#### DMCA

## Distributed Compressive Wide-Band Spectrum Sensing

Citations: | 52 - 6 self |

### Citations

3541 | Compressed Sensing,”
- Donoho
- 2006
(Show Context)
Citation Context ...ve sampling (CS) is a method for acquisition of sparse signals at rates significantly lower than the Nyquist sampling rate; signal reconstruction is a solution to an l1-norm optimization problem [2], =-=[6]-=-. In [14], [16] spectrum sensing schemes based on the principle of CS were presented for a single wide-band CR sensing receiver. In particular in [14], acquisition of the wide-band analog signal is pe... |

2679 | Atomic decomposition by basis pursuit.
- Chen, Donoho, et al.
- 1999
(Show Context)
Citation Context ... written as y = Φx = ΦΨs. (2) Reconstruction is achieved by solving the following l1-norm optimization problem ˆs = arg min s ‖s‖1 s.t. y = ΦΨs. (3) Linear programming techniques, e.g., basis pursuit =-=[4]-=-, or iterative greedy algorithms [11] can be used to solve (3). Distributed versions of CS have been considered in [5] and [7] in order to exploit the underlying correlation structures in the signal o... |

2559 | Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,”
- Candès, Romberg, et al.
- 2006
(Show Context)
Citation Context ...ressive sampling (CS) is a method for acquisition of sparse signals at rates significantly lower than the Nyquist sampling rate; signal reconstruction is a solution to an l1-norm optimization problem =-=[2]-=-, [6]. In [14], [16] spectrum sensing schemes based on the principle of CS were presented for a single wide-band CR sensing receiver. In particular in [14], acquisition of the wide-band analog signal ... |

434 | A survey of dynamic spectrum access - Zhao, Sadler - 2007 |

268 | Sparse solutions to linear inverse problems with multiple measurement vectors
- Cotter, Rao, et al.
- 2005
(Show Context)
Citation Context ...g min s ‖s‖1 s.t. y = ΦΨs. (3) Linear programming techniques, e.g., basis pursuit [4], or iterative greedy algorithms [11] can be used to solve (3). Distributed versions of CS have been considered in =-=[5]-=- and [7] in order to exploit the underlying correlation structures in the signal observations. The specific signal observation model of interest to us is the joint sparsity model JSM-2 [7], where the ... |

110 |
Compressed sensing for wideband cognitive radios
- Tian, Giannakis
- 2007
(Show Context)
Citation Context ...S) is a method for acquisition of sparse signals at rates significantly lower than the Nyquist sampling rate; signal reconstruction is a solution to an l1-norm optimization problem [2], [6]. In [14], =-=[16]-=- spectrum sensing schemes based on the principle of CS were presented for a single wide-band CR sensing receiver. In particular in [14], acquisition of the wide-band analog signal is performed using a... |

60 | Random Sampling for Analog-toInformation Conversion of Wideband Signals
- Laska, Kirolos, et al.
- 2006
(Show Context)
Citation Context ... analog-to-information converter (AIC). An AIC directly relates to the idea of sampling at the information rate of the signal. Practical approaches to the design of AICs have been considered in [10], =-=[12]-=-. An estimate of the original signal spectrum is then made based on CS reconstruction using a wavelet edge detector following the approach in [16]. It has however been observed that such a sensing sch... |

56 |
Sparsity and Incoherence
- Candes, Romberg
- 2007
(Show Context)
Citation Context ...n-zero (and large enough) entries si. It has been shown that x can be recovered using M = KO(log N) non-adaptive linear projection measurements on to an M × N basis matrix Φ that is incoherent with Ψ =-=[3]-=-. An example construction of Φ is given by choosing elements that are drawn independently from a random distribution, e.g., Gaussian, Bernoulli. The measurement vector y can be written as y = Φx = ΦΨs... |

46 | Signal reconstruction using sparse tree representations
- La, Do
- 2005
(Show Context)
Citation Context ...truction is achieved by solving the following l1-norm optimization problem ˆs = arg min s ‖s‖1 s.t. y = ΦΨs. (3) Linear programming techniques, e.g., basis pursuit [4], or iterative greedy algorithms =-=[11]-=- can be used to solve (3). Distributed versions of CS have been considered in [5] and [7] in order to exploit the underlying correlation structures in the signal observations. The specific signal obse... |

36 |
Spectrum sharing radios
- Cabric, O’Donnell, et al.
- 2006
(Show Context)
Citation Context ...ransmissions to such empty portions to meet regulatory requirements of limiting harmful interference to licensed systems. Future cognitive radios will be capable of scanning wide bands of frequencies =-=[1]-=-, in the order of a few GHz, and employ adaptive waveforms for transmission depending on the estimated spectrum of licensed systems. In this paper, we consider the problem of determining spectrum occu... |

17 | Spectral occupancy and interference studies in support of cognitive radio technology deployment - Roberson, Hood, et al. - 2006 |

15 | Practical issues in implementing analogto-information converters
- Kirolos, Ragheb, et al.
- 2006
(Show Context)
Citation Context ...ing an analog-to-information converter (AIC). An AIC directly relates to the idea of sampling at the information rate of the signal. Practical approaches to the design of AICs have been considered in =-=[10]-=-, [12]. An estimate of the original signal spectrum is then made based on CS reconstruction using a wavelet edge detector following the approach in [16]. It has however been observed that such a sensi... |