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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

Conclusions Restoration (EER)

  • 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…