Improved Decoding of Tanner Codes

Abstract

In this paper, we present improved decoding algorithms for expander-based Tanner codes. We begin by developing a randomized linear-time decoding algorithm that, under the condition that $ \delta d_0 > 2 $, corrects up to $ \alpha n $ errors for a Tanner code $ T(G, C_0) $, where $ G $ is a $ (c, d, \alpha, \delta) $-bipartite expander with $n$ left vertices, and $ C_0 \subseteq \mathbb{F}_2^d $ is a linear inner code with minimum distance $ d_0 $. This result improves upon the previous work of Shen, Shangguan, Ouyang and Cheng (IEEE TIT 2025), which required $ \delta d_0 > 3 $. We further derandomize the algorithm to obtain a deterministic linear-time decoding algorithm with the same decoding radius. Our algorithm improves upon the previous deterministic algorithm of Cheng et al.\ by achieving a decoding radius of $ \alpha n $, compared with the previous radius of $ \frac{2\alpha}{d_0(1 + 0.5c\delta) }n$. Additionally, we investigate the size-expansion trade-off introduced by the recent work of Chen, Cheng, Li, and Ouyang (IEEE TIT 2023), and use it to provide new bounds on the minimum distance of Tanner codes. Specifically, we prove that the minimum distance of a Tanner code $T(G,C_0)$ is approximately $f_\delta^{-1} \left( \frac{1}{d_0} \right) \alpha n $, where $ f_\delta(\cdot) $ is the Size-Expansion Function. As another application, we improve the decoding radius of our decoding algorithms from $\alpha n$ to approximately $f_\delta^{-1}\left(\frac{2}{d_0}\right)\alpha n$. Finally, we extend Viderman's find-erasures-and-decode framework (ACM TOCT 2013) to general linear inner codes, obtaining a deterministic linear-time decoder for $\delta d_{0}>1.8$ when $d_0=3$, thus pushing below the $\delta d_0 > 2$ threshold of our general result.