5G Air-to-Ground Network Design and Optimization: A Deep Learning Approach

TL;DR

This paper introduces a deep learning framework for designing and optimizing 5G air-to-ground networks, focusing on parameters like antenna angles and site distances to improve in-flight connectivity.

Contribution

It proposes a novel dual neural network architecture for approximating network behavior and optimizing deployment parameters, advancing system-level design methods for A2G networks.

Findings

The model accurately predicts user throughput in A2G networks.

Optimized parameters significantly improve network performance.

Simulation validates the effectiveness of the proposed approach.

Abstract

Direct air-to-ground (A2G) communications leveraging the fifth-generation (5G) new radio (NR) can provide high-speed broadband in-flight connectivity to aircraft in the sky. A2G network deployment entails optimizing various design parameters such as inter-site distances, number of sectors per site, and the up-tilt angles of sector antennas. The system-level design guidelines in the existing work on A2G network are rather limited. In this paper, a novel deep learning-based framework is proposed for efficient design and optimization of a 5G A2G network. The devised architecture comprises two deep neural networks (DNNs): the first DNN is used for approximating the 5G A2G network behavior in terms of user throughput, and the second DNN is developed as a function optimizer to find the throughput-optimal deployment parameters including antenna up-tilt angles and inter-site distances. Simulation results are provided to validate the proposed model and reveal system-level design insights.