One pixel image and RF signal based split learning for mmwave received power prediction
Koda, Yusuke; Park, Jihong; Bennis, Mehdi; Yamamoto, Koji; Nishio, Takayuki; Morikura, Masahiro (2019-12-31)
Yusuke Koda, Jihong Park, Mehdi Bennis, Koji Yamamoto, Takayuki Nishio, and Masahiro Morikura. 2019. One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction. In Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies (CoNEXT ’19). Association for Computing Machinery, New York, NY, USA, 54–56. DOI:https://doi.org/10.1145/3360468.3368176
© 2019 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019, https://doi.org/10.1145/3360468.3368176.
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https://urn.fi/URN:NBN:fi-fe2020050424732
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Abstract
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.
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