Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs

Romero, Daniel; Shrestha, Raju; Teganya, Yves; Chepuri, Sundeep Prabhakar

doi:10.48550/arxiv.2005.02432

Cited by 2 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kriging approach is the most commonly used spatial interpolation method since it can be tailored specifically to the statistical models of wireless channel shadowing. Furthermore, a variety of approaches were developed based on Bayesian inference, which normally rely on statistical channel models and source location as well [9]- [12]. Other standalone approaches were proposed based on low rank and sparse matrix reconstruction [13] and MMSE estimation [14].…”

Section: Introductionmentioning

confidence: 99%

Spatial Signal Strength Prediction using 3D Maps and Deep Learning

Krijestorac¹,

Hanna²,

Čabrić³

2020

Preprint

View full text Add to dashboard Cite

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data. Inspired by the successes of ANNs in physics modeling, we use deep neural networks (DNNs) to predict the radio signal strength field in an urban environment. Our algorithm relies on samples of signal strength collected across the prediction space and a 3D map of the environment, which enables it to predict the scattering of radio waves through the environment. While already extensive body of research exists in spatial signal strength prediction, our approach differs from most existing approaches in that it does not require the knowledge of the transmitter location, it does not require side channel information such as attenuation and shadowing parameters, and it is the first work, to the best of our knowledge, to use 3D maps to accomplish the task of signal strength prediction. This algorithm is developed for the purpose of placement optimization of a UAV or mobile robot to maximize the signal strength to or from a stationary transceiver but it also holds relevance to dynamic spectrum access networks, cellular coverage design, power control algorithms, etc.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatial Signal Strength Prediction using 3D Maps and Deep Learning

Krijestorac¹,

Hanna²,

Čabrić³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs

Cited by 2 publications

References 20 publications

Spatial Signal Strength Prediction using 3D Maps and Deep Learning

Spatial Signal Strength Prediction using 3D Maps and Deep Learning

Deep Completion Autoencoders for Radio Map Estimation

Contact Info

Product

Resources

About