On the minimal pseudo-codewords of codes from finite geometries

In order to understand the performance of a code under maximum-likelihood (ML) decoding, it is crucial to know the minimal codewords. In the context of linear programming (LP) decoding, it turns out to be necessary to know the minimal pseudo-codewords. This paper studies the minimal codewords and minimal pseudo-codewords of some families of codes derived from projective and Euclidean planes. Although our numerical results are only for codes of very modest length, they suggest that these code families exhibit an interesting property. Namely, all minimal pseudo-codewords that are not multiples of a minimal codeword have an AWGNC pseudo-weight that is strictly larger than the minimum Hamming weight of the code. This observation has positive consequences not only for LP decoding but also for iterative decoding

[1]  P. Vontobel,et al.  On the Relationship between Linear Programming Decoding and Min-Sum Algorithm Decoding , 2004 .

[2]  Ralf Koetter,et al.  Lower bounds on the minimum pseudoweight of linear codes , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[3]  Yuri L. Borissov,et al.  On the non-minimal codewords in the binary Reed-Muller code , 2001, Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252).

[4]  Martin J. Wainwright,et al.  Using linear programming to Decode Binary linear codes , 2005, IEEE Transactions on Information Theory.

[5]  D. Avis A Revised Implementation of the Reverse Search Vertex Enumeration Algorithm , 2000 .

[6]  P. Vontobel,et al.  Graph-covers and iterative decoding of nite length codes , 2003 .

[7]  Alexander Barg,et al.  Minimal Vectors in Linear Codes , 1998, IEEE Trans. Inf. Theory.

[8]  D. Mackay,et al.  Evaluation of Gallager Codes for Short Block Length and High Rate Applications , 2001 .

[9]  Erik Agrell,et al.  Voronoi regions for binary linear block codes , 1996, IEEE Trans. Inf. Theory.

[10]  Shu Lin,et al.  Low-density parity-check codes based on finite geometries: A rediscovery and new results , 2001, IEEE Trans. Inf. Theory.

[11]  Jon Feldman,et al.  Decoding error-correcting codes via linear programming , 2003 .

[12]  Peter F. Swaszek A lower bound on the error probability for signals in white Gaussian noise , 1995, IEEE Trans. Inf. Theory.

[13]  A. Vardy,et al.  Stopping sets in codes from designs , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..

[14]  Suquan Ding Iterative Decoding of L-step Majority-Logic Decodable Codes Based on Belief Propagation , 2007, International Conference on Wireless Communications, Networking and Mobile Computing.

[15]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[16]  D. Avis lrs : A Revised Implementation of the Rev rse Search Vertex Enumeration Algorithm , 1998 .

[17]  Shu Lin,et al.  Iterative decoding of one-step majority logic deductible codes based on belief propagation , 2000, IEEE Trans. Commun..

[18]  Tai-Yang Hwang Decoding linear block codes for minimizing word error rate (Corresp.) , 1979, IEEE Trans. Inf. Theory.

[19]  R. Koetter,et al.  On the Effective Weights of Pseudocodewords for Codes Defined on Graphs with Cycles , 2001 .

[20]  A. Barlotti,et al.  Combinatorics of Finite Geometries , 1975 .