Delay Optimization in Remote ID-Based UAV Communication via BLE and Wi-Fi Switching

TL;DR

This paper proposes an adaptive protocol switching method using deep reinforcement learning to minimize communication delay in UAV Remote ID systems, effectively reducing latency in dynamic environments.

Contribution

It introduces a delay model for Remote ID communications and a novel multi-agent deep Q-network based switching algorithm for BLE and Wi-Fi protocols.

Findings

Achieves 32.1% lower latency than static BLE 4.

Achieves 37.7% lower latency than static Wi-Fi.

Effective delay reduction in dynamic UAV density scenarios.

Abstract

The remote identification (Remote ID) broadcast capability allows unmanned aerial vehicles (UAVs) to exchange messages, which is a pivotal technology for inter-UAV communications. Although this capability enhances the operational visibility, low delay in Remote ID-based communications is critical for ensuring the efficiency and timeliness of multi-UAV operations in dynamic environments. To address this challenge, we first establish delay models for Remote ID communications by considering packet reception and collisions across both BLE 4 and Wi-Fi protocols. Building upon these models, we formulate an optimization problem to minimize the long-term communication delay through adaptive protocol selection. Since the delay performance varies with the UAV density, we propose an adaptive BLE/Wi-Fi switching algorithm based on the multi-agent deep Q-network approach. Experimental results demonstrate that in dynamic-density scenarios, our strategy achieves 32.1% and 37.7% lower latency compared to static BLE 4 and Wi-Fi modes respectively.