“We present JaX, a novel approach for detecting and cancelling high-power jammers in the scenarios when the traditional spread spectrum techniques and other jammer avoidance approaches are not sufficient. JaX does not require explicit probes, sounding, training sequences, channel estimation, or the cooperation of the transmitter. We identify and address multiple challenges, resulting in a convolutional neural network for a multi-antenna system to infer the existence of interference, the number of interfering emissions and their respective phases.”

Find the paper and full list of authors in the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks proceedings.