1 research outputs found
A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection
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.Comment: This pape has been accepted for publication in IEEE wireless
communications letter