Communication without Interception: Defense against Modulation Detection We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the intruder, while guaranteeing that the intended receiver can still recover the underlying message with the highest reliability. This is achieved by constellation perturbation at the encoder, similarly to adversarial attacks against classifiers in machine learning. In image classification, the perturbation is limited to be imperceptible to a human observer, while in our case the perturbation is constrained so that the message can still be reliably decoded by a legitimate receiver that is oblivious to the perturbation. Simulation results demonstrate the viability of our approach to make wireless communication secure against both stateoftheart deeplearning and decisiontreebased intruders with minimal sacrifice in the communication performance.

RealTime Automatic Modulation Classification Deep learning based techniques have shown promising results over traditional handcrafted methods for automatic modulation classification for radio signals. However, implementation of these deep learning models on specialized hardware can be challenging, as both latency and throughput performance are critical to achieving realtime response to overtheair radio signals. In this work, we meet our targets by designing an optimized ternarized convolutional neural network that leverages the RF capabilities offered by the Xilinx ZCU111 RFSoC platform. The implemented networks achieve highspeed realtime performance with a classification latency of xe2x89x888xc2xb5s, and an operational throughput of 488k classifications per second. On the challenging opensource RadioML dataset, we achieve up to 81.1% accuracy, which is competitive to existing stateoftheart softwareonly implementations.