GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication
Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Uncrewed 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 resea...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11072409/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849704023715741696 |
|---|---|
| author | Vendi Ardianto Nugroho Byung Moo Lee |
| author_facet | Vendi Ardianto Nugroho Byung Moo Lee |
| author_sort | Vendi Ardianto Nugroho |
| collection | DOAJ |
| description | Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Uncrewed 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 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% compared to exhaustive search methods with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not exceeding 1 dB. |
| format | Article |
| id | doaj-art-eae0cb6ef6cf4ec39be1bd2ec43270b4 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eae0cb6ef6cf4ec39be1bd2ec43270b42025-08-20T03:16:56ZengIEEEIEEE Access2169-35362025-01-011311706511707710.1109/ACCESS.2025.358659411072409GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave CommunicationVendi Ardianto Nugroho0https://orcid.org/0009-0008-2329-5725Byung Moo Lee1https://orcid.org/0000-0003-3675-929XDepartment of Intelligent Mechatronics Engineering, Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, South KoreaDepartment of Intelligent Mechatronics Engineering, Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, South KoreaMillimeter-wave (mmWave) communication enables high data rates for cellular-connected Uncrewed 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 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% compared to exhaustive search methods with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not exceeding 1 dB.https://ieeexplore.ieee.org/document/11072409/Millimeter wave communicationGPSdroneUAVdeep learningbeam prediction |
| spellingShingle | Vendi Ardianto Nugroho Byung Moo Lee GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication IEEE Access Millimeter wave communication GPS drone UAV deep learning beam prediction |
| title | GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication |
| title_full | GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication |
| title_fullStr | GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication |
| title_full_unstemmed | GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication |
| title_short | GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication |
| title_sort | gps aided deep learning for beam prediction and tracking in uav mmwave communication |
| topic | Millimeter wave communication GPS drone UAV deep learning beam prediction |
| url | https://ieeexplore.ieee.org/document/11072409/ |
| work_keys_str_mv | AT vendiardiantonugroho gpsaideddeeplearningforbeampredictionandtrackinginuavmmwavecommunication AT byungmoolee gpsaideddeeplearningforbeampredictionandtrackinginuavmmwavecommunication |