A hybrid solution for 2-UAV RAN slicing

TL;DR

This paper proposes a hybrid AI-optimization approach for efficient 2-UAV RAN slicing, optimizing drone placement and bandwidth allocation to improve service customization for different 5G use cases.

Contribution

It introduces a hybrid solution combining AI and optimization techniques to enhance 2-UAV RAN slicing performance over purely AI-based methods.

Findings

Achieves better slicing results than AI-only approaches.

Maintains reasonable computation times with the hybrid method.

Demonstrates the usefulness of combining optimization with AI in complex network problems.

Abstract

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 \cite{5gwhitepaper} 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 \textit{network 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.