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  • 1.
    Ainomae, Ahti
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Bengtsson, Mats
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Trump, Tonu
    Tallinn Univ Technol, Dept Radio & Telecommun Engn, EE-12616 Tallinn, Estonia..
    Distributed Largest Eigenvalue-Based Spectrum Sensing Using Diffusion LMS2018In: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, ISSN 2373-776X, Vol. 4, no 2, p. 362-377Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a distributed detection scheme for cognitive radio (CR) networks, based on the largest eigenvalues (LEs) of adaptively estimated correlation matrices (CMs), assuming that the primary user signal is temporally correlated. The proposed algorithm is fully distributed, there by avoiding the potential single point of failure that a fusion center would imply. Different forms of diffusion least mean square algorithms are used for estimating and averaging the CMs over the CR network for the LE detection and the resulting estimation performance is analyzed using a common framework. In order to obtain analytic results on the detection performance, the exact distribution of the CM estimates are approximated by a Wishart distribution, by matching the moments. The theoretical findings are verified through simulations.

  • 2.
    Ainomäe, Ahti
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Distributed Detection in Cognitive Radio Networks2017Licentiate thesis, monograph (Other academic)
    Abstract [en]

    One of the problems with the modern radio communication is the lack of availableradio frequencies. Recent studies have shown that, while the available licensed radiospectrum becomes more occupied, the assigned spectrum is significantly underutilized.To alleviate the situation, cognitive radio (CR) technology has been proposedto provide an opportunistic access to the licensed spectrum areas. Secondary CRsystems need to cyclically detect the presence of a primary user by continuouslysensing the spectrum area of interest. Radiowave propagation effects like fading andshadowing often complicate sensing of spectrum holes. When spectrum sensing isperformed in a cooperative manner, then the resulting sensing performance can beimproved and stabilized.

    In this thesis, two fully distributed and adaptive cooperative Primary User (PU)detection solutions for CR networks are studied.

    In the first part of this thesis we study a distributed energy detection schemewithout using any fusion center. Due to reduced communication such a topologyis more energy efficient. We propose the usage of distributed, diffusion least meansquare (LMS) type of power estimation algorithms with different network topologies.We analyze the resulting energy detection performance by using a commonframework and verify the theoretical findings through simulations.

    In the second part of this thesis we propose a fully distributed detection scheme,based on the largest eigenvalue of adaptively estimated correlation matrices, assumingthat the primary user signal is temporally correlated. Different forms of diffusionLMS algorithms are used for estimating and averaging the correlation matrices overthe CR network. The resulting detection performance is analyzed using a commonframework. In order to obtain analytic results on the detection performance, theadaptive correlation matrix estimates are approximated by a Wishart distribution.The theoretical findings are verified through simulations.

  • 3.
    Ainomäe, Ahti
    et al.
    KTH, School of Electrical Engineering (EES), Information Science and Engineering. Tallinn University of Technology, Estonia.
    Trump, T.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Distributed largest eigenvalue detection2017In: 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3519-3523, article id 7952811Conference paper (Refereed)
    Abstract [en]

    Cognitive radio (CR) systems need to detect the presence of a primary user (PU) signal by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. In this paper we study distributed spectrum sensing, based on the largest eigenvalue of adaptively estimated correlation matrices (CMs) of received signals. The PU signal is assumed to be temporally correlated. In this paper an Combine and Adapt (CTA) least Mean Square (LMS) diffusion based mean vector estimation scheme is proposed. No fusion center (FC) for estimation or detection is used. We analyse the resulting detection performance and verify the theoretical findings through simulations.

  • 4.
    Ainomäe, Ahti
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Tallinn Univ. of Technol., Tallinn, Estonia.
    Trump, Tõnu
    Tallinn Univ. of Technol., Tallinn, Estonia.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Distributed Diffusion LMS based Energy Detection2014In: Proceedings of 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2014, 2014, p. 176-183Conference paper (Refereed)
    Abstract [en]

    Cognitive radio (CR) is seen as a promising technology to make radio spectrum usage more effective by providing an opportunistic access for secondary users to the licensed spectrum areas. CR systems need to detect the presence of a primary user (PU) signal by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. This paper studies distributed spectrum sensing in a cognitive radio context. We investigate distributed energy detection schemes without using any fusion center. We propose the usage of distributed, diffusion least mean square (LMS) type of power estimation algorithms. In this paper an Adapt and Combine (ATC) diffusion based power estimation scheme is proposed and the performance is compared with the Combine and Adapt (CTA) and ring-around schemes in a common framework. The PU signal is assumed to be slowly fading. We analyse the resulting energy detection performance and verify the theoretical findings through simulations.

  • 5.
    Ainomäe, Ahti
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Department of Radio and Telecommunication Engineering, Tallinn University of Technology, Tallinn, Estonia .
    Trump, Tõnu
    Tallin University of Technology.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Distributed Recursive Energy Detection2014In: Proceedings of Wireless Communications and Networking Conference (WCNC), 2014, IEEE Communications Society, 2014, p. 1242-1247Conference paper (Refereed)
    Abstract [en]

    Recent studies have shown that, while the available licensed radio spectrum becomes more occupied, the assigned spectrum is significantly underutilized. To alleviate the situation, cognitive radio (CR) technology has been proposed to provide an opportunistic access to the licensed spectrum areas. CR systems are able to serve the secondary users for detecting and utilizing so called spectrum holes by sensing and adapting to the environment without causing harmful effects or interference to the licensed primary users (PU). CR systems need to detect the presence of a primary user by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. This paper studies distributed spectrum sensing in a cognitive radio context. We investigate a distributed energy detection scheme without using any fusion center. Due to reduced communication such a topology is more energy efficient. The PU signal is assumed to be in slow fading. A recursive distributed power estimation and detection scheme is proposed. The theoretical findings are verified through simulations.

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