二元线性码的神经校验多面体投影PDD译码器
A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection
摘要：
线性规划是一种重要的二元线性码译码技术。然而，LP解码的优点，例如低错误平层和强理论保证等，以高计算复杂度和低信噪比（SNR）区域的低性能为代价。在这封信中，我们采用惩罚对偶分解（PDD）的框架，并提出了一个PDD算法来解决基于基本多面体的最大似然（ML）解码问题。此外，我们建议将机器学习技术集成到PDD解码算法中最耗时的部分，即，检查多面体投影（CPP）。受多层感知（MLP）理论上可以逼近任何非线性映射函数的启发，提出了一种专门设计的神经CPP（NCPP）算法，以降低解码延迟。仿真结果验证了所提算法的有效性。
英文摘要：
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.
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