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Channel State Information Analysis for Jamming Attack Detection in Static and Dynamic UAV Networks -- An Experimental Study

Pavlo Mykytyn, Ronald Chitauro, Zoya Dyka, Peter Langendoerfer

TL;DR

<3-5 sentence high-level summary> This study tackles detecting jamming attacks in IEEE 802.11-based UAV networks by leveraging Channel State Information (CSI) captured from ESP32-S3 nodes. It experimentally assesses static and dynamic UAV-to-GCS links with a constant AWGN jammer implemented via USRP B210 and GNU Radio, analyzing how CSI metrics respond to interference. The results show that static links yield clear, easily detectable CSI changes and substantial PDR degradation under jamming, while dynamic UAV motion introduces volatility that requires more sophisticated analysis, though jammer effects remain observable. The work demonstrates the feasibility of lightweight CSI-based jamming detection for resource-constrained aerial networks and outlines directions to extend the approach to other jammer types and scenarios.

Abstract

Networks built on the IEEE 802.11 standard have experienced rapid growth in the last decade. Their field of application is vast, including smart home applications, Internet of Things (IoT), and short-range high throughput static and dynamic inter-vehicular communication networks. Within such networks, Channel State Information (CSI) provides a detailed view of the state of the communication channel and represents the combined effects of multipath propagation, scattering, phase shift, fading, and power decay. In this work, we investigate the problem of jamming attack detection in static and dynamic vehicular networks. We utilize ESP32-S3 modules to set up a communication network between an Unmanned Aerial Vehicle (UAV) and a Ground Control Station (GCS), to experimentally test the combined effects of a constant jammer on recorded CSI parameters, and the feasibility of jamming detection through CSI analysis in static and dynamic communication scenarios.

Channel State Information Analysis for Jamming Attack Detection in Static and Dynamic UAV Networks -- An Experimental Study

TL;DR

<3-5 sentence high-level summary> This study tackles detecting jamming attacks in IEEE 802.11-based UAV networks by leveraging Channel State Information (CSI) captured from ESP32-S3 nodes. It experimentally assesses static and dynamic UAV-to-GCS links with a constant AWGN jammer implemented via USRP B210 and GNU Radio, analyzing how CSI metrics respond to interference. The results show that static links yield clear, easily detectable CSI changes and substantial PDR degradation under jamming, while dynamic UAV motion introduces volatility that requires more sophisticated analysis, though jammer effects remain observable. The work demonstrates the feasibility of lightweight CSI-based jamming detection for resource-constrained aerial networks and outlines directions to extend the approach to other jammer types and scenarios.

Abstract

Networks built on the IEEE 802.11 standard have experienced rapid growth in the last decade. Their field of application is vast, including smart home applications, Internet of Things (IoT), and short-range high throughput static and dynamic inter-vehicular communication networks. Within such networks, Channel State Information (CSI) provides a detailed view of the state of the communication channel and represents the combined effects of multipath propagation, scattering, phase shift, fading, and power decay. In this work, we investigate the problem of jamming attack detection in static and dynamic vehicular networks. We utilize ESP32-S3 modules to set up a communication network between an Unmanned Aerial Vehicle (UAV) and a Ground Control Station (GCS), to experimentally test the combined effects of a constant jammer on recorded CSI parameters, and the feasibility of jamming detection through CSI analysis in static and dynamic communication scenarios.

Paper Structure

This paper contains 15 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Background on Jamming Attacks
  4. Background on CSI
  5. Related Work
  6. Experimental Setup and Configuration
  7. UAV-to-GCS Communication
  8. Jamming Hardware and Software Setup
  9. Jamming Experiments
  10. Static Communication Experiments
  11. Dynamic Communication Experiments
  12. Experimental Results
  13. Static Communication Experiments
  14. Dynamic Communication Experiments
  15. Conclusion and Future Work

Figures (3)

  • Figure 1: UAV (a) to GCS (b) communication link, set up for experimental testing.
  • Figure 2: Plot of the amplitude fluctuation of subcarrier 50 during the second static communication experiment.
  • Figure 3: Plot of the time delay fluctuation of consecutive packets during the second static communication experiment.