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

TL;DR

SIDSense is a novel database-free TV White Space sensing framework that enhances disaster-resilient connectivity for Small Island Developing States by ensuring continuous operation during outages.

Contribution

It introduces a CNN-based spectrum classification system coupled with a hybrid sensing workflow and a compliance controller, enabling database-free TVWS operation in disaster-prone regions.

Findings

Achieved 94.2% sensing accuracy in field tests

Maintained zero missed 5G L1 deadlines during outages

Demonstrated sustained connectivity during simulated PAWS failures

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.