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Due to the over-fitting problem caused by imbalance samples, there is stillroom to improve the performance of data-driven automatic modulationclassification (AMC) in noisy scenarios. By fully considering the signalcharacteristics, an AMC scheme based on adaptive fusion network (AFNet) isproposed in this work. The AFNet can extract and aggregate multi-scale spatialfeatures of in-phase and quadrature (I/Q) signals intelligently, thus improvingthe feature representation capability. Moreover, a novel confidence weightedloss function is proposed to address the imbalance issue and it is implementedby a two-stage learning scheme.Through the two-stage learning, AFNet can focuson high-confidence samples with more valid information and extract effectiverepresentations, so as to improve the overall classification performance. Inthe simulations, the proposed scheme reaches an average accuracy of 62.66% on awide range of SNRs, which outperforms other AMC models. The effects of the lossfunction on classification accuracy are further studied.

An Improved Automatic Modulation Classification Scheme Based on Adaptive Fusion Network