Compressive SNR estimation for wideband cognitive radio under correlated scenarios

Sharma, Shree Krishna; Chatzinotas, Symeon; Ottersten, Björn

doi:10.1109/wcnc.2014.6952155

Cited by 7 publications

(7 citation statements)

References 25 publications

(58 reference statements)

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“…Similar demands also appeal to a variety of applications like DoA estimation, channel estimation, signal-tonoise ratio (SNR) estimation, etc. [32]- [34]. Spectrum sensing mission is more like an inference problem.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Wideband Spectrum Sensing Algorithm for Unmanned Aerial Vehicle Communication Networks

Shu

Yan

et al. 2019

IEEE Internet Things J.

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With increasingly smaller size, more powerful sensing capabilities and higher level of autonomy, multiple unmanned aerial vehicles (UAVs) can form UAV networks to collaboratively complete missions more reliably, efficiently and economically. While UAV networks are promising for many applications, there are many outstanding issues to be resolved before large scale UAV networks are practically used. In this paper we study the application of cognitive radio technology for UAV communication networks, to provide high capacity and reliable communication with opportunistic and timely spectrum access. Compressive sensing is applied in the cognitive radio to boost the performance of spectrum sensing. However, the performance of existing compressive spectrum sensing schemes is constrained with non-strictly sparse spectrum. In addition, the reconstruction process applied in existing schemes has unnecessarily high computational complexity and low energy efficiency. We proposed a new compressive signal processing algorithm, called Iterative Compressive Filtering, to improve the UAV network communication performance. The key idea is using orthogonal projection as a bandstop filter in compressive domain. The components of primary users (PUs) in the recognized subchannels are adaptively eliminated in compressive domain, which can directly update the measurement for further detection of other active users. Experiment results showed increased efficiency of the proposed algorithm over existing compressive spectrum sensing algorithms. The proposed algorithm achieved higher detection probability in identifying the occupied subchannels under the condition of non-strictly sparse spectrum with large computational complexity reduction, which can provide strong support of reliable and timely communication for UAV networks.

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Section: Related Workmentioning

confidence: 99%

An Efficient Wideband Spectrum Sensing Algorithm for Unmanned Aerial Vehicle Communication Networks

Shu

Yan

et al. 2019

IEEE Internet Things J.

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“…However, in practice, it is highly desirable to estimate the primary SNR over the wideband spectrum in order to utilize the available spectrum opportunities effectively. In this context, authors in [90] recently studied an eigenvalue-based compressive SNR estimation problem for a wideband cognitive receiver utilizing the CS approach. The following two correlated scenarios have been studied considering the equal received power across all the carriers: (i) correlated noise, and (ii) correlated Multiple Measurement Vectors (MMVs).…”

Section: A Compressive Snr Estimationmentioning

confidence: 99%

“…Similarly, the correlated MMV case may arise due to channel correlation or imperfections in frequency selective filters present at the CR node. Figure 7 depicts the normalized Mean Square Error (MSE) versus SNR for the correlated noise scenario for both the compressive and full measurement cases assuming correlation knowledge at the CR receiver [90], [146]. It can be deduced from the figure that the compressive case with the compression ratio ρ = 0.8 has to sacrifice almost 0.3 % estimation error in comparison to the full measurement case at SNR = 1 dB.…”

Section: A Compressive Snr Estimationmentioning

confidence: 99%

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Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Sharma

Lagunas

Chatzinotas

et al. 2016

IEEE Commun. Surv. Tutorials

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205

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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“…From the presented results in [58], it can be noted that the effect of channel correlation dominates at higher SNR values while the effect of noise correlation dominates at low SNR values. Additionally, the SNR estimation problem for a wideband cognitive receiver has been studied in [144] under correlated noise and correlated multiple measurement vector scenarios. A tradeoff between sensing hardware cost (especially the cost of the ADC) and the estimation error has been observed while using compressive measurements.…”

Section: Noise/channel Correlationmentioning

confidence: 99%

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Sharma

Bogale

Chatzinotas

et al. 2015

IEEE Commun. Surv. Tutorials

Self Cite

196

111

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Abstract-Cognitive Radio (CR) has been considered as a potential candidate for addressing the spectrum scarcity problem of future wireless networks. Since its conception, several researchers, academic institutions, industries, regulatory and standardization bodies have put their significant efforts towards the realization of CR technology. However, as this technology adapts its transmission based on the surrounding radio environment, several practical issues may need to be considered. In practice, several imperfections such as noise uncertainty, channel/interference uncertainty, transceiver hardware imperfections, signal uncertainty, synchronization issues, etc. may severely deteriorate the performance of a CR system. To this end, the investigation of realistic solutions towards combating various practical imperfections is very important for successful implementation of the cognitive technology. In this direction, first, this survey paper provides an overview of the enabling techniques for CR communications. Subsequently, it discusses the main imperfections that may occur in the most widely used CR paradigms and then reviews the existing approaches towards addressing these imperfections. Finally, it provides some interesting open research issues.

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Compressive SNR estimation for wideband cognitive radio under correlated scenarios

Cited by 7 publications

References 25 publications

An Efficient Wideband Spectrum Sensing Algorithm for Unmanned Aerial Vehicle Communication Networks

An Efficient Wideband Spectrum Sensing Algorithm for Unmanned Aerial Vehicle Communication Networks

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Cognitive Radio Techniques Under Practical Imperfections: A Survey

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