FOLMS-AMDCNet: an automatic recognition scheme for multiple-antenna OFDM systems

doi:10.23919/JSEE.2023.000027

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (2): 307-323.doi: 10.23919/JSEE.2023.000027

• • 上一篇

收稿日期:2021-07-16 出版日期:2023-04-18 发布日期:2023-04-18

FOLMS-AMDCNet: an automatic recognition scheme for multiple-antenna OFDM systems

Yuyuan ZHANG(), Wenjun YAN(), Limin ZHANG(), Qing LING()

¹ Academy of Aeronautical Operations Service, Naval Aviation University, Yantai 264001, China

Received:2021-07-16 Online:2023-04-18 Published:2023-04-18
Contact: Wenjun YAN E-mail:2932484433@qq.com;wj_yan@foxmail.com;iamzlm@163.com;lingqing19870522@163.com
About author:
ZHANG Yuyuan was born in 1997. He received his B.S. degree from Naval Aviation University, Yantai, China, in 2019, where he is currently pursuing his M.S. degree. His current research interests include signal processing, blind identification of channel coding, and deep learning. E-mail: 2932484433@qq.com

YAN Wenjun was born in 1986. He received his M.S. and Ph.D. degrees from Naval Aviation University, Yantai, China, in 2015, where he is currently an associate professor. His current research interests include signal processing, cognitive radio, deep reinforcement learning, and the blind identification of channel coding. E-mail: wj_yan@foxmail.com

ZHANG Limin was born in 1966. He received his Ph.D. degree from Tanjin University, Tanjin, China, in 2005. He is currently a professor and a Ph.D. supervisor with Naval Aviation University, Yantai, China. His current research interests include signal processing, deep learning, blind identification of channel coding, and satellite reconnaissance. E-mail: iamzlm@163.com

LING Qing was born in 1987. She received her M.S. and Ph.D. degrees from Naval Aviation University, Yantai, China, in 2016, where she is currently an associate professor. Her current research interests include signal processing, cognitive radio, and wireless communication. E-mail: lingqing19870522@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (91538201) and the Taishan Scholar Foundation of China (ts201511020).

摘要/Abstract

Abstract:

The existing recognition algorithms of space-time block code (STBC) for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment. However, owing to the restrictions on the prior information and channel conditions, these existing algorithms cannot perform well under strong interference and non-cooperative communication conditions. To overcome these defects, this study introduces deep learning into the STBC-OFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum (FOLMS) and attention-guided multi-scale dilated convolution network (AMDCNet). The fourth-order lag moment vectors of the received signals are calculated, and vectors are stitched to form two-dimensional FOLMS, which is used as the input of the deep learning-based model. Then, the multi-scale dilated convolution is used to extract the details of images at different scales, and a convolutional block attention module (CBAM) is introduced to construct the attention-guided multi-scale dilated convolution module (AMDCM) to make the network be more focused on the target area and obtian the multi-scale guided features. Finally, the concatenate fusion, residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types. Simulation experiments show that the average recognition probability of the proposed method at ?12 dB is higher than 98%. Compared with the existing algorithms, the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances. In addition, the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise, which is more suitable for non-cooperative communication systems than the existing algorithms.

Key words: blind signal identification (BSI), space-time block code (STBC), orthogonal frequency-division multiplexing (OFDM), deep learning, fourth-order lag moment spectrum (FOLMS)

. [J]. Journal of Systems Engineering and Electronics, 2023, 34(2): 307-323.

Yuyuan ZHANG, Wenjun YAN, Limin ZHANG, Qing LING. FOLMS-AMDCNet: an automatic recognition scheme for multiple-antenna OFDM systems[J]. Journal of Systems Engineering and Electronics, 2023, 34(2): 307-323.

图/表 15

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Fusion method	$ {P_{{\text{train}}}} $	$ {T_{{\text{train}}}} $ /s	$ {T_{{\text{test}}}} $ /μs
Concatenation	1 878 915	1.04	183.82
Addition	830 339	1.03	169.12

FOLMS-AMDCNet: an automatic recognition scheme for multiple-antenna OFDM systems

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