TY - GEN
T1 - Simulation-based Beamforming Optimization in Moving Drones
AU - Bouras, Christos
AU - Diasakos, Damianos
AU - Kokkinos, Vasileios
AU - Prodromos, Nikolaos
AU - Gkamas, Apostolos
AU - Pouyioutas, Philippos
N1 - Publisher Copyright:
© 2024 IFIP.
PY - 2024
Y1 - 2024
N2 - The integration of drone technology with 5G networks presents novel opportunities for enhancing wireless communication systems. This paper explores the application of beamforming optimization techniques in dynamic environments, specifically focusing on moving drones in a simulated environment based on the DeepMIMO O1 scenario. By leveraging the unique properties of the O1 drone setup of DeepMIMO simulation environment, which simulates realistic urban mobility patterns at millimeter-wave (mmWave) frequencies, we propose a novel beamforming algorithm designed to optimize the signal quality and stability in highly mobile aerial networks. Key performance metrics used in this study include Signal-to-Noise Ratio (SNR), battery consumption, and power consumption of both the drones and the base station. Our findings indicate that the adaptive beamforming algorithm not only enhances the SNR and reduces power consumption but also optimizes battery usage compared to conventional beamforming methods. This study enhances the understanding of mmWave beamforming dynamics in aerial scenarios but also lays the groundwork for future advancements in drone-based communication networks.
AB - The integration of drone technology with 5G networks presents novel opportunities for enhancing wireless communication systems. This paper explores the application of beamforming optimization techniques in dynamic environments, specifically focusing on moving drones in a simulated environment based on the DeepMIMO O1 scenario. By leveraging the unique properties of the O1 drone setup of DeepMIMO simulation environment, which simulates realistic urban mobility patterns at millimeter-wave (mmWave) frequencies, we propose a novel beamforming algorithm designed to optimize the signal quality and stability in highly mobile aerial networks. Key performance metrics used in this study include Signal-to-Noise Ratio (SNR), battery consumption, and power consumption of both the drones and the base station. Our findings indicate that the adaptive beamforming algorithm not only enhances the SNR and reduces power consumption but also optimizes battery usage compared to conventional beamforming methods. This study enhances the understanding of mmWave beamforming dynamics in aerial scenarios but also lays the groundwork for future advancements in drone-based communication networks.
KW - 5G Drone Communication
KW - Adaptive Beamforming
KW - DeepMIMO O1 Scenario
KW - Millimeter-Wave Frequencies
KW - Signal-to-Noise Ratio (SNR) Optimization
UR - http://www.scopus.com/inward/record.url?scp=85213688772&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85213688772
T3 - Proceedings of the 2024 15th IFIP Wireless and Mobile Networking Conference, WMNC 2024
SP - 94
EP - 99
BT - Proceedings of the 2024 15th IFIP Wireless and Mobile Networking Conference, WMNC 2024
A2 - Fazio, Peppino
A2 - Calafate, Carlos
A2 - Amendola, Danilo
A2 - Tsiropoulou, Eirini Eleni
A2 - Diamanti, Maria
A2 - Mannone, Maria
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IFIP Wireless and Mobile Networking Conference, WMNC 2024
Y2 - 11 November 2024 through 12 November 2024
ER -