A Markovian technique is described to calculate the exact performance of the Viterbi algorithm used as either a channel decoder or a source encoder for a convolutional code. The probability of information bit error and the expected Hamming distortion are computed for codes of various rates and constraint lengths. The concept of tie-breaking rules is introduced and its influence on decoder performance is examined. Computer simulation is used to verify the accuracy of the results. Finally, we discuss the issue of when a coded system outperforms an uncoded system in light of the new results. >

On a technique to calculate the exact performance of a convolutional code

Analytic expressions for the exact probability of erasure for systematic, rate- 1/2 convolutional codes used to communicate over the binary erasure channel and decoded using the soft-input, soft-output (SISO) and a posteriori probability (APP) algorithms are given. An alternative forward-backward algorithm which produces the same result as the SISO algorithm is also given. This low-complexity implementation, based upon lookup tables, is of interest for systems which use convolutional codes, such as turbo codes.

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Exact probability of erasure and a decoding algorithm for convolutional codes on the binary erasure channel

Analytic expressions for the exact bit error probabilities of rate R=1/2, memory m=2 convolutional encoders are derived for a maximum-likelihood (ML) decoder and transmission over the binary-symmetric channel (BSC). The resulting expressions are rational functions of the crossover probability of the BSC. In addition to classical nonsystematic encoders without feedback, we consider also recursive systematic encoders, which became especially important as component encoders in concatenated coding schemes. To attest the validity of the results, they are compared to computer simulations. Based on the presented technique also the bit error probability and the probability distribution of the output log-likelihood ratios of the Max-Log-MAP algorithm are derived in analytic form.

Analytic expressions for the bit error probabilities of rate-1/2 memory 2 convolutional encoders

Viterbi decoding error probability for convolutional codes using finite Markov chain modeling

T. N. Morrissey

Papers

A Markovian analysis of Viterbi decoders for convolutional codes