Optimally DBS Placement In 6G Communication Networks Using Improved Gray Wolf Optimization Algorithm to Enhance Network Energy Efficiency
Subject Areas : Communication Systems & Devices
Hussein Shakir Diwan Al-Khulaifawi
1
,
Mahdi Nangir
2
*
1 - University of Tabriz, Faculty of Electrical and computer Enigeering
2 - University of Tabriz, Faculty of Electrical and computer Enigeering
Keywords: 6G communication networks, Drone Base Stations (DBSs), Internet of Things (IoT), Improved Gray Wolf Optimization (IGWO), Energy efficiency.,
Abstract :
The rapid evolution of sixth-generation (6G) communication networks brings forth unprecedented challenges and opportunities, particularly in meeting the stringent requirements of energy efficiency for large-scale Internet of Things (IoT) ecosystems. Among the promising solutions, Drone Base Stations (DBSs) have emerged as flexible and dynamic infrastructures capable of enhancing coverage and connectivity. However, the deployment of DBSs must be optimized to minimize energy consumption while maintaining network performance. This paper introduces an Improved Gray Wolf Optimization (IGWO) algorithm, a refined metaheuristic method specifically designed to address the complex problem of energy-efficient DBS placement. The deployment task is modeled as a power minimization problem, considering both the transmission and hovering energy of drones across heterogeneous propagation environments. The proposed IGWO algorithm incorporates adaptive parameters and enhanced exploration–exploitation balance, leading to superior convergence behavior and solution quality compared to conventional optimization techniques. Comprehensive simulation results reveal that IGWO significantly reduces total network power consumption while ensuring optimal coverage and connectivity. The algorithm’s robustness across various scenarios highlights its practical applicability in real-world 6G deployments. This study contributes to the growing body of research on sustainable network design by demonstrating the efficacy of advanced metaheuristic algorithms in solving high-dimensional, nonlinear optimization problems. The findings underscore the importance of intelligent resource management in shaping the future of energy-aware, scalable, and resilient 6G wireless communication systems.
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