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We study the problem of interference source identification, through the lensof recognizing one of 15 different channels that belong to 3 different wirelesstechnologies: Bluetooth, Zigbee, and WiFi. We employ deep learning algorithmstrained on received samples taken from a 10 MHz band in the 2.4 GHz ISM Band.We obtain a classification accuracy of around 89.5% using any of four differentdeep neural network architectures: CNN, ResNet, CLDNN, and LSTM, whichdemonstrate the generality of the effectiveness of deep learning at theconsidered task. Interestingly, our proposed CNN architecture requiresapproximately 60% of the training time required by the state of the art whileachieving slightly larger classification accuracy. We then focus on the CNNarchitecture and further optimize its training time while incurring minimalloss in classification accuracy using three different approaches: 1- BandSelection, where we only use samples belonging to the lower and uppermost 2 MHzbands, 2- SNR Selection, where we only use training samples belonging to asingle SNR value, and 3- Sample Selection, where we try various sub-Nyquistsampling methods to select the subset of samples most relevant to theclassification task. Our results confirm the feasibility of fast deep learningfor wireless interference identification, by showing that the training time canbe reduced by as much as 30x with minimal loss in accuracy.

Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection