Title :
Adaptive Linear Programming Decoding
Author :
Taghavi, Mohammad H. ; Siegel, Paul H.
Author_Institution :
Dept. of ECE, California Univ., San Diego, CA
Abstract :
The ability of linear programming (LP) decoding to detect failures, and its potential for improvement by the addition of new constraints, motivates the use of an adaptive approach in selecting the constraints for the underlying LP problem. In this paper, we show that the application of such adaptive methods can significantly reduce the complexity of the LP decoding algorithm, which, in the standard formulation, is exponential in the maximum row weight of the parity-check matrix. We further show that adaptively adding new constraints, e.g. by combining parity checks, can provide large gains in LP decoder performance
Keywords :
adaptive decoding; linear programming; matrix algebra; parity check codes; LP decoder performance; adaptive linear programming decoding; failure detection; maximum row weight; parity-check matrix; Degradation; Iterative algorithms; Iterative decoding; Iterative methods; Linear programming; Maximum likelihood decoding; Maximum likelihood detection; Parity check codes; Performance gain; Vectors;
Conference_Titel :
Information Theory, 2006 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
1-4244-0505-X
Electronic_ISBN :
1-4244-0504-1
DOI :
10.1109/ISIT.2006.262071
