@inproceedings{06cf923475e34a1b8206e8764059aa49,
title = "Environment Semantic Aided Communication: A Real World Demonstration for Beam Prediction",
abstract = "Millimeter-wave (mmWave) and terahertz (THz) communication systems adopt large antenna arrays to ensure adequate receive signal power. However, adjusting the narrow beams of these antenna arrays typically incurs high beam training overhead that scales with the number of antennas. Recently proposed vision-aided beam prediction solutions, which utilize raw RGB images captured at the basestation to predict the optimal beams, have shown initial promising results. However, they still have a considerable computational complexity, limiting their adoption in the real world. To address these challenges, this paper focuses on developing and comparing various approaches that extract the lightweight semantic information from the visual data. The results show that the proposed solutions can significantly decrease the computational requirements while achieving similar beam prediction accuracy compared to the previously proposed vision-aided solutions.",
keywords = "Millimeter wave, beam selection, camera, computer vision, deep learning, environment semantics",
author = "Shoaib Imran and Gouranga Charan and Ahmed Alkhateeb",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Communications Workshops, ICC Workshops 2023 ; Conference date: 28-05-2023 Through 01-06-2023",
year = "2023",
doi = "10.1109/ICCWorkshops57953.2023.10283602",
language = "English (US)",
series = "2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "48--53",
booktitle = "2023 IEEE International Conference on Communications Workshops",
}