Exploring the Limits of Digital Predistortion P. Draxler, I. Langmore*, D. Kimball*, J. Deng*, P.M....
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Exploring the Limits Exploring the Limits of of Digital Predistortion 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
Exploring the Limits of Digital Predistortion P. Draxler, I. Langmore*, D. Kimball*, J. Deng*, P.M. Asbeck* QUALCOMM, Inc. & UCSD – HSDG *University of
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
Slide 2
Predistortion with Memory Model Blue points instantaneous V out
vs. V in Purple line gain target Green line expected value of gain
Original measurement with DPD incl. memory
Slide 3
Outline 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
Slide 4
System Block Diagram DPD is the digital predistortion block PA
is the power amplifier (model or device) Ideal Gain block sets
system performance target
Slide 5
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
y n =G o x n is output of the Ideal Gain block (the target output
of the system) y n =G n (x n ) is the output of the PA block (with
memory)
Slide 6
Waveforms Identified x n is the input waveform xp n i is the
input waveform after digital pre-distortion y n i is the output
waveform y n is the target output waveform e c i is the current
error waveform e c (i-1) is the past error waveform
Slide 7
Contraction approximation Memoryless gain Gain with memory
effects xp n i correction equation x adjustment equation
Slide 8
Specific Application Model Based Generate xp n i Evaluation of
model Compare modeled vs. measured for xp n i Quantify the
predictive accuracy of the model Model
Slide 9
Specific Application Model Based
Slide 10
Error Vector Magnitude Over all sample points, n, of a single
measurement: Normalize average power of signals to unity: x , y
Generate the rms difference between the normalized vectors
Slide 11
Experimental values of alpha: Identify vector x n Sweep and
evaluate for optimal EVM. Function of: Memoryless nonlinearity
Memory effect nonlinearity Noise and chaotic amplifier behavior
Baseband envelope DAC/ADC quantization
Slide 12
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: x , y Generate
the rms difference between the normalized vectors
Slide 13
Typical EVM histogram with Ensemble EVM (N=16) Ensemble EVM is
typically in the lower range of the histogram members. As E{e c i }
becomes small, more ensemble members are needed to have confidence
in the ensemble means and variances.
Slide 14
Simple Test Amplifier Inexpensive catalog amplifier. WCDMA
waveform used amplifier configured for narrowband operation. Severe
ACPR asymmetry which switched sides and didnt improve after
memoryless predistortion.
Slide 15
Specific Application Experiment Based Memoryless
correctionOriginal I/O performance
Slide 16
Specific Application Experiment Based Correction with memory
compensation Original I/O performance
Slide 17
Non-optimal RF Power Amplifier
Slide 18
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
Slide 19
RF Power Amplifier using Envelope Elimination and Restoration
(EER)
Slide 20
Conclusions 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