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

arXiv: https://arxiv.org/abs/2505.17530 · HTML: https://mlsanity.com/paper/2505.17530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 22 of 22)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets](https://mlsanity.com/paper/2111.07574/) (2021)
- [LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications](https://mlsanity.com/paper/2203.05548/) (2022)
- [A Survey of Beam Management for mmWave and THz Communications Towards 6G](https://mlsanity.com/paper/2308.02135/) (2023)
- [Characterizing Driving Styles with Deep Learning](https://mlsanity.com/paper/1607.03611/) (2016)
- [Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?](https://mlsanity.com/paper/2205.09054/) (2022)
- [What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G](https://mlsanity.com/paper/2105.04842/) (2021)
- [Computer Vision Aided Beam Tracking in A Real-World Millimeter Wave Deployment](https://mlsanity.com/paper/2111.14803/) (2021)
- [Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction](https://mlsanity.com/paper/2205.12187/) (2022)
- [A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking](https://mlsanity.com/paper/2104.09204/) (2021)
- [Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration](https://mlsanity.com/paper/2412.04734/) (2024)
- [Hierarchical Codebook based Multiuser Beam Training for Millimeter Wave Massive MIMO](https://mlsanity.com/paper/2203.06438/) (2022)
- [Adaptive Beam Tracking with the Unscented Kalman Filter for Millimeter Wave Communication](https://mlsanity.com/paper/1804.08640/) (2018)
- [Beam Training based on Dynamic Hierarchical Codebook for Millimeter Wave Massive MIMO](https://mlsanity.com/paper/1901.01425/) (2019)
- [Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach](https://mlsanity.com/paper/2009.07478/) (2020)
- [Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration](https://mlsanity.com/paper/2308.01558/) (2023)
- [Beam Training and Tracking in MmWave Communication: A Survey](https://mlsanity.com/paper/2205.10169/) (2022)
- [Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction](https://mlsanity.com/paper/2211.07569/) (2022)

## Abstract (arXiv)

Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Unmanned Aerial Vehicles (UAVs). However, a robust beam management remains challenging due to significant path loss and the dynamic mobility of UAVs, which can destabilize the UAV-base station (BS) link. This research presents a GPS-aided deep learning (DL) model that simultaneously predicts current and future optimal beams for UAV mmWave communications, maintaining a Top-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.