cognitive rf front-end control
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By: Eyosias Yoseph Imana
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Motivation and significance
Underlying philosophy
The actual work and results Contributions
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We use multiple of them at the same time The gadgets use wireless connectivity
They generate a large amount of wireless traffic
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We need to address this challenge!!!
Source: CISCO
http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.html
Source: FCC
http://www.hightechforum.org/spectrum-deficit-disorder/
1000x
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The lower frequencies are already crowded The wireless industry needs to be allocated with additional
spectrum
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Exploiting sparsely used millimeter wave (mmWave) bands Spectrum sharing between the federal government and the
wireless industry through Dynamic Spectrum Access (DSA)
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Exploiting sparsely used millimeter wave (mmWave) bands Spectrum sharing between the federal government and the
wireless industry through Dynamic Spectrum Access (DSA)
We need to expedite the adoption of mmWave and DSAtechnologies into the main-street of the wireless industry
We do this by addressing the technical challenges related to
these technologies
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Poor receiver selectivity is a challenge for bothmmWave and DSA technologies
What is receiver selectivity?
Highly selective pre-selector Poorly selective pre-selector
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Consider a 1%, -10 dB bandwidth filterUsed in Verizon phones
A 1% filter has bandwidth of 10 MHz at1 GHz band
A 1% filter has bandwidth of 300 MHz at30 GHz band (mmWave band)
Source: Yaiyo Yuden
http://www.yuden.co.jp/productdata/sheet/B4UQ.pdf
Reception
Bandwidth > 300 MHz
Large guard
bands
Needs high-performance ADCs
> 300 MHz
Signal
Bandwidth < 20 MHz
Poor selectivity is expected to be a challenge in
mmWave-based communications
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In DSA, a secondary user uses a frequency band when the incumbent
user is not active
There may be multiple shared frequency bands
The incumbent may be frequency hopping
Frequency
Frequency
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DSA may have to use tunable filters Electronically-tunable filters are poorly selective
Source: Yaiyo Yudenhttp://www.yuden.co.jp/productdata/sheet/B4UQ.pdf
In average 15% selectivity
2% selectivity
Poor selectivity is expected to
be a challenge in DSA-based
communications
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Do not attempt to improve selectivity
Accept poor-selectivity
Design the rest of the receiver assuming poorselectivity
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A new modeling concept is developed for poorly-
selective receivers
Using this model, a Cognitive RF front-end (CogRF)
control mechanism is developed to improve the
performance of the poorly-selective receivers
A concept of using auxiliary path to combat very strong
neighboring-channel signals is also developed
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Motivation and significance
Underlying philosophy
The actual work and results Contributions
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fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPNo
Pre-selector
RF frequency
INPUTSEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DC
MIXER OUTPUT
DSP frequency
DCADCOUTPUT
+fs/2-fs/2
1st
Nyquisit Zone
fs-fs
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fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPNo
Pre-selector
RF frequency
INPUTSEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DC
MIXER OUTPUT
DSP frequency
DCADCOUTPUT
+fs/2-fs/2
1st
Nyquisit Zone
fs-fs
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fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPPre-selector
fs/2
RF frequency
INPUTSEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DC
MIXER OUTPUT
DSP frequency
DCADCOUTPUT
+fs/2-fs/2
1st
Nyquisit Zone
fs-fs
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fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPNo
Pre-selector
RF frequency
INPUTSEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DC
MIXER OUTPUT
DSP frequency
DCADCOUTPUT
+fs/2-fs/2
1st
Nyquisit Zone
fs-fs
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fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPNo
Pre-selector
RF frequency
INPUTSEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DC
MIXER OUTPUT
DSP frequency
DCADCOUTPUT
+fs/2-fs/2
1st
Nyquisit Zone
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fs/2
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPNo
Pre-selector
RF frequency
INPUTSEPECTRUM
RF frequency
fLO
LNAOUTPUT
Baseband frequency
DC
MIXER OUTPUT
DSP frequency
DCADCOUTPUT
+fs/2-fs/2
1st
Nyquisit Zone
The energy re-distribution in receivers is controllable
The energy re-distribution process can be controlled using the
sampling and Local Oscillator (LO) frequencies of the receiver
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CogRF intelligently controls the LO and sampling frequencies
of the receiver
Transforms an RF filtering problem to a DSP filtering problem
Spectrum sensing is integral part ofthe CogRF operation
CogRF uses the mathematical model
of a receiver to predict the
interference level corresponding to a
given receiver setting (it does not trial
and error)
The model should allow a simple
computation of interference level at
the output of the ADC given a
spectrum sensing data
1
2
3
4
5
end
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pin[1] pin[M]..
CSR of Receiver Input
LNAPre-selector MixerBaseband
filterBasebandamplifier
ADC
Digital BasebandFrequency
pout[1] pout[N]..
CSR of Receiver
Output
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,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,10 ,11 ,12in in in in in in in in in in in inp p p p p p p p p p p p inP
,5 1 1
,6 ,8 2 2
,9 3 3
,10 4 4
0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0
in
in in
in
in
p v v
p xp v vx
p v v
p v v
inP
pin[1] pin[M]..
CSR of Receiver Input
LNAPre-selector MixerBaseband
filter
Baseband
amplifier
ADC
Digital Baseband
Frequency
pout[1] pout[N]..
CSR of Receiver
Output
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Linearly models an inherently non-linear receiver
RF front-end
Readily captures the energy re-distribution processin receivers
Has various applications Cognitive engine design
Receiver characterization Spectrum sensing
Receiver aware frequency resource allocation in DSA
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The CSR model of the receiver changes as the LOand sampling frequency settings change
0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0
x
0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0
x
(fLO,A, fs,A) (fLO,B, fs,B)
Desired channel/raw
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1
2
3
4
5
end
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Spectrum sensing for CogRF presents unique
challenges
Estimation based spectrum sensing The sensing RF front-end is poorly selective
pin[1] pin[M]..
CSR of Receiver Input
LNAPre-selector MixerBaseband
filter
Baseband
amplifierADC
Digital Baseband
Frequency
pout[1] pout[N]..
CSR of Receiver
Output
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( , ) ( , ) ( , )LO s LO s LO s
Y f f A f f X V f f
CSR model of the
receiverZero-input CSR model
of the receiver
CSR of the received signalCSR of the ADC output
fLO fs
Antenna
LNA Mixer Baseband Filter ADC DSPNo
Pre-selector
X( , )LO sY f f
Is accessed by
spectrum
sensing
algorithms
Has to be
estimated
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,0 ,0 ,0
,1 ,1 ,1
( , ) ( , ) ( , )
( , ) ( , ) ( , )
... ... ...
LO s LO s LO s
LO s LO s LO s
Y f f A f f V f f
Y f f A f f X V f f
Y A X V
Raw output of multi-
band sensing
are known
,Z AX Z Y V
True spectrum
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Estimator Design
mY A X V N
Measured Measurement errorTo be estimated
Proposed estimator:
,mY X High-dynamic range vectors2
,
,
.arg min
m i i
X i m i
z a XX
y
called R-TSC
11 1T T
mX A R A A R Y
2 2 2
,0 ,1 ,2 ...m m mR diag y y y
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B encompasses the effects of different receiver impairments B
is easy to measure The metric has a single value The metric tells the performance of the receiver averaged across
different possibilities of the spectrum occupancy
1 in inout
m m m
p p p
P P
P = VA B
out inP = AP + V
1010log 1 B
For ideal receiver
0 For bad receiver
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0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0
v
v
v
v
v
B
IQ imbalance
0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0
v
v
v
v
v
B
Ideal receiver
0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0
w v
x v
y y v
x v
w v
B
Aliasing and IQ imbalance
0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0
w v
x v
x v
w v
B
Aliasing, IQ imbalance and DC offset
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PicoRF platform is used to carry out
the hardware experiments
The platform contains: Virtex-5 FPGA
RFIC5
RFIC5 has variable LO frequency
RFIC5 has variable samplingfrequency
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fLO= 915 MHz, fs= 15.625 Msa/s
Bmatrix
IQ Imbalance
Aliasing
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Baseband filters
3 dB bandwidth
Sampling rate
14 MHz 15.625 MSa/s (8 sub-bands) 0.81 dB
14 MHz 31.25 MSa/s (16 sub-bands) 7.33 dB
7 MHz 15.625 MSa/s (8 sub-bands) 1.86 dB
7 MHz 31.25 MSa/s (16 sub-bands) 9.47 dB
The best receiver setting
The worst receiver setting
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Baseband filters
3 dB bandwidth
Sampling rate
14 MHz 15.625 MSa/s (8 sub-bands) 0.81 dB
14 MHz 31.25 MSa/s (16 sub-bands) 7.33 dB
7 MHz 15.625 MSa/s (8 sub-bands) 1.86 dB
7 MHz 31.25 MSa/s (16 sub-bands) 9.47 dB
The best receiver setting
The worst receiver setting
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The corrected spectrum sensing data closely represents thestate of the spectrum occupancy
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Fixed settings
The level of undesired
power in the desired band
LO frequency under
CogRF control
Sampling frequency
under CogRF control
Experiment scenario: Tone signal injected into the receiver The frequency of the input is randomly varied between 890 MHz and 940 MHz The desired channel 13 (Dissertation Figure 6.3)
The spikes corresponding to CogRF are lessaggressive and less frequent
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The performance of pre-selector-less
receiver suffers as the number of active
neighboring channels increases
Selective receivers are insensitive to
neighboring-channel interference
CogRF enables a pre-selector-less
receiver to behave like a selective
receiver
CogRF virtually creases selectivity in a
pre-selector-less receiver
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The performance of pre-selector-less
receiver suffers as the number of active
neighboring channels increases
Selective receivers are insensitive to
neighboring-channel interference
CogRF enables a pre-selector-less
receiver to behave like a selective
receiver
CogRF virtually creases selectivity in a
pre-selector-less receiver
CogRF is a viable architecture to implementpoorly selective receivers
CogRF is a viable architecture to implementmmWave and DSA receivers
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Neighboring-channel signal exceeds the saturation level of the receiver CogRF may not be helpful in such scenarios Traditionally this scenario is addressed using Automatic Gain Control (AGC)
Power
Channels
Psat
Reception
bandwidth
Desired
signal
Interferer
Noise
There is SNR penalty associated with using AGCs
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Added Noise
cos
cos
D D
I I
D
I
x t D t
x
t
tt I t
1
2
( ) ( ) ( )( ) 1 sin ( ) sin ( )
2 sin ( ) sin ( ) cos
s in 1 ( ) sin 1 ( ) cos1
s in 1 ( ) sin 1 ( )1
s o
oI
k
I
k
k
t t I t x t V t t
Vk t k t k t
k
I tk t k t k t
k
I tk t k t
k
2
1
cos
sin ( ) sin ( )( ) ( ) ( ) cos
2
( ) ( ) 2 + sin ( ) sin ( ) cos ( )
I
I
I D
k
k t
k t k t I tt t t
t tk t k t k t x t
k
2
1 Dj
Fx n n D n e v n
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cos
cos
D D
I I
D
I
x t D t
x
t
tt I t
2
3
21 , for strong non-linearity
where,3
1 , for weak non-linearity2
Dj
Fx n n D n e v n
n
n
a I n
1
0,
cos ,
o
oo
I n V
n VI n V
I n
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Difficult to implement
Simpler to implement
Auxiliary Path Assisted Soft-Decoding
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Manual adjustable attenuator
to emulate an AGC
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APA-SD has up to 8 dB improvement over AGC
Or more
0 2 4 6 8 10 12 14 16 18 20
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
SNR, dB
Average
Throughput,bits/s/Hz
Simulation
Hardware
No-AGC,
no aux. path
Simulation
APA-SD,
Hardware
No-AGC,
no aux. path
Simulation
APA-SD,
Simulation
AGC
Hardware
QPSK, C = 8 dB
6 8 10 12 14 16 18 20 22 240
0.5
1
1.5
2
2.5
3
3.5
SNR, dB
Average
Throughput,bits/s/H
z
Simulation
APA-SD,
Simulation
APA-SD,
Hardware
No-AGC,
no aux. path
Simulation
Hardware
AGC
No-AGC, no aux. path
Simulation
16-QAM, C = 8 dB
Hardware
max20logo
I nCV
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APA-SD has up to 8 dB improvement over AGC Or more
16-QAM, SNR = 14 dB
0 2 4 6 8 100
0.5
1
1.5
2
2.5
3
3.5
C, dB
Average
Throughput,bits/s/H
z
AGC, Hardware
APA-SD, Hardware
AGC, Simulation
APA-SD, Simulation
max20log
o
I nC
V
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APA-SD has up to 8 dB improvement over AGC Or more
16-QAM, SNR = 14 dB
0 2 4 6 8 100
0.5
1
1.5
2
2.5
3
3.5
C, dB
Average
Throughput,bits/s/H
z
AGC, Hardware
APA-SD, Hardware
AGC, Simulation
APA-SD, Simulation
max20log
o
I nC
V
APA-SD provides up to expands the dynamic range of apoorly-selective receiver by 10 dB or more
This helps in shrinking receiver-exclusion zones around
radar transmitters
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Motivation and significance
Underlying philosophy
The actual work and results Contributions
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Channelized Spectrum Representation (CSR) A new receiver modeling concept
CSR models an inherently non-linear RF front-end using a
matrix-based linear model CSR was used to solve various engineering problems
CSR
Cognitive engine
design
Robust spectrum
sensing design
Single-valued receiver
performance metric
Receiver aware frequency
resource allocation
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CogRF Cognitive control over the sampling and LO frequencies of the
receiver
CogRF can transform an RF filtering problem to a DSP filteringproblem
CogRF allows a poorly-selective receiver to behave similar to a
highly-selective receiver
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Auxiliary-Path Assisted Soft-Decoding (APA-SD) Alternative to AGCs to handle strong neighboring-channel
signals
A new concept of informing the decoder about which of thereceived bits are likely erroneous
Information used by a decoder
Extrinsic information
Intrinsic information
Bit-quality information
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Thank you
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