Code optimization, frozen glassy phase and improved decoding algorithms for low-density parity-check codes
Haiping Huang

TL;DR
This paper investigates the physics of low-density parity-check codes, revealing a frozen glassy phase, the benefits of irregular code structures, and how reinforced belief propagation improves decoding thresholds near critical noise levels.
Contribution
It introduces a spin glass perspective on LDPC codes, identifies a frozen glassy phase, and demonstrates enhanced decoding algorithms that approach theoretical limits.
Findings
Irregular LDPC codes have higher decoding thresholds than regular ones.
A frozen glassy phase exists at low temperatures, affecting decoding.
Reinforced belief propagation improves decoding thresholds near the dynamical transition.
Abstract
The statistical physics properties of low-density parity-check codes for the binary symmetric channel are investigated as a spin glass problem with multi-spin interactions and quenched random fields by the cavity method. By evaluating the entropy function at the Nishimori temperature, we find that irregular constructions with heterogeneous degree distribution of check (bit) nodes have higher decoding thresholds compared to regular counterparts with homogeneous degree distribution. We also show that the instability of the mean-field calculation takes place only after the entropy crisis, suggesting the presence of a frozen glassy phase at low temperatures. When no prior knowledge of channel noise is assumed (searching for the ground state), we find that a reinforced strategy on normal belief propagation will boost the decoding threshold to a higher value than the normal belief…
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