An information density approach to analyzing and optimizing incremental redundancy with feedback
Haobo Wang, Nathan Wong, A. Baldauf, Christopher K. Bachelor, Sudarsan V. S. Ranganathan, D. Divsalar, Richard D. Wesel · 2017
This paper uses a case study of a tail-biting convolutional code (with successful decoding indicated by the reliability output Viterbi algorithm) to present an information density approach for analyzing and optimizing the throughput of systems using incremental redundancy controlled by feedback. Polyan-skiy's normal approximation combined with a linear model for the information gap of a rate-compatible code family provides a simple and accurate characterization of the behavior of feedback systems employing practical codes, such as convolutional or low-density parity-check codes. Especially for short message lengths on the order of k <; 50 message bits, the newly proposed model is more accurate than Vakilinia's model in which the rate of first successful decoding has a Gaussian probability density function.