Iterative subspace pursuit decoding of weighted Euclidean superimposed codes

2008 5th International Symposium on Turbo Codes and Related Topics, TURBOCODING

Volumn , Issue , 2008, Pages 402-407

  Dai, Wei ^a   Milenkovic, Olgica ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; DECODING; SIGNAL RECONSTRUCTION; TURBO CODES;

CODE WORDS; COMPRESSIVE SENSING; CONSTANT PARAMETERS; EUCLIDEAN; ITERATIVE ALGORITHMS; SP ALGORITHMS; SUB SPACES; SUPERIMPOSED CODES; UPPER BOUNDS;

ITERATIVE DECODING;

EID: 57849133176     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/TURBOCODING.2008.4658733     Document Type: Conference Paper

References (19)

1. W. Kautz and R. Singleton, "Nonrandom binary superimposed codes," IEEE Trans. Inform. Theory, vol. 10, no. 4, pp. 363-377, 1964.
2. n," IEEE Trans. Inform. Theory, vol. 34, no. 4, pp. 877-880, 1988.
3. W. Dai and O. Milenkovic, "Weighted euclidean superimposed codes for integer compressed sensing," in IEEE Information Theory Workshop (ITW), 2008, submitted.
4. -, "Weighted superimposed codes and constrained integer compressed sensing," IEEE Trans. Inform. Theory, submitted, 2008. [Online]. Available: http://arxiv.org/abs/0806.2682
5. D. Donoho, "Compressed sensing," IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
6. E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489-509, 2006.
7. E. J. Candès and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?" IEEE Trans. Inform. Theory, vol. 52, no. 12, pp. 5406-5425, 2006.
8. E. Candès and T. Tao, "Decoding by linear programming," Information Theory, IEEE Transactions on, vol. 51, no. 12, pp. 4203-4215, 2005.
9. G. Cormode and S. Muthukrishnan, "What's hot and what's not: Tracking most frequent items dynamically," IEEE Trans. Inform. Theory, vol. 50, no. 10, pp. 2231-2242, 2004.
10. J. A. Tropp, "Greed is good: algorithmic results for sparse approximation," IEEE Trans. Inform. Theory, vol. 50, no. 10, pp. 2231-2242, 2004.
11. D. Needell and R. Vershynin, "Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit," preprint, 2007.
12. -, "Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit," preprint, 2008.
13. D. L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit," submitted.
14. S. Sarvotham, D. Baron, and R. Baraniuk, "Compressed sensing reconstruction via belief propagation." Preprint, 2006.
15. R. S. Varga, Geršgorin and His Circles. Berlin: Springer-Verlag, 2004.
16. Y. Han, , C. Hartmann, and C.-C. Chen, "Efficient priority-first search maximum-likelihood soft-decision decoding of linear block codes," IEEE Trans. on Information Theory, vol. 51, pp. 1514-1523, 1993.
17. I. E. Nesterov, A. Nemirovskii, and Y. Nesterov, Interior-Point Polynomial Algorithms in Convex Programming. SIAM, 1994.
18. W. Dai and O. Milenkovic, "Subspace pursuit for compressive sensing: Closing the gap between performance and complexity," IEEE Trans. Inform. Theory, submitted, 2008. [Online]. Available: http://arxiv.org/abs/ 0803.0811v2
19. Å. Björck, Numerical Methods for Least Squares Problems. SIAM, 1996.