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随机 Reed-Solomon 码在线性大小字母表上达到列表译码容量

Random Reed-Solomon Codes Achieve List-Decoding Capacity With Linear-Sized Alphabets

Omar Alrabiah, Zeyu Guo, Venkatesan Guruswami, Ray Li, Zihan Zhang

AI总结:

本文证明随机 Reed-Solomon 码可在线性域大小 $O(n)$ 下达到列表译码容量,并以超图定向方法将该问题与 GM-MDS 定理直接联系,同时给出随机线性码的近优字母表结果。

摘要:

Reed-Solomon 码是一类经典纠错码,由有限域上低次多项式在某一互不相同的域元素序列上的求值构成。它们以最优的唯一译码能力而广为人知,但其列表译码能力尚未被完全理解。鉴于 Reed-Solomon 码的普遍存在,编码理论中的一个基本问题是确定 Reed-Solomon 码能否最优地达到列表译码容量。Brakensiek、Gopi 和 Makam 最近的一项突破证明,Reed-Solomon 码在组合意义上可以一直列表译码至容量。然而,他们的结果适用于指数级大域大小 $2^{O(n)}$ 上的随机删余 Reed-Solomon 码,其中 $n$ 是码的分组长度。一个自然的问题是,Reed-Solomon 码在更小的域上是否仍能达到容量。我们证明,Reed-Solomon 码在线性域大小 $O(n)$ 下可列表译码至容量,这在常数因子意义上显然是最优的。我们的技术还表明,随机线性码能够以最优列表大小 $O(1/\varepsilon)$ 和近最优字母表大小 $2^{O(1/\varepsilon^2)}$ 列表译码至容量,其中 $\varepsilon$ 是与容量的差距。据我们所知,以最优列表大小 $O(1/\varepsilon)$ 列表译码至容量,此前并不知道可由常数大小字母表上的任何线性码实现(即使是非构造性地),也不知道可由任意字母表大小上的随机线性码实现。我们的证明保持了列表译码问题的超图视角,并给出了 Brakensiek、Gopi 和 Makam 思想的另一种阐述,通过一个超图定向定理,将列表译码问题更直接地与 GM-MDS 定理联系起来。

英文摘要:

Reed-Solomon codes are a classic family of error-correcting codes consisting of evaluations of low-degree polynomials over a finite field on some sequence of distinct field elements. They are widely known for their optimal unique-decoding capabilities, but their list-decoding capabilities are not fully understood. Given the prevalence of Reed-Solomon codes, a fundamental question in coding theory is determining if Reed-Solomon codes can optimally achieve list-decoding capacity. A recent breakthrough by Brakensiek, Gopi, and Makam established that Reed-Solomon codes are combinatorially list-decodable all the way to capacity. However, their results hold for randomly-punctured Reed-Solomon codes over an exponentially large field size $2^{O(n)}$, where $n$ is the block length of the code. A natural question is whether Reed-Solomon codes can still achieve capacity over smaller fields. We show that Reed-Solomon codes are list-decodable to capacity with linear field size $O(n)$, which is evidently optimal up to a constant factor. Our techniques also show that random linear codes are list-decodable up to capacity with optimal list-size $O(1/\varepsilon)$ and near-optimal alphabet size $2^{O(1/\varepsilon^2)}$, where $\varepsilon$ is the gap to capacity. As far as we are aware, list-decoding up to capacity with optimal list-size $O(1/\varepsilon)$ was not known to be achievable with any linear code over a constant alphabet size (even non-constructively), and it was also not known to be achievable for random linear codes over any alphabet size. With our proof, which maintains a hypergraph perspective of the list-decoding problem, we include an alternate presentation of ideas from Brakensiek, Gopi, and Makam that more directly connects the list-decoding problem to the GM-MDS theorem via a hypergraph orientation theorem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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