Some New Nonlinear and Symbol Manipulation Techniques to Mitigate Adverse Effects of High PAPR in OFDM Wireless Communications

  • Byung Moo Lee
  • Rui J.P. de Figueiredo
Part of the Systems & Control: Foundations & Applications book series (SCFA)


Orthogonal frequency division multiplexing (OFDM) modulation has several attributes which favor it for high speed wireless communications. But its high peak-to-average power ratio (PAPR) seriously limits the linear range, and hence the power efficiency, of the transmitter’s high power amplifier (HPA). We present an overview of two complementary approaches to the solution of this problem. Details are provided in our papers listed in the References. The first approach uses an adaptive nonlinear pre-distorter (PD) to compensate for the nonlinearity in the HPA. The analytical expressions used for the HPA and the corresponding PD lead to satisfactory overall system operation up to the saturation region of the HPA, under rapidly time-varying conditions. The second approach reduces the PAPR to an acceptable range by means of several recently proposed PAPR reduction techniques. These techniques include (1) an enhanced version (denoted EIF-PTS) of the Cimini/Sollenberger iterative flipping procedure for implementation of the Muller/Huber partial transmit sequence (PTS) algorithm; (2) a decision-oriented tree-structured modification (denoted T-PTS) of the PTS algorithm, which seeks the best complexity/performance trade-off in the implementation of the resulting simplified PTS algorithm; (3) a combination of clipping and selected mapping techniques for fading channels; and (4) an extension of some of the underlying PAPR reduction concepts to multiple input, multiple output OFDM-based wireless communication systems.


Orthogonal Frequency Division Multiplex Orthogonal Frequency Division Multiplex System Nonlinear Distortion Orthogonal Frequency Division Multiplex Signal Additive White Gaussian Noise Channel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Birkhäuser Boston, a part of Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Infra LaboratorySouth Korea
  2. 2.California Institute for Telecommunications and Information TechnologyUniversity of CaliforniaIrvineUSA

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