Metaheuristic Algorithms for 6G wireless communications: Recent advances and applications
The widespread distribution of intelligent applications and devices, coupled with the vast array of mobile data, technologies, and architectures within Sixth Generation (6G) networks, highlights the importance of optimization strategies. Concurrently, state-of-the-art Metaheuristic Algorithms (MHAs)...
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| Vydáno v: | Ad hoc networks Ročník 158; s. 103474 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.05.2024
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| ISSN: | 1570-8705, 1570-8713 |
| On-line přístup: | Získat plný text |
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| Abstract | The widespread distribution of intelligent applications and devices, coupled with the vast array of mobile data, technologies, and architectures within Sixth Generation (6G) networks, highlights the importance of optimization strategies. Concurrently, state-of-the-art Metaheuristic Algorithms (MHAs) have emerged as a promising optimization approach for wireless networks, bridging game theory and convex optimization domains. Falling within the domain of Artificial Intelligence (AI), MHAs draw their inspiration from Evolutionary Algorithms (EAs), which are rooted in the principle of natural selection, the principles of Swarm Intelligence (SI), and Trajectory-based Algorithms (TAs). In recent times, MHAs have been implemented in the 6G network. These simple solutions with limited capabilities have proven effective in solving complex, challenging and high-dimensional problems. Nevertheless, the literature has yet to study the MHAs comprehensively, especially within 6G networks. The main objective of this study is to investigate how to merge the 6G and MHAs domains. We begin by giving a comprehensive overview of MHAs, from fundamental to popular optimization methods. Then, we examine the utilization of MHAs to address the challenges in 6G, such as network security, resource allocation, spectrum management, edge computing, wireless caching, and others. Finally, we identify the current literature’s limitations and suggest avenues for further research directions. |
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| AbstractList | The widespread distribution of intelligent applications and devices, coupled with the vast array of mobile data, technologies, and architectures within Sixth Generation (6G) networks, highlights the importance of optimization strategies. Concurrently, state-of-the-art Metaheuristic Algorithms (MHAs) have emerged as a promising optimization approach for wireless networks, bridging game theory and convex optimization domains. Falling within the domain of Artificial Intelligence (AI), MHAs draw their inspiration from Evolutionary Algorithms (EAs), which are rooted in the principle of natural selection, the principles of Swarm Intelligence (SI), and Trajectory-based Algorithms (TAs). In recent times, MHAs have been implemented in the 6G network. These simple solutions with limited capabilities have proven effective in solving complex, challenging and high-dimensional problems. Nevertheless, the literature has yet to study the MHAs comprehensively, especially within 6G networks. The main objective of this study is to investigate how to merge the 6G and MHAs domains. We begin by giving a comprehensive overview of MHAs, from fundamental to popular optimization methods. Then, we examine the utilization of MHAs to address the challenges in 6G, such as network security, resource allocation, spectrum management, edge computing, wireless caching, and others. Finally, we identify the current literature’s limitations and suggest avenues for further research directions. |
| ArticleNumber | 103474 |
| Author | Ouni, Bassem Guizani, Mohsen Aloqaily, Moayad Abasi, Ammar Kamal |
| Author_xml | – sequence: 1 givenname: Ammar Kamal orcidid: 0000-0003-0725-6167 surname: Abasi fullname: Abasi, Ammar Kamal email: ammar.abasi@mbzuai.ac.ae organization: Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates – sequence: 2 givenname: Moayad orcidid: 0000-0003-2443-7234 surname: Aloqaily fullname: Aloqaily, Moayad email: moayad.aloqaily@mbzuai.ac.ae organization: Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates – sequence: 3 givenname: Mohsen surname: Guizani fullname: Guizani, Mohsen email: mohsen.guizani@mbzuai.ac.ae organization: Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates – sequence: 4 givenname: Bassem surname: Ouni fullname: Ouni, Bassem email: Bassem.Ouni@tii.ae organization: Technology Innovation Institute (TII), Abu Dhabi, United Arab Emirates |
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| CitedBy_id | crossref_primary_10_1016_j_dsp_2024_104963 crossref_primary_10_1109_JSAC_2025_3560009 crossref_primary_10_1109_ACCESS_2025_3576109 crossref_primary_10_1016_j_phycom_2025_102701 crossref_primary_10_1038_s41598_025_14972_3 crossref_primary_10_1109_JSTARS_2025_3591406 crossref_primary_10_1016_j_comcom_2025_108128 crossref_primary_10_1016_j_enconman_2024_118844 crossref_primary_10_1109_TNSM_2025_3528235 crossref_primary_10_1080_23335777_2024_2449347 |
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