Maximum Likelihood Decoding of Convolutional Codes for Non-Gaussian Channels

The Viterbi algorithm plays a fundamental role in the design of receivers for digital communication systems corrupted by Gaussian noise. This algorithm arises as the maximum likelihood sequence detector of the transmitted data symbols in several applications, including equalization for channels subject to intersymbol interference, multiuser communications, and the detection of convolutionally encoded data. Although the Viterbi algorithm has been extensively studied and applied to several problems in communications involving Gaussian noise, little work has been done on these same problems for the case when the channel noise is impulsive and, therefore, non-Gaussian in nature. In this paper, we derive a general algorithm for maximum likelihood soft-decision sequence decoding of convolutionally encoded data, when the channel is corrupted by additive i.i.d. non-Gaussian noise following an arbitrary (but known) distribution. We then focus on the special case of Laplacian noise, for which our algorithm is particularly elegant and simple to implement.