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## Giannakis, “Online spectrum cartography via quantized measurements (2015)

Citations: | 1 - 1 self |

### Citations

2803 | Learning with Kernels
- Schölkopf, Smola
- 2002
(Show Context)
Citation Context ... one can resort to the regularization inductive principle, where a linear combination of a fitting cost Remp, called empirical risk, and a smoothness-enforcing penalty J , is minimized as (see, e.g., =-=[17]-=-) minimize l∈S Remp(l; {(x,φx, yx)}x∈X ) + λJ(l). (9) Here, S is the space of functions, assumed to be a RKHS of vector-valued functions from Rd to RM [10], and λ > 0 is a constant adjusted to attain ... |

863 | A tutorial on support vector regression
- Smola, Schölkopf
(Show Context)
Citation Context ...e ‖l‖S is the norm induced by the inner product of S. The empirical risk Remp is chosen to linearly penalize deviations from (8), using the so-called -insensitive loss u(y) := max(0, |y| − ) as in =-=[18]-=- Remp(l; {(x,φx, yx)}x∈X ) := 1 N ∑ x∈X u(yx − φTx l(x)). (10) This is a convex surrogate for the number of measurement errors, in the same way as the `1-norm is substituted for the `0-norm in the sp... |

156 | Beyond Nyquist: Efficient sampling of sparse, bandlimited signals - Tropp, Laska, et al. |

119 | On learning vector-valued functions
- Micchelli, Pontil
- 2005
(Show Context)
Citation Context ...e spatial power distribution of each channel. Spatial interpolation is accomplished using an online nonparametric regression technique that hinges on the framework of RKHSs of vector-valued functions =-=[10]-=-, where the coefficients of the frequency basis functions are estimated as spatial vector fields. This work was partially funded by the Spanish Government and the European Regional Development Fund (E... |

79 | Distributed spectrum sensing for cognitive radio networks by exploiting sparsity
- Bazerque, Giannakis
(Show Context)
Citation Context ...sing Kriging [2], [3], orthogonal matching pursuit [4], sparse Bayesian learning [5], and dictionary learning [6]. To account for the frequency dimension, a basis expansion model (BEM) was adopted in =-=[7]-=-, [8]. However, these techniques require exchanging raw measurements, which imposes stringent requirements on the control channel. This issue was mitigated in [9], but the spatial dimension was not ac... |

67 | Spectrum sensing for cognitive radio : State-of-the-art and recent advances
- Axell, Leus, et al.
- 2012
(Show Context)
Citation Context ...on, network planning, and in particular, in dynamic spectrum access (DSA) of cognitive radios, which aspire to opportunistically exploit underutilized spectral resources in time, space, and frequency =-=[1]-=-. Spatial interpolation of RF power measurements has been tackled using Kriging [2], [3], orthogonal matching pursuit [4], sparse Bayesian learning [5], and dictionary learning [6]. To account for the... |

28 | Cooperative spectrum sensing for cognitive radios using Kriged Kalman filtering
- Kim, Dall’Anese, et al.
(Show Context)
Citation Context ...adios, which aspire to opportunistically exploit underutilized spectral resources in time, space, and frequency [1]. Spatial interpolation of RF power measurements has been tackled using Kriging [2], =-=[3]-=-, orthogonal matching pursuit [4], sparse Bayesian learning [5], and dictionary learning [6]. To account for the frequency dimension, a basis expansion model (BEM) was adopted in [7], [8]. However, th... |

21 | Group-Lasso on Splines for Spectrum Cartography, Arxiv preprint arXiv:1010.0274
- Bazerque, Mateos, et al.
- 2010
(Show Context)
Citation Context ...Kriging [2], [3], orthogonal matching pursuit [4], sparse Bayesian learning [5], and dictionary learning [6]. To account for the frequency dimension, a basis expansion model (BEM) was adopted in [7], =-=[8]-=-. However, these techniques require exchanging raw measurements, which imposes stringent requirements on the control channel. This issue was mitigated in [9], but the spatial dimension was not account... |

15 | Design and implementation of a fully integrated compressed-sensing signal acquisition system
- Yoo, Becker, et al.
- 2012
(Show Context)
Citation Context ...imate the PSD map (2). In order to allow for estimation in wide frequency bands at low hardware cost and power consumption, acquisition via analog-to-information converters (AICs) is considered [13]– =-=[15]-=-. For each input block rx[b] := [rx[bL], . . . , rx[bL+(L− 1)]]T of L samples rx[k] := rx(kTs), where 1/Ts is the Nyquist rate, an AIC produces a linearly compressed block r̃x[b] of L̃ (< L) samples. ... |

14 |
Informed spectrum usage in cognitive radio networks: interference cartography
- Alaya-Feki, Jemaa, et al.
- 2008
(Show Context)
Citation Context ...ive radios, which aspire to opportunistically exploit underutilized spectral resources in time, space, and frequency [1]. Spatial interpolation of RF power measurements has been tackled using Kriging =-=[2]-=-, [3], orthogonal matching pursuit [4], sparse Bayesian learning [5], and dictionary learning [6]. To account for the frequency dimension, a basis expansion model (BEM) was adopted in [7], [8]. Howeve... |

12 | Practical compressed sensing: Modern data acquisition and signal processing
- Becker
- 2011
(Show Context)
Citation Context ...to estimate the PSD map (2). In order to allow for estimation in wide frequency bands at low hardware cost and power consumption, acquisition via analog-to-information converters (AICs) is considered =-=[13]-=-– [15]. For each input block rx[b] := [rx[bL], . . . , rx[bL+(L− 1)]]T of L samples rx[k] := rx(kTs), where 1/Ts is the Nyquist rate, an AIC produces a linearly compressed block r̃x[b] of L̃ (< L) sam... |

11 | Frugal sensing: Wideband power spectrum sensing from few bits
- Mehanna, Sidiropoulos
- 2013
(Show Context)
Citation Context ... expansion model (BEM) was adopted in [7], [8]. However, these techniques require exchanging raw measurements, which imposes stringent requirements on the control channel. This issue was mitigated in =-=[9]-=-, but the spatial dimension was not accounted for. The aim of the present work is to address these limitations through an estimator-interpolator approach that can afford lowcost, low-power sensing arc... |

10 | Wideband spectrum sensing from compressed measurements using spectral prior information
- Romero, Leus
(Show Context)
Citation Context ...ems obey transmission standards (e.g. DVB or ATSC in TV bands) and spectrum mask regulations, which fix the transmission parameters, such as bandwidth, carrier frequencies, and roll-off factors [11], =-=[12]-=-. If the functions φm(f), m = 1, . . . ,M − 1, are not known, our approach can still be used by adopting a general BEM [7], [8]. The received waveform at position x ∈ R, due to the M−1 uncorrelated tr... |

6 |
Spectrum sensing exploiting guard bands and weak channels
- Vázquez-Vilar, López-Valcarce
- 2011
(Show Context)
Citation Context ...y systems obey transmission standards (e.g. DVB or ATSC in TV bands) and spectrum mask regulations, which fix the transmission parameters, such as bandwidth, carrier frequencies, and roll-off factors =-=[11]-=-, [12]. If the functions φm(f), m = 1, . . . ,M − 1, are not known, our approach can still be used by adopting a general BEM [7], [8]. The received waveform at position x ∈ R, due to the M−1 uncorrela... |

4 | Giannakis, “Cognitive radio spectrum prediction using dictionary learning
- Kim, B
- 2013
(Show Context)
Citation Context ...pace, and frequency [1]. Spatial interpolation of RF power measurements has been tackled using Kriging [2], [3], orthogonal matching pursuit [4], sparse Bayesian learning [5], and dictionary learning =-=[6]-=-. To account for the frequency dimension, a basis expansion model (BEM) was adopted in [7], [8]. However, these techniques require exchanging raw measurements, which imposes stringent requirements on ... |

3 |
Improved performance of spectrum cartography based on compressive sensing in cognitive radio networks,”
- Jayawickrama, Dutkiewicz, et al.
- 2013
(Show Context)
Citation Context ...tically exploit underutilized spectral resources in time, space, and frequency [1]. Spatial interpolation of RF power measurements has been tackled using Kriging [2], [3], orthogonal matching pursuit =-=[4]-=-, sparse Bayesian learning [5], and dictionary learning [6]. To account for the frequency dimension, a basis expansion model (BEM) was adopted in [7], [8]. However, these techniques require exchanging... |

3 | Compression limits for random vectors with linearly parameterized second-order statistics
- Romero, López-Valcarce, et al.
- 2015
(Show Context)
Citation Context ...his operation can be represented as r̃x[b] = G̃rx[b] (3) where G̃ ∈ CL̃×L is the compression matrix. In order to guarantee the identifiability of the lm(x)’s, this matrix must satisfy the criteria in =-=[16]-=-. The whole observation frame rx := [rTx [0], . . . , r T x [B − 1]]T therefore produces r̃x = Grx, where r̃x := [r̃Tx [0], . . . , r̃ T x [B − 1]]T and G := IB ⊗ G̃ (⊗ denotes the Kronecker product).... |

3 | Giannakis, “Spectrum cartography using quantized observations
- Romero, Kim, et al.
- 2015
(Show Context)
Citation Context ...d certain requirements are met [10]. A simple construction is K(z,x) = diag { k(1)(z,x), . . . , k(M)(z,x) } , where k(m)(z,x) are valid scalar kernels. We developed a batch algorithm to solve (9) in =-=[19]-=-. However, the cost that batch approaches incur can grow prohibitively as the number of measurements increases. This motivates well the online algorithm here, which can incrementally update the estima... |

2 |
Cooperative radio source positioning and power map reconstruction: A sparse Bayesian learning approach
- Huang, Wu, et al.
(Show Context)
Citation Context ...spectral resources in time, space, and frequency [1]. Spatial interpolation of RF power measurements has been tackled using Kriging [2], [3], orthogonal matching pursuit [4], sparse Bayesian learning =-=[5]-=-, and dictionary learning [6]. To account for the frequency dimension, a basis expansion model (BEM) was adopted in [7], [8]. However, these techniques require exchanging raw measurements, which impos... |

1 | Online Learning with Multiple Operator-valued Kernels
- Audiffren, Kadri
(Show Context)
Citation Context ...s can track slow temporal variations of the fields of interest. An elegant approach to derive online algorithms for kernel machines is based on stochastic gradient descent in the function space [20], =-=[21]-=-. First, one can define the instantaneous regularized error as Rinst(l,φ,x, y) := u(y − φT l(x)) + λ‖l‖2S . (11) Note that the objective of (9) is just the sample average of Rinst. Suppose that per s... |