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.

A Deep LearningAided Detection Method for FTNBased NOMA The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the nextgeneration radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, fasterthanNyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTNbased transmission signals on the same physical recourses. Considering the complicated intra and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel slidingwindow detection method by incorporating the stateoftheart deep learning (DL) technology. The datadriven offline training is first applied to derive a nearoptimal receiver for FTNbased NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared errorfrequency domain equalization (MMSEFDE) and can even approach the performance of the maximum likelihoodbased receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.