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Sample covariance matrix eigenvalues based blind SNR estimation
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences. (wireless@kth)
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences.ORCID iD: 0000-0001-5429-7223
Royal Institute of Technology (KTH), Communication Systems.
2014 (English)In: 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014, 718-722 p.Conference paper (Refereed)
Abstract [en]

In this paper, a newly developed SNR estimation algorithm is presented. The new algorithm is based on the eigenvalues of the samples covariance matrix of the recieved signal. The presented algorithm is blind in the sense that both the noise and the signal power are unknown and estimated from the received samples. The Minimum Descriptive Length (MDL) criterion is used to split the signal and noise corresponding eigenvalues. The experimental results are judged using the Normalized Mean Square Error (NMSE) between the estimated and the actual SNRs. The results show that depending on the value of received vectors size, N, and the number of received vectors, L, the NMSE is changed and down to −55 dB NMSE can be achieved for the highest used values of N and L.

Place, publisher, year, edition, pages
2014. 718-722 p.
Series
IEEE Instrumentation and Measurement Technology Conference, ISSN 1091-5281
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hig:diva-16231DOI: 10.1109/I2MTC.2014.6860836ISI: 000346477200138Scopus ID: 2-s2.0-84905695374ISBN: 978-146736385-3 OAI: oai:DiVA.org:hig-16231DiVA: diva2:692355
Conference
I2MTC 2014, Montevideo, Uruguay, May 12-15, 2014
Available from: 2014-01-30 Created: 2014-01-30 Last updated: 2016-08-10Bibliographically approved
In thesis
1. On Spectrum Sensing for Secondary Operation in Licensed Spectrum: Blind Sensing, Sensing Optimization and Traffic Modeling
Open this publication in new window or tab >>On Spectrum Sensing for Secondary Operation in Licensed Spectrum: Blind Sensing, Sensing Optimization and Traffic Modeling
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There has been a recent explosive growth in mobile data consumption. This, in turn, imposes many challenges for mobile services providers and regulators in many aspects. One of these primary challenges is maintaining the radio spectrum to handle the current and upcoming expansion in mobile data traffic. In this regard, a radio spectrum regulatory framework based on secondary spectrum access is proposed as one of the solutions for the next generation wireless networks. In secondary spectrum access framework, secondary (unlicensed) systems coexist with primary (licensed) systems and access the spectrum on an opportunistic base.

In this thesis, aspects related to finding the free of use spectrum portions - called spectrum opportunities - are treated. One way to find these opportunities is spectrum sensing which is considered as an enabler of opportunistic spectrum access. In particular, this thesis investigates some topics in blind spectrum sensing where no priori knowledge about the possible co-existing systems is available.

As a standalone contribution in blind spectrum sensing arena, a new blind sensing technique is developed in this thesis. The technique is based on discriminant analysis statistical framework and called spectrum discriminator (SD). A comparative study between the SD and some existing blind sensing techniques was carried out and showed a reliable performance of the SD.

The thesis also contributes by exploring sensing parameters optimization for two existing techniques, namely, energy detector (ED) and maximum-minimum eigenvalue detector (MME). For ED, the sensing time and periodic sensing interval are optimized to achieve as high detection accuracy as possible. Moreover, a study of sensing parameters optimization in a real-life coexisting scenario, that is, LTE cognitive femto-cells, is carried out with an objective of maximizing cognitive femto-cells throughput. In association with this work, an empirical statistical model for LTE channel occupancy is accomplished. The empirical model fits the channels' active and idle periods distributions to a linear combination of multiple exponential distributions. For the MME, a novel solution for the filtering problem is introduced. This solution is based on frequency domain rectangular filtering. Furthermore, an optimization of the observation bandwidth for MME with respect to the signal bandwidth is analytically performed and verified by simulations.

After optimizing the parameters for both ED and MME, a two-stage fully-blind self-adapted sensing algorithm composed of ED and MME is introduced. The combined detector is found to outperform both detectors individually in terms of detection accuracy with an average complexity lies in between the complexities of the two detectors. The combined detector is tested with measured TV and wireless microphone signals.

The performance evaluation in the different parts of the thesis is done through measurements and/or simulations. Active measurements were performed for sensing performance evaluation. Passive measurements on the other hand were used for LTE downlink channels occupancy modeling and to capture TV and wireless microphone signals.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. xv, 75 p.
Series
TRITA-ICT-COS, ISSN 1653-6347 ; 1502
Keyword
Cognitive Radio, Spectrum Sensing, Sensing Optimization, Blind Sensing, Traffic Modelling, Energy detection, Maximumum-minimum Eigenvalue Detection, Discriminant anlysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:hig:diva-19028 (URN)
Public defence
2015-03-13, 99:131, Hus 99, Högskolan i Gävle, 13:15 (English)
Opponent
Supervisors
Available from: 2015-02-19 Created: 2015-02-19 Last updated: 2016-08-10Bibliographically approved

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