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학술지
A Supplementary Explanation for Experimental Environment of Implementation Methodologies of Deep Learning-based Signal Detection for Conventional MIMO Transmitters
- 발행일
- 202103
- 출처
- IEEE Transactions on Broadcasting, v.67 no.1, pp.356-357
- ISSN
- 0018-9316
- 출판사
- IEEE
- 협약과제
- 19ZR1500, 동일 채널에서의 기계 학습 기반 다중 RF 신호 송수신 기술 개발, 최동준
- 초록
- To solve practical challenges for implementing deep learning-based algorithm in MIMO signal detector such as handling complex number or designing proper neural network for a specific communication system, (Baek et al., 2019) has proposed candidate implementation methodologies with simple verification experiments. According to (Baek et al., 2019), it was shown that the proposed algorithms can achieve the optimal ML performance. However, due to the lack of explanation on the experimental environment, it is difficult for readers to reproduce the presented experiments and obtain the same results. This document precisely explains on the experimental environments of (Baek et al., 2019) including the exact channel profiles.
- KSP 제안 키워드
- Communication system, Complex Number, Learning-based, MIMO transmitters, Signal detector, Verification experiments, deep learning(DL), neural network, signal detection