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arXiv 2004.13247cs.ITmath.ITmath.PR
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随机线性码的列表译码与列表恢复界

Bounds for list-decoding and list-recovery of random linear codes

Venkatesan Guruswami, Ray Li, Jonathan Mosheiff, Nicolas Resch, Shashwat Silas, Mary Wootters

AI总结:

本研究推导了随机线性码在码率趋近容量时列表译码与列表恢复的列表大小界,明确二元随机线性码对应列表大小的三个可能取值,揭示其与完全随机码的性能差异。

摘要:

若一个纠错码族能够从错误比例$p$下进行列表译码,则对于该码族中的每一个码,任意分数半径为$p$的汉明球内的码字数量都小于某个与码长无关的整数$L$。若一个码族对于输入列表大小$\boldsymbol{\rm \textell}$具有列表恢复能力,则对于每一个足够大的码字子集(大小为$L$或更大),都存在一个坐标,使得这些码字在该坐标上的取值超过$\textell$种。在上述两种情形中,参数$L$均被称为“列表大小”。已知当列表大小$L \to \boldsymbol{\rm \textinfty}$时,这些概念对应的容量(即最大可能码率)为:列表译码的容量是$1-h_q(p)$,列表恢复的容量是$1-\text{log}_q \textell$,其中$q$为码族的字母表大小。\n在本研究中,我们探讨了当码率趋近于容量时,随机线性码在列表译码和列表恢复两种场景下的列表大小。我们证明,在码的随机选择下,以下结论以高概率成立(其中$\boldsymbol{\rm \textepsilon}> 0$为与容量的差距):\n(1)码率为$1 - \text{log}_q(\textell) - \textepsilon$的随机线性码,在输入列表大小为$\textell$的列表恢复任务中,所需列表大小满足$L \boldsymbol{\rm \textge} \textell^{\boldsymbol{\rm \textOmega}(1/\textepsilon)}$。这一结果与完全随机码的情况形成了惊人的对比——对于完全随机码,$L = O(\textell/\textepsilon)$以高概率即可满足要求。\n(2)当$\textepsilon$足够小时,码率为$1 - h_q(p) - \textepsilon$的随机线性码,在错误比例为$p$的列表译码任务中,所需列表大小满足$L \boldsymbol{\rm \textge} \boldsymbol{\rm \textlfloor} h_q(p)/\textepsilon+0.99 \boldsymbol{\rm \textrfloor}$。\n(3)码率为$1 - h_2(p) - \textepsilon$的随机二元线性码,能够在平均错误比例$p$下完成列表译码,且列表大小满足$L \boldsymbol{\rm \textle} \boldsymbol{\rm \textlfloor} h_2(p)/\textepsilon \boldsymbol{\rm \textrfloor} + 2$。\n第二项与第三项结果相结合,精确地将二元随机线性码在列表译码和平均半径列表译码中的列表大小限定在三个可能的取值范围内。

英文摘要:

A family of error-correcting codes is list-decodable from error fraction $p$ if, for every code in the family, the number of codewords in any Hamming ball of fractional radius $p$ is less than some integer $L$ that is independent of the code length. It is said to be list-recoverable for input list size $\ell$ if for every sufficiently large subset of codewords (of size $L$ or more), there is a coordinate where the codewords take more than $\ell$ values. The parameter $L$ is said to be the "list size" in either case. The capacity, i.e., the largest possible rate for these notions as the list size $L \to \infty$, is known to be $1-h_q(p)$ for list-decoding, and $1-\log_q \ell$ for list-recovery, where $q$ is the alphabet size of the code family. In this work, we study the list size of random linear codes for both list-decoding and list-recovery as the rate approaches capacity. We show the following claims hold with high probability over the choice of the code (below, $ε> 0$ is the gap to capacity). (1) A random linear code of rate $1 - \log_q(\ell) - ε$ requires list size $L \ge \ell^{Ω(1/ε)}$ for list-recovery from input list size $\ell$. This is surprisingly in contrast to completely random codes, where $L = O(\ell/ε)$ suffices w.h.p. (2) A random linear code of rate $1 - h_q(p) - ε$ requires list size $L \ge \lfloor h_q(p)/ε+0.99 \rfloor$ for list-decoding from error fraction $p$, when $ε$ is sufficiently small. (3) A random binary linear code of rate $1 - h_2(p) - ε$ is list-decodable from average error fraction $p$ with list size with $L \leq \lfloor h_2(p)/ε\rfloor + 2$. The second and third results together precisely pin down the list sizes for binary random linear codes for both list-decoding and average-radius list-decoding to three possible values.

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