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  • 1.
    Mylonakis, Michail
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Stavrou, Photios A.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Empirical Coordination with Multiple Descriptions2019In: 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1074-1081, article id 8919668Conference paper (Refereed)
    Abstract [en]

    We extend the framework of empirical coordination to a distributed setup where for a given action by nature, multiple descriptions of the action of the decoder are available. We adopt the coding strategy applied by El Gamal and Cover in [1] to get a lower bound of the coordination region. Then, we improve this region by applying the coding scheme applied by Zhang and Berger in [2].

  • 2.
    Stavrou, Fotios
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Asymptotic Reverse Waterfilling Algorithm of NRDF for Certain Classes of Vector Gauss-Markov ProcessesManuscript (preprint) (Other academic)
    Abstract [en]

    In this paper, we revisit the asymptotic reverse-waterfilling characterization of the nonanticipative rate distortion function (NRDF) derived for a time-invariant multidimensional Gauss-Markov processes with mean-squared error (MSE) distortion in \cite{stavrou:2018cdc}. We show that for certain classes of time-invariant multidimensional Gauss-Markov processes, the specific characterization behaves as a reverse-waterfilling algorithm obtained in {\it matrix form} ensuring that the numerical approach of \cite[Algorithm 1]{stavrou:2018cdc} is optimal. In addition, we give an equivalent characterization that utilizes the {\it eigenvalues of the involved matrices} reminiscent of the well-known reverse-waterfilling algorithm in information theory. For the latter, we also propose a novel numerical approach to solve the algorithm optimally. The efficacy of our proposed iterative scheme compared to similar existing schemes is demonstrated via experiments. Finally, we use our new results to derive an analytical solution of the asymptotic NRDF for a correlated time-invariant two-dimensional Gauss-Markov process.

    Download full text (pdf)
    fulltext
  • 3.
    Stavrou, Photios
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Skoglund, Mikael
    KTH, Superseded Departments (pre-2005), Signals, Sensors and Systems.
    The Zero-Delay RDF for Gaussian Processes with Encoder-Decoder Causal Side InformationManuscript (preprint) (Other academic)
    Download full text (pdf)
    fulltext
  • 4.
    Stavrou, Photios
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Østergaard, Jan
    Department of Electronic Systems, Aalborg University, Aalborg, Denmark .
    Fixed-Rate Zero-Delay Source Coding for Stationary Vector-Valued Gauss-Markov Sources2018In: 2018 Data Compression Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 257-266Conference paper (Refereed)
    Abstract [en]

    We consider a fixed-rate zero-delay source coding problem where a stationary vector-valued Gauss-Markov source is compressed subject to an average mean-squared error (MSE) distortion constraint. We address the problem by considering the Gaussian nonanticipative rate distortion function (NRDF) which is a lower bound to the zero-delay Gaussian RDF. Then, we use its corresponding optimal “test-channel” to characterize the stationary Gaussian NRDF and evaluate the corresponding information rates. We show that the Gaussian NRDF can be achieved by p-parallel fixed-rate scalar uniform quantizers of finite support with dithering signal up to a multiplicative distortion factor and a constant rate penalty. We demonstrate our framework with a numerical example.

    Download full text (pdf)
    dcc_2018
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