Lei Guo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Lei Guo has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Cong Hu
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
1 shared publication - Jianjun Zhang
TOEC technology Co.,LTD, China
1 shared publication - Tao Liu
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
1 shared publication - Xuyang Zhao
China Academy of Information and Communication Technology, China
1 shared publication - Yanjiang Li
Beijing University of Posts and Telecommunications, China
1 shared publication - Yuanxiang Chen
Beijing University of Posts and Telecommunications, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 23 February 2024
- Published by Association for Computing Machinery (ACM)
- p. 165-171
Automatic Modulation Classification Under Large Frequency Offset: A Robust Approach Integrating Pre-classification and Compensation Mechanism
Automatic Modulation Classification (AMC) is a critical technology in the field of non-cooperative communication. Carrier frequency offset is an inevitable impairment factor in communication systems, and it significantly affects the performance of AMC, especially when the range is large. Therefore, frequency offset estimation and compensation are essential for automatic modulation classification. To address this issue, this paper proposes an innovative cascading network based on deep learning, primarily comprising pre-classification and compensation mechanisms. With the pre-classification mechanism, we categorize the eleven unknown modulation formats into three major classes: PSK, FSK, and QAM. Subsequently, we utilize deep neural networks to estimate the frequency offset of signals within these major classes, followed by performing the corresponding signal compensation. The experimental results demonstrate that the pre-classification mechanism effectively reduces the error in frequency offset estimation, minimizing the error (mean-square error) to as low as -3.9. Under various signal-to-noise ratio (SNR) ranges, the classification accuracy achieved through pre-classification and compensation is up to 14% higher than the accuracy without this method. Specifically, at SNR = 10 dB, it can reach 99%. Moreover, the proposed scheme is robust and performs stably across different frequency offsets range, outperforming other existing network classification schemes to some extent.
- 23 February 2024
- Published by Association for Computing Machinery (ACM)
- p. 165-171
Open AccessAutomatic Modulation Classification Under Large Frequency Offset: A Robust Approach Integrating Pre-classification and Compensation Mechanism
Automatic Modulation Classification (AMC) is a critical technology in the field of non-cooperative communication. Carrier frequency offset is an inevitable impairment factor in communication systems, and it significantly affects the performance of AMC, especially when the range is large. Therefore, frequency offset estimation and compensation are essential for automatic modulation classification. To address this issue, this paper proposes an innovative cascading network based on deep learning, primarily comprising pre-classification and compensation mechanisms. With the pre-classification mechanism, we categorize the eleven unknown modulation formats into three major classes: PSK, FSK, and QAM. Subsequently, we utilize deep neural networks to estimate the frequency offset of signals within these major classes, followed by performing the corresponding signal compensation. The experimental results demonstrate that the pre-classification mechanism effectively reduces the error in frequency offset estimation, minimizing the error (mean-square error) to as low as -3.9. Under various signal-to-noise ratio (SNR) ranges, the classification accuracy achieved through pre-classification and compensation is up to 14% higher than the accuracy without this method. Specifically, at SNR = 10 dB, it can reach 99%. Moreover, the proposed scheme is robust and performs stably across different frequency offsets range, outperforming other existing network classification schemes to some extent.