Improved Maximally Recoverable LRCs using Skew Polynomials
Sivakanth Gopi, Venkatesan Guruswami

TL;DR
This paper presents an explicit algebraic construction of maximally recoverable local reconstruction codes (LRCs) using skew polynomials, achieving near-optimal field sizes and improving over previous methods.
Contribution
It introduces a new construction of MR LRCs over smaller fields using skew polynomials, matching lower bounds in certain parameter regimes.
Findings
Constructs MR LRCs with field size bounded by a polynomial in max{r, n/r}
Achieves optimal field size in specific parameter ranges where r=Θ(√n)
Demonstrates potential applications of skew polynomials in coding theory
Abstract
An -Local Reconstruction Code (LRC) is a linear code over of length , whose codeword symbols are partitioned into local groups each of size . Each local group satisfies `' local parity checks to recover from `' erasures in that local group and there are further global parity checks to provide fault tolerance from more global erasure patterns. Such an LRC is Maximally Recoverable (MR), if it offers the best blend of locality and global erasure resilience -- namely it can correct all erasure patterns whose recovery is information-theoretically feasible given the locality structure (these are precisely patterns with up to `' erasures in each local group and an additional erasures anywhere in the codeword). Random constructions can easily show the existence of MR LRCs over very large fields, but a major algebraic challenge is to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Data Storage Technologies · Error Correcting Code Techniques · Coding theory and cryptography
