Semi-supervised Convolutional Neural Networks for Identifying Wi-Fi Interference Sources

Krista Longi, Teemu Pulkkinen, Arto Klami
Proceedings of the Ninth Asian Conference on Machine Learning, PMLR 77:391-406, 2017.

Abstract

We present a convolutional neural network for identifying radio frequency devices from signal data, in order to detect possible interference sources for wireless local area networks. Collecting training data for this problem is particularly challenging due to a high number of possible interfering devices, difficulty in obtaining precise timings, and the need to measure the devices in varying conditions. To overcome this challenge we focus on semi-supervised learning, aiming to minimize the need for reliable training samples while utilizing larger amounts of unsupervised labels to improve the accuracy. In particular, we propose a novel structured extension of the pseudo-label technique to take advantage of temporal continuity in the data and show that already a few seconds of training data for each device is sufficient for highly accurate recognition.

Cite this Paper

BibTeX
@InProceedings{pmlr-v77-longi17a, title = {Semi-supervised Convolutional Neural Networks for Identifying Wi-Fi Interference Sources}, author = {Longi, Krista and Pulkkinen, Teemu and Klami, Arto}, booktitle = {Proceedings of the Ninth Asian Conference on Machine Learning}, pages = {391--406}, year = {2017}, editor = {Zhang, Min-Ling and Noh, Yung-Kyun}, volume = {77}, series = {Proceedings of Machine Learning Research}, address = {Yonsei University, Seoul, Republic of Korea}, month = {15--17 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v77/longi17a/longi17a.pdf}, url = {https://proceedings.mlr.press/v77/longi17a.html}, abstract = {We present a convolutional neural network for identifying radio frequency devices from signal data, in order to detect possible interference sources for wireless local area networks. Collecting training data for this problem is particularly challenging due to a high number of possible interfering devices, difficulty in obtaining precise timings, and the need to measure the devices in varying conditions. To overcome this challenge we focus on semi-supervised learning, aiming to minimize the need for reliable training samples while utilizing larger amounts of unsupervised labels to improve the accuracy. In particular, we propose a novel structured extension of the pseudo-label technique to take advantage of temporal continuity in the data and show that already a few seconds of training data for each device is sufficient for highly accurate recognition.} }
Endnote
%0 Conference Paper %T Semi-supervised Convolutional Neural Networks for Identifying Wi-Fi Interference Sources %A Krista Longi %A Teemu Pulkkinen %A Arto Klami %B Proceedings of the Ninth Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Min-Ling Zhang %E Yung-Kyun Noh %F pmlr-v77-longi17a %I PMLR %P 391--406 %U https://proceedings.mlr.press/v77/longi17a.html %V 77 %X We present a convolutional neural network for identifying radio frequency devices from signal data, in order to detect possible interference sources for wireless local area networks. Collecting training data for this problem is particularly challenging due to a high number of possible interfering devices, difficulty in obtaining precise timings, and the need to measure the devices in varying conditions. To overcome this challenge we focus on semi-supervised learning, aiming to minimize the need for reliable training samples while utilizing larger amounts of unsupervised labels to improve the accuracy. In particular, we propose a novel structured extension of the pseudo-label technique to take advantage of temporal continuity in the data and show that already a few seconds of training data for each device is sufficient for highly accurate recognition.
APA
Longi, K., Pulkkinen, T. & Klami, A.. (2017). Semi-supervised Convolutional Neural Networks for Identifying Wi-Fi Interference Sources. Proceedings of the Ninth Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 77:391-406 Available from https://proceedings.mlr.press/v77/longi17a.html.

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