Upper bounds on sequential decoding performance parameters

This paper presents the best obtainable random coding and expurgated upper bounds on the probabilities of undetectable error, of -order failure (advance to depth into an incorrect subset), and of likelihood rise in the incorrect subset, applicable to sequential decoding when the metric bias is arbitrary. Upper bounds on the Pareto exponent are also presented. The -values optimizing each of the parameters of interest are determined, and are shown to lie in intervals that in general have nonzero widths. The -optimal expurgated bound on undetectable error is shown to agree with that for maximum likelihood decoding of convolutional codes, and that on failure agrees with the block code expurgated bound. Included are curves evaluating the bounds for interesting choices of and SNR for a binary-input quantized-output Gaussian additive noise channel.