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  • 1.
    Hamid, Mohamed
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Electronics. Department of Communication System and wireless@ kth, KTH Royal Institute of Technology, Stockholm.
    Björsell, Niclas
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Electronics.
    Slimane, Ben
    Department of Communication System and wireless@ kth, KTH Royal Institute of Technology, Stockholm.
    Energy and Eigenvalue-Based Combined Fully-Blind Self-Adapted Spectrum Sensing Algorithm2016In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, no 2, p. 630-642Article in journal (Refereed)
    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.

  • 2.
    Zenteno, Efrain
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Electronics. Department of Signal Processing, Royal Institute of Technology KTH, Stockholm, Sweden.
    Khan, Zain Ahmed
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Electronics. Department of Signal Processing, Royal Institute of Technology KTH, Stockholm, Sweden.
    Isaksson, Magnus
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Electronics.
    Händel, Peter
    Department of Signal Processing, Royal Institute of Technology KTH, Stockholm, Sweden.
    Finding Structural Information about RF Power Amplifiers using an Orthogonal Nonparametric Kernel Smoothing Estimator2016In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, no 5, p. 2883-2889, article id 7109926Article in journal (Refereed)
    Abstract [en]

    A non-parametric technique for modeling the behavior of power amplifiers is presented. The proposed technique relies on the principles of density estimation using the kernel method and is suited for use in power amplifier modeling. The proposed methodology transforms the input domain into an orthogonal memory domain. In this domain, non-parametric static functions are discovered using the kernel estimator. These orthogonal, non-parametric functions can be fitted with any desired mathematical structure, thus facilitating its implementation. Furthermore, due to the orthogonality, the non-parametric functions can be analyzed and discarded individually, which simplifies pruning basis functions and provides a tradeoff between complexity and performance. The results show that the methodology can be employed to model power amplifiers, therein yielding error performance similar to state-of-the-art parametric models. Furthermore, a parameter-efficient model structure with 6 coefficients was derived for a Doherty power amplifier, therein significantly reducing the deployment’s computational complexity. Finally, the methodology can also be well exploited in digital linearization techniques.

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