  Due to the over-fitting problem caused by imbalance samples, there is still
room to improve the performance of data-driven automatic modulation
classification (AMC) in noisy scenarios. By fully considering the signal
characteristics, an AMC scheme based on adaptive fusion network (AFNet) is
proposed in this work. The AFNet can extract and aggregate multi-scale spatial
features of in-phase and quadrature (I/Q) signals intelligently, thus improving
the feature representation capability. Moreover, a novel confidence weighted
loss function is proposed to address the imbalance issue and it is implemented
by a two-stage learning scheme.Through the two-stage learning, AFNet can focus
on high-confidence samples with more valid information and extract effective
representations, so as to improve the overall classification performance. In
the simulations, the proposed scheme reaches an average accuracy of 62.66% on a
wide range of SNRs, which outperforms other AMC models. The effects of the loss
function on classification accuracy are further studied.
