adaptive residual gated recurrent unit Device-to-device communication Hybrid manta-ray foraging with chef based optimization Joint relay selection and resource allocation |
| url | https://doi.org/10.1038/s41598-025-08290-x |
| work_keys_str_mv | AT chrameshbabu towardsenergyefficientjointrelayselectionandresourceallocationford2dcommunicationusinghybridheuristicbaseddeeplearning AT snandakumar towardsenergyefficientjointrelayselectionandresourceallocationford2dcommunicationusinghybridheuristicbaseddeeplearning |