基于多尺度特征增强与注意力机制的调制识别算法OA

Automatic modulation recognition(AMR)plays an important role in noncooperative communication sys-tems.In recent years,deep learning-based automatic modulation recognition(DL-AMR)methods have achieved no-table improvements in recognition accuracy and robustness compared to traditional approaches.However,existing DL-AMR methods generally suffer from limited feature extraction capabilities and difficulty in balancing accuracy and the complexity of the computational model.To address these limitations,we propose a modulation recognition architecture referred to as a multi-scale feature enhancement and attention mechanism network(MFEA-Net).The proposed network employs convolution kernels with different receptive fields to extract multiscale signal features,which are then further compressed and fused through convolutional layers.For feature enhancement,depthwise convolution(DW-Conv)and dilated convolution(D-Conv)were used to replace standard convolutions to strengthen multiscale representations while reducing the number of parameters and computational cost.Squeeze-and-excitation(SE)attention was adopted to em-phasize the informative channel features.Moreover,multi-head self-attention(MHSA)was introduced to model global dependencies at the sequence level.Global maximum pooling(GMP)and global average pooling(GAP)were com-bined to capture the global statistical features,and the final recognition results were produced through fully connected layers.Comparative experimental results show that,in the SNR≥0 dB range on both the RML2016.10a and RML2016.10b datasets,MFEA-Net achieves recognition accuracies exceeding 90%for the vast majority of modulation types.In addition,the overall accuracy(OA)of MFEA-Net respectively reached 63.01%and 64.61%on the two datas-ets despite the relatively low complexity of the model.Furthermore,the results of ablation studies verified the contribu-tions of each module to the overall performance and thus demonstrate the rationality of the proposed architecture.Thus,the results demonstrate that MFEA-Net achieved a good balance between performance and complexity and showed promise for applications in resource-constrained scenarios.