System-Level Methods and Models for Coordination and Resource Management in UAV Networks with Directional Antennas
Gaydamaka, Anna (2025)
Tampere University
2025
Tieto- ja sähkötekniikan tohtoriohjelma - Doctoral Programme in Computing and Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Väitöspäivä
2025-06-17
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-03-3980-7
https://urn.fi/URN:ISBN:978-952-03-3980-7
Tiivistelmä
The emergence and development of fifth generation (5G) and sixth generation (6G) cellular systems promises remarkable data transmission speeds and seamless connectivity, unlocking a wide range of innovative applications such as autonomous systems, immersive virtual reality, and smart city technologies. Among the areas benefiting from these advancements is the development of non-terrestrial networks. Within this context, the concept of a “user” extends beyond humans to include unmanned aerial vehicles (UAVs).
UAVs hold immense potential in diverse fields, including disaster management, logistics, and surveillance, due to their mobility and adaptability. However, integrating UAVs into non-terrestrial networks presents significant challenges. These include operating in environments lacking traditional infrastructure, ensuring robust situational awareness, achieving optimal content dissemination, and making efficient use of limited network resources. To address these challenges, it is essential to develop novel algorithms and methods tailored to the unique requirements of UAV swarm management in 5G enabled systems.
This dissertation advances 5G network technologies by tackling the critical issues surrounding UAV swarm integration. It begins by proposing a distributed framework for organizing and maintaining swarm topology and communication. Next, it introduces a methodology leveraging mmWave/sub-THz radars and directional antennas to ensure reliable situational awareness in dynamic environments. Finally, it applies machine learning techniques to optimize user-to-group assignment in 5G multicasting, enhancing communication efficiency and scalability.
The thesis results demonstrate that the proposed methods effectively address the challenges of UAV swarm integration in 5G networks. Specifically, the use of information from just two-hop neighbors achieves a network topology replication with 90-95% similarity allowing for efficient overhead-controlled routing in dynamic UAV swarms. Detection performance is shown to be highly sensitive to both UAV density and coverage radius, with increase in these parameters causing a significant decline in performance. Additionally, the findings reveal that employing antennas with smaller half-power beamwidths (HPBWs) improves the probability of successful detection, albeit at the cost of longer scanning times. Finally, it was shown that tree-based algorithms provide an optimal trade-off between accuracy and computational complexity for multicast grouping.
UAVs hold immense potential in diverse fields, including disaster management, logistics, and surveillance, due to their mobility and adaptability. However, integrating UAVs into non-terrestrial networks presents significant challenges. These include operating in environments lacking traditional infrastructure, ensuring robust situational awareness, achieving optimal content dissemination, and making efficient use of limited network resources. To address these challenges, it is essential to develop novel algorithms and methods tailored to the unique requirements of UAV swarm management in 5G enabled systems.
This dissertation advances 5G network technologies by tackling the critical issues surrounding UAV swarm integration. It begins by proposing a distributed framework for organizing and maintaining swarm topology and communication. Next, it introduces a methodology leveraging mmWave/sub-THz radars and directional antennas to ensure reliable situational awareness in dynamic environments. Finally, it applies machine learning techniques to optimize user-to-group assignment in 5G multicasting, enhancing communication efficiency and scalability.
The thesis results demonstrate that the proposed methods effectively address the challenges of UAV swarm integration in 5G networks. Specifically, the use of information from just two-hop neighbors achieves a network topology replication with 90-95% similarity allowing for efficient overhead-controlled routing in dynamic UAV swarms. Detection performance is shown to be highly sensitive to both UAV density and coverage radius, with increase in these parameters causing a significant decline in performance. Additionally, the findings reveal that employing antennas with smaller half-power beamwidths (HPBWs) improves the probability of successful detection, albeit at the cost of longer scanning times. Finally, it was shown that tree-based algorithms provide an optimal trade-off between accuracy and computational complexity for multicast grouping.
Kokoelmat
- Väitöskirjat [5165]