Optimally DBS Placement In 6G Communication Networks Using Improved Gray Wolf Optimization Algorithm to Enhance Network Energy Efficiency
محورهای موضوعی : Communication Systems & Devices
Hussein Shakir Diwan Al-Khulaifawi
1
,
Mahdi Nangir
2
1 - Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
2 - Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
کلید واژه: 6G communication networks, Drone Base Stations (DBSs), Internet of Things (IoT), Improved Gray Wolf Optimization (IGWO), Energy Efficiency,
چکیده مقاله :
The transition to sixth-generation (6G) networks demands highly energy-efficient solutions for large-scale IoT services. Drone Base Stations (DBSs) offer flexible coverage, but their three-dimensional placement must be optimized to reduce both transmission and hovering energy. This paper, model DBS deployment as a power-minimization problem and introduce an Improved Grey Wolf Optimization (IGWO) algorithm that integrates adaptive control parameters, exponential weighting of leader contributions (alpha/beta/delta), and a dynamic control structure that progressively favors elite solutions. This design improves search efficiency in high-dimensional, nonlinear spaces and reduces the risk of premature convergence. Extensive MATLAB simulations across multiple propagation environments demonstrate that IGWO achieves lower network power consumption and faster convergence compared to standard metaheuristics, while preserving coverage and connectivity. Specifically, the simulation results demonstrate that the proposed method achieves a remarkable superiority over other optimization algorithms, showing more than a 2% improvement compared to the best among them the standard GWO algorithm—thereby confirming its effectiveness and efficiency in low-power network scenarios.
The transition to sixth-generation (6G) networks demands highly energy-efficient solutions for large-scale IoT services. Drone Base Stations (DBSs) offer flexible coverage, but their three-dimensional placement must be optimized to reduce both transmission and hovering energy. This paper, model DBS deployment as a power-minimization problem and introduce an Improved Grey Wolf Optimization (IGWO) algorithm that integrates adaptive control parameters, exponential weighting of leader contributions (alpha/beta/delta), and a dynamic control structure that progressively favors elite solutions. This design improves search efficiency in high-dimensional, nonlinear spaces and reduces the risk of premature convergence. Extensive MATLAB simulations across multiple propagation environments demonstrate that IGWO achieves lower network power consumption and faster convergence compared to standard metaheuristics, while preserving coverage and connectivity. Specifically, the simulation results demonstrate that the proposed method achieves a remarkable superiority over other optimization algorithms, showing more than a 2% improvement compared to the best among them the standard GWO algorithm—thereby confirming its effectiveness and efficiency in low-power network scenarios.
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