[1] An ChunyanJi HongLi YiZhang Xiaoliang,. Discrete-time Markov-based dynamic control approachfor compressed sampling [J]. Journal of Southeast University (English Edition), 2012, 28 (3): 287-291. [doi:10.3969/j.issn.1003-7985.2012.03.006]

Copy

Discrete-time Markov-based dynamic control approachfor compressed sampling()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

28

Issue:

2012 3

Page:

287-291

Research Field:

Information and Communication Engineering

Publishing date:

2012-09-30

Info

Title:

Discrete-time Markov-based dynamic control approachfor compressed sampling

Author(s):

An ChunyanJi HongLi YiZhang Xiaoliang

Key Laboratory of Universal Wireless Communication of Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China

Keywords:

compressed sampling;  signal sparsity level prediction;  discrete-time Markov chain

PACS:

TN91

DOI:

10.3969/j.issn.1003-7985.2012.03.006

Abstract:

To solve the problem that the signal sparsity level is time-varying and not known as a priori in most cases, a signal sparsity level prediction and optimal sampling rate determination scheme is proposed. The discrete-time Markov chain is used to model the signal sparsity level and analyze the transition between different states. According to the current state, the signal sparsity level state in the next sampling period and its probability are predicted. Furthermore, based on the prediction results, a dynamic control approach is proposed to find out the optimal sampling rate with the aim of maximizing the expected reward which considers both the energy consumption and the recovery accuracy. The proposed approach can balance the tradeoff between the energy consumption and the recovery accuracy. Simulation results show that the proposed dynamic control approach can significantly improve the sampling performance compared with the existing approach.

References:

[1] Donoho D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[2] Tropp J A, Laska J N, Duarte M F, et al. Beyond Nyquist: efficient sampling of sparse bandlimited signals [J]. IEEE Transactions on Information Theory, 2010, 56(1): 520-544.
[3] Hormati A, Vetterli M. Compressive sampling of multiple sparse signals having common support using finite rate of innovation principles [J]. IEEE Signal Processing Letters, 2011, 18(5): 331-334.
[4] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
[5] Candès E J, Romberg J, Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information [J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
[6] Amini A, Marvasti F. Deterministic construction of binary, bipolar, and ternary compressed sensing matrices [J]. IEEE Transactions on Information Theory, 2011, 57(4): 2360-2370.
[7] Bajwa W U, Haupt J, Sayeed A M, et al. Compressed channel sensing: a new approach to estimating sparse multipath channels [J]. Proceedings of the IEEE, 2010, 98(6): 1058-1076.
[8] Berger C R, Wang Z H, Huang J Z, et al. Application of compressive sensing to sparse channel estimation [J]. IEEE Communications Magazine, 2010, 48(11): 164-174.
[9] Fazel F, Fazel M, Stojanovic M. Random access compressed sensing for energy-efficient underwater sensor networks [J]. IEEE Journal on Selected Areas in Communications, 2011, 29(8): 1660-1670.
[10] Malioutov D M, Sanghavi S R, Willsky A S. Sequential compressed sensing [J]. IEEE Journal on Selected Topics in Signal Processing, 2010, 4(2): 435-444.

Memo

Memo:

Biographies: An Chunyan(1987—), female, graduate; Ji Hong(corresponding author), female, doctor, professor, jihong@bupt.edu.cn.
Foundation items: Innovation Funds for Outstanding Graduate Students in School of Information and Communication Engineering in BUPT, the National Natural Science Foundation of China(No.61001115, 61271182).
Citation: An Chunyan, Ji Hong, Li Yi, et al. Discrete-time Markov-based dynamic control approach for compressed sampling[J].Journal of Southeast University(English Edition), 2012, 28(3):287-291.[doi:10.3969/j.issn.1003-7985.2012.03.006]

Common functions