Multi-Label Wireless Interference Classification with Convolutional Neural Networks
Sergej Grunau, Dimitri Block, Uwe Meier · 2018
The steadily growing use of license-free frequency bands require reliable coexistence management and therefore proper wireless interference classification (WIC). In this work, we propose a WIC approach based upon a deep convolutional neural network (CNN) which classifies multiple IEEE 802.15.1, IEEE 802.11 b/g and IEEE 802.15.4 interfering signals in the presence of a utilized signal. The generated multi-label dataset contains frequency- and time-limited sensing snapshots with the bandwidth of 10MHz and duration of 12.8 μs, respectively. Each snapshot combines one utilized signal with up to multiple interfering signals. The approach shows promising results for same-technology interference with a classification accuracy of approximately 100% for narrow-band IEEE 802.15.1 and IEEE 802.15.4 signals. For cross-technology interference, wide-band IEEE 802.11 b/g signals achieve an accuracy above 90 %.