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SIDSense: Database-Free TV White Space Sensing for Disaster-Resilient Connectivity

George M. Gichuru, Zoe Aiyanna M. Cayetano

TL;DR

This work presents SIDSense, an edge AI framework for database-free TVWS operation that preserves regulatory intent through a compliance-gated controller, audit logging, and graceful degradation, and looks to contribute some of the components of the SIDSense pipeline to the open source community to accelerate resilient connectivity deployments in climate-vulnerable regions.

Abstract

Small Island Developing States (SIDS) are disproportionately exposed to climate-driven disasters, yet often rely on fragile terrestrial networks that fail when they are most needed. TV White Space (TVWS) links offer long-range, low-power coverage; however, current deployments depend on Protocol to Access White Spaces (PAWS) database connectivity for channel authorization, creating a single point of failure during outages. We present SIDSense, an edge AI framework for database-free TVWS operation that preserves regulatory intent through a compliance-gated controller, audit logging, and graceful degradation. SIDSense couples CNN-based spectrum classification with a hybrid sensing-first, authorization-as-soon-as-possible workflow and co-locates sensing and video enhancement with a private 5G stack on a maritime vessel to sustain situational-awareness video backhaul. Field experiments in Barbados demonstrate sustained connectivity during simulated PAWS outages, achieving 94.2% sensing accuracy over 470-698 MHz with 23 ms mean decision latency, while maintaining zero missed 5G Layer-1 (L1) deadlines under GPU-aware scheduling. We release an empirical Caribbean TVWS propagation and occupancy dataset and look to contribute some of the components of the SIDSense pipeline to the open source community to accelerate resilient connectivity deployments in climate-vulnerable regions.

SIDSense: Database-Free TV White Space Sensing for Disaster-Resilient Connectivity

TL;DR

This work presents SIDSense, an edge AI framework for database-free TVWS operation that preserves regulatory intent through a compliance-gated controller, audit logging, and graceful degradation, and looks to contribute some of the components of the SIDSense pipeline to the open source community to accelerate resilient connectivity deployments in climate-vulnerable regions.

Abstract

Small Island Developing States (SIDS) are disproportionately exposed to climate-driven disasters, yet often rely on fragile terrestrial networks that fail when they are most needed. TV White Space (TVWS) links offer long-range, low-power coverage; however, current deployments depend on Protocol to Access White Spaces (PAWS) database connectivity for channel authorization, creating a single point of failure during outages. We present SIDSense, an edge AI framework for database-free TVWS operation that preserves regulatory intent through a compliance-gated controller, audit logging, and graceful degradation. SIDSense couples CNN-based spectrum classification with a hybrid sensing-first, authorization-as-soon-as-possible workflow and co-locates sensing and video enhancement with a private 5G stack on a maritime vessel to sustain situational-awareness video backhaul. Field experiments in Barbados demonstrate sustained connectivity during simulated PAWS outages, achieving 94.2% sensing accuracy over 470-698 MHz with 23 ms mean decision latency, while maintaining zero missed 5G Layer-1 (L1) deadlines under GPU-aware scheduling. We release an empirical Caribbean TVWS propagation and occupancy dataset and look to contribute some of the components of the SIDSense pipeline to the open source community to accelerate resilient connectivity deployments in climate-vulnerable regions.
Paper Structure (29 sections, 3 equations, 1 figure)

This paper contains 29 sections, 3 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Contributions
  3. Related Work
  4. TV White Space Systems and Database Governance
  5. Spectrum Sensing Techniques
  6. Disaster Communications and Network Resilience
  7. AI-RAN and Edge Computing Architectures
  8. System Model and Problem Formulation
  9. Deployment Scenario
  10. Channel Model
  11. Spectrum Sensing Model
  12. Problem Statement
  13. SIDSense Architecture
  14. System Overview
  15. CNN-Based Spectrum Sensing Pipeline
  16. ...and 14 more sections

Figures (1)

  • Figure 1: SIDSense reference architecture: a UAV video source uplinks to a vessel-hosted private 5G network with UPF local breakout and edge AI services; a compliance gate authorizes TVWS backhaul based on PAWS grants or sensing-first emergency policy; all decisions are audit-logged and exported to shore when connectivity permits.