GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication

This work has been published in the IEEE Access with DOI: 10.1109/ACCESS.2025.3586594. Abstract --Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Uncrewed Aerial V ehicles (UA Vs). However, a robust beam management remains challenging due to significant path loss and the dynamic mobility of UA Vs, which can destabilize the UA V-base station (BS) link. This research presents a GPS-aided deep learning (DL) model that simultaneously predicts current and future optimal beams for UA V mmWave communications, maintaining a T op-1 prediction accuracy exceeding 70% and an average power loss below 0.6 dB across all prediction steps. These outcomes stem from a proposed data set splitting method ensuring balanced label distribution, paired with a GPS preprocessing technique that extracts key positional features, and a DL architecture that maps sequential position data to beam index predictions. The model reduces overhead by approximately 93% (requiring the training of 2 3 beams instead of 32 beams) with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not exceeding 1 dB. Uncrewed Aerial V ehicles (UA V) are expected to serve two roles in wireless networks both as user equipment (UE) that accesses cellular network (cellular-connected UA V) and as UA V -assisted communication platforms providing aerial base stations (BS) and relays for terrestrial users [1] As the high path loss characteristic of mmWave, deploying large antenna arrays on the BS side helps mitigate it by generating narrow beams with strong beamforming gains [4]. As a result, mmWave communications rely heavily on efficient beam management--including beam training and tracking--to quickly select the appropriate beams during intra-and inter-cell mobility, minimizing the risk of beam misalignment [5].