Autonomous Self-Healing UAV Swarms for Robust 6G Non-Terrestrial Networks

TL;DR

This paper presents RASHND, a self-healing UAV network design that enhances robustness and interference resilience in 6G NTN applications through adaptive algorithms and real-world testing.

Contribution

Introduction of RASHND, a novel self-healing, adaptive UAV network framework utilizing intelligent algorithm selection for improved reliability in dynamic conditions.

Findings

RASHND significantly improves network reliability.

The system enhances interference resilience.

Successful real-world UAV tests validate the approach.

Abstract

Recent years have seen an increased interest in the use of Non-terrestrial networks (NTNs), especially the unmanned aerial vehicles (UAVs) to provide cost-effective global connectivity in next-generation wireless networks. We introduce a resilient, adaptive, self-healing network design (RASHND) to optimize signal quality under dynamic interference and adversarial conditions. RASHND leverages inter-node communication and an intelligent algorithm selection process, incorporating combining techniques like distributed-Maximal Ratio Combining (d-MRC), distributed-Linear Minimum Mean Squared Error Estimation(d-LMMSE), and Selection Combining (SC). These algorithms are selected to improve performance by adapting to changing network conditions. To evaluate the effectiveness of the proposed RASHND solutions, a software-defined radio (SDR)-based hardware testbed afforded initial testing and evaluations. Additionally, we present results from UAV tests conducted on the AERPAW testbed to validate our solutions in real-world scenarios. The results demonstrate that RASHND significantly enhances the reliability and interference resilience of UAV networks, making them well-suited for critical communications.