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
Hlavní autoři: Abasi, Ammar Kamal, Aloqaily, Moayad, Guizani, Mohsen, Ouni, Bassem
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.05.2024
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ISSN:1570-8705, 1570-8713
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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.
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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Keywords Metaheuristics algorithms (MHAs)
Sixth-generation (6G)
Artificial intelligence (AI)
Optimization
Beyond 5G (B5G)
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Snippet The widespread distribution of intelligent applications and devices, coupled with the vast array of mobile data, technologies, and architectures within Sixth...
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StartPage 103474
SubjectTerms Artificial intelligence (AI)
Beyond 5G (B5G)
Metaheuristics algorithms (MHAs)
Optimization
Sixth-generation (6G)
Title Metaheuristic Algorithms for 6G wireless communications: Recent advances and applications
URI https://dx.doi.org/10.1016/j.adhoc.2024.103474
Volume 158
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