基于互相关特征图和扩张稠密卷积网络的SFBC-OFDM识别方法

针对传统的空频分组码(space-frequency block code, SFBC)识别方法存在人工提取特征困难、低信噪比(signal to noise ratio, SNR)下识别准确率低和不适用于非协作通信的问题, 提出一种基于互相关特征图和扩张稠密卷积网络的SFBC自动识别方法。首先，计算接收端频域上的互相关函数并进行维度变换, 得到二维互相关特征图。然后, 对得到的特征图进行预处理以扩大卷积核感受的有效区域, 去除图像冗余信息。最后，构建扩张稠密卷积网络以自动提取预处理图像特征, 实现SFBC分类识别。仿真结果表明, SNR为-8 dB时, 该方法对SFBC信号的识别准确率达到了96.1%。相比于传统算法, 该方法具有更好的抗低SNR和特征自提取能力, 验证了深度学习方法在SFBC识别领域的有效性, 为该领域的后续研究奠定了基础。

SFBC-OFDM recognition method based on cross-correlation feature map and dilated dense convolutional neural networks

Aiming at the problems of the traditional space-frequency block code (SFBC) recognition method, such as the difficulty of extracting features manually, low recognition accuracy under low signal to noise ratio (SNR) and not suitable for non-cooperative comm unication, a SFBC automatic recognition method based on cross-correlation feature map and extended dense convolutional network is proposed. Firstly, the cross-correlation function in the frequency domain of the receiving end is computed and the dimensional transformation to obtain the two-dimensional cross-correlation characteristic graph is carried out. Then, the obtained feature map is preprocessed to enlarge the effective region of convolution kernel perception and remove the image redundancy information. Finally, the extended dense convolutional network is constructed to automatically extract the preprocessing image features and realize the SFBC classification and recognition. Simulation results show that when the SNR is -8 dB, the recognition accuracy of the SFBC signal of the proposed method reaches 96.1%. Compared with the traditional algorithm, the proposed method has better anti-low SNR and feature self-extraction ability, which verifies the effectiveness of deep learning method in the field of SFBC recognition, and lays a foundation for the subsequent research in this field.