مصطفي الجميلي

With the aim of achieving pervasive an‎d intelligent connectivity, the transition to the sixth generation (6G) of wireless communication networks represents a paradigm shift in the global telecommunications industry. However, one of the challenges of this generation is the "coverage gap" that has been identified in densely populated an‎d rural areas where traditional ground-based base stations are not feasible. Based on their mobility an‎d ability to create line-of-sight (LoS) communications necessary for high-frequency 6G communications, this thesis proposes the use of Drone Base Stations (DBS) to fill this gap. The research work proposes a State-Adaptive Multi-Objective Genetic Algorithm (SAMOGA) to enhance 3D placement an‎d resource allocation in order to address the significant trade-off between network performance an‎d the limited battery life of Unmanned Aerial Vehicles (UAVs). The proposed invention is based on a dynamic fitness component that varies in real-time according to environmental input such as the variation in IoT traffic requirements an‎d battery state. The SAMOGA framework effectively traverses the large search space to discover Pareto-optimal solutions by taking into account realistic 6G channel models an‎d comprehensive energy consumption models that account for both propulsion an‎d communication energy. According to simulation data, the suggested SAMOGA achieves a 95% coverage ratio an‎d an average latency of 24.33 ms, hence fulfilling the deman‎ding requirements for ultra-reliable low-latency communications (URLLC). Moreover, the approach yields a clear rise in energy efficiency over conventional non-adaptive methods. This study provides a solid theoretical an‎d practical basis for creating effective aerial network layers in the 6G-IoT ecosystem.

شبكه‌هاي 6G , ايستگاه‌هاي پايه پهپادي (DBS) , الگوريتم ژنتيك چندهدفه (MOGA) , اينترنت اشيا (IoT) , بهره‌وري انرژي , بهينه‌سازي جاي‌گذاري سه‌بعدي

6G Networks , Drone Base Stations (DBS) , Multi-Objective Genetic Algorithm (MOGA) , Internet of Things (IoT) , Energy Efficiency , 3D Placement Optimization

Mustafa Aljumaili

Dr. Javad Vahidi