List-Recovery of Random Linear Codes over Small Fields
By: Dean Doron , Jonathan Mosheiff , Nicolas Resch and more
Potential Business Impact:
Makes data recovery from errors much faster.
We study list-recoverability of random linear codes over small fields, both from errors and from erasures. We consider codes of rate $\epsilon$-close to capacity, and aim to bound the dependence of the output list size $L$ on $\epsilon$, the input list size $\ell$, and the alphabet size $q$. Prior to our work, the best upper bound was $L = q^{O(\ell/\epsilon)}$ (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 $L=O(\ell/\epsilon)$, we know that $L \ge \ell^{\Omega(1/\epsilon)}$ 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 $\epsilon$ and go beyond the Zyablov-Pinsker bound for the first time. Specifically, when $q$ is constant and $\epsilon$ approaches zero: - For list-recovery from erasures over prime fields, we show that $L \leq C_1/\epsilon$. By prior work, such a result cannot be obtained for low-characteristic fields. - For list-recovery from errors over arbitrary fields, we prove that $L \leq C_2/\epsilon$. 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 $q\leq 2^{(1/\epsilon)^c}$ for some small universal constant $c>0$.
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