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

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.

Koda Yusuke, Park Jihong, Bennis Mehdi, Yamamoto Koji, Nishio Takayuki, Morikura Masahiro

A4 Article in conference proceedings

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

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: