The Experts below are selected from a list of 36564 Experts worldwide ranked by ideXlab platform

T. Berger - One of the best experts on this subject based on the ideXlab platform.

  • robust Distributed source Coding
    IEEE Transactions on Information Theory, 2008
    Co-Authors: Jun Chen, T. Berger
    Abstract:

    We consider a Distributed source Coding system in which several observations must be encoded separately and communicated to the decoder by using limited transmission rate. We introduce a robust Distributed Coding scheme which flexibly trades off between system robustness and compression efficiency. The optimality of this Coding scheme is proved for various special cases.

  • robust Distributed source Coding
    arXiv: Information Theory, 2006
    Co-Authors: Jun Chen, T. Berger
    Abstract:

    We consider a Distributed source Coding system in which several observations are communicated to the decoder using limited transmission rate. The observations must be separately coded. We introduce a robust Distributed Coding scheme which flexibly trades off between system robustness and compression efficiency. The optimality of this Coding scheme is proved for various special cases.

  • ISIT - Robust Coding schemes for Distributed sensor networks with unreliable sensors
    International Symposium onInformation Theory 2004. ISIT 2004. Proceedings., 1
    Co-Authors: Jun Chen, T. Berger
    Abstract:

    A Distributed sensor network in which several observations are communicated to the fusion center using limited transmission rate is considered in this paper. The observations must be separately coded and introduces a class of robust Distributed Coding schemes, which flexibly trade off between system robustness and compression efficiency.

Jun Chen - One of the best experts on this subject based on the ideXlab platform.

  • robust Distributed source Coding
    IEEE Transactions on Information Theory, 2008
    Co-Authors: Jun Chen, T. Berger
    Abstract:

    We consider a Distributed source Coding system in which several observations must be encoded separately and communicated to the decoder by using limited transmission rate. We introduce a robust Distributed Coding scheme which flexibly trades off between system robustness and compression efficiency. The optimality of this Coding scheme is proved for various special cases.

  • robust Distributed source Coding
    arXiv: Information Theory, 2006
    Co-Authors: Jun Chen, T. Berger
    Abstract:

    We consider a Distributed source Coding system in which several observations are communicated to the decoder using limited transmission rate. The observations must be separately coded. We introduce a robust Distributed Coding scheme which flexibly trades off between system robustness and compression efficiency. The optimality of this Coding scheme is proved for various special cases.

  • ISIT - Robust Coding schemes for Distributed sensor networks with unreliable sensors
    International Symposium onInformation Theory 2004. ISIT 2004. Proceedings., 1
    Co-Authors: Jun Chen, T. Berger
    Abstract:

    A Distributed sensor network in which several observations are communicated to the fusion center using limited transmission rate is considered in this paper. The observations must be separately coded and introduces a class of robust Distributed Coding schemes, which flexibly trade off between system robustness and compression efficiency.

Ertem Tuncel - One of the best experts on this subject based on the ideXlab platform.

  • ISIT - High-resolution predictive Wyner-Ziv Coding of Gaussian sources
    2009 IEEE International Symposium on Information Theory, 2009
    Co-Authors: Xuechen Chen, Ertem Tuncel
    Abstract:

    A predictive scheme for zero-delay Wyner-Ziv Coding of sources with memory is proposed and analyzed in the high-resolution regime for Gaussian source-side information pairs. Incorporating the propagation of distortion due to occasional deCoding errors into the analysis, the quantizers and first-order filter parameters are simultaneously optimized. The rate-distortion performance of the scheme is compared with those of non-Distributed Coding and Wyner-Ziv Coding with a naive choice of prediction filters (minimizing temporal correlation), as well as the asymptotic Wyner-Ziv rate-distortion function.

J M Cioffi - One of the best experts on this subject based on the ideXlab platform.

  • the capacity region of frequency selective gaussian interference channels under strong interference
    IEEE Transactions on Communications, 2007
    Co-Authors: Seong Taek Chung, J M Cioffi
    Abstract:

    This paper presents the capacity region of frequency-selective Gaussian interference channels under the condition of strong interference, assuming an average power constraint per user. First, a frequency-selective Gaussian interference channel is modeled as a set of independent parallel memoryless Gaussian interference channels. Using nonfrequency selective results, the capacity region of frequency-selective Gaussian interference channels under strong interference is expressed mathematically. Exploiting structures inherent in the problem, a dual problem is constructed for each independent memoryless channel, in which both mathematical and numerical analysis are performed. Furthermore, three suboptimal methods are compared to the capacity-achieving Coding and power allocation scheme. Iterative waterfilling, a suboptimal scheme, provides close-to-optimum performance and has a Distributed Coding and power allocation scheme, which are attractive in practice.

Seong Taek Chung - One of the best experts on this subject based on the ideXlab platform.

  • the capacity region of frequency selective gaussian interference channels under strong interference
    IEEE Transactions on Communications, 2007
    Co-Authors: Seong Taek Chung, J M Cioffi
    Abstract:

    This paper presents the capacity region of frequency-selective Gaussian interference channels under the condition of strong interference, assuming an average power constraint per user. First, a frequency-selective Gaussian interference channel is modeled as a set of independent parallel memoryless Gaussian interference channels. Using nonfrequency selective results, the capacity region of frequency-selective Gaussian interference channels under strong interference is expressed mathematically. Exploiting structures inherent in the problem, a dual problem is constructed for each independent memoryless channel, in which both mathematical and numerical analysis are performed. Furthermore, three suboptimal methods are compared to the capacity-achieving Coding and power allocation scheme. Iterative waterfilling, a suboptimal scheme, provides close-to-optimum performance and has a Distributed Coding and power allocation scheme, which are attractive in practice.

  • the capacity region of frequency selective gaussian interference channels under strong interference
    International Conference on Communications, 2003
    Co-Authors: Seong Taek Chung
    Abstract:

    This paper presents the capacity region of frequency-selective Gaussian interference channels under the condition of strong interference, assuming an average power constraint per user. First, a frequency-selective Gaussian interference channel is modeled as a set of independent parallel memoryless Gaussian interference channels. Using non-frequency selective results, the capacity region of frequency-selective Gaussian interference channels under strong interference is expressed mathematically. Exploiting structures inherent in the problem, a dual problem is constructed for each independent memoryless channel, and solved. Furthermore, three suboptimal methods are compared with the capacity-achieving Coding and power allocation scheme. Iterative waterfilling, a suboptimal scheme, provides close-to-optimum performance and has a Distributed Coding and power allocation scheme, which are attractive in practice.