Cognitive RF Front-End Control

7/21/2019 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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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


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


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


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


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]..



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]..



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


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




The best receiver setting

The worst receiver setting


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Baseband filters

3 dB bandwidth

Sampling rate





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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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

Documents

Cognitive RF Front-End Control