Neural Normalized Min-Sum Message-Passing vs. Viterbi Decoding for the CCSDS Line Product Code

Jonathan Nguyen, Linfang Wang, Chester Hulse, Sahil Dani, Amaael Antonini, Todd Chauvin, Divsalar Dariush, Richard Wesel

The Consultative Committee for Space Data Systems (CCSDS) 141.11-O-1 Line Product Code (LPC) provides a rare opportunity to compare maximum-likelihood decoding and message passing. The LPC considered in this paper is intended to serve as the inner code in conjunction with a (255,239) Reed Solomon (RS) code whose symbols are bytes of data. This paper represents the 141.11-O-1 LPC as a bipartite graph and uses that graph to formulate both maximum likelihood (ML) and message passing algorithms. ML decoding must, of course, have the best frame error rate (FER) performance. However, a fixed point implementation of a Neural-Normalized MinSum (N-NMS) message passing decoder closely approaches ML performance with a significantly lower complexity.

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Knowledge Graph

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