Location-Free Spectrum Cartography

Teganya, Yves; Romero, Daniel; Lopez-Ramos, Luis M.; Beferull-Lozano, Baltasar

doi:10.1109/tsp.2019.2923151

Cited by 16 publications

(17 citation statements)

References 41 publications

(88 reference statements)

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“…Schemes to construct power maps, which provide the received signal strength across space, have been developed using kriging [1], [9]- [11], dictionary learning [12], sparse Bayesian learning [13]- [15], and matrix completion [16]. Power spectral density (PSD) maps can be estimated using kernel-based learning [17]- [19], sparse learning [18], and tensor completion [20], [21]. The problem of estimating channel-gain maps has been addressed in [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Shrestha¹,

Romero²,

Chepuri³

2022

Preprint

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Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous techniques have been proposed to construct radio maps from spatially distributed measurements, the locations of such measurements are assumed predetermined beforehand. In contrast, this paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. This is performed in two steps. First, two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. These algorithms offer complementary benefits and feature constant complexity per measurement. Second, the uncertainty metric is used to plan the trajectory of the UAV to gather measurements at the most informative locations. To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time. Numerical experiments conducted on a realistic dataset confirm that the proposed scheme constructs accurate radio maps quickly.

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Section: Introductionmentioning

confidence: 99%

Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Shrestha¹,

Romero²,

Chepuri³

2022

Preprint

Self Cite

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“…Most approaches are based on some interpolation algorithm. For example, power maps have been constructed through kriging [1], [9], dictionary learning [10], [11], compressive sensing [3], Bayesian models [12], matrix completion [13], and kernel methods [14], [15]. PSD maps have also been constructed by exploiting the sparsity of power across space and frequency [2] as well as by applying thin-plate spline regression [16] and kernel-based learning [8], [17].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Spectrum Cartography via Deep Completion Autoencoders

Teganya

Romero

2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Spectrum maps, which provide RF spectrum metrics such as power spectral density for every location in a geographic area, find numerous applications in wireless communications such as interference control, spectrum management, resource allocation, and network planning to name a few. Spectrum cartography techniques construct these maps from a collection of measurements collected by spatially distributed sensors. Due to the nature of the propagation of electromagnetic waves, spectrum maps are complicated functions of the spatial coordinates. For this reason, model-free approaches have been preferred. However, all existing schemes rely on some interpolation algorithm unable to learn from data. This work proposes a novel approach to spectrum cartography where propagation phenomena are learned from data. The resulting algorithms can therefore construct a spectrum map from a significantly smaller number of measurements than existing schemes since the spatial structure of shadowing and other phenomena is previously learned from maps in other environments. Besides the aforementioned new paradigm, this is also the first work to perform spectrum cartography with deep neural networks. To exploit the manifold structure of spectrum maps, a deep network architecture is proposed based on completion autoencoders.

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“…have been constructed through kriging [2], [16]- [18], compressive sensing [4], dictionary learning [19], [20], matrix completion [21], Bayesian models [22], radial basis functions [23], [24], and kernel methods [25]. PSD map estimators have been developed using sparse learning [3], thin-plate spline regression [26], kernel-based learning [11], [27], and tensor completion [28], [29].…”

Section: Introductionmentioning

confidence: 99%

Deep Completion Autoencoders for Radio Map Estimation

Teganya¹,

Romero²

2020

Preprint

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Radio maps provide metrics such as power spectral density for every location in a geographic area and find numerous applications such as UAV communications, interference control, spectrum management, resource allocation, and network planning to name a few. Radio maps are constructed from measurements collected by spectrum sensors distributed across space. Since radio maps are complicated functions of the spatial coordinates due to the nature of electromagnetic wave propagation, model-free approaches are strongly motivated. Nevertheless, all existing schemes rely on interpolation algorithms unable to learn from data. In contrast, this paper proposes a novel approach in which the spatial structure of propagation phenomena such as shadowing is learned beforehand from a data set with measurements in other environments. Relative to existing schemes, a significantly smaller number of measurements is therefore required to estimate a map with a prescribed accuracy. As an additional novelty, this is also the first work to estimate radio maps using deep neural networks. Specifically, a deep completion autoencoder architecture is developed to effectively exploit the manifold structure of this class of maps.

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Location-Free Spectrum Cartography

Cited by 16 publications

References 41 publications

Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Data-Driven Spectrum Cartography via Deep Completion Autoencoders

Deep Completion Autoencoders for Radio Map Estimation

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