Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

• Published in: IEEE access Vol. 9; pp. 70419 - 70429
• Main Authors: Abdel-Basset, Mohamed, Abdel-Fatah, Laila, Eldrandaly, Khalid A., Abdel-Aziz, Nabil M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 6G mobile communication; 6G technology; Algorithms; Artificial intelligence; computational intelligence; Computer architecture; Constraints; Covariance matrix; Crossovers; Evolutionary algorithms; High altitude; Low altitude; Low earth orbit satellites; Low earth orbits; non-terrestrial base stations; Optimization; Particle swarm optimization; Radio equipment; reconfigurable intelligent surfaces; Search algorithms; Slime; Space vehicles; Stations; Statistical analysis; Swarm intelligence; three-dimensional coverage optimization problem; Three-dimensional displays; Two dimensional displays; Unmanned aerial vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3078585

The next generation 6G communication network is typically characterized by the full connectivity and coverage of Users Equipment (UEs). This leads to the need for moving beyond the traditional two-dimensional (2D) coverage service to the three-dimensional (3D) full-service one. The 6G 3D architecture leverages different types of non-terrestrial or aerial nodes that can act as mobile Base Stations (BSs) such as Unmanned Aerial Vehicles (UAVs), Low Altitude Platforms (LAPs), High-Altitude Platform Stations (HAPSs), or even Low Earth Orbit (LEO) satellites. Moreover, aided technologies have been added to the 6G architecture to dynamically increase its coverage efficiency such as the Reconfigurable Intelligent Surfaces (RIS). In this paper, an enhanced Computational Intelligence (CI) algorithm is introduced for optimizing the coverage of UAV-BSs with respect to their location from RIS in the 3D space of 6G architecture. The regarded problem is formulated as a constrained 3D coverage optimization problem. In order to increase the convergence of the proposed algorithm, it is hybridized with a crossover operator. For the validation of the proposed method, it is tested on different scenarios with large-scale coordinates and compared with many recent and hybrid CI algorithms, as Slime Mould Algorithm (SMA), Lévy Flight Distribution (LFD), hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and hybrid Grey Wolf Optimizer and Cuckoo Search (GWOCS). The experiment and the statistical analysis show the significant efficiency of the proposed algorithm in achieving complete coverage with a lower number of UAV-BSs and without constraints violation.