Abstract
This study presents AirWave, an innovative modeling framework tailored to address the challenges associated with integrating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. With the rising prominence of UAVs across various industries, ensuring uninterrupted connectivity and efficient mobility management within cellular networks becomes paramount. AirWave offers a comprehensive simulation environment, amalgamating network and physics simulations using ns-3 for network simulation, Gazebo for physics simulation, and integrating PX4 autopilot for realistic UAV flight dynamics. This framework facilitates in-depth investigations into UAV mobility and handover management, including a trajectory optimization technique based on reinforcement learning for cellular-connected drones. Extensive simulations demonstrate the versatility and effectiveness of AirWave, showcasing notable improvements in UAV range, connectivity, and management within cellular networks. These findings hold significant promise for advancing research and practical applications in UAV communications.
Original language | English |
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Title of host publication | 2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 295-302 |
Number of pages | 8 |
ISBN (Electronic) | 9798350374100 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 - Bhopal, India Duration: 24 Feb 2024 → 25 Feb 2024 |
Publication series
Name | 2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 |
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Conference
Conference | 2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 |
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Country/Territory | India |
City | Bhopal |
Period | 24/02/24 → 25/02/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- 5G and beyond
- Handover management
- machine learning
- Reinforcement learning
- UAV communications