Linear Programming (LP) is an important decoding technique for binary linear
codes. However, the advantages of LP decoding, such as low error floor and
strong theoretical guarantee, etc., come at the cost of high computational
complexity and poor performance at the low signal-to-noise ratio (SNR) region.
In this letter, we adopt the penalty dual decomposition (PDD) framework and
propose a PDD algorithm to address the fundamental polytope based maximum
likelihood (ML) decoding problem. Furthermore, we propose to integrate machine
learning techniques into the most time-consuming part of the PDD decoding
algorithm, i.e., check polytope projection (CPP). Inspired by the fact that a
multi-layer perception (MLP) can theoretically approximate any nonlinear
mapping function, we present a specially designed neural CPP (NCPP) algorithm
to decrease the decoding latency. Simulation results demonstrate the
effectiveness of the proposed algorithms.

本文提出了基于惩罚对偶分解（PDD）框架的 PDD 算法，并将机器学习技术集成到 PDD 解码算法的最耗时部分 —— 检查多面体投影（CPP）中，提出了特别设计的神经 CPP（NCPP）算法来降低解码延迟。仿真结果表明，所提出的算法是有效的