논문 해외 국제전문학술지(SCI급) Deep learning approach for physical-layer security in Gaussian multiple access wiretap channel
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2023-03
- 저자명 방인규
- 학술지명 ICT Express
- 발행처명 ELSEVIER
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 3
홈페이지논문 초록 (Abstract)
Deep learning (DL) has exhibited great potential in communication systems. Recent advances in DL-based physical-layer techniques have shown that the communication system can be modeled as an autoencoder (AE), which performs end-to-end learning tasks. In this article, we investigate an AE-based deep learning framework for physical-layer security where multiple transmitters send their own data to a common receiver under an eavesdropping scenario (i.e., Gaussian multiple access wiretap channel). We have newly designed an integrated loss function with respect to secrecy performance in terms of symbol error rate among multiple users. Further, we verify that our training approach based on the proposed loss function can achieve better secrecy performance compared with the conventional training one.