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Exploring the Limits of Digital Predistortion. P. Draxler, I. Langmore*, D. Kimball*, J. Deng*, P.M. Asbeck* QUALCOMM, Inc. & UCSD – HSDG *University of California, San Diego, HSDG September 14 th , 2004. Predistortion with Memory Model. Original measurement. with DPD incl. memory.

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exploring the limits of digital predistortion

Exploring the Limits of Digital Predistortion

P. Draxler, I. Langmore*, D. Kimball*, J. Deng*, P.M. Asbeck*


*University of California, San Diego, HSDG

September 14th, 2004

predistortion with memory model
Predistortion with Memory Model

Original measurement

with DPD incl. memory

Blue points – instantaneous Vout vs. Vin

Purple line – gain target

Green line – expected value of gain

  • Introduction
  • Contraction approximation for nonlinear systems
  • Memory effect compensation – model based
  • Error Vector Magnitude (EVM) metric
  • Memory effect compensation – measurement based
  • Results from 2 RF Power Amplifiers
  • Conclusions
system block diagram
System Block Diagram
  • DPD is the digital predistortion block
  • PA is the power amplifier (model or device)
  • Ideal Gain block sets system performance target
notation and relationships
Notation and Relationships
  • n is the sample index
  • i is compensated waveform iteration index
  • x: vectors are denoted with underbars
  • {} curly brackets denote multiple signals in an ensemble
  • yn=Goxn is output of the “Ideal Gain” block (the target output of the system)
  • y’n=Gn(xn) is the output of the “PA” block (with memory)
waveforms identified
Waveforms Identified
  • xn is the input waveform
  • xpni is the input waveform after digital pre-distortion
  • y’ni is the output waveform
  • yn is the target output waveform
  • eci is the current error waveform
  • ec(i-1) is the past error waveform
contraction approximation
Contraction approximation

Memoryless gain

Gain with memory effects

xpni correction equation

Δx adjustment equation

specific application model based


Specific Application – Model Based
  • Generate xpni
  • Evaluation of model
    • Compare modeled vs. measured for xpni
  • Quantify the predictive accuracy of the model
error vector magnitude
Error Vector Magnitude
  • Over all sample points, n, of a single measurement:
    • Normalize average power of signals to unity: xα, yα
  • Generate the rms difference between the normalized vectors
experimental values of alpha
Experimental values of alpha: α
  • Identify vector Δxn
  • Sweep α and evaluate for optimal EVM.
  • Function of:
    • Memoryless nonlinearity
    • Memory effect nonlinearity
    • Noise and chaotic amplifier behavior
    • Baseband envelope DAC/ADC quantization
ensemble average error vector magnitude
Ensemble Average Error Vector Magnitude
  • Perform an ensemble average over many measurements: E{.}
  • Over all sample points: n
    • Normalize average power of both signals to unity: xα, yα
  • Generate the rms difference between the normalized vectors
typical evm histogram with ensemble evm n 16
Typical EVM histogram with Ensemble EVM (N=16)
  • Ensemble EVM is typically in the lower range of the histogram members.
  • As E{eci} becomes small, more ensemble members are needed to have confidence in the ensemble means and variances.
simple test amplifier
Simple Test Amplifier
  • Inexpensive catalog amplifier.
  • WCDMA waveform used – amplifier configured for narrowband operation.
  • Severe ACPR asymmetry which switched sides and didn’t improve after memoryless predistortion.
specific application experiment based
Specific Application – Experiment Based

Memoryless correction

Original I/O performance

specific application experiment based1
Specific Application – Experiment Based

Correction with memory compensation

Original I/O performance

eer amplifier
EER Amplifier
  • Power Amplifier
    • Motorola LDMOS
    • Vdd amplifier included
    • PAE: 31.5%
  • Signal
    • WCDMA signal
    • >9dB peak to average
    • Pin: 3.35 Watts
    • Pout: 29.0 Watts
  • A new metric – ensemble average EVM – has been defined to separate out the deterministic EVM components from the random EVM components.
  • An measurement based algorithm has been realized that enables one to compensate for deterministic components of the output waveform.
  • This metric and compensation technique is insightful during:
    • component evaluation and characterization of amplifiers,
    • amplifier modeling and model evaluation,
    • identification of optimal performance targets,
    • in support of development of real time adaptive blocks…