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Deep learning has been recently applied to many problems in wirelesscommunications including modulation classification and symbol decoding. Many ofthe existing end-to-end learning approaches demonstrated robustness to signaldistortions like frequency and timing errors, and outperformed classical signalprocessing techniques with sufficient training. However, deep learningapproaches typically require hundreds of thousands of floating pointsoperations for inference, which is orders of magnitude higher than classicalsignal processing approaches and thus do not scale well for long sequences.Additionally, they typically operate as a black box and without insight on howtheir final output was obtained, they can't be integrated with existingapproaches. In this paper, we propose a novel neural network architecture thatcombines deep learning with linear signal processing typically done at thereceiver to realize joint modulation classification and symbol recovery. Theproposed method estimates signal parameters by learning and corrects signaldistortions like carrier frequency offset and multipath fading by linearprocessing. Using this hybrid approach, we leverage the power of deep learningwhile retaining the efficiency of conventional receiver processing techniquesfor long sequences. The proposed hybrid approach provides good accuracy insignal distortion estimation leading to promising results in terms of symbolerror rate. For modulation classification accuracy, it outperforms many stateof the art deep learning networks.

Combining Deep Learning and Linear Processing for Modulation Classification and Symbol Decoding