Turbo compressed sensing with partial DFT sensing matrix

IEEE Signal Processing Letters

Volumn 22, Issue 2, 2015, Pages 158-161

  Ma, Junjie ^a   Yuan, Xiaojun ^b   Ping, Li ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b SHANGHAITECH UNIVERSITY   (China)

Author keywords

Approximate message passing (AMP); compressed sensing; partial DFT matrix; replica method; state evolution

Indexed keywords

COMPRESSED SENSING; DISCRETE FOURIER TRANSFORMS; EVOLUTIONARY ALGORITHMS;

APPROXIMATE MESSAGE PASSING (AMP); DFT MATRIX; NUMERICAL RESULTS; REPLICA METHOD; STATE EVOLUTIONS;

MESSAGE PASSING;

EID: 84907215853     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2014.2351822     Document Type: Article

References (11)

1. E. J. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, 2006.
2. D. L. Donoho, A. Maleki, and A. Montanari, "Message-passing algorithms for compressed sensing," Proc. Nat. Acad. Sci., vol. 106, no. 45, pp. 18 914-18 919, 2009.
3. M. Bayati and A. Montanari, "The dynamics of message passing on dense graphs, with applications to compressed sensing," IEEE Trans. Inf. Theory, vol. 57, no. 2, pp. 764-785, Feb. 2011.
4. S. Rangan, "Generalized approximate message passing for estimation with random linear mixing," 2010 [Online]. Available: http://arxiv.org/abs/1010.5141, preprint
5. A. Tulino, G. Caire, S. Verdu, and S. Shamai, "Support recovery with sparsely sampled free random matrices," IEEE Trans. Inf. Theory, vol. 59, no. 7, pp. 4243-4271, Jul. 2013.
6. C. K.Wen and K. K.Wong, "Analysis of compressed sensing with spatially-coupled orthogonal matrices," 2014 [Online]. Available: http://arxiv.org/abs/1402.3215, preprint
7. C. Berrou and A. Glavieux, "Near optimum error correcting coding and decoding: Turbo-codes," IEEE Trans. Commun., vol. 44, no. 10, pp. 1261-1271, Oct. 1996.
8. S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Upper Saddle River, NJ, USA: Prentice-Hall PTR, 1993.
9. Q. Guo and D. Huang, "A concise representation for the soft-in soft-out lmmse detector," IEEE Commun. Lett., vol. 15, no. 5, pp. 566-568, May 2011.
10. X.Yuan, L. Ping, C. Xu, andA. Kavcic, "Achievable rates ofmimo systems with linear precoding and iterative lmmse detection," 2011 [Online]. Available: http://arxiv.org/ftp/arxiv/papers/1106/1106.0178.pdf, preprint
11. H.-A. Loeliger, J.Dauwels, J. Hu,S.Korl, L. Ping, and F.Kschischang, "The factor graph approach to model-based signal processing," Proc. IEEE, vol. 95, no. 6, pp. 1295-1322, Jun. 2007.