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 - University of Tabriz, Faculty of Electrical and computer Enigeering
2 - University of Tabriz, Faculty of Electrical and computer Enigeering
کلید واژه: 6G communication networks, Drone Base Stations (DBSs), Internet of Things (IoT), Improved Gray Wolf Optimization (IGWO), Energy efficiency.,
چکیده مقاله :
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.
[1] M. Fathi, “An Analysis of the Signal-to-Interference Ratio in UAV-based Telecommunication Networks,” Journal of Information Systems and Telecommunication (JIST), vol. 1, no. 45, pp. 49, 2024.
[2] S. H. Mostafavi-Amjad, V. Solouk, and H. Kalbkhani, “Energy-efficient user pairing and power allocation for granted uplink-NOMA in UAV communication systems,” Journal of Information Systems and Telecommunication (JIST), vol. 4, no. 40, pp. 312, 2022.
[3] W. Shafik, M. Ghasemzadeh, and S. M. Matinkhah, “A fast machine learning for 5G beam selection for unmanned aerial vehicle applications,” Journal of Information Systems and Telecommunication (JIST), vol. 4, no. 28, pp. 262, 2020.
[4] L. Liu, A. Wang, G. Sun, and J. Li, “Multiobjective optimization for improving throughput and energy efficiency in UAV-enabled IoT,” IEEE Internet of Things Journal, vol. 9, no. 20, pp. 20763-20777, 2022.
[5] H. B. Salameh, A. E. Masadeh, and G. El Refae, “Intelligent drone-base-station placement for improved revenue in B5G/6G systems under uncertain fluctuated demands,” IEEE Access, vol. 10, pp. 106740-106749, 2022.
[6] Y. Luo and G. Fu, “UAV based device to device communication for 5G/6G networks using optimized deep learning models,” Wireless Networks, pp. 1-15, 2023.
[7] S. Khosroabadi and H. A. Alaboodi, “Innovative Drone Base Station Placement in 6G Networks: A Marine Predators Algorithm Approach,” Journal of AI and Data Mining, vol. 13, no. 2, pp. 175-182, 2025.
[8] V. Loganathan, S. Veerappan, P. Manoharan, and B. Derebew, “Optimizing Drone-Based IoT Base Stations in 6G Networks Using the Quasi-opposition-Based Lemurs Optimization Algorithm,” International Journal of Computational Intelligence Systems, vol. 17, no. 1, pp. 218, 2024.
[9] H. Alsolai et al., “Optimization of Drone Base Station Location for the Next-Generation Internet-of-Things Using a Pre-Trained Deep Learning Algorithm and NOMA,” Mathematics, vol. 11, no. 8, pp. 1947, 2023.
[10] M. Q. Alsudani et al., “Positioning Optimization of UAV (Drones) Base Station in Communication Networks,” Malaysian Journal of Fundamental and Applied Sciences, vol. 19, no. 3, pp. 429-439, 2023.
[11] X. Zhu et al., “Multi-objective Deployment Optimization of UAVs for Energy-Efficient Wireless Coverage,” IEEE Transactions on Communications, 2024.
[12] J. Carvajal-Rodríguez et al., “3D Placement Optimization in UAV-Enabled Communications: A Systematic Mapping Study,” IEEE Open Journal of Vehicular Technology, 2024.
[13] F. Pasandideh et al., “An improved particle swarm optimization algorithm for UAV base station placement,” Wireless Personal Communications, vol. 130, no. 2, pp. 1343-1370, 2023.
[14] M. H. Zahedi et al., “Fuzzy based efficient drone base stations (DBSs) placement in the 5G cellular network,” Iranian Journal of Fuzzy Systems, vol. 17, no. 2, pp. 29-38, 2020.
[15] D. Pliatsios et al., “Drone-base-station for next-generation internet-of-things: A comparison of swarm intelligence approaches,” IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 32-47, 2021.