liu.seSearch for publications in DiVA
Change search
Refine search result
1 - 19 of 19
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Axell, Erik
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Spectrum Sensing Algorithms Based on Second-Order Statistics2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cognitive radio is a new concept of reusing spectrum in an opportunistic manner. Cognitive radio is motivated by recent measurements of spectrum utilization, showing unused resources in frequency, time and space. Introducing cognitive radios in a primary network inevitably creates increased interference to the primary users. Secondary users must sense the spectrum and detect primary users' signals at very low SNR, to avoid causing too much interference.This dissertation studies this detection problem, known as spectrum sensing.

    The fundamental problem of spectrum sensing is to discriminate an observation that contains only noise from an observation that contains a very weak signal embedded in noise. In this work, detectors are derived that exploit known properties of the second-order moments of the signal. In particular, known structures of the signal covariance are exploited to circumvent the problem of unknown parameters, such as noise and signal powers or channel coefficients.

    The dissertation is comprised of six papers, all in different ways related to spectrum sensing based on second-order statistics. In the first paper, we considerspectrum sensing of orthogonal frequency-division multiplexed (OFDM) signals in an additive white Gaussian noise channel. For the case of completely known noise and signal powers, we set up a vector-matrix model for an OFDM signal with a cyclic prefix and derive the optimal Neyman-Pearson detector from first principles. For the case of completely unknown noise and signal powers, we derive a generalized likelihood ratio test (GLRT) based on empirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise or signal powers.

    In the second paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities. We derive the GLRT for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio.

    The general result of the second paper is used as a building block, in the third and fourth papers, for spectrum sensing of second-order cyclostationary signals received at multiple antennas and orthogonal space-time block coded (OSTBC) signals respectively. The proposed detector of the third paper exploits both the spatial and the temporal correlation of the received signal, from knowledge of the fundamental period of the cyclostationary signal and the eigenvalue multiplicities of the temporal covariance matrix.

    In the fourth paper, we consider spectrum sensing of signals encoded with an OSTBC. We show how knowledge of the eigenvalue multiplicities of the covariance matrix are inherent owing to the OSTBC, and propose an algorithm that exploits that knowledge for detection. We also derive theoretical bounds on the performance of the proposed detector. In addition, we show that the proposed detector is robust to a carrier frequency offset, and propose another detector that deals with timing synchronization using the detector for the synchronized case as a building block.

    A slightly different approach to covariance matrix estmation is taken in the fifth paper. We consider spectrum sensing of Gaussian signals with structured covariance matrices, and propose to estimate the unknown parameters of the covariance matrices using covariance matching estimation techniques (COMET). We also derive the optimal detector based on a Gaussian approximation of the sample covariance matrix, and show that this is closely connected to COMET.

    The last paper deals with the problem of discriminating samples that containonly noise from samples that contain a signal embedded in noise, when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one andit has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio.

    List of papers
    1. Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance
    Open this publication in new window or tab >>Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance
    2011 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 29, no 2, p. 290-304Article in journal (Refereed) Published
    Abstract [en]

    We consider spectrum sensing of OFDM signals in an AWGN channel. For  the case of completely known noise and signal powers, we set up  a vector-matrix model for an OFDM signal with a cyclic prefix and  derive the optimal Neyman-Pearson detector from first  principles. The optimal detector exploits the inherent correlation  of the OFDM signal incurred by the repetition of data in the cyclic  prefix, using knowledge of the length of the cyclic prefix and the  length of the OFDM symbol. We compare the optimal detector to the energy  detector numerically. We show that the energy detector is  near-optimal (within 1 dB SNR) when the noise variance is  known. Thus, when the noise power is known, no substantial gain can  be achieved by using any other detector than the energy detector.

    For the case of completely unknown noise and signal powers, we  derive a generalized likelihood ratio test (GLRT) based onempirical second-order statistics of  the received data. The proposed GLRT detector exploits the  non-stationary correlation structure of the OFDM signal and does not  require any knowledge of the noise power or the signal power. The  GLRT detector is compared to state-of-the-art OFDM signal detectors,  and shown to improve the detection performance with 5 dB SNR in  relevant cases.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2011
    Keywords
    spectrum sensing, signal detection, OFDM, cyclic prefix, subspace detection, second-order statistics
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-58515 (URN)10.1109/JSAC.2011.110203 (DOI)000286676500003 ()
    Note

    ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Erik Axell and Erik G. Larsson, Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance, 2011, IEEE Journal on Selected Areas in Communications, (29), 2, 290-304. http://dx.doi.org/10.1109/JSAC.2011.110203

    The previous status of this article was Manuskript.

    Available from: 2010-08-12 Created: 2010-08-12 Last updated: 2017-12-12Bibliographically approved
    2. A Unified Framework for GLRT-Based Spectrum Sensing of Signals with Covariance Matrices with Known Eigenvalue Multiplicities
    Open this publication in new window or tab >>A Unified Framework for GLRT-Based Spectrum Sensing of Signals with Covariance Matrices with Known Eigenvalue Multiplicities
    2011 (English)In: Proceedings of the IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP), IEEE conference proceedings, 2011, p. 2956-2959Conference paper, Published paper (Refereed)
    Abstract [en]

    In this paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities. We derive the generalized likelihood-ratio test (GLRT) for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio and show that the GLRT for these applications, of which some are already known, are special cases of the general result.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2011
    Series
    IEEE International Conference on Acoustics, Speech and SignalProcessing, ISSN 1520-6149
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-64320 (URN)10.1109/ICASSP.2011.5946277 (DOI)000296062403092 ()
    Note
    ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Erik Axell and Erik G. Larsson, A Unified Framework for GLRT-Based Spectrum Sensing of Signals with Covariance Matrices with Known Eigenvalue Multiplicities, 2011, Proceedings of the IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP), 2956-2959. http://dx.doi.org/10.1109/ICASSP.2011.5946277 Available from: 2011-01-19 Created: 2011-01-19 Last updated: 2016-08-31
    3. Multiantenna Spectrum Sensing of a Second-Order Cyclostationary Signal
    Open this publication in new window or tab >>Multiantenna Spectrum Sensing of a Second-Order Cyclostationary Signal
    2011 (English)In: Proceedings of the 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP'11), 2011, p. 329-332Conference paper, Published paper (Refereed)
    Abstract [en]

    We consider spectrum sensing of a second-order cyclostationary signal receivedat multiple antennas. The proposed detector exploits both the spatial andthe temporal correlation of the received signal, from knowledge of thefundamental period of the cyclostationary signal and the eigenvaluemultiplicities of the temporal covariance matrix. All other parameters, suchas the channel gains or the noise power, are assumed to be unknown. The proposeddetector is shown numerically to outperform state-of-the-art detectors forspectrum sensing of anOFDM signal, both when using a single antenna and with multiple antennas.

    Keywords
    spectrum sensing, multiple antennas, cyclostationarity, GLRT
    National Category
    Communication Systems Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-70858 (URN)10.1109/CAMSAP.2011.6136017 (DOI)978-1-4577-2103-8 (ISBN)
    Conference
    4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), December 13-16 2011, San Juan, Puerto Rico (USA)
    Funder
    eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research Council
    Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2016-08-31Bibliographically approved
    4. Spectrum Sensing of Orthogonal Space-Time Block Coded Signals with Multiple Receive Antennas
    Open this publication in new window or tab >>Spectrum Sensing of Orthogonal Space-Time Block Coded Signals with Multiple Receive Antennas
    2010 (English)In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2010, p. 3110-3113Conference paper, Published paper (Other academic)
    Abstract [en]

    We consider detection of signals encoded with orthogonal space-time block codes (OSTBC), using multiple receive antennas. Such signals contain redundancy and they have a specific structure, that can be exploited for detection. We derive the optimal detector, in the Neyman-Pearson sense, when all parameters are known. We also consider unknown noise variance, signal variance and channel coefficients. We propose a number of GLRT based detectors for the different cases, that exploit the redundancy structure of the OSTBC signal. We also propose an eigenvalue-based detector for the case when all parameters are unknown. The proposed detectors are compared to the energy detector. We show that when only the noise variance is known, there is no gain in exploiting the structure of the OSTBC. However, when the noise variance is unknown there can be a significant gain.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2010
    Series
    Acoustics Speech and Signal Processing (ICASSP), ISSN 1520-6149, E-ISSN 2379-190X
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-51745 (URN)10.1109/ICASSP.2010.5496088 (DOI)000287096003014 ()9781424442959 (ISBN)9781424442966 (ISBN)
    Conference
    IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, 14-19 March, Dallas, Texas, U.S.A.
    Note

    ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.: Erik Axell and Erik G. Larsson, Spectrum Sensing of Orthogonal Space-Time Block Coded Signals with Multiple Receive Antennas, 2010, Proceedings of the 35th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'10).

    The previous status of this articel was Manuscript.

    Available from: 2009-11-17 Created: 2009-11-17 Last updated: 2018-02-02Bibliographically approved
    5. Spectrum Sensing of Signals with Structured Covariance Matrices Using Covariance Matching Estimation Techniques
    Open this publication in new window or tab >>Spectrum Sensing of Signals with Structured Covariance Matrices Using Covariance Matching Estimation Techniques
    2011 (English)In: Proceedings of the IEEE Global Communications Conference (GLOBECOM), 2011, p. 1-5Conference paper, Published paper (Refereed)
    Abstract [en]

    In this work, we consider spectrum sensing of Gaussian signals with structured covariance matrices. We show that the optimal detector based on the probability distribution of the sample covariance matrix is equivalent to the optimal detector based on the raw data, if the covariance matrices are known. However, the covariance matrices are unknown in general. Therefore, we propose to estimate the unknown parameters using covariance matching estimation techniques (COMET). We also derive the optimal detector based on a Gaussian approximation of the sample covariance matrix, and show that this is closely connected to COMET.

    Keywords
    spectrum sensing, sample covariance, COMET
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-69639 (URN)10.1109/GLOCOM.2011.6133506 (DOI)978-1-4244-9267-1 (ISBN)978-1-4244-9266-4 (ISBN)
    Conference
    IEEE Global Communications Conference (GLOBECOM), 3-7 December, Anaheim, California, USA
    Available from: 2011-07-08 Created: 2011-07-08 Last updated: 2016-08-31
    6. A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection
    Open this publication in new window or tab >>A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection
    2009 (English)In: Proceedings of the 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'09), 2009, p. 2333-2336Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter.

    Series
    Acoustics, Speech and Signal Processing, ISSN 1520-6149 ; 2009
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-25592 (URN)10.1109/ICASSP.2009.4960088 (DOI)978-1-4244-2354-5 (ISBN)978-1-4244-2353-8 (ISBN)
    Conference
    34th IEEE international conference on acoustics, speech and signal processing,19-24 April, Taipei, Taiwan
    Note
    ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Erik Axell and Erik G. Larsson, A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection, 2009, Proceedings of the 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'09), 2333-2336. http://dx.doi.org/10.1109/ICASSP.2009.4960088Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2016-08-31Bibliographically approved
  • 2.
    Axell, Erik
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Topics in Spectrum Sensing for Cognitive Radio2009Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cognitive radio is a new concept of reusing licensed spectrum in an unlicensed manner. Cognitive radio is motivated by recent measurements of spectrum utilization, showing unused resources in frequency, time and space. The spectrum must be sensed to detect primary user signals, in order to allow cognitive radios in a primary system. In this thesis we study some topics in spectrum sensing for cognitive radio.

    The fundamental problem of spectrum sensing is to discriminate samples that contain only noise from samples that contain a very weak signal embedded in noise. We derive detectors that exploit known structures of the signal, for the cases of an OFDM modulated signal and an orthogonal space-time block coded signal. We derive optimal detectors, in the Neyman-Pearson sense, for a few different cases when all parameters are known. Moreover we study detection when the parameters, such as noise variance, are unknown. We propose solutions the problem of unknown parameters.

    We also study system aspects of cognitive radio. More specifically, we investigate spectrum reuse of geographical spectrum holes in a frequency planned primary network. System performance is measured in terms of the achievable rate for the cognitive radio system. Simulation results show that a substantial sum-rate could be achieved if the cognitive radios communicate over small distances. However, the spectrum hole gets saturated quite fast, due to interference caused by the cognitive radios.

    List of papers
    1. A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection
    Open this publication in new window or tab >>A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection
    2009 (English)In: Proceedings of the 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'09), 2009, p. 2333-2336Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter.

    Series
    Acoustics, Speech and Signal Processing, ISSN 1520-6149 ; 2009
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-25592 (URN)10.1109/ICASSP.2009.4960088 (DOI)978-1-4244-2354-5 (ISBN)978-1-4244-2353-8 (ISBN)
    Conference
    34th IEEE international conference on acoustics, speech and signal processing,19-24 April, Taipei, Taiwan
    Note
    ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Erik Axell and Erik G. Larsson, A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection, 2009, Proceedings of the 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'09), 2333-2336. http://dx.doi.org/10.1109/ICASSP.2009.4960088Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2016-08-31Bibliographically approved
    2. On the Optimal K-term Approximation of a Sparse Parameter Vector MMSE Estimate
    Open this publication in new window or tab >>On the Optimal K-term Approximation of a Sparse Parameter Vector MMSE Estimate
    2009 (English)In: Proceedings of the 2009 IEEE Workshop on Statistical Signal Processing (SSP'09), IEEE , 2009, p. 245-248Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper considers approximations of marginalization sums thatarise in Bayesian inference problems. Optimal approximations ofsuch marginalization sums, using a fixed number of terms, are analyzedfor a simple model. The model under study is motivated byrecent studies of linear regression problems with sparse parametervectors, and of the problem of discriminating signal-plus-noise samplesfrom noise-only samples. It is shown that for the model understudy, if only one term is retained in the marginalization sum, thenthis term should be the one with the largest a posteriori probability.By contrast, if more than one (but not all) terms are to be retained,then these should generally not be the ones corresponding tothe components with largest a posteriori probabilities.

    Place, publisher, year, edition, pages
    IEEE, 2009
    Keywords
    MMSE estimation, Bayesian inference, marginalization
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-25591 (URN)10.1109/SSP.2009.5278594 (DOI)000274988800062 ()978-1-4244-2709-3 (ISBN)
    Note
    ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Erik Axell, Erik G. Larsson and Jan-Åke Larsson, On the Optimal K-term Approximation of a Sparse Parameter Vector MMSE Estimate, 2009, Proceedings of the 2009 IEEE Workshop on Statistical Signal Processing (SSP'09), 245-248. http://dx.doi.org/10.1109/SSP.2009.5278594 Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2016-08-31Bibliographically approved
    3. Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance
    Open this publication in new window or tab >>Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance
    2011 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 29, no 2, p. 290-304Article in journal (Refereed) Published
    Abstract [en]

    We consider spectrum sensing of OFDM signals in an AWGN channel. For  the case of completely known noise and signal powers, we set up  a vector-matrix model for an OFDM signal with a cyclic prefix and  derive the optimal Neyman-Pearson detector from first  principles. The optimal detector exploits the inherent correlation  of the OFDM signal incurred by the repetition of data in the cyclic  prefix, using knowledge of the length of the cyclic prefix and the  length of the OFDM symbol. We compare the optimal detector to the energy  detector numerically. We show that the energy detector is  near-optimal (within 1 dB SNR) when the noise variance is  known. Thus, when the noise power is known, no substantial gain can  be achieved by using any other detector than the energy detector.

    For the case of completely unknown noise and signal powers, we  derive a generalized likelihood ratio test (GLRT) based onempirical second-order statistics of  the received data. The proposed GLRT detector exploits the  non-stationary correlation structure of the OFDM signal and does not  require any knowledge of the noise power or the signal power. The  GLRT detector is compared to state-of-the-art OFDM signal detectors,  and shown to improve the detection performance with 5 dB SNR in  relevant cases.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2011
    Keywords
    spectrum sensing, signal detection, OFDM, cyclic prefix, subspace detection, second-order statistics
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-58515 (URN)10.1109/JSAC.2011.110203 (DOI)000286676500003 ()
    Note

    ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Erik Axell and Erik G. Larsson, Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance, 2011, IEEE Journal on Selected Areas in Communications, (29), 2, 290-304. http://dx.doi.org/10.1109/JSAC.2011.110203

    The previous status of this article was Manuskript.

    Available from: 2010-08-12 Created: 2010-08-12 Last updated: 2017-12-12Bibliographically approved
    4. Spectrum Sensing of Orthogonal Space-Time Block Coded Signals with Multiple Receive Antennas
    Open this publication in new window or tab >>Spectrum Sensing of Orthogonal Space-Time Block Coded Signals with Multiple Receive Antennas
    2010 (English)In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2010, p. 3110-3113Conference paper, Published paper (Other academic)
    Abstract [en]

    We consider detection of signals encoded with orthogonal space-time block codes (OSTBC), using multiple receive antennas. Such signals contain redundancy and they have a specific structure, that can be exploited for detection. We derive the optimal detector, in the Neyman-Pearson sense, when all parameters are known. We also consider unknown noise variance, signal variance and channel coefficients. We propose a number of GLRT based detectors for the different cases, that exploit the redundancy structure of the OSTBC signal. We also propose an eigenvalue-based detector for the case when all parameters are unknown. The proposed detectors are compared to the energy detector. We show that when only the noise variance is known, there is no gain in exploiting the structure of the OSTBC. However, when the noise variance is unknown there can be a significant gain.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2010
    Series
    Acoustics Speech and Signal Processing (ICASSP), ISSN 1520-6149, E-ISSN 2379-190X
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-51745 (URN)10.1109/ICASSP.2010.5496088 (DOI)000287096003014 ()9781424442959 (ISBN)9781424442966 (ISBN)
    Conference
    IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, 14-19 March, Dallas, Texas, U.S.A.
    Note

    ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.: Erik Axell and Erik G. Larsson, Spectrum Sensing of Orthogonal Space-Time Block Coded Signals with Multiple Receive Antennas, 2010, Proceedings of the 35th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'10).

    The previous status of this articel was Manuscript.

    Available from: 2009-11-17 Created: 2009-11-17 Last updated: 2018-02-02Bibliographically approved
    5. Capacity Considerations for Uncoordinated Communication in Geographical Spectrum Holes
    Open this publication in new window or tab >>Capacity Considerations for Uncoordinated Communication in Geographical Spectrum Holes
    2009 (English)In: Physical Communication, ISSN 1874-4907, Vol. 2, no 1-2, p. 3-9Article in journal (Refereed) Published
    Abstract [en]

    Cognitive radio is a new concept of reusing a licensed spectrum in an unlicensed manner. The motivation for cognitive radio is various measurements of spectrum utilization, that generally show unused resources in frequency, time and space. These "spectrum holes" could be exploited by cognitive radios. Some studies suggest that the spectrum is extremely underutilized, and that these spectrum holes could provide ten times the capacity of all existing wireless devices together. The spectrum could be reused either during time periods where the primary system is not active, or in geographical positions where the primary system is not operating. In this paper, we deal primarily with the concept of geographical reuse, in a frequency-planned primary network. We perform an analysis of the potential for communication in a geographical spectrum hole, and in particular the achievable sum-rate for a secondary network, to some order of magnitude. Simulation results show that a substantial sum-rate could be achieved if the secondary users communicate over small distances. For a small number of secondary links, the sum-rate increases linearly with the number of links. However, the spectrum hole gets saturated quite fast, due to interference caused by the secondary users. A spectrum hole may look large, but it disappears as soon as someone starts using it.

    Place, publisher, year, edition, pages
    Elsevier, 2009
    Keywords
    achievable rate, capacity, cognitive radio, spectrum hole
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-21547 (URN)10.1016/j.phycom.2009.03.002 (DOI)
    Note
    Original Publication: Erik Axell, Erik G. Larsson and Danyo Danev, Capacity Considerations for Uncoordinated Communication in Geographical Spectrum Holes, 2009, Physical Communication, (2), 1-2, 3-9. http://dx.doi.org/10.1016/j.phycom.2009.03.002 Copyright: Elsevier Science B.V., Amsterdam http://www.elsevier.com/ Available from: 2009-10-03 Created: 2009-10-02 Last updated: 2016-08-31Bibliographically approved
  • 3.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Geijer Lundin, Erik
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Gunnarsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Englund, Eva
    Wang Helmersson, Ke
    Coexistence of Speech and Best Effort Services in Enhanced Uplink WCDMA2005In: in Proc. of Radiovetenskap och Kommunikation (RVK), 2005Conference paper (Refereed)
    Abstract [en]

    An evaluation of the performance of coexistent voice and best effort data users in Enhanced Uplink WCDMA is studied in this paper. The main focus is on deriving the capacity regions and compare with previous WCDMA releases. It is shown that the Enhanced Uplink yields a large capacity gain in many aspects for all fractions of voice users compared to previous WCDMA releases. It is also shown, by the cumulative distribution functions of noise rise at the capacity limits, that the best effort data users experience bad quality at lower noise rise than voice users. This means that the capacity is in fact limited by the best effort users.

  • 4.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection2009In: Proceedings of the 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'09), 2009, p. 2333-2336Conference paper (Refereed)
    Abstract [en]

    This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter.

  • 5.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    A Unified Framework for GLRT-Based Spectrum Sensing of Signals with Covariance Matrices with Known Eigenvalue Multiplicities2011In: Proceedings of the IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP), IEEE conference proceedings, 2011, p. 2956-2959Conference paper (Refereed)
    Abstract [en]

    In this paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities. We derive the generalized likelihood-ratio test (GLRT) for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio and show that the GLRT for these applications, of which some are already known, are special cases of the general result.

  • 6.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Comments on "Multiple Antenna Spectrum Sensing in Cognitive Radios"2011In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 10, no 5, p. 1678-1680Article in journal (Refereed)
    Abstract [en]

    We point out an error in a derivation in the recent paper [1], and provide a correct and much shorter calculation of the result in question. In passing, we also connect the results in [1] to the literature on array signal processing and on principal component analysis, and show that the main findings of [1] follow as special cases of standard results in these fields.

  • 7.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Eigenvalue-Based Spectrum Sensing of Orthogonal Space-Time Block Coded Signals2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 12, p. 6724-6728Article in journal (Refereed)
    Abstract [en]

    We consider spectrum sensing of signals encoded with an orthogonal space-time block code (OSTBC). We propose a CFAR detector based on knowledge of the eigenvalue multiplicities of the covariance matrix which are inherent owing to the OSTBC and derive theoretical performance bounds. In addition, we show that the proposed detector is robust to a carrier frequency offset, and propose a detector that deals with timing synchronization using the detector for the synchronized case as a building block. The proposed detectors are shown numerically to perform well.

  • 8.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Multiantenna Spectrum Sensing of a Second-Order Cyclostationary Signal2011In: Proceedings of the 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP'11), 2011, p. 329-332Conference paper (Refereed)
    Abstract [en]

    We consider spectrum sensing of a second-order cyclostationary signal receivedat multiple antennas. The proposed detector exploits both the spatial andthe temporal correlation of the received signal, from knowledge of thefundamental period of the cyclostationary signal and the eigenvaluemultiplicities of the temporal covariance matrix. All other parameters, suchas the channel gains or the noise power, are assumed to be unknown. The proposeddetector is shown numerically to outperform state-of-the-art detectors forspectrum sensing of anOFDM signal, both when using a single antenna and with multiple antennas.

  • 9.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Optimal and Near-Optimal Spectrum Sensing of OFDM Signals in AWGN Channels2010In: Proceedings of the International Workshop on Cognitive Information Processing (CIP), 2010Conference paper (Refereed)
    Abstract [en]

    We consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely unknown noise and signal powers, we  derive a GLRT detector based on empirical second-order statistics of  the received data. The proposed GLRT detector exploits the  non-stationary correlation structure of the OFDM signal and does not  require any knowledge of the noise power or the signal power. The  GLRT detector is compared to state-of-the-art OFDM signal detectors,  and shown to improve the detection performance with 5 dB SNR in  relevant cases.

    For the case of completely known noise power and signal power, we present a brief  derivation of the optimal Neyman-Pearson detector from first  principles. We compare the optimal detector to the energy  detector numerically, and show that the energy detector is  near-optimal (within 0.2 dB SNR) when the noise variance is  known. Thus, when the noise power is known, no substantial gain can  be achieved by using any other detector than the energy detector.

  • 10.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Optimal and Sub-Optimal Spectrum Sensing of OFDM Signals in Known and Unknown Noise Variance2011In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 29, no 2, p. 290-304Article in journal (Refereed)
    Abstract [en]

    We consider spectrum sensing of OFDM signals in an AWGN channel. For  the case of completely known noise and signal powers, we set up  a vector-matrix model for an OFDM signal with a cyclic prefix and  derive the optimal Neyman-Pearson detector from first  principles. The optimal detector exploits the inherent correlation  of the OFDM signal incurred by the repetition of data in the cyclic  prefix, using knowledge of the length of the cyclic prefix and the  length of the OFDM symbol. We compare the optimal detector to the energy  detector numerically. We show that the energy detector is  near-optimal (within 1 dB SNR) when the noise variance is  known. Thus, when the noise power is known, no substantial gain can  be achieved by using any other detector than the energy detector.

    For the case of completely unknown noise and signal powers, we  derive a generalized likelihood ratio test (GLRT) based onempirical second-order statistics of  the received data. The proposed GLRT detector exploits the  non-stationary correlation structure of the OFDM signal and does not  require any knowledge of the noise power or the signal power. The  GLRT detector is compared to state-of-the-art OFDM signal detectors,  and shown to improve the detection performance with 5 dB SNR in  relevant cases.

  • 11.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Spectrum Sensing of Orthogonal Space-Time Block Coded Signals with Multiple Receive Antennas2010In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2010, p. 3110-3113Conference paper (Other academic)
    Abstract [en]

    We consider detection of signals encoded with orthogonal space-time block codes (OSTBC), using multiple receive antennas. Such signals contain redundancy and they have a specific structure, that can be exploited for detection. We derive the optimal detector, in the Neyman-Pearson sense, when all parameters are known. We also consider unknown noise variance, signal variance and channel coefficients. We propose a number of GLRT based detectors for the different cases, that exploit the redundancy structure of the OSTBC signal. We also propose an eigenvalue-based detector for the case when all parameters are unknown. The proposed detectors are compared to the energy detector. We show that when only the noise variance is known, there is no gain in exploiting the structure of the OSTBC. However, when the noise variance is unknown there can be a significant gain.

  • 12.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Spectrum Sensing of Signals with Structured Covariance Matrices Using Covariance Matching Estimation Techniques2011In: Proceedings of the IEEE Global Communications Conference (GLOBECOM), 2011, p. 1-5Conference paper (Refereed)
    Abstract [en]

    In this work, we consider spectrum sensing of Gaussian signals with structured covariance matrices. We show that the optimal detector based on the probability distribution of the sample covariance matrix is equivalent to the optimal detector based on the raw data, if the covariance matrices are known. However, the covariance matrices are unknown in general. Therefore, we propose to estimate the unknown parameters using covariance matching estimation techniques (COMET). We also derive the optimal detector based on a Gaussian approximation of the sample covariance matrix, and show that this is closely connected to COMET.

  • 13.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Danev, Danyo
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Capacity Considerations for Uncoordinated Communication in Geographical Spectrum Holes2009In: Physical Communication, ISSN 1874-4907, Vol. 2, no 1-2, p. 3-9Article in journal (Refereed)
    Abstract [en]

    Cognitive radio is a new concept of reusing a licensed spectrum in an unlicensed manner. The motivation for cognitive radio is various measurements of spectrum utilization, that generally show unused resources in frequency, time and space. These "spectrum holes" could be exploited by cognitive radios. Some studies suggest that the spectrum is extremely underutilized, and that these spectrum holes could provide ten times the capacity of all existing wireless devices together. The spectrum could be reused either during time periods where the primary system is not active, or in geographical positions where the primary system is not operating. In this paper, we deal primarily with the concept of geographical reuse, in a frequency-planned primary network. We perform an analysis of the potential for communication in a geographical spectrum hole, and in particular the achievable sum-rate for a secondary network, to some order of magnitude. Simulation results show that a substantial sum-rate could be achieved if the secondary users communicate over small distances. For a small number of secondary links, the sum-rate increases linearly with the number of links. However, the spectrum hole gets saturated quite fast, due to interference caused by the secondary users. A spectrum hole may look large, but it disappears as soon as someone starts using it.

  • 14.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Jan-Åke
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    On the Optimal K-term Approximation of a Sparse Parameter Vector MMSE Estimate2009In: Proceedings of the 2009 IEEE Workshop on Statistical Signal Processing (SSP'09), IEEE , 2009, p. 245-248Conference paper (Refereed)
    Abstract [en]

    This paper considers approximations of marginalization sums thatarise in Bayesian inference problems. Optimal approximations ofsuch marginalization sums, using a fixed number of terms, are analyzedfor a simple model. The model under study is motivated byrecent studies of linear regression problems with sparse parametervectors, and of the problem of discriminating signal-plus-noise samplesfrom noise-only samples. It is shown that for the model understudy, if only one term is retained in the marginalization sum, thenthis term should be the one with the largest a posteriori probability.By contrast, if more than one (but not all) terms are to be retained,then these should generally not be the ones corresponding tothe components with largest a posteriori probabilities.

  • 15.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Leus, Geert
    Delft University of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Overview of Spectrum Sensing for Cognitive Radio2010In: Proceedings of the International Workshop on Cognitive Information Processing (CIP), 2010, p. 322-327Conference paper (Refereed)
    Abstract [en]

    We present a survey of state-of-the-art algorithms for spectrum  sensing in cognitive radio. The algorithms discussed range from  energy detection to sophisticated feature detectors. The feature  detectors that we present all have in common that they exploit some  known structure of the transmitted signal.  In particular we treat  detectors that exploit cyclostationarity properties of the signal,  and detectors that exploit a known eigenvalue structure of the  signal covariance matrix.  We also consider cooperative  detection. Specifically we present data fusion rules for soft and  hard combining, and discuss the energy efficiency of several  different sensing, sleeping and censoring schemes in detail.

  • 16.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Leus, Geert
    Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Poor, H. Vincent
    Princeton University, Department of Electrical Engineering.
    Spectrum sensing for cognitive radio: State-of-the-art and recent advances2012In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 29, no 3, p. 101-116Article in journal (Refereed)
    Abstract [en]

    The ever-increasing demand for higher data rates in wireless communications in the face of limited or underutilized spectral resources has motivated the introduction of cognitive radio. Traditionally, licensed spectrum is allocated over relatively long time periods and is intended to be used only by licensees. Various measurements of spectrum utilization have shown substantial unused resources in frequency, time, and space [1], [2]. The concept behind cognitive radio is to exploit these underutilized spectral resources by reusing unused spectrum in an opportunistic manner [3], [4]. The phrase cognitive radio is usually attributed to Mitola [4], but the idea of using learning and sensing machines to probe the radio spectrum was envisioned several decades earlier (cf., [5]).

  • 17.
    Blad, Anton
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Axell, Erik
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Spectrum Sensing of OFDM Signals in the Presence of CFO: New Algorithms and Empirical Evaluation Using USRP2012In: Proceedings of the 13th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE , 2012, p. 159-163Conference paper (Refereed)
    Abstract [en]

    In this work, we consider spectrum sensing of OFDM signals. We deal withthe inevitable problem of a carrier frequency offset, and propose modificationsto some state-of-the-art detectors to cope with that. Moreover, the (modified)detectors are implemented using GNU radio and USRP, and evaluated over aphysical radio channel. Measurements show that all of the evaluated detectorsperform quite well, and the preferred choice of detector depends on thedetection requirements and the radio environment.

  • 18.
    Danev, Danyo
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Axell, Erik
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Spectrum Sensing Methods for Detection of DVB-T Signals in AWGN and Fading Channels2010In: Proceedings of the 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2010, p. 2721-2726Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider spectrum sensing of DVB-T signals in differentfading environments. We compare state-of-the-art detectorsincluding detectors based on pilot subcarriers, as well as detectors for general OFDM signals that exploit the correlation structure incurred by the cyclicprefix. Energy detection is also included for comparison. We shownumerically that the choice of detector depends on the scenario, the detectorrequirements, and on the available prior knowledge. We also show that it ispossible to obtain good detection performance by exploiting the correlation,even in a frequency selective channel.

  • 19.
    Eliardsson, Patrik
    et al.
    FOI.
    Wiklundh, Kia
    FOI.
    Axell, Erik
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Johansson, Björn
    FOI.
    Stenumgaard, Peter
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Analysis of the local HF interference environment at a military platform2013In: Nordic HF Conference Proceedings 2013, 2013, p. 3.4-Conference paper (Refereed)
    Abstract [en]

    High frequency (HF) communications are of vital importance for modern military operations. However, HF channels are touchy and unpredictable, prone to noise, fading, jamming, and interference. Therefore, a number of prediction tools for channel selection have been developed. However, existing tools do not consider the local actual electromagnetic interference at receivers located on navy and army platforms. Measurements on military platforms show that also the local interference environment can be crucial and has large variations in frequency and time. In this paper we analyze the levels and dynamics of local interference from a typical military platform. We show that the variations regarding interference waveform can be very large between two consecutive seconds of measurement. This means that the interference impact in terms of bit error probability also will be very large between such consecutive seconds. The overall conclusion is that future methods for HF-frequency selection would be significantly improved by considering the characteristics of local interference from electrical equipment.

1 - 19 of 19
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf