UAV Communications Based on Non-Orthogonal Multiple Access

TL;DR

This paper explores a new framework for UAV networks utilizing NOMA, including performance modeling, joint trajectory and power optimization, and machine learning-based placement strategies for dynamic environments.

Contribution

It introduces a comprehensive framework combining stochastic geometry, trajectory design, and machine learning for NOMA-enabled UAV networks, addressing static and dynamic scenarios.

Findings

Performance evaluation using stochastic geometry shows NOMA benefits.

Joint trajectory and power optimization improves network efficiency.

Machine learning enhances UAV placement in roaming user scenarios.

Abstract

This article proposes a novel framework for unmaned aerial vehicle (UAV) networks with massive access capability supported by non-orthogonal multiple access (NOMA). In order to better understand NOMA enabled UAV networks, three case studies are carried out. We first provide performance evaluation of NOMA enabled UAV networks by adopting stochastic geometry to model the positions of UAVs and ground users. Then we investigate the joint trajectory design and power allocation for static NOMA users based on a simplified two-dimensional (2D) model that UAV is flying around at fixed height. As a further advance, we demonstrate the UAV placement issue with the aid of machine learning techniques when the ground users are roaming and the UAVs are capable of adjusting their positions in three-dimensions (3D) accordingly. With these case studies, we can comprehensively understand the UAV systems from fundamental theory to practical implementation.