Focusing on the received power prediction of millimeter-wave (mmWave) radio-frequency (RF) signals, we propose a multimodal split learning (SL) framework that integrates RF received signal powers and depth-images observed by physically separated entities. To improve its communication efficiency while preserving data privacy, we propose an SL neural network architecture that compresses the communication payload, i.e., images. Compared to a baseline solely utilizing RF signals, numerical results show that SL integrating only one pixel image with RF signals achieves higher prediction accuracy while maximizing both communication efficiency and privacy guarantees.
@article{arxiv.1911.01682,
title = {One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction},
author = {Yusuke Koda and Jihong Park and Mehdi Bennis and Koji Yamamoto and Takayuki Nishio and Masahiro Morikura},
journal= {arXiv preprint arXiv:1911.01682},
year = {2019}
}
Comments
3 pages, Accepted in ACM CoNEXT 2019 Poster Session