CNN Autoencoder Resizer: A Power-Efficient LoS/NLoS Detector in MIMO-enabled UAV Networks

TL;DR

This paper introduces CNN Autoencoder Resizer (CAR), a power-efficient framework that significantly improves LoS/NLoS detection accuracy in UAV networks, aiding in reliable communication without extra power use.

Contribution

The paper presents a novel CNN autoencoder resizer that enhances LoS/NLoS detection accuracy in UAV networks without increasing power consumption.

Findings

LoS/NLoS detection accuracy increased from 66% to 86%.

CAR maintains consistent power consumption levels.

CAR can serve as a preprocessing tool for other methods.

Abstract

Optimizing the design, performance, and resource efficiency of wireless networks (WNs) necessitates the ability to discern Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios across diverse applications and environments. Unmanned Aerial Vehicles (UAVs) exhibit significant potential in this regard due to their rapid mobility, aerial capabilities, and payload characteristics. Particularly, UAVs can serve as vital non-terrestrial base stations (NTBS) in the event of terrestrial base station (TBS) failures or downtime. In this paper, we propose CNN autoencoder resizer (CAR) as a framework that improves the accuracy of LoS/NLoS detection without demanding extra power consumption. Our proposed method increases the mean accuracy of detecting LoS/NLoS signals from 66% to 86%, while maintaining consistent power consumption levels. In addition, the resolution provided by CAR shows that it can be employed as a preprocessing tool in other methods to enhance the quality of signals.