• DocumentCode
    23006
  • Title

    Capacity Analysis of Linear Operator Channels Over Finite Fields

  • Author

    Shenghao Yang ; Siu-Wai Ho ; Jin Meng ; En-Hui Yang

  • Author_Institution
    Inst. for Theor. Comput. Sci., Tsinghua Univ., Beijing, China
  • Volume
    60
  • Issue
    8
  • fYear
    2014
  • fDate
    Aug. 2014
  • Firstpage
    4880
  • Lastpage
    4901
  • Abstract
    Motivated by communication through a network employing linear network coding, capacities of linear operator channels (LOCs) with arbitrarily distributed transfer matrices over finite fields are studied. Both the Shannon capacity C and the subspace coding capacity CSS are analyzed. By establishing and comparing lower bounds on C and upper bounds on CSS, various necessary conditions and sufficient conditions such that C = CSS are obtained. A new class of LOCs such that C = CSS is identified, which includes LOCs with uniform-given-rank transfer matrices as special cases. It is also demonstrated that CSS is strictly less than C for a broad class of LOCs. In general, an optimal subspace coding scheme is difficult to find because it requires to solve the maximization of a nonconcave function. However, for an LOC with a unique subspace degradation, CSS can be obtained by solving a convex optimization problem over rank distribution. Classes of LOCs with a unique subspace degradation are characterized. Since LOCs with uniform-given-rank transfer matrices have unique subspace degradations, some existing results on LOCs with uniform-given-rank transfer matrices are explained from a more general way.
  • Keywords
    channel capacity; linear codes; matrix algebra; network coding; optimisation; LOC; Shannon capacity; arbitrarily distributed transfer matrices; capacity analysis; convex optimization problem; finite fields; linear network coding; linear operator channels; maximization; nonconcave function; optimal subspace coding scheme; rank distribution; subspace coding capacity; uniform-given-rank transfer matrices; unique subspace degradation; Channel coding; Degradation; Network coding; Random variables; Symmetric matrices; Vectors; Linear operator channel; network coding; subspace coding;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/TIT.2014.2326976
  • Filename
    6822565