The document discusses improving the efficiency and linearity of RF power amplifiers. It proposes using a technique called outphasing which decomposes the input signal into constant amplitude signals. Additionally, it introduces using specially optimized nonlinear Q-filters to process the decomposed signals in order to improve the spectral content without sacrificing the peak-to-average power ratio. This enhances the linearity and relaxes the stringent alignment requirements of traditional outphasing amplifiers, making the technique more practical to implement. The key innovation is the use of these nonlinear Q-filters applied in the digital domain to optimize the tradeoff between spectral content and signal crest factor.

1. Smart Power Amplifier
using variable number of constant amplitude decomposed signals
--- and a dual phase modulator ---
Phased array architecture for linear and efficient power amplification
using an adaptive system of massively parallel power amplifiers
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2. Power Amplifiers Classification
nonlinearity and efficiency in RF power amplifiers
Type* Linearity* Efficiency*
A very high very low (~50%)
A/B
B high low (~78%)
B/C
C medium medium (~80%)
:
: low high
:
E very low very high (~90%)
*Linearity: for EDGE, 56 dBc at 400 KHz offset from carrier is “very high”
*Efficiency when transistor is at full power
*Type/Class E is “switched-mode”
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3. Power Amplifiers Classification
nonlinearity concerns in RF power amplifiers
• Linearity is defined by standard bodies: FCC, ETSI
• Makes sure not to affect neighboring channels
• Enforcing less distortion => Less interference
=> Less loss of information
• Usually specified as Signal to Interferer Noise Ratio
• Linearity specification is technology dependant
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4. Power Amplifiers Classification
efficiency needs in RF power amplifiers
• Efficiency == Less Wasted Energy
• Efficiency => Less Operational Expenses (OP-EX)
=> Lower Electrical Bills
=> Money Gain (+$)
• Efficiency => Long Battery Life for Mobiles
• Efficiency => Environmentally Friendly
• Inefficiency => Heat Dissipation
=> Cooling Requirements
=> Noise because of Fans
=> More Heat Sink
=> Significantly Increased Size and Weight
=> Money Loss (-$)
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5. Outphasing Power Amplifiers
of Enhanced Data Rates for GSM Evolution (EDGE) Signals
I 1 (t ) ⎫
⎪
Ie ⎬ S1 (t )
⎧ I (t ) EDGE filter Nonlinear
Q1 (t ) ⎪
⎪ Map ⎭ Modulator PA
⎪
⎪
S (t )⎨ Se RF Combiner
I 2 (t ) ⎫
⎪ ⎪
⎪ ⎬ S 2 (t )
Q2 (t ) ⎪
Nonlinear
⎪Q(t )
⎩ EDGE filter Map ⎭ Modulator PA
Qe
EDGE Signal Generation Modulation, amplification, and combining
reconstructs the original EDGE signal Se
Outphasing means ( I e , Qe )
At any point in time, Se=S1+S2,
decomposing the signal vector but S1 and S2 have constant
Se
Se into two vectors S1 and S2 radiuses.
with constant amplitudes S2 The intersection of those
(0,0)
(vectors rotating in circles of circles defines the outphased
constant radiuses) S1 signals S1 and S2
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6. Prior Art Search
for RF power amplification techniques
Competing Technologies
a) Digital Pre-Distortion
b) Outphasing
Implementation of Outphasing
1) Lookup Tables
2) Inverse Sine & Inverse Cosine Computations
3) Complex Computation of the Square Root Function
Types of Decomposition
A) 2-Phase Decomposition
B) N-Phase Decomposition (Fixed Number of Signals = N)
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7. Closest Prior Art
Ericsson fixed number of decomposed signals
Title: Linear amplification system and methods
using more than two constant length vectors
Inventor: Paul Wilkinson Dent
Assignee: Ericsson Inc., Research Triangle Park, NC (US)
Number: US 6,311,046
Date: October 30, 2001
Issues:
» fixed number of decomposed signals
» complex combiner hardware
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8. The New Idea
Smart Power Amplifier Concept and Computations
for Generating Variable Number of Constant Amplitude Decomposed Signals in Real-Time
S5 S5
S4
S1
S3 ( I e , Qe ) ( I e , Qe )
S4
S2 Se
S3
S1 Se
S2
(0,0) (0,0)
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9. The Invention
Smart Power Amplifier (SPA) - Phased Array Architecture
Adaptive PA Array
S1 aS1
S2 aS2
S3 aS3
S aS
Decomposer Combiner
Si aSi
Promise
Sn aSn
Linearity of Type A
together with the
Efficiency of Type E
Power Amplification
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10. Demonstrations
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11. Demonstrations
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12. Advantages of SPA
characteristics and promises
1. Efficiency is not compromised for linearity as done with the conventional
outphasing power amplifiers.
2. By using constant amplitude signals, we completely by-pass the issues of signal
distortion due to the nonlinearity of individual power amplifiers.
3. Performs variable phase decomposition at the cost of two phase decomposition plus
a minimal constant overhead, regardless of the number of available PAs.
4. For a given design, accuracy and efficiency requirements can be met by identifying
the maximum number of PAs for the proposed architecture.
5. Can be used with other kinds of signals, and not just with EDGE signals.
6. Selection of maximum number of PAs is performed only once for each design
(EDGE, CDMA, … etc.), and offline.
7. Can be used in base-stations as well as in wireless handsets.
8. Can be efficiently implemented as software or hardware (requires few cycles).
9. Solution is transparent to receivers.
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13. Dual Phase Modulator
for improved efficiency and linearity of RF power amplifiers
An extremely inexpensive and novel solution for the alignment problems in
Outphasing Power Amplifiers using
Q-filter Techniques
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14. Problem and Solution
Problem: Outphasing is a technique used to improve RF power
amplifier efficiency without trading-off linearity.
It is rarely used nowadays due to stringent branch alignment
requirements imposed by stringent spectral requirements for digital
wireless transmission standards. Usually phase misalignment as little
as 3 degrees between the two branches can cause spectral emissions
and ACPR failures.
Solution: We are proposing a new architecture where the two
outphased signals go through special DSP filtering (using nonlinear
Q-filters) optimized to improve spectral content (and thus relaxing the
alignment requirement up to 8 degrees or higher) without sacrificing
the desirable low Peak-To-Average Power Ratio (Crest Factor) of the
outphased signals.
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15. I1 (t ) ⎫
⎪
Ie
Nonlinear ⎬ S 1 (t )
⎧ I (t ) EDGE filter Q 1 (t ) ⎪ Modulator
⎪ Map ⎭ PA
⎪
⎪
S (t ) ⎨ Se RF Combiner
I 2 (t ) ⎫
⎪ ⎪
⎪ ⎬ S 2 (t )
Nonlinear
⎪Q (t )
⎩ EDGE filter Q 2 (t ) ⎪
⎭ Modulator PA
Qe Map
e.g. EDGE Signal Modulation, amplification, and combining
Generation
Existing Outphasing Solution reconstructs the original EDGE signal Se
Adding specially optimized DSP filtering (with nonlinear Q-filters) can
dramatically improve spectral content without sacrificing low Crest Factor
I1 (t ) ⎫
⎪
Ie
Nonlinear ⎬ S 1 (t )
⎧ I (t ) EDGE filter Q 1 (t ) ⎪
Optimized Modulator PA
⎪ Map ⎭ Q-Filter
⎪
⎪
S (t ) ⎨ Se RF Combiner
I 2 (t ) ⎫
⎪ ⎪
⎪ ⎬ S 2 (t ) Optimized
Nonlinear
⎪Q (t )
⎩ EDGE filter Q 2 (t ) ⎪
⎭ Modulator PA
Qe Map Q-Filter
e.g. EDGE Signal Modulation, amplification, and combining
Generation
Q-filter Based Solution reconstructs the original EDGE signal Se
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16. Spectral Contents
improved by about 10dB, thus leading to more relaxed alignment requirements
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17. Crest Factor
degraded by only 0.67dB, thus still maintaining low Peak-To-Average Power Ratio
(Our experiments show that using FIR linear filters does not maintain good Crest Factor, while
optimizing the Q-filter showed good control and trading-off spectral contents versus Crest Factor)
Original EDGE Outphased Signal After Applying Optimized
Signal Before Q-filtering Nonlinear Q-filter
CF=3.5dB CF=0dB CF=0.67dB
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18. Using Linear
FIR-Filter
Crest Factor gets
worse than
original signal
Outphased 1 FIR-Filtered 1
Good Crest Factor Bad Crest Factor
Bad Spectral Content Good Spectral Content
Outphased 2 FIR-Filtered 2
Good Crest Factor Bad Crest Factor
Bad Spectral Content Good Spectral Content
Original Signal Amplify then Combine
(e.g. EDGE) to Get Original Signal
Constellation
Diagrams
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19. Using Nonlinear
Q-Filter
Note that Crest
Factor and Spectral
Content gets better
Outphased 1 Q-Filtered 1
Good Crest Factor Good Crest Factor
Bad Spectral Content Good Spectral Content
Outphased 2 Q-Filtered 2
Good Crest Factor Good Crest Factor
Bad Spectral Content Good Spectral Content
Original Signal Amplify then Combine
(e.g. EDGE) to Get Original Signal
Constellation
Diagrams
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20. Phase Misalignment Effect
10o
60o
Error Vector Magnitude (EVM) = 1 – 2*sin(30+10/2)= 14%
3GPP Spec ~ 17%
Thus, up to 10o of phase error is tolerable.
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21. Prior Art
Linear amplification systems and methods using more than two constant length vectors:
6311046, Ericsson Inc., Oct 30, 2001
http://www.google.com/patents?vid=USPAT6311046&id=Q2sIAAAAEBAJ&dq=outphasing+ericsson
Describes a method to improve alignment and spectral contents, but relies on using four branches with conjugate signals,
which degrades efficiency considerably and adds need to four modulators and more complex architecture.
Hybrid Chireix/Doherty amplifiers and methods:
6133788, Ericsson Inc., Oct 17, 2000
http://www.google.com/patents?vid=USPAT6133788&id=KQcGAAAAEBAJ&dq=outphasing+ericsson
Describes a way to increase efficiency by using special combining technique, but will still suffer from traditional outphasing
problems, like branch alignment accuracy …etc.
Outphasing modulator:
7009447, Intel Corporation, Mar 7, 2006
http://www.google.com/patents?vid=USPAT7009447&id=_bJ3AAAAEBAJ&dq=outphasing
Does not describe how to solve the alignment problem. It details a method to outphase the signals, but will still suffer from
traditional outphasing problems, like branch alignment accuracy …etc.
Method and apparatus to match output impedance of combined outphasing power amplifiers:
7030714, Intel Corporation, Apr 18, 2006
http://www.google.com/patents?vid=USPAT7030714&id=g9J3AAAAEBAJ&dq=outphasing
Describes a unique way of combining both branches to achieve maximum efficiency, but will still suffer from traditional
outphasing problems, like branch alignment accuracy …etc.
RF power amplifier and methods for improving the efficiency thereof:
6825719, Intel Corporation, Nov 30, 2004
http://www.google.com/patents?vid=USPAT6825719&id=d9oRAAAAEBAJ&dq=outphasing
Describes a way to control the phase at the output of the PA, but will still suffer from traditional outphasing problems, like
branch alignment accuracy …etc.
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22. Benefits
• Improve user experience by improving battery life. It is estimated that using this
technique, PA can achieve about 25% more efficiency than traditional methods.
• Improve cost by using much cheaper and smaller nonlinear PA operating in Class C. It is
estimated that we only need half the size PA when using filtered outphasing to achieve
same PA performance, besides no need to any linearity requirements on the PA thus
improving yield.
• Eliminate need of costly outphasing alignment techniques.
• Shift PA performance optimization to the digital domain instead of the RF domain, giving
much more controllability and ease of design, thus improving Time-To-Market.
• Achieve a new method that allows improving PA efficiency (and thus battery life) without
compromising linearity, and have all the controls in the digital domain.
• Easy to implement and far superior to other outphasing or PA linearization techniques.
• Achieve a practical architecture for current generation wireless standards with high Crest
Factor (like HSUPA), and for next generation wireless standards with high Crest Factor
(like WiMAX and other OFDM standards).
• High Crest Factor is imposing battery life constrains and cost constrains (due to need of
bigger linear PA) that can be solved by using very low Crest Factor techniques.
• Extendable to Smart Power Amplifiers.
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23. Backup
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24. Q-Measure Concept
Fuzzy Measure Axioms Q-Measure Extensions (2003)
Let A, B ⊂ X be non-intersecting sets for any choice of λ >-1, λ!=0, define:
• Boundary conditions:
m ( Φ ) = 0, m ( X ) = 1 ∏ (1 + λf i ) − 1
xi ∈ A
q ( A) = , λ ≥ -1, λ ≠ 0
• Monotonicity:
A1 ⊂ A2 → m ( A1 ) ≤ m ( A2 ) ∏ (1 + λf
xi ∈ X
i
) −1
• Continuity: where fi ε [0,1] are density generators
guaranteed for discrete spaces
Convergence Behavior of Q-Measures
Probability Measure (1933)
1.05
replaces monotonicity by additivity: 1.025
p ( A U B ) = p ( A) + p ( B ) Scaling Factor, fn 1
0.975 C as e 1
C as e 2
Sugeno λ-Measure (1975)
0.95 C as e 3
0.925
adds one more axiom: 0.9
g ( A U B ) = g ( A) + g ( B ) + λ g ( A) g ( B ) 0.875
0 2 4 6 8 10 12
for a unique λ that satisfies g(X)=1
I te ra ti o n , n
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25. Q-Measures
in a nutshell
X
x1 x2 x3
A∪ B = X
A
q-measures provide A∩ B = Φ
x6 x5 x4
more expressive and α = f ( A) > 0
computationally attractive β = f ( B) > 0
B=Ac
nonlinear models λ ≥ −1
x7 x8 x9 ρ = f ( A ∪ B) = α + β + λαβ > 0
for
uncertainty α α
q ( A) = =
q(Ac)
management ρ α + β + λαβ
when modeling a q( B) =
β
=
β
1.0
λ<0
complex system, ρ α + β + λαβ
belief
α/ρ it’s an oversimplification
λ=0 ∂q( A)
probability to assume that the >0
β/ρ
∂α
interdependency among ∂q( A)
<0
λ>0 ∂β
plausibility information sources is
∂q( A)
linear <0
∂λ
q(A)
0 β/ρ α/ρ 1.0
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26. Q-Filter Node
and evidence accumulation for n-tap window and m-level signal
S(t)
s1 ... sn
e(m-1)(t)
m-1 s1 (m-1) sn (m-1)
. . . . . . .
. . . . . . .
. . . . . . .
e(i)(t)
i Ai q(Ai)
. . . . . . .
H(i,j) .
.
.
.
.
.
.
.
.
.
.
.
.
. + e(t)
e(3)(t)
3
e(2)(t)
2
e(1)(t)
1 s1 (1) sn (1)
f Processor
λ
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27. Q-Filter Computations
N=5 Tap Case - Nonlinearity, Adaptivity, and Model Capacity
h4
Signal Value h(xi) Aα
h3
α
h1
h2
h5
i
∏ (1 + λf
xi ∈ A
i
) −1 x1 x2 x3 x4 x5 Window Slots
q ( A) = , λ ≥ -1, λ ≠ 0 f1 f2 f3 f4 f5
∏ (1 + λf
xi ∈ X
i
) −1 Density Generators
q(Aα) λ Nonlinearity Controller
q({x4, x3, x1, x2, x5})=1.0
q({x4, x3, x1, x2}) Total area is the Q-Filter output value
q({x4, x3, x1})
q({x4, x3})
Adaptive Weight
q({x4})
α
q(Φ)=0.0
h5 h 2 h 1 h 3 h4 Threshold
h5<h2< h1<h3< h4 Case
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28. Kernel Structure
and model capacities
ξ00000
ξ10000 ξ01000 ξ00100 ξ00010 ξ00001
ξ11000 ξ10100 ξ10010 ξ10001 ξ01100 ξ01010 ξ01001 ξ00110 ξ00101 ξ00011
ξ11100 ξ11010 ξ11001 ξ10110 ξ10101 ξ10011 ξ01110 ξ01101 ξ01011 ξ00111
ξ11110 ξ11101 ξ11011 ξ10111 ξ01111
ξ11111
Lattice representation for a q-measure of size, n=5
ξ b , …, b = q({xj | bj=1, j=1, …n})
1 n
i.e. ξ00000 = q(Φ) and ξ01101 = q({x2,x3 ,x5})
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29. Fast Q-Filter
1. A fast method that replaces resorting (quadratic complexity for worst case) of
moving window contents by deletions and insertions (linear complexity for worst
case) when processing q-filter operations.
2. Data structures to implement the method.
3. Static memory allocation.
h5
h5
f3 f2 f0 f4 f1
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30. Sample Execution
1 2 3 4 5 6 7 8 9 Time Slot
9 3 5 7 2 4 6 1 8 Time Series
5 2 3 4 1 1-Index
4 1 5 2 3 2-Index
3 4 1 5 2 3-Index
5 2 3 4 1 4-Index
4 1 2 3 5 5-Index
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