ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G

TL;DR

ProSky is an AI-driven framework that combines NEAT with NOMA-mmWave technology to optimize UAV network performance, achieving faster learning and higher spectral and energy efficiency compared to traditional methods.

Contribution

This paper introduces ProSky, a novel NEAT-based AI approach for managing NOMA-mmWave UAV networks, outperforming existing deep reinforcement learning schemes in speed and efficiency.

Findings

ProSky outperforms model-based methods in spectral efficiency and energy efficiency.

ProSky learns 5.3 times faster than deep reinforcement learning schemes.

ProSky provides a practical AI solution for UAV network management.

Abstract

Rendering to their abilities to provide ubiquitous connectivity, flexibly and cost effectively, unmanned aerial vehicles (UAVs) have been getting more and more research attention. To take the UAVs' performance to the next level, however, they need to be merged with some other technologies like non-orthogonal multiple access (NOMA) and millimeter wave (mmWave), which both promise high spectral efficiency (SE). As managing UAVs efficiently may not be possible using model-based techniques, another key innovative technology that UAVs will inevitably need to leverage is artificial intelligence (AI). Designing an AI-based technique that adaptively allocates radio resources and places UAVs in 3D space to meet certain communication objectives, however, is a tough row to hoe. In this paper, we propose a neuroevolution of augmenting topologies NEAT framework, referred to as ProSky, to manage NOMA-mmWave-UAV networks. ProSky exhibits a remarkable performance improvement over a model-based method. Moreover, ProSky learns 5.3 times faster than and outperforms, in both SE and energy efficiency EE while being reasonably fair, a deep reinforcement learning DRL based scheme. The ProSky source code is accessible to use here: https://github.com/Fouzibenfaid/ProSky