Error Propagation Mitigation in Sliding Window Decoding of Braided Convolutional Codes

TL;DR

This paper addresses error propagation in sliding window decoding of braided convolutional codes and proposes adaptive schemes like window extension, resynchronization, and retransmission to significantly improve bit error rate performance and reduce complexity.

Contribution

It introduces novel error mitigation strategies for sliding window decoding of BCCs, enhancing performance and efficiency in streaming scenarios.

Findings

BER improved by up to four orders of magnitude

Proposed schemes effectively mitigate error propagation

Soft BER stopping reduces computational complexity

Abstract

We investigate error propagation in sliding window decoding of braided convolutional codes (BCCs). Previous studies of BCCs have focused on iterative decoding thresholds, minimum distance properties, and their bit error rate (BER) performance at small to moderate frame length. Here, we consider a sliding window decoder in the context of large frame length or one that continuously outputs blocks in a streaming fashion. In this case, decoder error propagation, due to the feedback inherent in BCCs, can be a serious problem.In order to mitigate the effects of error propagation, we propose several schemes: a \emph{window extension algorithm} where the decoder window size can be extended adaptively, a resynchronization mechanism where we reset the encoder to the initial state, and a retransmission strategy where erroneously decoded blocks are retransmitted. In addition, we introduce a soft BER stopping rule to reduce computational complexity, and the tradeoff between performance and complexity is examined. Simulation results show that, using the proposed window extension algorithm, resynchronization mechanism, and retransmission strategy, the BER performance of BCCs can be improved by up to four orders of magnitude in the signal-to-noise ratio operating range of interest, and in addition the soft BER stopping rule can be employed to reduce computational complexity.