Systematic feed-forward convolutional encoders are better than other encoders with an M-algorithm decoder

Consider nonbacktracking convolutional decoders that keep a fixed number of trellis survivors. It is shown that the error performance of these depends on the early part of the distance profile and on the number of survivors kept, and not on the free distance or the details of the code generators. Particularly, the encoder may be feedforward systematic without loss. Furthermore, this kind of encoder solves the correct path loss problem in reduced-search decoders. Other kinds do not. Therefore, with almost any other decoding method than the Viterbi algorithm, systematic feed-forward encoders should be used. The conclusions in this correspondence run counter to much accepted wisdom about convolutional codes