  The anti-interference capability of wireless links is a physical layer
problem for edge computing. Although convolutional codes have inherent error
correction potential due to the redundancy introduced in the data, the
performance of the convolutional code is drastically degraded due to multipath
effects on the channel. In this paper, we propose the use of a Hidden Markov
Model (HMM) for the reconstruction of convolutional codes and decoding by the
Viterbi algorithm. Furthermore, to implement soft-decision decoding, the
observation of HMM is replaced by Gaussian mixture models (GMM). Our method
provides superior error correction potential than the standard method because
the model parameters contain channel state information (CSI). We evaluated the
performance of the method compared to standard Viterbi decoding by numerical
simulation. In the multipath channel, the hybrid HMM decoder can achieve a
performance gain of 4.7 dB and 2 dB when using hard-decision and soft-decision
decoding, respectively. The HMM decoder also achieves significant performance
gains for the RSC code, suggesting that the method could be extended to turbo
codes.
