基于改进Res2Net与自适应多尺度窗口池化的调制识别方法OA

With the rapid development of modern communication technology,automatic modulation recognition(AMR)has become increasingly important in spectrum resource management,and deep learning-based AMR methods have become a current research hotspot due to their superior performance.To address the problems of insufficient multi-scale feature fu⁃sion capability and the difficulty in balancing the effectiveness and complexity of feature tokenization under complex chan⁃nel conditions in existing methods,this thesis proposed a modulation recognition method termed Res2-AMWP based on an improved Res2Net and adaptive multi-scale window pooling.In the feature extraction stage,the improved Res2Net was ad⁃opted to group features by channel and fuse them progressively,while the squeeze-and-excitation(SE)attention mechanism was introduced to perform adaptive channel re-calibration.In the feature fusion stage,an adaptive multi-scale window pool⁃ing(AMWP)module was proposed to transform multi-scale features into more discriminative token representations,and a bidirectional long short-term memory network(BiLSTM)was employed to capture contextual dependencies among tokens.The attention-based classification head further highlighted key token representations through an attention pooling mecha⁃nism,and the final recognition results were obtained by fully connected layers.Experimental results on the public datasets RadioML2016.10a,RadioML2016.10b,and RML22 demonstrated that Res2-AMWP achieved overall recognition accura⁃cies of 63.51％,65.36％,and 70.30％,respectively,outperforming multiple baseline methods by 1.01％～7.33％,0.32％～6.5％,and 0.75％～8.40％on the three datasets.Moreover,the model complexity remained at a relatively low level,achiev⁃ing a good balance between accuracy and complexity.