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학술대회 Distortion Element Estimation Technique based on Deep Learning for Self-Interference Cancellation of Full Duplex Communication System
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저자
백명선, 정준영, 김흥묵, 이현우, 최동준
발행일
201906
출처
International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) 2019, pp.1-4
DOI
https://dx.doi.org/10.1109/BMSB47279.2019.8971898
협약과제
19ZR1500, 동일 채널에서의 기계 학습 기반 다중 RF 신호 송수신 기술 개발, 최동준
초록
This paper presents the deep learning-based distortion element estimation technique of self-interference signal for DOCSIS 3.1 system with full duplex. In the DOCSIS 3.1 system with full duplex, the self-interference signal estimation and cancellation are the most important issues to enhance the system performance. Proposed technique estimates channel and nonlinear element of self-interference 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 self-interference signal in time-domain. The evaluation results of the deep learning-based proposed technique is better than that of existing algorithm-based estimation technique.
키워드
Deep learning, distortion element estimation, DOCSIS 3.1, full duplex
KSP 제안 키워드
Communication system, DOCSIS 3.1, Deep learning method, Deep neural network(DNN), Estimation Technique, Full-Duplex(FuDu), Full-duplex communication, Learning-based, Neural network structure, Self-Interference Cancellation, System performance