Distortion Element Estimation Technique based on Deep Learning for SelfInterference Cancellation of Full Duplex Communication System This paper presents the deep learningbased distortion element estimation technique of selfinterference signal for DOCSIS 3.1 system with full duplex. In the DOCSIS 3.1 system with full duplex, the selfinterference signal estimation and cancellation are the most important issues to enhance the system performance. Proposed technique estimates channel and nonlinear element of selfinterference signal by using deep learning method. The practical challenge of application of deep learning technique to communication system is that it is difficult to find proper deep neural network structure for a specific purpose of the system. The proposed estimation technique uses simple deep neural network structure to estimate channel and nonlinear element of selfinterference signal in timedomain. The evaluation results of the deep learningbased proposed technique is better than that of existing algorithmbased estimation technique.

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.