Short low-error-floor Tanner codes with Hamming nodes

While it is fairly easy to design good low-density parity-check codes with medium or large block lengths, and with code rates 1/2 or greater, much more effort is required for short lengths and low rates. We propose a code structure which incorporates Hamming codes into a code's graph, leading to a type of a Tanner code. The incorporation of Hamming codes into the graph, which we call "Hamming code doping", tends to lead to larger minimum distances and hence low error-rate floors. We present an iterative decoding algorithm tailored to graphs possessing "Hamming nodes". Further, a density evolution analysis based on the Gaussian approximation is presented, which is a useful tool for finding good codes. Finally, we present numerical results of some Hamming-doped Tanner codes simulated on the AWGN channel. The simulated codes exhibit remarkably low error floors, while simultaneously displaying good decoding thresholds

[1]  Stephan ten Brink,et al.  Convergence behavior of iteratively decoded parallel concatenated codes , 2001, IEEE Trans. Commun..

[2]  Stephan ten Brink,et al.  Design of low-density parity-check codes for modulation and detection , 2004, IEEE Transactions on Communications.

[3]  J. Boutros,et al.  Generalized low density (Tanner) codes , 1999, 1999 IEEE International Conference on Communications (Cat. No. 99CH36311).

[4]  D.J.C. MacKay,et al.  Good error-correcting codes based on very sparse matrices , 1997, Proceedings of IEEE International Symposium on Information Theory.

[5]  Dariush Divsalar,et al.  Iterative turbo decoder analysis based on density evolution , 2001, IEEE J. Sel. Areas Commun..

[6]  Sae-Young Chung,et al.  Analysis of sum-product decoding of low-density parity-check codes using a Gaussian approximation , 2001, IEEE Trans. Inf. Theory.

[7]  Robert Michael Tanner,et al.  A recursive approach to low complexity codes , 1981, IEEE Trans. Inf. Theory.

[8]  John Cocke,et al.  Optimal decoding of linear codes for minimizing symbol error rate (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[9]  Michael Lentmaier,et al.  Iterative decoding of generalized low-density parity-check codes , 1998, Proceedings. 1998 IEEE International Symposium on Information Theory (Cat. No.98CH36252).

[10]  Peter Elias,et al.  Error-free Coding , 1954, Trans. IRE Prof. Group Inf. Theory.

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

[12]  Ramesh Pyndiah,et al.  Near-optimum decoding of product codes: block turbo codes , 1998, IEEE Trans. Commun..

[13]  A. Glavieux,et al.  Near Shannon limit error-correcting coding and decoding: Turbo-codes. 1 , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.

[14]  Hesham El Gamal,et al.  Analyzing the turbo decoder using the Gaussian approximation , 2001, IEEE Trans. Inf. Theory.

[15]  Dariush Divsalar,et al.  Accumulate repeat accumulate codes , 2004, ISIT.

[16]  Rüdiger L. Urbanke,et al.  Design of capacity-approaching irregular low-density parity-check codes , 2001, IEEE Trans. Inf. Theory.

[17]  Evangelos Eleftheriou,et al.  Progressive edge-growth Tanner graphs , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).