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.

Deep Learning Aided Signal Detection in OFDM Systems with TimeVarying Channels * In this paper, we propose a deep learning aided approach for signal detection in orthogonal frequencydivision multiplexing (OFDM) systems with timevarying channels. The method simplifies the architecture of OFDM systems by treating OFDM receivers as a black box. We utilize fullyconnected deep neural network (FCDNN) properly and successfully simulate an endtoend timevarying OFDM system. Compared with two conventional algorithms welldesigned to deal with OFDM systems in timevarying environment, the proposed method does not need to estimate channel parameters to detect signals. Simulation results also demonstrate that the trained DNN model has the ability to remember the characteristics of wireless timevarying channels and provide more accurate and robust signal recovery performance.