English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Chase-Pyndiah decoding is widely used for decoding product codes. However,this method is suboptimal and requires scaling the soft information exchangedduring the iterative processing. In this paper, we propose a framework forobtaining the scaling coefficients based on maximizing the generalized mutualinformation. Our approach yields gains up to 0.11 dB for product codes withtwo-error correcting extended BCH component codes over the binary-inputadditive white Gaussian noise channel compared to the original Chase-Pyndiahdecoder with heuristically obtained coefficients. We also introduce anextrinsic version of the Chase-Pyndiah decoder and associate product codes witha turbo-like code ensemble to derive a Monte Carlo-based density evolutionanalysis. The resulting iterative decoding thresholds accurately predict theonset of the waterfall region.

Soft-Information Post-Processing for Chase-Pyndiah Decoding Based on Generalized Mutual Information