  Blindly decoding a signal requires estimating its unknown transmit
parameters, compensating for the wireless channel impairments, and identifying
the modulation type. While deep learning can solve complex problems, digital
signal processing (DSP) is interpretable and can be more computationally
efficient. To combine both, we propose the dual path network (DPN). It consists
of a signal path of DSP operations that recover the signal, and a feature path
of neural networks that estimate the unknown transmit parameters. By
interconnecting the paths over several recovery stages, later stages benefit
from the recovered signals and reuse all the previously extracted features. The
proposed design is demonstrated to provide 5% improvement in modulation
classification compared to alternative designs lacking either feature sharing
or access to recovered signals. The estimation results of DPN along with its
blind decoding performance are shown to outperform a blind signal processing
algorithm for BPSK and QPSK on a simulated dataset. An over-the-air
software-defined-radio capture was used to verify DPN results at high SNRs. DPN
design can process variable length inputs and is shown to outperform relying on
fixed length inputs with prediction averaging on longer signals by up to 15% in
modulation classification.
