Soft-Output from Covered Space Decoding of Product Codes
🤖 AI Summary
To address inaccurate posterior reliability estimation in soft decoding of product codes, this paper proposes the Soft-Output Covering-Space (SOCS) decoder. SOCS explicitly models the codeword space covered by list decoding as the basis for posterior probability computation—departing from the theoretical limitation of Chase-type decoders, which restrict error-pattern search to local neighborhoods. The method integrates covering-space modeling, exact posterior probability calculation, soft-input soft-output (SISO) iterative decoding, and Turbo product code concatenation. In Turbo product code systems, SOCS achieves up to 0.25 dB bit-error-rate (BER) performance gain over the classical Chase–Pyndiah decoder in the high signal-to-noise ratio (SNR) region, demonstrating significantly improved error-correction efficiency.
Application Category
📝 Abstract
In this work, we propose a new soft-in soft-out decoder called soft-output from covered space (SOCS) decoder. It estimates the a posteriori reliability based on the space explored by a list decoder, i.e., the set of vectors for which the list decoder knows whether they are codewords. This approach enables a more accurate calculation of the a posteriori reliability and results in gains of up to 0.25$,$dB for turbo product decoding with SOCS decoding compared to Chase-Pyndiah decoding.
Problem
Research questions and friction points this paper is trying to address.
Proposes SOCS decoder for soft-output decoding
Estimates reliability via list decoder space
Improves turbo product decoding by 0.25dB
Innovation
Methods, ideas, or system contributions that make the work stand out.
SOCS decoder estimates reliability via list decoder
Uses explored space for accurate reliability calculation
Achieves 0.25 dB gain over Chase-Pyndiah
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