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Deep learning methods achieve great success in many areas due to theirpowerful feature extraction capabilities and end-to-end training mechanism, andrecently they are also introduced for radio signal modulation classification.In this paper, we propose a novel deep learning framework called SigNet, wherea signal-to-matrix (S2M) operator is adopted to convert the original signalinto a square matrix first and is co-trained with a follow-up CNN architecturefor classification. This model is further accelerated by integrating 1Dconvolution operators, leading to the upgraded model SigNet2.0. The simulationson two signal datasets show that both SigNet and SigNet2.0 outperform a numberof well-known baselines. More interestingly, our proposed models behaveextremely well in small-sample learning when only a small training dataset isprovided. They can achieve a relatively high accuracy even when 1\% trainingdata are kept, while other baseline models may lose their effectiveness muchmore quickly as the datasets get smaller. Such result suggests thatSigNet/SigNet2.0 could be extremely useful in the situations where labeledsignal data are difficult to obtain. The visualization of the output featuresof our models demonstrates that our model can well divide different modulationtypes of signals in the feature hyper-space.

SigNet: A Novel Deep Learning Framework for Radio Signal Classification