Convergence behavior of iteratively decoded parallel concatenated codes

TLDR

The exchange of extrinsic information is visualized as a decoding trajectory in the extrinsic information transfer chart (EXIT chart). The authors propose using mutual information transfer characteristics of soft in/soft out decoders to better understand iterative decoding convergence and to predict turbo cliff position and bit error rate after any number of iterations. They study how code memory, code polynomials, and constituent codes influence convergence behavior of parallel concatenated codes using EXIT chart analysis. A code search guided by EXIT charts identified new recursive systematic convolutional constituent codes that achieve turbo cliffs at lower signal‑to‑noise ratios than previously known codes.

Abstract

Mutual information transfer characteristics of soft in/soft out decoders are proposed as a tool to better understand the convergence behavior of iterative decoding schemes. The exchange of extrinsic information is visualized as a decoding trajectory in the extrinsic information transfer chart (EXIT chart). This allows the prediction of turbo cliff position and bit error rate after an arbitrary number of iterations. The influence of code memory, code polynomials as well as different constituent codes on the convergence behavior is studied for parallel concatenated codes. A code search based on the EXIT chart technique has been performed yielding new recursive systematic convolutional constituent codes exhibiting turbo cliffs at lower signal-to-noise ratios than attainable by previously known constituent codes.