### Abstract

Original language | English |
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Journal | European Signal Processing Conference (EUSIPCO) |

Volume | 21 |

Number of pages | 5 |

ISSN | 2076-1465 |

Publication status | Published - 2013 |

Event | European Signal Processing Conference EUSIPCO 2013 - Marrakech, Moroco Duration: 9 Sep 2013 → 13 Sep 2013 |

### Conference

Conference | European Signal Processing Conference EUSIPCO 2013 |
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City | Marrakech, Moroco |

Period | 09/09/2013 → 13/09/2013 |

### Fingerprint

### Keywords

- compressed sensing
- multi-coset
- sampling
- nonuniform sampling
- reconstruction algorithm

### Cite this

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**Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling.** / Grigoryan, Ruben; Jensen, Tobias Lindstrøm; Arildsen, Thomas; Larsen, Torben.

Research output: Contribution to journal › Conference article in Journal › Research › peer-review

TY - GEN

T1 - Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling

AU - Grigoryan, Ruben

AU - Jensen, Tobias Lindstrøm

AU - Arildsen, Thomas

AU - Larsen, Torben

PY - 2013

Y1 - 2013

N2 - This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.

AB - This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.

KW - compressed sensing

KW - multi-coset

KW - sampling

KW - nonuniform sampling

KW - reconstruction algorithm

M3 - Conference article in Journal

VL - 21

JO - Proceedings of the European Signal Processing Conference

JF - Proceedings of the European Signal Processing Conference

SN - 2076-1465

ER -