• DocumentCode
    640260
  • Title

    Concavity of mutual information rate of finite-state channels

  • Author

    Yonglong Li ; Guangyue Han

  • Author_Institution
    Univ. of Hong Kong, Hong Kong, China
  • fYear
    2013
  • fDate
    7-12 July 2013
  • Firstpage
    2114
  • Lastpage
    2118
  • Abstract
    The computation of the capacity of a finite-state channel (FSC) is a fundamental and long-standing open problem in information theory. The capacity of a memoryless channel can be effectively computed via the classical Blahut-Arimoto algorithm (BAA), which, however, does not apply to a general FSC. Recently Vontobel et al. [1] generalized the BAA to compute the capacity of a finite-state machine channel with a Markovian input. Their proof of the convergence of this algorithm, however, depends on the concavity conjecture posed in their paper. In this paper, we confirm the concavity conjecture for some special FSCs. On the other hand, we give examples to show that the conjecture is not true in general.
  • Keywords
    Markov processes; channel capacity; convergence of numerical methods; finite state machines; information theory; memoryless systems; Blahut-Arimoto algorithm; Markovian input; algorithm convergence; concavity conjecture; finite state channel; finite state machine channel; information theory; memoryless channel capacity; mutual information rate concavity; Entropy; Hidden Markov models; Markov processes; Mutual information; Power capacitors; Signal to noise ratio;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory Proceedings (ISIT), 2013 IEEE International Symposium on
  • Conference_Location
    Istanbul
  • ISSN
    2157-8095
  • Type

    conf

  • DOI
    10.1109/ISIT.2013.6620599
  • Filename
    6620599