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

Sambrama Hegde, Venkata Srirama Rohit Kantheti, Liang C Chu, Erik Blasch, Shih-Chun Lin

TL;DR

The paper addresses reliable connectivity for UAV swarms in 6G non-terrestrial networks by proposing RASHND, a self-healing framework that dynamically switches among d-MRC, d-LMMSE, and SC based on BER-driven state decisions. It leverages inter-device links for distributed signal fusion and rotates the leader role to avoid single points of failure, validated through indoor/outdoor SDR-based testbeds and the AERPAW UAV platform. Key contributions include the architectural model, the state-machine-driven algorithm selection, and comprehensive hardware validation showing improved data rates and interference resilience across bitstream and image data. The findings demonstrate that autonomous, device-to-device coordination in UAV swarms can deliver robust, high-quality communications suitable for critical NTN-enabled 6G applications.

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.

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

TL;DR

The paper addresses reliable connectivity for UAV swarms in 6G non-terrestrial networks by proposing RASHND, a self-healing framework that dynamically switches among d-MRC, d-LMMSE, and SC based on BER-driven state decisions. It leverages inter-device links for distributed signal fusion and rotates the leader role to avoid single points of failure, validated through indoor/outdoor SDR-based testbeds and the AERPAW UAV platform. Key contributions include the architectural model, the state-machine-driven algorithm selection, and comprehensive hardware validation showing improved data rates and interference resilience across bitstream and image data. The findings demonstrate that autonomous, device-to-device coordination in UAV swarms can deliver robust, high-quality communications suitable for critical NTN-enabled 6G applications.

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.
Paper Structure (22 sections, 5 equations, 13 figures)

This paper contains 22 sections, 5 equations, 13 figures.

Table of Contents

  1. Introduction
  2. System Architecture
  3. Proposed Architectural Model
  4. Experimental Testbed Setup
  5. Indoor Testbed
  6. Outdoor Testbed
  7. Self-healing Proposal
  8. Algorithms Implemented in Self-Healing
  9. Distributed Maximal Ratio Combining (d-MRC)
  10. Distributed Linear Minimum Mean Square Error Estimation (d-LMMSE)
  11. Selection Combining (SC)
  12. State Machine Model for Self-Healing
  13. Inter-Device Links
  14. Over the air Implementation
  15. Self-Healing on Indoor Testbed
  16. ...and 7 more sections

Figures (13)

  • Figure 1: Proposed RASHND architecture.
  • Figure 2: Indoor testbed for $1\times3$ SIMO testbed.
  • Figure 3: Outdoor testbed for $1\times3$ SIMO testbed.
  • Figure 4: State machine for self-healing.
  • Figure 5: Flow diagram for inter-device links.
  • ...and 8 more figures