AI-Empowered Low-Altitude Economy: Cooperative Sensing With Fixed Wireless Access

TL;DR

This paper presents an AI-driven cooperative sensing framework using fixed wireless access devices to detect and localize UAVs efficiently, meeting 3GPP safety standards.

Contribution

It introduces a novel two-stage AI-based cooperative sensing pipeline leveraging uplink CSI for UAV detection and localization with high accuracy.

Findings

Missed detection probability reduced to 0.63%

Achieves 6.50 m positioning error at 95% confidence

Satisfies 3GPP safety and performance requirements

Abstract

The rapid growth of the low-altitude economy has intensified safety concerns arising from unauthorized unmanned aerial vehicles (UAVs), positioning UAV supervision as a key use case in 3GPP. To precisely sense such UAVs with wide coverage and low cost, we leverage fixed wireless access (FWA) customer premises equipment (CPEs), static, densely deployed devices that serve as wireless cameras for the radio environment. We develop an artificial intelligence-empowered two-stage cooperative sensing pipeline that exploits uplink channel state information (CSI) from multiple base station-CPE pairs for UAV detection and localization. In cooperative detection, lightweight CSI features are first individually extracted by neural network, and then adaptively integrated through an attention-based scheme to declare UAV presence. The learned attention scores effectively identify the critical pairs during detection, while facilitating UAV-affected pair selection for subsequent localization. For cooperative localization, neural network initially generates individual estimates and extract CSI features from selected pairs. These estimates, together with features and pair indexes, are fused using a Transformer to produce a precise cooperative estimate. Simulations show that cooperative schemes significantly reduce the missed detection probability to 0.63% and realize a 95%-confidence positioning error of 6.50 m, satisfying 3GPP requirements and showing the potential of FWA-assisted cooperative sensing. Dataset and codes are available on GitHub.