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One pixel image and RF signal based split learning for mmwave received power prediction

conference contribution
posted on 2019-01-01, 00:00 authored by Y Koda, K Yamamoto, Jihong ParkJihong Park, T Nishio, M Bennis, M Morikura
© 2019 held by the owner/author(s). 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.

History

Event

CoNEXT '19

Pagination

54 - 56

Publisher

ACM

Location

Orlando, FL, USA

Start date

2019-12-09

End date

2019-12-12

ISBN-13

9781450370066

Publication classification

E3.1 Extract of paper

Title of proceedings

CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019

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