A Novel Catastrophic Condition for Periodically Time-varying Convolutional Encoders Based on Time-varying Equivalent Convolutional Encoders

TL;DR

This paper introduces a new, computationally efficient condition to identify catastrophic behavior in periodically time-varying convolutional encoders, improving over existing methods by reducing complexity.

Contribution

It presents a novel catastrophic condition based on time-varying equivalent encoders, enabling simpler and faster analysis compared to state transition table methods.

Findings

New condition reduces analysis complexity from exponential to linear

Technique to convert catastrophic encoders into non-catastrophic ones

Applicable to periodically time-varying convolutional encoders

Abstract

A convolutional encoder is said to be catastrophic if it maps an information sequence of infinite weight into a code sequence of finite weight. As a consequence of this mapping, a finite number of channel errors may cause an infinite number of information bit errors when decoding. This situation should be avoided. A catastrophic condition to determine if a time-invariant convolutional encoder is catastrophic or not is stated in \cite{Massey:LSC}. Palazzo developed this condition for periodically time-varying convolutional encoders in \cite{Palazzo:Analysis}. Since Palazzo's condition is based on the state transition table of the constituent encoders, its complexity increases exponentially with the number of memory elements in the encoders. A novel catastrophic condition making use of time-varying equivalent convolutional encoders is presented in this letter. A technique to convert a catastrophic periodically time-varying convolutional encoder into a non-catastrophic one can also be developed based on these encoders. Since they do not involve the state transitions of the convolutional encoder, the time complexity of these methods grows linearly with the encoder memory.