Probabilistic bounds on the trapping redundancy of linear codes
Yu Tsunoda, Yuichiro Fujiwara

TL;DR
This paper establishes a rigorous, sharper upper bound on the trapping redundancy of linear codes, improving previous probabilistic bounds and applicable to a wider range of trapping sets, aiding in code design and analysis.
Contribution
It provides a more general, mathematically rigorous upper bound on trapping redundancy, surpassing previous bounds based on the Lovász Local Lemma, and applicable to all relevant trapping sets.
Findings
The new bound is sharper than previous probabilistic bounds.
It can exactly determine trapping redundancy in many cases.
The bound applies to all potentially avoidable trapping sets with small size.
Abstract
The trapping redundancy of a linear code is the number of rows of a smallest parity-check matrix such that no submatrix forms an -trapping set. This concept was first introduced in the context of low-density parity-check (LDPC) codes in an attempt to estimate the number of redundant rows in a parity-check matrix suitable for iterative decoding. Essentially the same concepts appear in other contexts as well such as robust syndrome extraction for quantum error correction. Among the known upper bounds on the trapping redundancy, the strongest one was proposed by employing a powerful tool in probabilistic combinatorics, called the Lov\'{a}sz Local Lemma. Unfortunately, the proposed proof invoked this tool in a situation where an assumption made in the lemma does not necessarily hold. Hence, although we do not doubt that nonetheless the proposed bound actually holds, for it to be a…
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