A Novel Approach Mitigating the Doppler Effect in UAV-LoRa Networks: DQN-Based Dynamic Resource Management

Abstract

This study proposes a novel Deep Reinforcement Learning (DRL)-based resource allocation architecture that dynamically mitigates physical layer impairments in Long Range (LoRa) communication networks established with Unmanned Aerial Vehicles (UAVs) operating at tactical speeds. Traditional Adaptive Data Rate (ADR) algorithms used in LoRaWAN networks misinterpret the Doppler shift under high mobility as path loss, leading to an unwarranted increase in the spreading factor and subsequent communication link failures. In this work, a cross-layer Deep Q-Network (DQN) agent is designed to incorporate UAV velocity into the state space, autonomously selecting the optimal spreading factor and transmission power by predicting frequency shifts at the physical layer. Simulations conducted in a realistic Rayleigh fading channel model demonstrate that the proposed method increases the Packet Delivery Ratio (PDR) to over 85% at high speeds, significantly outperforming conventional algorithms.