A hybrid solution for 2-UAV RAN slicing
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Abstract (arXiv)
It's possible to distribute the Internet to users via drones. However it is then necessary to place the drones according to the positions of the users. Moreover, the 5th Generation (5G) New Radio (NR) technology is designed to accommodate a wide range of applications and industries. The NGNM 5G White Paper cite5gwhitepaper groups these vertical use cases into three categories: - enhanced Mobile Broadband (eMBB) - massive Machine Type Communication (mMTC) - Ultra-Reliable Low-latency Communication (URLLC). Partitioning the physical network into multiple virtual networks appears to be the best way to provide a customised service for each application and limit operational costs. This design is well known as textitnetwork slicing. Each drone must thus slice its bandwidth between each of the 3 user classes. This whole problem (placement + bandwidth) can be defined as an optimization problem, but since it is very hard to solve efficiently, it is almost always addressed by AI in the litterature. In my internship, I wanted to prove that viewing the problem as an optimization problem can still be useful, by building an hybrid solution involving on one hand AI and on the other optimization. I use it to achieve better results than approaches that use only AI, although at the cost of slightly larger (but still reasonable) computation times.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 13 references that are in the map. First posted on arXiv 15 Sep 2024.
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3 of 13 references.
References · 3
- 2018Deep Reinforcement Learning for Resource Management in Network Slicingcited by 27
- 2021Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Pointscited by 4
- 2022Deep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicingcited by 1
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