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On Spectrum Sensing for Secondary Operation in Licensed Spectrum: Blind Sensing, Sensing Optimization and Traffic Modeling
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences. KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS and Center for Wireless Systems, Wireless@kth . (Elektronik)ORCID iD: 0000-0003-3860-5964
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 [en]
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: urn:nbn:se:hig:diva-19028OAI: oai:DiVA.org:hig-19028DiVA: diva2:789530
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
List of papers
1. Spectrum sensing through spectrum discriminator and maximum minimum eigenvalue detector: a comparative study
Open this publication in new window or tab >>Spectrum sensing through spectrum discriminator and maximum minimum eigenvalue detector: a comparative study
2012 (English)In: 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), New York: IEEE conference proceedings, 2012, 2252-2256 p.Conference paper (Refereed)
Abstract [en]

In this paper we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator and compare it with the maximum minimum eigenvalue detector. The common feature between those two techniques is that neither prior knowledge about the system noise level nor the primary user signal, that might occupy the band under sensing, is required. Instead the system noise level will be derived from the received signal. The main difference between both techniques is that the spectrum discriminator is a non-parametric technique while the maximum minimum eigenvalue detector is a parametric technique. The comparative study between both has been done based on two performance metrics: the probability of false alarm and the probability of detection. For the spectrum discriminator an accuracy factor called noise uncertainty is defined as the level over which the noise energy may vary. Simulations are performed for different values of noise uncertainty for the spectrum discriminator and different values for the number of received samples and smoothing factor for the maximum minimum eigenvalue detector.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2012
Series
, IEEE Instrumentation and Measurement Technology Conference, ISSN 1091-5281
National Category
Telecommunications
Identifiers
urn:nbn:se:hig:diva-12701 (URN)10.1109/I2MTC.2012.6229452 (DOI)000309449100428 ()2-s2.0-84864252477 (ScopusID)978-1-4577-1773-4 (ISBN)978-1-4577-1771-0 (ISBN)
Conference
2012 IEEE International International Instrumentation and Measurement Conference (I2MTC), 13-16 May 2012, Graz, Austria
Available from: 2012-08-28 Created: 2012-08-28 Last updated: 2016-08-10Bibliographically approved
2. Blind Spectrum Sensing for Cognitive Radios Using Discriminant Analysis: A Novel Approach
Open this publication in new window or tab >>Blind Spectrum Sensing for Cognitive Radios Using Discriminant Analysis: A Novel Approach
Show others...
2013 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, Vol. 62, no 11, 2912-2921 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator. The presented technique uses the knowledge of the noise uncertainty and a probabilistic validation to overcome the limitations of the discriminant analysis. A comparative study between the proposed technique and the maximum-minimum eigenvalue detection has been performed based on two performance metrics: the probability of false alarm and the probability of detection. The spectrum discriminator has been further developed to a peel-off technique where all primary users can be detected. The performance of the spectrum discriminator and the peel-off technique has been tested on simulations and experimentally verified. The comparative study is based on simulations as well as measurements.

Keyword
Blind detection; cognitive radio; discriminant analysis; maximum-minimum eigenvalues detection; spectrum discriminator; spectrum sensing
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:hig:diva-14912 (URN)10.1109/TIM.2013.2267456 (DOI)000325820200004 ()2-s2.0-84885958005 (ScopusID)
Available from: 2013-07-18 Created: 2013-07-18 Last updated: 2016-08-10Bibliographically approved
3. A Novel Approach for Energy Detector Sensing Time and Periodic Sensing Interval Optimization in Cognitive Radios
Open this publication in new window or tab >>A Novel Approach for Energy Detector Sensing Time and Periodic Sensing Interval Optimization in Cognitive Radios
2011 (English)In: Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management, New York: ACM Press, 2011Conference paper (Refereed)
Abstract [en]

In this paper a new approach of optimizing the sensing time and periodic sensing interval for energy detectors has been explored. This new approach is built upon maximizing the probability of right detection, captured opportunities and transmission efficiency. The probability of right detection is defined as the probability of having no false alarm and correct detection. Optimization of the sensing time relies on maximizing the summation of the probability of right detection and the transmission efficiency while optimization of periodic sensing interval subjects to maximizing the summation of transmission efficiency and the captured opportunities. The optimum sensing time and periodic sensing interval are dependent on each other, hence, iterative approach to optimize them is applied and convergence criterion is defined. The simulations show that both converged sensing time and periodic sensing interval increase with the increase of the channel utilization factor, moreover, the probability of false alarm, the probability of detection, the probability of right detection, the transmission efficiency and the captured opportunities have been taken as the detector performance metrics and evaluated for different values of channel utilization factor and signal-to-noise ratio.

Place, publisher, year, edition, pages
New York: ACM Press, 2011
Keyword
Energy detector, Sensing time, Periodic sensing interval, Probability of right detection, Transmission efficiency, Captured opportunities
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-11460 (URN)10.1145/2093256.2093314 (DOI)2-s2.0-84856325209 (ScopusID)978-1-4503-0912-7 (ISBN)
Conference
CogART 2011, 4th International Conference on Cognitive Radio and Advanced Spectrum Management, 26-29 October, 2011, Barcelona, Catalonia, Spain
Projects
QUASAR
Available from: 2012-02-09 Created: 2012-02-09 Last updated: 2016-08-10Bibliographically approved
4. On Spectrum Sharing and Dynamic Spectrum Allocation: MAC Layer Spectrum Sensing in Cognitive Radio Networks
Open this publication in new window or tab >>On Spectrum Sharing and Dynamic Spectrum Allocation: MAC Layer Spectrum Sensing in Cognitive Radio Networks
2010 (English)Conference paper, Abstract (Refereed)
Abstract [en]

One of the most critical issues regarding wireless networksregulation agencies is how to manage the available electromagneticradio spectrum in a way that satisfies the needs of the huge growingin wireless systems both economically and technically, especiallywith the recent crowding in the available spectrum. Hence, buildingcognitive radio systems supporting dynamic access to the availablespectrum has appeared recently as a novel solution for the wirelesssystem huge expansion. In this paper we investigate the MAC layersensing schemes in cognitive radio networks, where both reactiveand proactive sensing are considered. In proactive sensing theadapted and non-adapted sensing periods schemes are also assessed.The assessment of these sensing schemes has been held via twoperformance metrics: available spectrum utilization and idlechannel search delay. Simulation results show that with proactivesensing adapted periods we achieve the best performance but withan observable overhead computational tasks to be done by thenetwork nodes

Place, publisher, year, edition, pages
IEEE Communications Society, 2010
National Category
Communication Systems
Identifiers
urn:nbn:se:hig:diva-13643 (URN)10.1109/CMC.2010.342 (DOI)
Conference
2010 International Conference on Communications and Mobile Computing (CMC'10) April 12-14, 2010, Shenzhen, China
Available from: 2013-01-11 Created: 2013-01-11 Last updated: 2015-02-19Bibliographically approved
5. Empirical Statistical Model for LTE Downlink Channel Occupancy
Open this publication in new window or tab >>Empirical Statistical Model for LTE Downlink Channel Occupancy
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper develops an empirical statistical channel occupancy model for downlink long-term evolution (LTE) cellular systems. The model is based on statistical distributions mixtures for the holding times of the channels. Moreover, statistical distribution of the time when the channels are free is also considered. The data is obtained through an extensive measurement campaign performed in Stockholm, Sweden. Two types of mixtures are considered, namely, exponential and log-normal distributions to fit the measurement findings. The log-likelihood of both mixtures is used as a quantitative measure of the goodness of fit. Moreover, finding the optimal number of linearly combined distributions using the Akaike information criterion (AIC) is investigated. The results show that good fitting can be obtained by using either exponential or log-normal distributions mixture. Even though, the fitting is done for a representative case with a tempo-spatial consideration, the model is yet applicable in general for LTE and other cellular systems in a wider sense.

Keyword
LTE, Cellular Traffic, Channel Occupancy Model, Exponential Mixture Fitting, Log-normal Mixture Fitting, Akaike Information Criterion (AIC).
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:hig:diva-19029 (URN)
Available from: 2015-02-03 Created: 2015-02-19 Last updated: 2016-08-10Bibliographically approved
6. Downlink Throughput Driven Channel Access Framework for Cognitive LTE Femto-Cells
Open this publication in new window or tab >>Downlink Throughput Driven Channel Access Framework for Cognitive LTE Femto-Cells
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper proposes an optimized sensing based channel access framework for the LTE cognitive femto cells with an objective of maximizing femto-cells downlink throughput. Energy detection is used by the LTE cognitive femto cells to locate and thereafter utilize the free channels. Moreover, periodic sensing is adopted to detect any changes of the sensing outcomes. The developed channel access framework is based on an objective of maximization the femto-cell downlink throughput which varies with the macro-cell channel occupancy. Therefore, the LTE macro-cell occupancy statistics are empirically modelled using exponential distributions mixture. The LTE cognitive femto-cell downlink throughput is maximized by compromising the transmission efficiency, the explored spectrum opportunities and the interference from the macro-cell. An analytical solution for the optimal periodic sensing interval that maximizes the throughput is found and verified by simulations. The obtained results show that there is indeed a single periodic sensing interval value that maximizes the LTE cognitive femto-cell downlink throughput which changes with the change of the macro-cell channel occupancy. Yet, at a specific channel occupancy statistical parameters, our framework provides the optimal throughput. At the peak of the macro-cell traffic, our framework increased the femto-cell throughput by $\simeq 15\%$ compared to the senseless case. The impact of the available channels for opportunistic access is studied. The simulation results show that increasing the number of the available channels becomes less significant for more than three channels.

Keyword
Two tier LTE network, Cognitive femto-cell, Channel occupancy, Downlink throughput, Periodic sensing, Energy detection.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-19030 (URN)
Available from: 2015-02-03 Created: 2015-02-19 Last updated: 2016-08-10Bibliographically approved
7. Maximum Minimum Eigenvalues Based Spectrum Scanner for Cognitive Radios
Open this publication in new window or tab >>Maximum Minimum Eigenvalues Based Spectrum Scanner for Cognitive Radios
2012 (English)In: 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), New York: IEEE conference proceedings, 2012, 2248-2251 p.Conference paper (Refereed)
Abstract [en]

In this paper we introduce a technique for spectrum scanning with the maximum minimum eigenvalue detection based spectrum sensing. The fundamental problem we address in this paper is the inability of using maximum minimum eigenvalue detection with filtering in time domain where the white noise becomes coloured. The solution we propose here is based on frequency domain rectangular filtering. By frequency domain rectangular filtering we take the spectral lines inside each sub-band and throw out the rest. After doing the frequency domain rectangular filtering, we generate the corresponding time domain signal and inject it to the maximum minimum eigenvalue detector. An experimental verification has been performed and the obtained results show that the technique is implementable with a performance better than the energy detector as a reference technique in terms of the probability of detection when both technique have the same probability of false alarm.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2012
Series
, IEEE Instrumentation and Measurement Technology Conference, ISSN 1091-5281
National Category
Telecommunications
Identifiers
urn:nbn:se:hig:diva-12711 (URN)10.1109/I2MTC.2012.6229450 (DOI)000309449100427 ()2-s2.0-84864249987 (ScopusID)978-1-4577-1773-4 (ISBN)978-1-4577-1771-0 (ISBN)
Conference
2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 13-16 May 2012, Graz, Austria
Available from: 2012-08-28 Created: 2012-08-28 Last updated: 2016-08-10Bibliographically approved
8. Signal Bandwidth Impact on Maximum-Minimum Eigenvalue Detection
Open this publication in new window or tab >>Signal Bandwidth Impact on Maximum-Minimum Eigenvalue Detection
2015 (English)In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 19, no 3, 395-398 p., 7001062Article in journal (Refereed) Published
Abstract [en]

The impact of the signal bandwidth and observation bandwidth on the detection performance of the maximumminimum eigenvalue detector is studied in this letter. The considered signals are the Gaussian signals. The optimum ratio between the signal and the observation bandwidth is analytically proven to be 0.5 when reasonable values of the system dimensionality are used. The analytical proof is verified by simulations.

Keyword
Bandwidth; Covariance matrices; Eigenvalues and eigenfunctions; Gaussian noise;Sensors;Signal to noise ratio; Detection bandwidth; Marchenko Pastur densit; Maximum-minimum eigenvalue detection; Occupation bandwidth
National Category
Signal Processing
Identifiers
urn:nbn:se:hig:diva-18675 (URN)10.1109/LCOMM.2014.2387287 (DOI)000351407800022 ()2-s2.0-84924911982 (ScopusID)
Available from: 2015-01-07 Created: 2015-01-07 Last updated: 2016-08-10Bibliographically approved
9. Energy and Eigenvalue-Based Combined Fully-Blind Self-Adapted Spectrum Sensing Algorithm
Open this publication in new window or tab >>Energy and Eigenvalue-Based Combined Fully-Blind Self-Adapted Spectrum Sensing Algorithm
2016 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, no 2, 630-642 p.Article in journal (Refereed) 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.

Keyword
Blind sensing, Energy detector, Maximum-minimum eigenvalue detector, Multi-stage sensing, Noise estimation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-18957 (URN)10.1109/TVT.2015.2401132 (DOI)000370754000012 ()2-s2.0-84962176492 (ScopusID)
Available from: 2015-02-10 Created: 2015-02-10 Last updated: 2016-08-10Bibliographically approved
10. Sample covariance matrix eigenvalues based blind SNR estimation
Open this publication in new window or tab >>Sample covariance matrix eigenvalues based blind SNR estimation
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.

Series
, IEEE Instrumentation and Measurement Technology Conference, ISSN 1091-5281
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-16231 (URN)10.1109/I2MTC.2014.6860836 (DOI)000346477200138 ()2-s2.0-84905695374 (ScopusID)978-146736385-3 (ISBN)
Conference
I2MTC 2014, Montevideo, Uruguay, May 12-15, 2014
Available from: 2014-01-30 Created: 2014-01-30 Last updated: 2016-08-10Bibliographically approved

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