• DocumentCode
    1014156
  • Title

    Duality bounds on the cutoff rate with applications to Ricean fading

  • Author

    Lapidoth, Amos ; Miliou, Natalia

  • Author_Institution
    Dept. of Inf. Technol. & Electr. Eng., Swiss Fed. Inst. of Technol., Zurich, Switzerland
  • Volume
    52
  • Issue
    7
  • fYear
    2006
  • fDate
    7/1/2006 12:00:00 AM
  • Firstpage
    3003
  • Lastpage
    3018
  • Abstract
    We propose a technique to derive upper bounds on Gallager´s cost-constrained random coding exponent function. Applying this technique to the noncoherent peak-power or average-power limited discrete time memoryless Ricean fading channel, we obtain the high signal-to-noise ratio (SNR) expansion of this channel´s cutoff rate. At high SNR, the gap between channel capacity and the cutoff rate approaches a finite limit. This limit is approximately 0.26 nats per channel-use for zero specular component (Rayleigh) fading and approaches 0.39 nats per channel-use for very large values of the specular component. We also compute the asymptotic cutoff rate of a Rayleigh-fading channel when the receiver has access to some partial side information concerning the fading. It is demonstrated that the cutoff rate does not utilize the side information as efficiently as capacity, and that the high SNR gap between the two increases to infinity as the imperfect side information becomes more and more precise.
  • Keywords
    Rayleigh channels; Rician channels; channel capacity; discrete systems; memoryless systems; random codes; Gallager cost-constrained random coding; Rayleigh channel; Ricean fading; channel capacity; discrete time memoryless channel; duality bound; exponent function; Channel capacity; Costs; Fading; H infinity control; Lagrangian functions; Memoryless systems; Network address translation; Rayleigh channels; Signal to noise ratio; Upper bound; Asymptotic; Lagrange; Ricean fading; Rician fading; channel capacity; cutoff rate; duality; fading; high signal-to-noise ratio (SNR);
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/TIT.2006.876349
  • Filename
    1650352