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

doi:10.23919/JSEE.2023.000027

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)

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.

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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

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