Green Communication for Sixth-Generation Intent-Based Networks: An Architecture Based on Hybrid Computational Intelligence Algorithm

The sixth-generation (6G) is envisioned as a pivotal technology that will support the ubiquitous seamless connectivity of substantial networks. The main advantage of 6G technology is leveraging Artificial Intelligence (AI) techniques for handling its interoperable functions. The pairing of 6G networ...

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Veröffentlicht in:Wireless communications and mobile computing Jg. 2021; H. 1
Hauptverfasser: Eldrandaly, Khalid A., Abdel-Fatah, Laila, Abdel-Basset, Mohamed, El-hoseny, Mohamed, Abdel-Aziz, Nabil M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Hindawi 2021
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Artificial intelligence
Clustering
Clusters
Communication
Energy consumption
Fuzzy logic
Game theory
Hybrid systems
Infrastructure
Network management systems
Networks
Normal distribution
Optimization
Power consumption
Power management
Psychology
Statistical analysis
Traffic congestion
Unmanned aerial vehicles
Wireless networks
ISSN:1530-8669, 1530-8677
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Zusammenfassung:The sixth-generation (6G) is envisioned as a pivotal technology that will support the ubiquitous seamless connectivity of substantial networks. The main advantage of 6G technology is leveraging Artificial Intelligence (AI) techniques for handling its interoperable functions. The pairing of 6G networks and AI creates new needs for infrastructure, data preparation, and governance. Thus, Intent-Based Network (IBN) architecture is a key infrastructure for 6G technology. Usually, these networks are formed of several clusters for data gathering from various heterogeneities in devices. Therefore, an important problem is to find the minimum transmission power for each node in the network clusters. This paper presents hybridization between two Computational Intelligence (CI) algorithms called the Marine Predator Algorithm and the Generalized Normal Distribution Optimization (MPGND). The proposed algorithm is applied to save power consumption which is an important problem in sustainable green 6G-IBN. MPGND is compared with several recently proposed algorithms, including Augmented Grey Wolf Optimizer (AGWO), Sine Tree-Seed Algorithm (STSA), Archimedes Optimization Algorithm (AOA), and Student Psychology-Based Optimization (SPBO). The experimental results with the statistical analysis demonstrate the merits and highly competitive performance of the proposed algorithm.
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ISSN:1530-8669
1530-8677
DOI:10.1155/2021/9931677