Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs

TL;DR

This paper discusses the challenges of providing reliable and secure cellular connectivity for UAVs, proposing ANN-based solutions to enhance performance and security in various UAV applications.

Contribution

It introduces artificial neural network-based methods to address wireless and security challenges in cellular-connected UAV systems, with preliminary simulation validation.

Findings

ANN solutions improve resource utilization for UAVs

Enhanced security measures protect against cyber-physical attacks

Simulation results demonstrate effectiveness in real-time scenarios

Abstract

Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.