Title :
Experiments on Decoding LDPC Codes Using Trees and Random Forests
Author :
Gunther, Jake ; Pound, Andrew ; Moon, Todd
Author_Institution :
Dept. of Electr. & Comput. Eng., Utah State Univ., Logan, UT
Abstract :
This paper explores two alternatives to the standard message passing approach to decoding LDPC codes: regression trees and random forests. Regression trees are capable of approximating complicated functions. This paper reports on the performance of a tree-based low-density parity-check code (LDPC) decoder in which a regression tree is trained to approximate the message passing update function. This approach offers potential advantages over the message passing decoder. Single trees as well as groups of trees (i.e. forests) produce their outputs using only threshold comparisons. Therefore, the proposed decoders are arithmetic free.
Keywords :
decoding; parity check codes; regression analysis; trees (mathematics); arithmetic frees; low density parity check codes; message passing update function; random forests; regression trees; Code standards; Computational complexity; Iterative decoding; Message passing; Moon; Neural networks; Parity check codes; Regression tree analysis; Training data; Vectors; LDPC decoding; random forests; regression tree;
Conference_Titel :
Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th
Conference_Location :
Marco Island, FL
Print_ISBN :
978-1-4244-3677-4
Electronic_ISBN :
978-1-4244-3677-4
DOI :
10.1109/DSP.2009.4785998