Error Vector Magnitude Optimization for OFDM Systems With a Deterministic Peak-to-Average Power Ratio Constraint

Orthogonal frequency division multiplexing (OFDM) has been adopted by several wireless transmission standards. A major disadvantage of OFDM is the large dynamic range of its time-domain waveforms, making OFDM vulnerable to nonlinearities (including clipping effects) of the power amplifier (PA) and c...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2009-06, Vol.3 (3), p.418-429
Main Authors: Qijia Liu, Baxley, R.J., Ma, X., Zhou, G.T.
Format: Article
Language:English
Subjects:
Communication standards
Constraint optimization
Dynamic range
Error vector magnitude (EVM)
Masks
Mathematical analysis
Nonlinear distortion
Nonlinearity
OFDM
Optimization
Orthogonal Frequency Division Multiplexing
orthogonal frequency division multiplexing (OFDM)
Peak to average power ratio
peak-to-average power ratio (PAR)
Power conversion
power efficiency
Radio frequency
Signal to noise ratio
Spectra
Studies
Time domain analysis
Transmitters
Vectors (mathematics)
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Summary:Orthogonal frequency division multiplexing (OFDM) has been adopted by several wireless transmission standards. A major disadvantage of OFDM is the large dynamic range of its time-domain waveforms, making OFDM vulnerable to nonlinearities (including clipping effects) of the power amplifier (PA) and causing the PA to yield low efficiency on the RF to dc power conversion. A commonly used metric to characterize a signal's dynamic range is the peak-to-average power ratio (PAR). To suppress the nonlinear effects, one may want to reduce the signal PAR. However, this results in the increase of error vector magnitude (EVM), and may violate the spectral mask. In this paper, we formulate the problem as an EVM optimization task subject to a deterministic PAR constraint and a spectral mask constraint. A low-complexity customized interior-point algorithm is developed to solve the optimization problem. We also discuss extensions of the optimization framework, whereby we optimize the parameters with respect to two metrics on signal-to-noise-and-distortion ratio (SNDR) and mutual information, respectively.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2009.2020239