Informed spectrum usage in cognitive radio networks: Interference cartography

Alaya-Feki, Afef Ben Hadj; Jemaa, Sana Ben; Sayraç, Berna; Houze, P.; Moulines, Éric

doi:10.1109/pimrc.2008.4699911

Cited by 101 publications

(89 citation statements)

References 10 publications

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“…This extends our earlier work on spatial estimation problems in wireless networks [2,10], and also illustrates how radio environment map (REM) techniques [9,12] can be applied in the cellular network context as discussed in [3]. While conventional drive tests can only uncover coverage holes in regions where the measurements are actively conducted, our approach can estimate their presence in adjacent regions as well, based on the use of spatial prediction techniques.…”

Section: Introductionsupporting

confidence: 57%

Improving coverage estimation for cellular networks with spatial bayesian prediction based on measurements

Sayraç

Riihijärvi

Mähönen

et al. 2012

Proceedings of the 2012 ACM SIGCOMM Workshop on Cellular Networks: Operations, Challenges, and Future Design

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Cellular operators routinely use sophisticated planning tools to estimate the coverage of the network based on building and terrain data combined with detailed propagation modeling. Nevertheless, coverage holes still emerge due to equipment failures, or unforeseen changes in the propagation environment. For detecting these coverage holes, drive tests are typically used. Since carrying out drive tests is expensive and time consuming, there is significant interest in both improving the quality of the coverage estimates obtained from a limited number of drive test measurements, as well as enabling the incorporation of measurements from mobile terminals. In this paper we introduce a spatial Bayesian prediction framework that can be used for both of these purposes. We show that using techniques from modern spatial statistics we can significantly increase the accuracy of coverage predictions from drive test data. Further, we carry out a detailed evaluation of our framework in urban and rural environments, using realistic coverage data obtained from an operator planning tool for an operational cellular network. Our results indicate that using spatial prediction techniques can more than double the likelihood of detecting coverage holes, while retaining a highly acceptable false alarm probability.

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Section: Introductionsupporting

confidence: 57%

Improving coverage estimation for cellular networks with spatial bayesian prediction based on measurements

Sayraç

Riihijärvi

Mähönen

et al. 2012

Proceedings of the 2012 ACM SIGCOMM Workshop on Cellular Networks: Operations, Challenges, and Future Design

Self Cite

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“…In order to compare the results with the sampling rate used in previous works, a decimated rate (DR) is defined as DR = DN N S , where DN corresponds to the total number of original data and N S is the amount of data used before the data cube construction. We select a model that uses spatial interpolation [14], and other that uses compressive sensing to create cartography maps solving the "Orthogonal Matching Pursuit" (OMP) algorithm [15]. VI.…”

Section: Resultsmentioning

confidence: 99%

Compressive multispectral model for spectrum sensing in cognitive radio networks

Marin

Martinez

Betancur

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required traditionally. This paper presents a model that addresses the Spectral Sensing problem in Cognitive Radio using Compressive Sensing as an effective way of decreasing the number of samples required in the sensing process. This model is based on Compressive Spectral Imaging (CSI) architectures where a centralized spectrum manager selects what power data must be delivered by the different wireless devices using binary patterns, and builds a multispectral data cube image with the geographical and spectral data power information. The results show that this multispectral data cube can be built with only a 50% of the samples generated by the devices and, therefore reducing the data traffic dramatically.

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“…It refers to any system manipulating geographical referenced data for capture, storage, analysis and management purposes. The main spatial interpolation techniques are the Inverse Distance Weighting (IDW), the Nearest Neighbor Interpolation, Splines and Kriging [153]. Determining the location and power level of the active PTs considering the sparsity feature of the primary activity significantly helps in constructing an REM.…”

Section: B Related Literaturementioning

confidence: 99%

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Sharma

Lagunas

Chatzinotas

et al. 2016

IEEE Commun. Surv. Tutorials

210

112

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Abstract-Compressive Sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and Cognitive Radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the underutilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CSrelated works applied to different categories such as wideband sensing, signal parameter estimation and Radio Environment Map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.

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Informed spectrum usage in cognitive radio networks: Interference cartography

Cited by 101 publications

References 10 publications

Improving coverage estimation for cellular networks with spatial bayesian prediction based on measurements

Improving coverage estimation for cellular networks with spatial bayesian prediction based on measurements

Compressive multispectral model for spectrum sensing in cognitive radio networks

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

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