Non-parametric spectrum cartography using adaptive radial basis functions

Hamid, Mohamed; Beferull-Lozano, Baltasar

doi:10.1109/icassp.2017.7952827

Cited by 32 publications

(21 citation statements)

References 17 publications

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“…Bayesian learning [13], or kernel-based learning [14], [15]. Since these works can only map power distribution across space but not across frequency, different schemes have been devised to construct PSD maps, for instance by exploiting the sparsity of power distributions over space and frequency with a basis expansion model [3], [16] or by leveraging the framework of kernel-based learning [5].…”

Section: Introductionmentioning

confidence: 99%

Location-Free Spectrum Cartography

Teganya

Romero

Lopez-Ramos

et al. 2019

IEEE Trans. Signal Process.

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Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.

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Section: Introductionmentioning

confidence: 99%

Location-Free Spectrum Cartography

Teganya

Romero

Lopez-Ramos

et al. 2019

IEEE Trans. Signal Process.

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“…There are several techniques proposed for spectrum cartography including Kriging interpolation [5], [6], dictionary learning [7], sparsity aware regression [8], basis expansion [9], matrix completion [10] and reproducing kernel Hilbert space (RKHS) regression [11]- [13]. These techniques are briefly surveyed in [14]. To the best of the authors' knowledge, even though there exist diverse schemes for spectrum cartography, each of these schemes however has at least one of the following three limitations [14].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques are briefly surveyed in [14]. To the best of the authors' knowledge, even though there exist diverse schemes for spectrum cartography, each of these schemes however has at least one of the following three limitations [14].…”

Section: Introductionmentioning

confidence: 99%

“…In [14], the aforementioned limitations are overcome by developing a spectrum cartography algorithm that adapts the parameters of the basis functions based on a relatively lower density of measurements with no prior information about the transmitters. This goal is achieved by using RBF that are centered at strategic informative centroids locations instead of using kernels directly centered on sensors locations.…”

Section: Introductionmentioning

confidence: 99%

“…In view of the theoretical foundations of adaptive RBF based spectrum cartography provided in [14], this paper presents an experimental validation of the algorithm in two aspects. At first, the functionality of the algorithm is shown in real radio propagation environment.…”

Section: Introductionmentioning

confidence: 99%

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Spectrum cartography using adaptive radial basis functions: Experimental validation

Idsøe

Hamid

Jordbru

et al. 2017

2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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In this paper, we experimentally validate the functionality of a developed algorithm for spectrum cartography using adaptive Gaussian radial basis functions (RBF). The RBF are strategically centered around representative centroid locations in a machine learning context. We assume no prior knowledge about neither the power spectral densities (PSD) of the transmitters nor their locations. Instead, the received signal power at each location is estimated as a linear combination of different RBFs. The weights of the RBFs, their Gaussian decaying parameters and locations are jointly optimized using expectation maximization with a least squares loss function and a quadratic regularizer. The performance of adaptive RBFs based spectrum cartography is shown through measurements using a universal software radio peripheral, a customized node and LabView framework. The obtained results verify the ability of adaptive RBF to construct spectrum maps with an acceptable performance measured by normalized mean square error (NMSE).

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Radio Map Reconstruction With Adaptive Spatial Feature Learning

Yang,

Guo

2024

IET Signal Processing

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Radio map reconstruction is a fundamental problem of great relevance in numerous real‐world applications, such as network planning and fingerprint localization. Sampling the complete radio map is prohibitively costly in practice and difficult to achieve. Such methods for reconstructing radio maps from a subset of measurements are now gaining additional attention. In this paper, we first explore the spatial features of signals on the radio map and formulate the reconstruction problem as an optimization problem with feature penalties. Then, we propose an iteration algorithm with spatial feature learning to reconstruct signals on the radio map, which improves the reconstruction accuracy by using an adaptive feature dictionary. Numerical examples are given to demonstrate the viability and performance of our method at last.

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Non-parametric spectrum cartography using adaptive radial basis functions

Cited by 32 publications

References 17 publications

Location-Free Spectrum Cartography

Location-Free Spectrum Cartography

Spectrum cartography using adaptive radial basis functions: Experimental validation

Radio Map Reconstruction With Adaptive Spatial Feature Learning

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