A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection
This work addresses decoding efficiency in communication systems, but it is incremental as it builds on existing PDD and neural approximation methods.
The paper tackles the high computational complexity and poor low-SNR performance of LP decoding for binary linear codes by proposing a PDD algorithm with a neural check polytope projection to reduce decoding latency.
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.