Wireless interference identification with convolutional neural networks

By Malte Schmidt, Dimitri Block, Ueli Meier. 2017

144 citations recorded by the source corpus.

The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference avoidance, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 μs and an acquisition bandwidth of 10 MHz. The CNN distinguishes between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95 % for signal-to-noise ratio of at least −5 dB.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, neutral): “[169], where a wireless interference identifica-”
Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks (2018, background): “1 devices in order to classify wireless devices by allocated frequency channel and the type of wireless technology [25].”
End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017, methodology): “[12] for sharing the ‘‘Wireless interference’’ dataset.”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “As opposed to LSTM, that are good at temporal modeling, CNNs are appropriate at reducing frequency variations which therefore makes them suitable for applications that deal with spatial data such as interference identification in wireless networks [85].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “As for learning from the environment, we are able to harness DNNs trained by the data gleaned over the air for the sake of channel estimation [312], interference identification [313], localization [314]–[318], etc.”