A Closed Form Expression for the Exact Bit Error Probability for Viterbi Decoding of Convolutional Codes

TL;DR

This paper presents a new method to derive a closed-form expression for the exact bit error probability in Viterbi decoding of convolutional codes, extending previous results to more complex encoders and channels.

Contribution

A novel approach using a recurrent matrix equation to obtain a closed-form expression for the bit error probability in Viterbi decoding, applicable to various encoder configurations.

Findings

Derived a general matrix equation for bit error probability

Obtained closed-form expressions matching previous special cases

Extended the approach to quantized AWGN channels

Abstract

In 1995, Best et al. published a formula for the exact bit error probability for Viterbi decoding of the rate R=1/2, memory m=1 (2-state) convolutional encoder with generator matrix G(D)=(1 1+D) when used to communicate over the binary symmetric channel. Their formula was later extended to the rate R=1/2, memory m=2 (4-state) convolutional encoder with generator matrix G(D)=(1+D^2 1+D+D^2) by Lentmaier et al. In this paper, a different approach to derive the exact bit error probability is described. A general recurrent matrix equation, connecting the average information weight at the current and previous states of a trellis section of the Viterbi decoder, is derived and solved. The general solution of this matrix equation yields a closed form expression for the exact bit error probability. As special cases, the expressions obtained by Best et al. for the 2-state encoder and by Lentmaier et al. for a 4-state encoder are obtained. The closed form expression derived in this paper is evaluated for various realizations of encoders, including rate R=1/2 and R=2/3 encoders, of as many as 16 states. Moreover, it is shown that it is straightforward to extend the approach to communication over the quantized additive white Gaussian noise channel.