Predistortion at Baseband (Digital Domain) · Predistortion can be used at baseband, IF or RF. The...

Predistortion at Baseband

(Digital Domain)

Schematic Wireless Transmitter

Symbol

generator

FIR

Filter DAC

Reconst

ruction

filter Quadrature

modulator Filter

VGA PA Duplexer filter

or T/R switch Antenna

LO

cos sin FIR

Filter DAC

Reconst

ruction

filter

Baseband

DSP

I channel

Q channel

-20 -15 -10 -5 0 5 10 15 20 250

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

function y=gain2(x)

%%% table based amplifier gain function

%%% expresses gain in dB as function of input in dBm

%%%

pin=[-1000,-10,-5,0, 5, 10, 12, 14, 16, 18, 20, 50, 100]';

ga=[7, 7,7.2,8,9,10,10.5,11,11.3,10.2,8.5,-21.5,-71.5]';

y=interp1(pin,ga,x);

end

Example of Amplifier AM-AM Distortion

Pin (dBm)

Ga

in(d

B)

Matlab

Representation

(table with interpolation)

Earlier chart

function y=phase2a(x)

%%% table based amplifier phase function

%%%expresses phase in degrees as function of input in dBm

%%%

pin=[-1000,-10,0,5,8,10,12,14,16,18,20,50,100]';

ph=[ 0, 0,0,0,1, 3,4.5,8,12,18,20,20,20]';

y=interp1(pin,-ph,x);

end

Example of Amplifier AM-PM Distortion

Pin (dBm)

Ph

as

e (

de

gre

es

)

Matlab

Representation

(table with interpolation)

-20 -15 -10 -5 0 5 10 15 20 25-25

-20

-15

-10

-5

0

5

Earlier chart

Matlab Program for CDMA Signal Generation % cdmawaveform

% This program creates an IS-95 OQPSK waveform of n symbols(normalized to

% 0dBm).

% This program reads in one file containing the baseband FIR filter

% coeffecients, is95taps .

% Written by Kevin Gard 7/20/98 with modifications by Asbeck

%

%%%% Load FIR filter coefficients

load is95taps;

%%%% Set the number of I and Q symbols

n=2^14;

b=200;

n=n+b;

% Set up the random input I/Q bits

bitsI=sign(randn(n,1));

bitsQ=sign(randn(n,1));

chipbitsI=zeros(4*n,1);

chipbitsQ=chipbitsI;

chipbitsO_Q=chipbitsI;

lchip=1:n;

% Insert zeros for 4x oversampling without interpolation (1 bit 3 zeros)

chipbitsI((lchip-1)*4+1)=bitsI(lchip);

chipbitsO_Q((lchip-1)*4+3)=bitsQ(lchip);

chipbitsQ((lchip-1)*4+1)=bitsQ(lchip);

% Filter the I and Q data with FIR filter

Ichan=filter(is95taps,1,chipbitsI);

Qchan=filter(is95taps,1,chipbitsQ);

O_Qchan=filter(is95taps,1,chipbitsO_Q);

% Add I and Q in quadrature and scale final signal to 0dBm power

CDMA_O=0.1591603919596*(Ichan(b+1:n)+j*O_Qchan(b+1:n));

%% CDMA_O is the complex CDMA signal, sampled at 4x the chip rate

env=abs(CDMA_O);

How Can You Generate and Analyze Modulation Signals?

50 100 150 200 250 300 350

0.1

0.15

0.2

0.25

0.3

0.35

|en

velo

pe|

time Pave=7;

xdBm=20*log10(abs(xcdma))+Pave;

ycdma=10.^(gain2(xdBm)/20).*exp(j*phase2(xdBm)*0.0174532).*xcdma;

Use gain, phase tables to compute distorted signal

Matlab Program for CDMA Signal Generation % cdmawaveform

% This program creates an IS-95 OQPSK waveform of n symbols(normalized to

% 0dBm).

% This program reads in one file containing the baseband FIR filter

% coeffecients, is95taps .

% Written by Kevin Gard 7/20/98 with modifications by Asbeck

%

%%%% Load FIR filter coefficients

load is95taps;

%%%% Set the number of I and Q symbols

n=2^14;

b=200;

n=n+b;

% Set up the random input I/Q bits

bitsI=sign(randn(n,1));

bitsQ=sign(randn(n,1));

chipbitsI=zeros(4*n,1);

chipbitsQ=chipbitsI;

chipbitsO_Q=chipbitsI;

lchip=1:n;

% Insert zeros for 4x oversampling without interpolation (1 bit 3 zeros)

chipbitsI((lchip-1)*4+1)=bitsI(lchip);

chipbitsO_Q((lchip-1)*4+3)=bitsQ(lchip);

chipbitsQ((lchip-1)*4+1)=bitsQ(lchip);

% Filter the I and Q data with FIR filter

Ichan=filter(is95taps,1,chipbitsI);

Qchan=filter(is95taps,1,chipbitsQ);

O_Qchan=filter(is95taps,1,chipbitsO_Q);

% Add I and Q in quadrature and scale final signal to 0dBm power

CDMA_O=0.1591603919596*(Ichan(b+1:n)+j*O_Qchan(b+1:n));

%% CDMA_O is the complex CDMA signal, sampled at 4x the chip rate

env=abs(CDMA_O);

How Can You Generate and Analyze Modulation Signals?

50 100 150 200 250 300 350

0.1

0.15

0.2

0.25

0.3

0.35

|en

velo

pe|

time Pave=7;

xdBm=20*log10(abs(xcdma))+Pave;

ycdma=10.^(gain2inv(xdBm)/20).*exp(j*phase2inv(xdBm)*0.0174532).*xcdma;

Use inverse tables to generate predistorted signal

Schematic Wireless Transmitter

Symbol

generator

FIR

Filter DAC

Recons

truction

filter Quadrature

modulator Filter

VGA PA Duplexer

filter or T/R

switch

Antenna

LO

cos sin FIR

Filter DAC

Recons

truction

filter

Baseband

DSP

I channel

Q channel

Pre

dis

tort

ion

Calc

ula

tio

n

Predistortion Equations

x y z Fpd(x) GPA(y)

Vout

Vin

z

x y

Desired output

Choose y instead of x to get z


x y z Fpd(x) GPA(y)

Vout

Vin

z

x y More realistic !

y stays within

bounds Choose y instead of x to get z


x y z Fpd(x) GPA(y)

Measure GPA(y)

Choose a linear gain Go

Determine Fpd(x)

such that GPA(Fpd(x)) = Go

Fpd(x)=GPA-1(Go x)

Vout

Vin

z

x y Vpd

Vin

x

y ymax

ymax

ymax

Slope 1

Slope Go

Voutmax

Adaptive Digital Pre-Distortion

• Adaptation allows tracking of environmental variations

– An extra receiver is required

• The benefits of feedback without bandwidth limitations

Output Spectrum Before and After Predistortion

Before PD After PD

Input Spectrum Before and After Predistortion

Before PD After PD

Time Domain Response of Power Amplifiers

Input and output

waveforms vs time

(CDMA signal)

Vout vs Vin

No correction Memoryless correction Full correction

(with memory effect)

14

2.95 3 3.05 3.1 3.15 3.2 3.25

x 10-4

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Time

Vo

ltag

e

Vin and Normalized Vout

inputNormalized PA outputV

olt

ag

e (

V)

Time (100usec)

Normalized Input Envelope Voltage No

rma

lize

d O

utp

ut

En

ve

lop

e V

olt

ag

e


rma

lize

d O

utp

ut

En

ve

lop

e V

olt

ag

e


rma

lize

d O

utp

ut

En

ve

lop

e V

olt

ag

e

Characteristics of Adaptive Digital Predistortion

Technique is similar to feedback schemes, except that the

feedback is not continuous

The input signal is applied to a memoryless nonlinearity

complementary to that of the power amplifier

Feedback is only used for adaptation of the predistorted

nonlinearity

Technique is insensitive to loop delay and frequency of

operation

Technique is insensitive to aging and environmental factors

if the feedback path sensitivity to these factors is negligible

Predistortion can be used at baseband, IF or RF. The most

practical approach is at baseband.

PA Linearization Techniques: Comparison

• Adaptive DPD: the ideal approach to achieve wide-band, accurate linearization, in the era of affordable DSP

– Widely accepted in base-station transmitters

• DPD for handsets is becoming really attractive!

Linearization

Technique

Linearization

Performance

Modulation

Bandwidth Complexity Comments

Feedforward Best Widest High Best Performance

Not suitable for handsets

Polar /

Cartesian

Feedback

Good Narrow Moderate Tracks Environment Variations

Loop Stability vs. Bandwidth

Analog

Pre-distortion Low Wide Low

Simple

Difficult to Adapt

Adaptive Digital

Pre-distortion Good Wide Moderate

Tracks Environment Variations

Wideband Linearization

Depends on DSP

Requires a Receiver

How To Calculate Predistorted Input in Real System?

Vpd~xpredistorted as function of Vin~ xin

could be computed by evaluating polynomial

Generally this is too expensive in time and power

Typical approach: LUT

Compute once xpredistorted for appropriate values of xin and

then store them in lookup table --- which stores dG and dF

Then for each input xin(n) compute dG *xin(n) *exp(jdF)

Gain-Based (Cavers) Predistorter

Compute xpred for appropriate values of xin and then store them

in lookup table --- which stores dG and dF vs |xin|

Then for each input xin(n) compute dG *xin(n) *exp(jdF)

Adaptation algorithm

LUT Organization Issues

• Number of entries

• Equal spacing or nonuniform spacing of bins defining inputs / addresses

F(xm)

Xmk-1 Xmk Xmk+1

Dg

Fi = F(xmi)

Where

Xmi=Dg (i+1/2)

Dg is bin spacing

0

-20

-40

-60

-80 0 0.1 0.2 0.3 0.4 0.5

Frequency F/Fs

Po

wer

sp

ectr

al

den

sit

y (

dB

c) A: PA, no PD, PB0=0.22 dB

B: PA with 32 pt PD, PB0=0.22 dB

C: PA with 64 pt PD, PB0=0.22 dB

D: PA, no PD, PB0=30 dB

Representative results for 16QAM signal

[Cavers, 1990]

Typical practice: for handsets: LUT size 16 to 64 entries

for basestatons: LUT size 64 to 256 entries

Filling in LUT Entries How to find the right values?

1) Precalibrate: Simplest approach is to precompute the LUT

for a given amplifier design, or to calibrate it at

manufacture

=> Not generally accurate enough; need to correct in

real time due to changes in T, power level, supply

voltage, antenna impedance

2) “One-Shot Calculation”: Can collect output data over an

extended "record", downconvert to base-band, and compute a

new LUT as a "one-shot" computation

3) Iterative Loop: Can collect output data continuously, and

update LUT by small increments continuously in background

(as an iterative loop)

How to Determine Coefficients (1)

Computation using Block of Data

Send envelope x(t) to upconverter and PA

Measure output, downconvert and sample to

find y(t)

Normalize and time align x(t) and y(t)

2.95 3 3.05 3.1 -0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Time (unit~100usec)

Vo

ltag

e


input

Normalized PA output

|vo| vs |vin|

D phase vs |vin|

Scaling: compute average Vin, Vout and normalize

Time alignment: compute correlation function C, time offset is

value to get peak of C

Envelope Domain Power Series Approaches

Memory-less nonlinearity in envelope domain can be expressed as

complex power series

y(n) = a1 x(n) + a2 |x(n)| x(n) + a3 |x(n)|2 x(n) + a4 |x(n)|3 x(n) + …

n is index of time step (sampled time on envelope scale)

a1, a2, etc are unknown complex coefficients

Determine coefficients by using data set

x(n) <=> y(n)

Typically used to fit the data and then compute LUT

Experimental data set: x(t) => ym(t)

How to Determine Coefficients (1 continued)

Computation using Block of Data

Model: Use for simplicity all real coefficients, real inputs, outputs


Find "best guess" coefficients a1, a2, a3,

by minimizing error

Define Error= sum( |ym(n)-y(n)|2 )

M unknowns

y=M a Solve for ai => Can't do this exactly

equations are overdetermined

(measured result, contains noise, errors, etc)

y(n) = a1 x(n) + a2 x(n) 2 + a3 x(n) 3 + a4 x(n) 4 + …

y(1) x(1) x 2(1) x 3(1) … a1

y(2) x(2) x 2(2) x 3(2) … a2

y(3) = x(3) x 2(3) x 3(3) … a3

y(4) x(4) x 2(4) x 3(4) …

y(5) x(5) x 2(5) x 3(5) …

N e

qu

ati

on

s

ym: measured values y(n)=calculated with sum

(3 in this example)

Calculation of Coefficients

MT y = MT M a

a = (MTM)-1 MT y

Error= sum( |ym(n)-y(n)|2 )

N data points

M c

oeff

icie

nts

Simple operation in matlab!

a=M \ y

Matrix left divide

If M is square, M \ y multiplies y by inverse of M

Here M is not square : m x n with m>>n

Equations are overdetermined

Now \ computes the pseudoinverse--- gives

best value for result a in the least squares

sense

y= M a

MT y= MT M a

(MTM)-1 MT y = a

The pseudoinverse can be found by a simple heuristic

If M is not square, cannot find M-1 But MT M is square!

Polynomial Fitting in Matlab

Least Squares Fitting - Formal Theory

Linear Case

Basis functions Unknown coefficients

M=[x' x.^2' x.^3' x.^4' x.^5' x.^6' x.^7' x.^8' x.^9' x.^10' x.^11' x.^12' ];

a=M \ y ';

xtest=(0:100)*0.01;

Mtest=[xtest' xtest.^2' xtest.^3' xtest.^4' xtest.^5' xtest.^6' xtest.^7' xtest.^8'

xtest.^9' xtest.^10' xtest.^11' xtest.^12'];

ycomp=Mtest*a;

Polynomial Fitting Example in Matlab

x= vector of input data

y= vector of output data

y

ycomp

x

%x=[0 xnorm]; %used for modeling

%y=[0 ynorm]; %used for modeling

y=[0 xnorm]; %use for predistortion

x =[0 ynorm]; % use for predistortion

How to Invert Predistortion Equations?

PA provides y=F(x)

Wish to have Yd= Go x

Yd= F(xpred)

Need to decide what is Go !!

Go x= F(xpred)

xpred=F-1(Go x)

Using same data set x(n), y(n)

Only need to plot x (= x pred) vs y (=Go x)

F-1 can be expressed as polynomial fit

y=Go x

xp

red

How to Determine Coefficients (2) Successive Approximation Solution to Coefficient Determination

For each time sample n, measure ym(n) and compare with desired output

y(n)=Go x(n)

Use difference ym(n)-y(n) to adjust the coefficients by a small amount

Follows approach of LMS algorithm used widely for adaptive linear filters

Instead of a block of data with error(n) known for a large set of x(n) and y(n)

Now we have only one sample:

Error(n)= ym(n) -M(x(n)) a

Determine updates da to the various components of a

By using gradient descent strategy error

ai

Change ai by a little bit, in

proportion to your estimate of

Gradient (error) in a space

Memory-Less DPD Using Successive Approximation

C. Presti

S is a small coefficient chosen to tradeoff convergence time and accuracy

Time Dependence of ACPR After LUT Reset

Standard

algorithm

Refined

algorithm

2.95 3 3.05 3.1 3.15 3.2 3.25

x 10-4

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Time

Vo

lta

ge


inputNormalized PA output

Waveform Predistortion and

Memory Effect Correction

Input and output

waveforms vs time

(CDMA signal)

Vout vs Vin

No correction Memoryless correction Full correction

(with memory effect)

Frequency Dependent - “Memory Effects”

Inherent to the active device itself

– Thermal Effects and Trapping

Imposed by external circuitry

– Bias Networks and Matching Networks

Gain and phase at time t don’t just depend on input at time t

But also on inputs at earlier times (on baseband time scale)!

May not have proper bandwidth for

baseband signal

Including Memory in Power Series

1) Memory-less nonlinearity in envelope domain can be expressed as

complex power series y(n) = a1 x(n) + a2 |x(n)| x(n) + a3 |x(n)|2 x(n) + a4 |x(n)|3 x(n) + …


2) "Memory" in envelope time scale is expressed as linear filter y(n)= b0 x(n) + b1 x(n-1) + b2 x(n-2) + b3 x(n-3) + …

3) Nonlinearity with memory: Volterra series

y(n)= c10 x(n) + c20 |x(n)| x(n) + c30 |x(n)|2 x(n) + c40 |x(n)|3 x(n) + …

c11 x(n-1) + c210 |x(n)| x(n-1) + c310 |x(n)|2 x(n-1) + c410 |x(n)|3 x(n-1) + …

+ c211 |x(n-1)| x(n) + c311 |x(n)x(n-1)|2 x(n-1) + c41 |x(n)2x(n-1)| x(n-1)

c12 x(n-2) + c220 |x(n)| x(n-2) + c320 |x(n)|2 x(n-2) + c420 |x(n)|3 x(n-2) + …

+ c221 |x(n-2)| x(n) + c321 |x(n)x(n-1)|2 x(n-2) + c421|x(n)2x(n-1)| x(n-2)

+ …

Very many terms!!!

FIR Filter With Adapted Tapweights

Describes memory at baseband

(but not nonlinearity)

Approximations to Power Series with Memory “Pruned” Volterra series

Memory Polynomial (parallel Wiener model)

Hammerstein Model

Wiener Model

Dynamic Deviation Reduction Model (Anding Zhu)

Linear

filter

Memory-less

nonlinearity

Linear

filter

Memory-less

nonlinearity

Z-1

F0

F1

F2 Z-1

+

Nonlinear

y(n)~ b0a1 x(n) + b0a2 |x(n)| x(n) + b0a3 |x(n)|2 x(n) + b0a4 |x(n)|3 x(n) + …

b1a1 x(n-1) + b1a2 |x(n-1)| x(n-1) + b1a3 |x(n-1)|2 x(n-1) + b1a4 |x(n-1)|3 x(n-1) + …



y(n)~ c10 x(n) + c20 |x(n)| x(n) + c30 |x(n)|2 x(n) + c40 |x(n)|3 x(n) +

c11 x(n-1) + c21 |x(n-1)| x(n-1) + c31 |x(n-1)|2 x(n-1) + c41 |x(n-1)|3 x(n-1) + …



(Ex: 8 coefficients)

(Ex: 16 coefficients)

y(n)~ g10 x(n) + g30 |x(n)|2 x(n) + g50 |x(n)|4 x(n) + …

g11 x(n-1) + g31 |x(n )|2 x(n-1) +g51 |x(n)|4 x(n-1) + …

g12 x(n-2) + g32 |x(n)|2 x(n-2) + g52 |x(n)|4 x(n-2) + …

g13 x(n-3) + g33 |x(n)|2 x(n-3) + g53 |x(n)|4 x(n-3) + …

(odd orders only)

Spectrum of Input & Output Signals

With & Without Predistortion

Spectrum of Input Signal Without Pre-

distortion

Spectrum of Output Signal Without Pre-

distortion

Spectrum of Input Signal With Pre-

distortion

Spectrum of Output Signal With Pre-

distortion

1024 QAM Modulation

98 Msymbols/s (0.98 Gb/s)

Memory

Mitigation

Algorithm

Memory Polynomial

Memory length: 8

Nonlinearity order: 9

Minicircuits PA At 2 GHz

EVM: 0.7% BER<1e-6 expected

I-Q constellation diagrams

Future Power Amplifiers

Multiband and multimode power amplifiers

Broadband power amplifiers

Tunable and adaptive power amplifiers

Digital microwave signals and switching power

amplifiers

Integrated RF front-ends

PAs for mm-wave wireless systems

Free-space power combining

I IMT 1920 - 1980 2110 - 2170

II PCS 1850 - 1910 1930 - 1990

III DCS 1710 - 1785 1805 - 1880

IV AWS 1710 - 1755 2110 - 2155

V CLR 824 - 849 869 - 894

VI 830 - 840 875 - 885

VII IMT-E 2500 - 2570 2620 - 2690

VIII GSM 880 - 915 925 - 960

IX 1749.9 - 1784.9 1844.9 - 1879.9

X 1710 - 1770 2110 - 2170

XI 1427.9 - 1447.9 1475.9 - 1495.9

XII SMH 698 - 716 728 - 746

XIII SMH 777 - 787 746 - 756

XIV SMH 788 - 798 758 - 768

Operating Band Uplink Downlink

Proliferation of Operating Bands

+ Carrier Aggregation

Single Golden PA

Applications of DSP in Power Amplifiers

Instead of “High Efficiency and Linear Power Amplifier”

===> Focus Becomes “High Efficiency and Linear Transmitter”

Need to integrate PA design into overall transmitter design

Generate input signals at baseband

Generate reference signals (eg: envelope)

Generate control signals for PA (eg: Vgg control)

Predistort input signal (at baseband)

Generate waveforms for PWM converter

Generate rf waveforms

Application of Switching Mode Amplifier with Non-Constant Envelope

Class S Amplifier: Class D Amplifier

Fed with PWM Signal To Get Linear Operation

Application of Delta-Sigma Modulation

to RF Bandpass Signals

Bogdan Staszewski (Texas Instruments)

Digitally Controlled Pre-Power Amplifier

Bin.-to-Therm.

Decoder Amplitude

Control Word

Tunable

Matching

Circuit

Vdd

Modulated

Signal

Phase-modulated

Signal 127 Unit Cells

3 Binary Cells

1 x

1 x

1 x

1/2 x

1/8 x

DPA core

Decoder

Decoder

Input Output

0

10

20

30

40

50

60

70

80

-20 -10 0 10 20 30

Output Power [dBm]

Po

ut/

Pd

c [

%]

Vdd = 2.1 V, throughVdd = 1.5 V, throughVdd = 1.0 V, throughVdd = 0.5 V, through

Vdd = 2.1 V, input attenuationVdd = 1.5 V, input attenuationVdd = 1.0 V, input attenuationVdd = 0.5 V, input attenuation

Digitally-Modulated CMOS PA

Power controlled by number of

"on" transistors

Further Challenges for Digital PA

Digital techniques can provide

flexible / programmable operation

high efficiency from switching mode operation

architectures that scale with technology node

integration into Systems-on-a-chip

Difficult areas: Reduction of spurious outputs

2) Signals introduced into RX band

Downlink Uplink

20MHz 60MHz

PCS Band Band II

1960MHz

80MHz

RX band noise allowed from TX:

-174dBm/Hz-6dB=-180 dBm/Hz

Duplexer filter: Suppression by

45 dB today (handset)

=> PA spurious in RX band

should be reduced to -135

dBm/HZ

Power Combining Techniques

Spatial power combining

Spatial Power Combining -- Products by Wavestream

• First demonstration of FET stacking at W-band

• Highest reported Pout at 90 GHz from CMOS

90 GHz Stacked FET PAs

CMOS SOI 45 nm

-20

-15

-10

-5

0

5

10

75 80 85 90 95 100 105 110

S -

pa

ram

ete

rs (

dB

)

Freq (GHz)

4

6

8

10

12

14

16

18

86 87 88 89 90 91 92 93 94

Psa

t (d

Bm

), D

E,

PA

E (

%)

Freq (GHz)

17.3dBm

9.5%

8dB

Mm-wave System Features

Major difficulties in packaging and interconnects

Small and directional antennas

For f = 94GHz

lo = 3.2 mm

Bond wire inductance=0.3nH

=> j 177 ohms at 94 GHz

Spatial Power Combining for Integrated Power Arrays

+ Minimize interconnect losses

+ Phased array possibility

+ Simple connection to Si ICs

8 Antenna Array 8 push-pull PAs

DC PADS

DC PADS

G

S

Ba

lun

+ P

ha

se

Sh

ifter D

rive

r 1

1 to 4 Wilkinson Divider

1 to

2 W

ilkin

son D

ivid

er

1 to 4 Wilkinson Divider

RFIN

Ant X axis Pitch = 1400 um = 0.4375λ

Ant Y

axis

Pitch =

1800 um

=0.56 λ

Y axis

height =

3.6 mm

Driver 2

Driver 2

G

S

94 GHz Antenna & PA Chip Design

55

100-1000x higher capacity

10-100 more connected elements

10x quality of experience

Gbps data rates everywhere

Latency of the order of 1 mS

10x longer battery life

300MHz 3GHz 30GHz 300GHz

56

Few antennas Massive MIMO

Few transceivers many transceivers

TRX integration very important

Output power / PA decreases

PA cost must decrease

Bandwidth must increase to>100MHz

Power per transceiver must decrease

Microwaves Mm-waves

Tolerable Massive computation computation

Hybrid beamforming

TRX

chip

TRX

chip

TRX

chip

TRX

chip

Summary / Outlook

•PA’s are crucial elements in modern wireless communications

•To be efficient, PA is necessarily nonlinear - but the nonlinearity can

degrade transmission of spectrally band-limited signals

•Classical architectures have tradeoff of efficiency and linearity

efficiency drops off as power level decreases. But:

•Advanced architectures can provide better efficiency!!

•Advanced architectures can provide better linearity!!

•Future developments will provide better devices,

and more complex algorithms

•Overall system optimization requires considering PA along with signal and

air interface design => integrated transmitter

=> There are lots of exciting possibilities for

better PAs and better systems!!!

Predistortion at Baseband (Digital Domain) · Predistortion can be used at baseband, IF or RF. The...

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Transcript of Predistortion at Baseband (Digital Domain) · Predistortion can be used at baseband, IF or RF. The...