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ISAC-over-NTN: HAPS-UAV Framework for Post-Disaster Responsive 6G Networks

Berk Ciloglu, Ozgun Ersoy, Metin Ozturk, Ali Gorcin

TL;DR

This work addresses the challenge of maintaining reliable communication and situational awareness in post-disaster scenarios where terrestrial networks are compromised. It proposes an integrated sensing and communication (ISAC) over non-terrestrial networks (NTN) architecture that combines multiple UAV-BS nodes with a high-altitude platform station (HAPS) to deliver resilient connectivity and ground sensing. The method jointly employs MU-MIMO downlink transmissions and monostatic sensing within shared beams, powered by SUS scheduling and MMSE-ZF beamforming, while Doppler-based mobility cues are extracted via two-UAV geometry and Doppler reconstruction. The framework demonstrates high sensing accuracy (up to 90% motion-detection sensitivity and 88% detection accuracy) and robust connectivity under varying levels of base-station failure, highlighting the practical potential of NTN-backed ISAC for disaster response and resilience.

Abstract

In disaster scenarios, ensuring both reliable communication and situational awareness becomes a critical challenge due to the partial or complete collapse of terrestrial networks. This paper proposes an integrated sensing and communication (ISAC) over non-terrestrial networks (NTN) architecture referred to as ISAC-over-NTN that integrates multiple uncrewed aerial vehicles (UAVs) and a high-altitude platform station (HAPS) to maintain resilient and reliable network operations in post-disaster conditions. We aim to achieve two main objectives: i) provide a reliable communication infrastructure, thereby ensuring the continuity of search-and-rescue activities and connecting people to their loved ones, and ii) detect users, such as those trapped under rubble or those who are mobile, using a Doppler-based mobility detection model. We employ an innovative beamforming method that simultaneously transmits data and detects Doppler-based mobility by integrating multi-user multiple-input multiple-output (MU-MIMO) communication and monostatic sensing within the same transmission chain. The results show that the proposed framework maintains reliable connectivity and achieves high detection accuracy of users in critical locations, reaching 90% motion detection sensitivity and 88% detection accuracy.

ISAC-over-NTN: HAPS-UAV Framework for Post-Disaster Responsive 6G Networks

TL;DR

This work addresses the challenge of maintaining reliable communication and situational awareness in post-disaster scenarios where terrestrial networks are compromised. It proposes an integrated sensing and communication (ISAC) over non-terrestrial networks (NTN) architecture that combines multiple UAV-BS nodes with a high-altitude platform station (HAPS) to deliver resilient connectivity and ground sensing. The method jointly employs MU-MIMO downlink transmissions and monostatic sensing within shared beams, powered by SUS scheduling and MMSE-ZF beamforming, while Doppler-based mobility cues are extracted via two-UAV geometry and Doppler reconstruction. The framework demonstrates high sensing accuracy (up to 90% motion-detection sensitivity and 88% detection accuracy) and robust connectivity under varying levels of base-station failure, highlighting the practical potential of NTN-backed ISAC for disaster response and resilience.

Abstract

In disaster scenarios, ensuring both reliable communication and situational awareness becomes a critical challenge due to the partial or complete collapse of terrestrial networks. This paper proposes an integrated sensing and communication (ISAC) over non-terrestrial networks (NTN) architecture referred to as ISAC-over-NTN that integrates multiple uncrewed aerial vehicles (UAVs) and a high-altitude platform station (HAPS) to maintain resilient and reliable network operations in post-disaster conditions. We aim to achieve two main objectives: i) provide a reliable communication infrastructure, thereby ensuring the continuity of search-and-rescue activities and connecting people to their loved ones, and ii) detect users, such as those trapped under rubble or those who are mobile, using a Doppler-based mobility detection model. We employ an innovative beamforming method that simultaneously transmits data and detects Doppler-based mobility by integrating multi-user multiple-input multiple-output (MU-MIMO) communication and monostatic sensing within the same transmission chain. The results show that the proposed framework maintains reliable connectivity and achieves high detection accuracy of users in critical locations, reaching 90% motion detection sensitivity and 88% detection accuracy.
Paper Structure (18 sections, 18 equations, 4 figures, 1 algorithm)

This paper contains 18 sections, 18 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Topology
  4. Channel Models
  5. User Association Model
  6. User Distribution and Mobility Models
  7. Transmission and Beamforming
  8. Sensing Model
  9. Two-UAV-BS Geometry-Based Speed Estimation
  10. Doppler-Based Distance Reconstruction
  11. Methodology: Beamforming and Sensing
  12. Multi-Slot Scheduler and User Selection
  13. Doppler--Based Motion Detection
  14. Problem Formulation
  15. Performance Evaluation
  16. ...and 3 more sections

Figures (4)

  • Figure 1: The system model illustrates multiple UAVs performing ISAC channel under the coordination of a HAPS.
  • Figure 2: CDF of per-user SINR for NTN and TN benchmarks under different BS failure ratios, $\gamma$.
  • Figure 3: CM for Doppler-based mobility detection with an accuracy of 0.884, precision of 0.905, and F1-score of 0.938.
  • Figure 4: Two-UAV-BS geometric motion tracking. (a) Estimated versus true user speed in the left $y$-axis. (b) True versus estimated user distance in the right $y$-axis.