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List-Recovery of Random Linear Codes over Small Fields

Dean Doron, Jonathan Mosheiff, Nicolas Resch, João Ribeiro

TL;DR

The paper studies list-recoverability of random linear codes over small fields near capacity, focusing on both erasures and errors. It introduces a δ-mixing framework and leverages prime-field worst-case mixing, along with puffed-up list-recovery balls, to obtain near-capacity bounds with linear dependence on 1/ε. The main results show L = O(1/ε) for list-recovery from erasures over prime fields and from errors over arbitrary fields, improving on the Zyablov–Pinsker bounds in many small-field regimes. The approach builds on mixing properties, increasing chains, and concentration tools to bridge random-code intuition with linear-code structure, and it highlights field-insensitive behavior for errors but field-sensitive behavior for erasures. These findings advance understanding of the trade-offs between linearity and list-size near capacity in the small alphabet regime, with implications for pseudorandomness and coding-theoretic constructions.

Abstract

We study list-recoverability of random linear codes over small fields, both from errors and from erasures. We consider codes of rate $ε$-close to capacity, and aim to bound the dependence of the output list size $L$ on $ε$, the input list size $\ell$, and the alphabet size $q$. Prior to our work, the best upper bound was $L = q^{O(\ell/ε)}$ (Zyablov and Pinsker, Prob. Per. Inf. 1981). Previous work has identified cases in which linear codes provably perform worse than non-linear codes with respect to list-recovery. While there exist non-linear codes that achieve $L=O(\ell/ε)$, we know that $L \ge \ell^{Ω(1/ε)}$ is necessary for list recovery from erasures over fields of small characteristic, and for list recovery from errors over large alphabets. We show that in other relevant regimes there is no significant price to pay for linearity, in the sense that we get the correct dependence on the gap-to-capacity $ε$ and go beyond the Zyablov-Pinsker bound for the first time. Specifically, when $q$ is constant and $ε$ approaches zero: - For list-recovery from erasures over prime fields, we show that $L \leq C_1/ε$. By prior work, such a result cannot be obtained for low-characteristic fields. - For list-recovery from errors over arbitrary fields, we prove that $L \leq C_2/ε$. Above, $C_1$ and $C_2$ depend on the decoding radius, input list size, and field size. We provide concrete bounds on the constants above, and the upper bounds on $L$ improve upon the Zyablov-Pinsker bound whenever $q\leq 2^{(1/ε)^c}$ for some small universal constant $c>0$.

List-Recovery of Random Linear Codes over Small Fields

TL;DR

The paper studies list-recoverability of random linear codes over small fields near capacity, focusing on both erasures and errors. It introduces a δ-mixing framework and leverages prime-field worst-case mixing, along with puffed-up list-recovery balls, to obtain near-capacity bounds with linear dependence on 1/ε. The main results show L = O(1/ε) for list-recovery from erasures over prime fields and from errors over arbitrary fields, improving on the Zyablov–Pinsker bounds in many small-field regimes. The approach builds on mixing properties, increasing chains, and concentration tools to bridge random-code intuition with linear-code structure, and it highlights field-insensitive behavior for errors but field-sensitive behavior for erasures. These findings advance understanding of the trade-offs between linearity and list-size near capacity in the small alphabet regime, with implications for pseudorandomness and coding-theoretic constructions.

Abstract

We study list-recoverability of random linear codes over small fields, both from errors and from erasures. We consider codes of rate -close to capacity, and aim to bound the dependence of the output list size on , the input list size , and the alphabet size . Prior to our work, the best upper bound was (Zyablov and Pinsker, Prob. Per. Inf. 1981). Previous work has identified cases in which linear codes provably perform worse than non-linear codes with respect to list-recovery. While there exist non-linear codes that achieve , we know that is necessary for list recovery from erasures over fields of small characteristic, and for list recovery from errors over large alphabets. We show that in other relevant regimes there is no significant price to pay for linearity, in the sense that we get the correct dependence on the gap-to-capacity and go beyond the Zyablov-Pinsker bound for the first time. Specifically, when is constant and approaches zero: - For list-recovery from erasures over prime fields, we show that . By prior work, such a result cannot be obtained for low-characteristic fields. - For list-recovery from errors over arbitrary fields, we prove that . Above, and depend on the decoding radius, input list size, and field size. We provide concrete bounds on the constants above, and the upper bounds on improve upon the Zyablov-Pinsker bound whenever for some small universal constant .
Paper Structure (24 sections, 22 theorems, 101 equations)

This paper contains 24 sections, 22 theorems, 101 equations.

Key Result

Theorem 1.1

If $\ell$ divides $\mathrm{char}(\mathds{F}_q)$, then with high probability over the choice of the linear code, the output list size $L$ cannot be taken smaller than $\ell^{\Omega(1/\varepsilon)}$.

Theorems & Definitions (49)

  • Theorem 1.1: informal; see GLMRSW22
  • Theorem 1.2: list recovery from erasures over prime fields; see \ref{['thm:erasures']}
  • Theorem 1.3: list recovery from errors; see \ref{['thm:errors']}
  • Remark 1.4: on the field insensitivity
  • Remark 1.5: on the dependence of $L$ on the various parameters
  • Remark 1.6: comparison to ZP81
  • Proposition 2.1
  • Definition 2.2: list recovery from erasures
  • Definition 2.3: list-recovery ball
  • Theorem 2.4: list-recovery from erasures capacity
  • ...and 39 more