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Energy and Eigenvalue-Based Combined Fully-Blind Self-Adapted Spectrum Sensing Algorithm
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för elektronik, matematik och naturvetenskap, Elektronik. Department of Communication System and wireless@ kth, KTH Royal Institute of Technology, Stockholm.
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för elektronik, matematik och naturvetenskap, Elektronik.ORCID-id: 0000-0001-5429-7223
Department of Communication System and wireless@ kth, KTH Royal Institute of Technology, Stockholm.
2016 (engelsk)Inngår i: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, nr 2, s. 630-642Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In this paper, a comparison between energy and maximum-minimum eigenvalue detectors is performed. The comparison has been made concerning the sensing complexity and the sensing accuracy in terms of the receiver operating characteristics curves. The impact of the signal bandwidth compared to the observation bandwidth is studied for each detector. For the energy detector, the probability of detection increases monotonically with the increase of the signal bandwidth. For the maximum-minimum eigenvalue detector, an optimal value of the ratio between the signal bandwidth and the observation bandwidth is found to be 0.5 when reasonable values of the system dimensionality are used. Based on the comparison findings, a combined two-stage detector is proposed. The combined detector performance is evaluated based on simulations and measurements. The combined detector achieves better sensing accuracy than the two individual detectors with a complexity lies in between the two individual complexities. The combined detector is fully-blind and self-adapted as the maximum-minimum eigenvalue detector estimates the noise and feeds it back to the energy detector. The performance of the noise estimation process is evaluated in terms of the normalized mean square error.

sted, utgiver, år, opplag, sider
2016. Vol. 65, nr 2, s. 630-642
Emneord [en]
Blind sensing, Energy detector, Maximum-minimum eigenvalue detector, Multi-stage sensing, Noise estimation
HSV kategori
Identifikatorer
URN: urn:nbn:se:hig:diva-18957DOI: 10.1109/TVT.2015.2401132ISI: 000370754000012Scopus ID: 2-s2.0-84962176492OAI: oai:DiVA.org:hig-18957DiVA, id: diva2:787341
Tilgjengelig fra: 2015-02-10 Laget: 2015-02-10 Sist oppdatert: 2018-03-13bibliografisk kontrollert
Inngår i avhandling
1. On Spectrum Sensing for Secondary Operation in Licensed Spectrum: Blind Sensing, Sensing Optimization and Traffic Modeling
Åpne denne publikasjonen i ny fane eller vindu >>On Spectrum Sensing for Secondary Operation in Licensed Spectrum: Blind Sensing, Sensing Optimization and Traffic Modeling
2015 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2015. s. xv, 75
Serie
TRITA-ICT-COS, ISSN 1653-6347 ; 1502
Emneord
Cognitive Radio, Spectrum Sensing, Sensing Optimization, Blind Sensing, Traffic Modelling, Energy detection, Maximumum-minimum Eigenvalue Detection, Discriminant anlysis
HSV kategori
Forskningsprogram
Informations- och kommunikationsteknik
Identifikatorer
urn:nbn:se:hig:diva-19028 (URN)
Disputas
2015-03-13, 99:131, Hus 99, Högskolan i Gävle, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2015-02-19 Laget: 2015-02-19 Sist oppdatert: 2018-03-13bibliografisk kontrollert

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