• DocumentCode
    464962
  • Title

    Application of Sequential Monte Carlo to M-QAM Schemes in the Presence of Nonlinear Solid-State Power Amplifiers

  • Author

    Shabany, Mahdi ; Gulak, P. Glenn

  • Author_Institution
    Toronto Univ., Ont.
  • fYear
    2007
  • fDate
    27-30 May 2007
  • Firstpage
    2295
  • Lastpage
    2298
  • Abstract
    This paper presents a sequential Monte Carlo (SMC) framework in order to compensate for the nonlinear distortion caused by solid-state power amplifiers (SSPA) in M-QAM schemes. The performance of this new approach is shown for low and high-order constellation schemes for different values of input backoff (IBO). The results reveal that, in low-IBO regimes, the SMC method shows a significant improvement, relative to the conventional methods where the predistorter is used before the amplifier, especially for high order constellations. Moreover, the SMC method is shown to have more robust behavior to the constellation scaling. Finally, an adaptive sequential Monte Carlo receiver is proposed that adapts itself efficiently to variations in amplifier parameters.
  • Keywords
    Monte Carlo methods; nonlinear distortion; power amplifiers; quadrature amplitude modulation; receivers; sequential circuits; M-QAM schemes; adaptive sequential Monte Carlo receiver; high-order constellation schemes; input backoff; low constellation schemes; nonlinear distortion; predistorter; sequential Monte Carlo framework; solid-state power amplifiers; Amplitude modulation; Filtering; High power amplifiers; Monte Carlo methods; Nonlinear distortion; Operational amplifiers; Phase distortion; Power amplifiers; Sliding mode control; Solid state circuits;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on
  • Conference_Location
    New Orleans, LA
  • Print_ISBN
    1-4244-0920-9
  • Electronic_ISBN
    1-4244-0921-7
  • Type

    conf

  • DOI
    10.1109/ISCAS.2007.378846
  • Filename
    4253133