We introduce a novel family of expander-based error correcting codes. These codes can be sampled with randomness linear in the block-length, and achieve list-decoding capacity (among other local properties). Our expander-based codes can be made starting from any family of sufficiently low-bias codes, and as a consequence, we give the first construction of a family of algebraic codes that can be sampled with linear randomness and achieve list-decoding capacity. We achieve this by introducing the notion of a pseudorandom puncturing of a code, where we select $n$ indices of a base code $C\subset \mathbb{F}_q^m$ via an expander random walk on a graph on $[m]$. Concretely, whereas a random linear code (i.e. a truly random puncturing of the Hadamard code) requires $O(n^2)$ random bits to sample, we sample a pseudorandom linear code with $O(n)$ random bits. We show that pseudorandom puncturings satisfy several desirable properties exhibited by truly random puncturings. In particular, we extend a result of (Guruswami Mosheiff FOCS 2022) and show that a pseudorandom puncturing of a small-bias code satisfies the same local properties as a random linear code with high probability. As a further application of our techniques, we also show that pseudorandom puncturings of Reed Solomon codes are list-recoverable beyond the Johnson bound, extending a result of (Lund Potukuchi RANDOM 2020). We do this by instead analyzing properties of codes with large distance, and show that pseudorandom puncturings still work well in this regime.
翻译:伪随机线性码可以列表解码到容量
我们介绍了一种新颖的基于展开图的纠错码家族。这些代码可以使用线性随机性进行采样,并实现列表解码容量(在其他局部属性中)。我们的扩展器基于码可以从任何足够低偏差码系列开始制作,因此,我们给出了第一个可以使用线性随机性进行采样并实现列表解码容量的代数码系列的构造。我们通过引入伪随机码的概念来实现,其中通过[m]上的图上的展开随机游走选择[math] n [/math]基础码 $C\subset \mathbb{F}_q^m$ 的索引。具体而言,随机线性码(即真正的Hadamard码的随机删除)需要[math]O(n^2)[/ math]随机比特数量进行采样,而我们使用[math]O(n)[/ math]个随机比特对伪随机线性码进行采样。我们展示了伪随机删除满足真正的随机删除表现的若干理想属性。特别是,我们扩展了(Guruswami Mosheiff FOCS 2022)的一个结果,并显示小偏差代码的伪随机删除具有相同的局部属性,与高概率的随机线性代码相同。作为我们技术的进一步应用,我们还展示了Reed Solomon代码的伪随机删除超出了Johnson界限可以进行列表恢复,扩展了(Lund Potukuchi RANDOM 2020)的结果。我们通过分析具有大距离特性的代码属性来完成此操作,并展示伪随机删除在此范围内仍然有效。