Minjae Kim
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Minjae Kim has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Daejin Kim
Department of Electronic and Computer Engineering, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Korea
1 shared publication - Seungwon Choi
Department of Electronic Engineering, Hanyang University, Seoul 04763, Korea
1 shared publication - Zhongfeng Zhang
Department of Electronic Engineering, Hanyang University, Seoul 04763, Korea
1 shared publication
Latest Publications
- Open Access
- 18 July 2020
- Published by MDPI AG in Electronics
- Vol. 9 (7) , 1170
Deep-Learning-Based Frame Format Detection for IEEE 802.11 Wireless Local Area Networks
Backward compatibility is one of the key issues for radio equipment that supports IEEE 802.11, which is a typical communication protocol for wireless local area networks (WLANs). For achieving successful packet decoding with backward compatibility, frame format detection is the core precondition. In this study, we present a novel, deep-learning-based frame format detection method for IEEE 802.11 WLANs. Considering that the detection performance of conventional methods is mainly degraded because of poor performance in symbol synchronization and/or channel estimation in environments with a low signal-to-noise ratio, we propose a novel detection method based on a deep learning network to replace conventional detection procedures. The proposed deep-learning network method achieves robust detection directly from the received (Rx) data. Through extensive computer simulations performed in multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), we confirmed that the proposed method exhibits significantly higher frame format detection performance than that of the conventional method.
- 18 July 2020
- Published by MDPI AG in Electronics
- Vol. 9 (7) , 1170
Open AccessDeep-Learning-Based Frame Format Detection for IEEE 802.11 Wireless Local Area Networks
Backward compatibility is one of the key issues for radio equipment that supports IEEE 802.11, which is a typical communication protocol for wireless local area networks (WLANs). For achieving successful packet decoding with backward compatibility, frame format detection is the core precondition. In this study, we present a novel, deep-learning-based frame format detection method for IEEE 802.11 WLANs. Considering that the detection performance of conventional methods is mainly degraded because of poor performance in symbol synchronization and/or channel estimation in environments with a low signal-to-noise ratio, we propose a novel detection method based on a deep learning network to replace conventional detection procedures. The proposed deep-learning network method achieves robust detection directly from the received (Rx) data. Through extensive computer simulations performed in multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), we confirmed that the proposed method exhibits significantly higher frame format detection performance than that of the conventional method.