Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems

TL;DR

This paper introduces a data-driven framework for collaborative wideband spectrum sensing and scheduling among networked UAVs in UTM systems, utilizing multi-class classification and reinforcement learning to optimize spectrum utilization.

Contribution

It presents a novel integrated approach combining multi-class spectrum sensing fusion and RL-based scheduling for UAV networks in UTM environments.

Findings

Enhanced spectrum sensing accuracy through data fusion.

Effective dynamic spectrum allocation via reinforcement learning.

Realistic simulation framework for UAV spectrum management.

Abstract

In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the secondary users to opportunistically utilize detected spectrum holes. To this end, we propose a multi-class classification problem for wideband spectrum sensing to detect vacant spectrum spots based on collected I/Q samples. To enhance the accuracy of the spectrum sensing module, the outputs from the multi-class classification by each individual UAV are fused at a server in the unmanned aircraft system traffic management (UTM) ecosystem. In the spectrum scheduling phase, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users (i.e., UAVs). To evaluate the proposed methods, we establish a comprehensive simulation framework that generates a near-realistic synthetic dataset using MATLAB LTE toolbox by incorporating base-station~(BS) locations in a chosen area of interest, performing ray-tracing, and emulating the primary users channel usage in terms of I/Q samples. This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices.