Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications
Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power alloc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/13/2/44 |
| _version_ | 1797298584765333504 |
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| author | Mohamed Kamel Benbraika Okba Kraa Yassine Himeur Khaled Telli Shadi Atalla Wathiq Mansoor |
| author_facet | Mohamed Kamel Benbraika Okba Kraa Yassine Himeur Khaled Telli Shadi Atalla Wathiq Mansoor |
| author_sort | Mohamed Kamel Benbraika |
| collection | DOAJ |
| description | Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions. |
| first_indexed | 2024-03-07T22:36:58Z |
| format | Article |
| id | doaj.art-d8aa0944d1f445a5b2bb5376a5d9c749 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-07T22:36:58Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-d8aa0944d1f445a5b2bb5376a5d9c7492024-02-23T15:12:55ZengMDPI AGComputers2073-431X2024-02-011324410.3390/computers13020044Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device CommunicationsMohamed Kamel Benbraika0Okba Kraa1Yassine Himeur2Khaled Telli3Shadi Atalla4Wathiq Mansoor5Department of Computer Science, University of El Oued, El Oued 39000, AlgeriaEnergy Systems Modelling (MSE) Laboratory, Mohamed Khider University, Biskra 07000, AlgeriaCollege of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab EmiratesEnergy Systems Modelling (MSE) Laboratory, Mohamed Khider University, Biskra 07000, AlgeriaCollege of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab EmiratesCollege of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab EmiratesDevice-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions.https://www.mdpi.com/2073-431X/13/2/445Gbee life algorithmbio-inspired algorithmsdevice-to-device communicationgenetic algorithmparticle swarm optimization |
| spellingShingle | Mohamed Kamel Benbraika Okba Kraa Yassine Himeur Khaled Telli Shadi Atalla Wathiq Mansoor Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications Computers 5G bee life algorithm bio-inspired algorithms device-to-device communication genetic algorithm particle swarm optimization |
| title | Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications |
| title_full | Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications |
| title_fullStr | Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications |
| title_full_unstemmed | Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications |
| title_short | Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications |
| title_sort | interference management based on meta heuristic algorithms in 5g device to device communications |
| topic | 5G bee life algorithm bio-inspired algorithms device-to-device communication genetic algorithm particle swarm optimization |
| url | https://www.mdpi.com/2073-431X/13/2/44 |
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