GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913938725142528 |
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| author | Nugroho, Vendi Ardianto Lee, Byung Moo |
| author_facet | Nugroho, Vendi Ardianto Lee, Byung Moo |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_17530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication Nugroho, Vendi Ardianto Lee, Byung Moo Signal Processing Information Theory Machine Learning 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. |
| title | GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2505.17530 |