Wideband Cyclostationary Spectrum Sensing and Modulation Classification

Wideband Cyclostationary Spectrum Sensing and Modulation Classification

Eric RebeizAdvisor: Prof. Danijela Cabric

UCLA Electrical Engineering Department

Ph.D. Defense08/19/2013

D. Markovic / Slide 2

Wideband Cognitive Radio Concept & Vision

Cognitive Radios (CRs) opportunistically access the spectrum

2

How can we achieve this goal?

Future Promise of Wideband CR– Increase radio throughput– Support more users

Sensing Layer PHY Layer

MAC Layer

Higher Layers

In order to achieve this vision, practical wideband spectrum sensing challenges should be considered


Wideband Spectrum Sensing & Classification Design Goal #1: Interference mitigation through sensing and classification Goal #2: Energy efficient processing

3

Sensing Requirements– False alarm rate– Detection probability– Short sensing time– Minimum SNR

Classification Requirements– Classification accuracy– Blind classification– Differentiate among M-QAM,

M-PSK, GMSK, M-PAM

Frequency800 MHz 1.6 GHz 2.5 GHz

CRPo

wer

Spe

ctru

m D

ensi

ty O

bser

ved

by C

Rs


Algorithmic ChallengesImplementation Challenges

Wideband Sensing & Classification Challenges

4

– Robust sensing and classification to impairments

– Short sensing time

– Energy efficiency

– High computational complexity

– Carrier & sampling offsets

– Front-end nonlinearities

Wideband LO

ADC

LPF

AGC BPF @ f2

y2[n]

LNA

RF Front-end

Digital Back-End

BPF @ f1

y1[n]Spectrum Sensing

BPF @ fK

yK[n]

•••

Carrier frequency & Sampling clock offsets

Spectrum Sensing

Sele

ct B

ands

to S

ense

Spectrum Sensing

YesModulation

Classification

•••

Occupied?

Occupied?

Occupied?

Yes

YesModulation

Classification

Modulation Classification

Use channelNo

Use channelNo

Use channelNo


Research Contributions

Proposed a sensing and classification method that is robust to carrier and sampling clock offsets– TSP’13, Asilomar’12

Proposed an energy efficient sensing and classification processor in blind scenarios– TCAS’13, Globecom’11, Milcom’11

Analyzed the impact of receiver nonlinearities on the sensing performance and proposed algorithmic solutions– TSP (to be submitted), Crowncom’13

5


Outline

Cyclostationary feature detection overview

Spectrum sensing and classification under receiver impairments

Blind energy efficient sensing and classification

Impact of nonlinearities and their compensation

Summary of contributions

6







Cyclostationary Detection (CD) Overview

CD can perform spectrum sensing and modulation classification

CD estimates the Cyclic Auto-Correlation (CAC) function (C-CAC*and NC-CAC)or the Spectral Correlation Function (SCF)

CAC at SCF

7

5

0.30

Cyclic Frequency (MHz)

CAC

Valu

e

0.250.200.150.100.05

010 15 20 25 30 35 40 45 500

0.450.400.35

0.50

Fsym = 6.25 MHzFcar = 12.5 MHz

BPSK

Fsym

2Fcar

2Fcar–Fsym 2Fcar+Fsym


Cyclic Features of Different Modulation Types

Modulation type determines the present cyclic featuresFeatures at functions of and

8

Class Signals Cyclic Features at f(Fsym, Fcar)

1 M-QAM / M-PSK2 M-PAM / BPSK , , , 3 GMSK , ,

5

0.30

Frequency (MHz)

CAC

Valu

e

0.250.200.150.100.05

010 15 20 25 30 35 40 45 500

0.450.400.35

0.50


QAMFsym

5

0.30

Frequency (MHz)

CAC

Valu

e

0.250.200.150.100.05

010 15 20 25 30 35 40 45 500

0.450.400.35

0.50


MSKFsym

2Fcar–0.5Fsym

2Fcar+0.5Fsym


Outline

9




Impact of nonlinearities and compensation


- E. Rebeiz, P. Urriza, D. Cabric, Optimizing Wideband Cyclostationary Spectrum Sensing under Receiver Impairments, in IEEE Transactions on Signal Processing, vol. 61, no. 15, pp. 3931-3943, Aug. 2013

- E. Rebeiz, P. Urriza, D. Cabric, Experimental Analysis of Cyclostationary Detectors Under Cyclic Frequency Offsets, in Proc. Asilomar Conference on Signals, Systems and Computers, Nov. 2012


Feature Detection Under Receiver Impairments

10

What is the impact on sensing and classification?

Frequency

Modulation Type 1 Modulation Type 2 Modulation Type 3

Thermal noisePS

D

BPF

LNA

Wideband Front-End

AGC ADC

LPF

Cycl

ic A

utoC

orre

latio

n

Sampling Clock Offset

Imperfect Cyclic Frequencies (LO mismatch, Doppler)

<>|Rα1(ν)| γ

<>|Rα2(ν)| γ

<>|RαK(ν)| γ

•••

α1

α2

α3 αK

Wideband LO

B1

f1

B2

f2

B3

f3

BK

fK

Dec

isio

n Si

nk


Ideal feature computed by

Feature under cyclic offset computed at

Signification Degradation in Low SNR

11

Cycl

ic F

eatu

re D

egra

datio

n (d

B)

-15

-10

-5

0

500 1000 1500 2000 2500 3000

Δα = 500 ppm

Δα = 1000 ppm

Δα = 2000 ppm

Sensing Time (N)High SNRs Low SNRs


Robust Feature Detection

Proposed Multi-Frame CAC

where is the total sensing time

– yields the conventional CAC

Tradeoff: - N reduces effect of CFO - M yields non-coherent integration

Resulting composite relationship of CAC to ideal one

12

Term with N Term with M


Tradeoff Between N and M

Single frame processing quickly degrades with CFO

Multi-frame processing spreads the energy across SCO and CFO

How to optimize M and N?13

Single Frame (M=1) Multiple Frames (M = 10)

Cyc

lic F

eatu

re

Cyc

lic F

eatu

re


Optimization Design Strategy

CFO and SCO are non-deterministic circuit impairments– Design strategy is to optimize the average cyclic SNR

Conditional cyclic SNR defined as – Derived in closed form and given by

where , are functions of the pulse shape filter, N and M

14


Performance Gains over Conventional Feature Detectors CFO and SCO zero mean normally distributed,

Under what ratios can we expect performance gains?

15

1

Aver

age

Cycl

ic S

NR

0

2

3

4

2 4 6 8 10 12

Theoretical

Empirical

Number of Frames (M)

x10-3

0.2

Probability of False AlarmPr

obab

ility

of D

etec

tion

0.10

0.40.3

0.60.5

0.80.7

0.91.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

M = 1

M = 2

M = 6

SNR = -5 dBNT = 5000

0.9 1

M = 12


Best and Worst Case Performance Scenarios

Most gains achieved when CFO is more severe than SCO

16

Total Number of Samples NT

Aver

age

Cycl

ic S

NR

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5x104

100

101

102

sco/cfo = 1/2

N = Nmin w/ resampling

sco/cfo > 1

Conventional CAC (M=1)

sco/cfo = 1/4sco/cfo = 1/20


Contributions to Cyclic Feature Detectors

Analyzed performance loss due to carrier and sampling offsets

Proposed a new multi-frame statistic that achieves robust detection with optimum (N,M) pair

Significant improvement obtained when

17


Outline

18






- E. Rebeiz, F. Yuan, P. Urriza, D. Markovic, D. Cabric, Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks, to appear in IEEE Transactions on Circuits and Systems I

- E. Rebeiz, D. Cabric, Blind Modulation Classification Based on Spectral Correlation and Its Robustness to Timing Mismatch, in Proc. IEEE Military Communications Conference, Nov. 2011

- E. Rebeiz, D. Cabric, Low Complexity Feature-based Modulation Classifier and its Non-Asymptotic Analysis, in Proc. IEEE Global Communications Conference, Dec. 2011


Blind Sensing and Modulation Classification

What do we mean by blind?– Signals can appear anywhere in the wideband channel– Signals are not standard compliant

Spectrum sensing performed via a cyclic frequency search

Cyclostationary detection is not robust to CFO– Number of CAC computations becomes a burden

Cyclic detection not energy efficient in blind scenarios

19

10

0.30


CAC

Valu

e

0.250.200.150.100.05

020 30 40 50 60 70 80 90 1000

0.450.400.35

0.50

Δα = 100 Hzαr = 0 : Δα : 100MHz

Compute |Rα(ν)|, Compare to γ

10

0.30


CAC

Valu

e

0.250.200.150.100.05

020 30 40 50 60 70 80 90 1000

0.450.400.35

0.50

Δα = 10 KHzαr = 0 : Δα : 100MHz

Compute |Rα(ν)|, Compare to γ


Proposed Hybrid Detection and Classification Processor

Most processing is in estimating signal parameters– What accuracy is required in the pre-processor?

20

Spectrum Sensing

FFT based Energy

Detection

No Knowledge of Parameters

- Detection of active signals- Coarse bandwidth estimate- Coarse carrier freq. estimate

Modulation Classification

I/Q Samples

Sensing Outputs

- Fine parameter estimation- Modulation type

Classification Outputs

ADC

Pres

ent M

odul

ation

SCF Computatio

nat (αi,fi) Co

ncat

enat

e

arg min|F – Vi|

2F

Fsym and Fcar

estimates

CAC @ at αi V1 V2 V3

1 2 36

Asymptotic Feature Vectors

I/Q

Dat

a

Parameter Estimation

Fsym and Fcar

estimates

0.1

CAC

0

0.2

4.5 5 5.5Cyclic Frequency x 106

arg max |Rx*|

0.5

CAC

0

1

6.35 6.4 6.45Cyclic Frequency x 107

arg max |Rx|

Symbol RateEstimation

Carrier FrequencyEstimation

Coarse estimates can result in CFOs on the order of 105 ppm


Design Strategy to Minimize Consumed Energy

Discretization required for implementation purposes (– Total # CAC computations: ,

All blocks use CAC minimizing energy = minimizing # samples

21

Symbol-Rate Estimator

(CAC)

Modulation Type Classifier

(CAC)

Modulation Database

T

Carrier Frequency Estimator

(CAC)

NT,ΔαT

Nf,Δαf

fc

Nc

From CIC

Nc = # Samples for mod. type. class.NT = # Samples for symbol rate est.Nf = # Samples for carrier freq. est

ΔαT = symbol rate est. resolutionΔαf = carrier feq. est. resolution


Tradeoffs Between Pre-Processor Resolutionand Classification Accuracy Feature at is the weakest among all features

– determined by signals in class 1 (M-QAM, M-PSK)

For every SNR, there is a maximum CFO tolerable to meet Pc

22

SNR (dB) max (ppm)

10 1000

8 900

6 800

4 500

2 400

0 100


Feasible Region for Classification

All triplets that meet form the feasible region

Features at the carrier frequency are stronger: looser requirement

23

400 500 600 700 800 900 10003600380040004200440046004800500052005400

Δ αf (

ppm

)

Nc

= 5

20

Nc

= 5

35

Nc

= 7

50

Nc

= 6

20Nc

= 5

50ΔαT (ppm)

Nc

= 5

00

Nc

= 5

10

Feasible Region

SNR = 10 dB


Optimum Operating Point in Feasible Region

Operating near the boundary of feasible region is most efficient

24

0 100 200 300 400 500 600 700 800 900 10001000

1500

2000

2500

3000

3500

4000

45005000

Feasible Region

Δ αf (

ppm

)

ΔαT (ppm)

10.3 µJ

24.9 µJ

59.5 µJ


Blind Modulation Classification Contributions

The tradeoff between parameter estimation and the resulting classification accuracy was analyzed

An optimization strategy has been developed to minimize the total consumed energy while meeting the classification requirement

25


Outline

26






- E. Rebeiz, A. Shahed, M. Valkama, and D. Cabric, Analysis of Spectrum Sensing under RF Non-Linearities and Compensation Algorithm, in preparation for submission to IEEE Transactions on Signal Processing

- E. Rebeiz, A. Shahed, M. Valkama, D. Cabric, Suppressing RF Front-End Nonlinearities in Wideband Spectrum Sensing, in Proc. IEEE CROWNCOM 2013


Wideband Receiver Nonlinearities

Typical LNA IIP3 of -10 dBm, LNA linear gain of 35 dB

Mixer nonlinearity are also important, but have less of an impact – IIP2 dBm, IIP3 dBm

Def: IIP3 = Input power at which linear term power equals power of 3rd order term27

0.1Out

put P

ower

(mW

)

0

0.2

0.4

0.6

0.8

Nonlinear LNA

Input Power (mW)0 0.5 1 1.5 2 2.5

Linear LNA

x 10-3

Wideband LO LPF

AGC

RF Front-End

yw[n]LNA ADC

x[n]


Received Signal with Intermodulation Terms

SOI @ , blockers @ , such that Due to nonlinearity, intermodulation (IMD) term appears at

How does the IMD term affect the sensing performance?

28

Wideband LO

ADCyW[n]

BPF

AGC

BPF @ fIF

y[n]LNA

Spectrum Sensing(CD or ED)

RF Front-end DSP

fb1 fb2 fc=2fb2-fb1

z1

z0z2

fIF

y[n]

f1 f2 fIF=2f2-f1

β1z1

β1z0β1z2


SIR = -67 dB, SNR = 10 dB, N = 500 Samples

Cyclostationary Detection Energy Detection

Severe degradation in sensing performanceWhat are possible compensation methods?

Degradation Depends on Blocker Modulation

29

Prob

abili

ty o

f Det

ectio

n 1.0

0.2

0.4

0.6

0.05 0.1 0.15 0.2 0.25 0.3Probability of False Alarm

0

z1[n] QPSK, z2[n] QPSK, z0[n] 4PAMIdeal LNA, z0[n] 4PAM

z1[n] 4PAM, z2[n] 4PAM, z0[n] 4PAMz1[n] QPSK, z2[n] 4PAM, z0[n] 4PAMz1[n] 4PAM, z2[n] QPSK, z0[n] 4PAM

Analytical Simulation

Prob

abili

ty o

f Det

ectio

n 1.0

0.2

0.4

0.6

0.05 0.1 0.15 0.2 0.25 0.3Probability of False Alarm

0

z1[n] QPSK, z2[n] QPSK, z0[n] 4PAMIdeal LNA, z0[n] 4PAM

z1[n] 4PAM, z2[n] 4PAM, z0[n] 4PAMz1[n] QPSK, z2[n] 4PAM, z0[n] 4PAMz1[n] 4PAM, z2[n] QPSK, z0[n] 4PAM



Increasing Sensing Time is not Effective

Recall that under – How do we set the decision threshold?

30

Det

ectio

n Pr

obab

ility 0.9

0.5

0.7

1000 2000 3000 4000 5000Sensing Time (N)

0.3

1

CD - IdealED - Ideal

CD - UncompensatedED - Uncompensated

SNR = 3 dB, SIR = -70 dB

6000



Impact of Uncertainties on False Alarm

Setting the threshold requires knowledge of – Blocker and noise power– Blocker modulation

Accurate estimation of parameters needed for threshold setting

CD is more robust to uncertainties than ED31

Prob

abili

ty o

f Fal

se A

larm

0.8

0.2

0.4

0.6

-0.6 -0.2 0.2 0.6 1IIP3 Uncertainty (dB)

-10

1

CD – SIR = -75 dBED – SIR = -75 dB

Target False AlarmCD – SIR = -70 dBED – SIR = -70 dB

SNR = 0 dB, N = 500Analytical Simulation

Prob

abili

ty o

f Fal

se A

larm

0.8

0.2

0.4

0.6

-0.6 -0.2 0.2 0.6 1Blocker Power Uncertainty (dB)

-10

1

CD – SIR = -75 dBED – SIR = -75 dB

Target False AlarmCD – SIR = -70 dBED – SIR = -70 dB

SNR = 0 dB, N = 500Analytical Simulation

– Receiver IIP3


Actual IMD term , Estimated IMD term Compensation method:

Performance of compensation is modulation dependent

Intermodulation Term Compensation

32

0.3

SIR (dB)

Prob

abili

ty o

f Det

ectio

n

0.20.1

0.50.4

0.70.6

0.90.8

1.0

-74 -72 -70 -68 -66 -64 -62 -60

ED CD

Linear LNANonlinear LNA

Compensated

SNR = 3 dB Analytical Simulation

z1, z2, z0 4PAM

0.3

SIR (dB)

Prob

abili

ty o

f Det

ectio

n

0.20.1

0.50.4

0.70.6

0.90.8

1.0

-74 -72 -70 -68 -66 -64


Compensated

SNR = 3 dB

-62 -60


ED CD

z1, z2 QPSK, z0 4PAMBPF @ fIF

y[n] CAC + Thresh.

Decisiony[n]~

z[n]^

BPF @ f1

BPF @ f2

fIF=2f2-f1

fIF=2f2-f1

y = β1z0+ 1.5β3z1*z2

2

z[n]^

z1[n]~

z2[n]~

Compute

________

β1 3

z2[n]2~z1[n]*~

3β3/2


Compensation requires– Modulation type of blockers– Blocker strength

Modulation Dependent Compensation Algorithm

33

– Receiver IIP3

ADC

Estimate Noise Power

Compensated Detector

Choo

se T

hres

hold

w

/Noi

se O

nly

CAC

Com

puta

tion

+ Th

resh

old

Com

paris

on

Sens

ing

Resu

ltDemodulate + Remodulate Blockers + Estimate IMD term

Estimate Blockers’ Powers

Slow Varying Parameters

yW[n]

BPF @ fIF

y[n]

BPF @ f1

BPF @ f2

z1[n]~

z2[n]~

β3 ^

Fast Varying Parameters

z2[n]2~z1[n]*~ /β13

Estimate Blockers’ Modulation

BPF @ fIFy[n]

Adaptive Estimation of β3

LMS Adaptive Algorithm

Delay


Performance Gains due to Demodulation / Remodulation

34

Prob

abili

ty o

f Det

ectio

n

0.8

0.2

0.4

0.6

-5 0 5 10 15 20SNR (dB)

1.0

-10

SIR = -65 dB

ED – ProposedΔIIP3 = 0.25 dB, Δp = 100.4/20 CD – ProposedΔIIP3 = 0.25 dB, ΔP = 100.4/20


ED CD


Compensated Alg#1

Prob

abili

ty o

f Det

ectio

n

0.8

0.2

0.4

0.6

-5 0 5 10 15 20SNR (dB)

1.0

-10

SIR = -65 dB

ED – ProposedΔIIP3 = 0.25 dB, ΔP = 101/20

CD – ProposedΔIIP3 = 0.25 dB, ΔP = 101/20

Analytical SimulationLinear LNANonlinear LNA

Compensated Alg#1

ED CD

When estimates are off, residual term degrades detection performance


Nonlinearity Contributions

Analyzed the degradation in sensing performance due to IMD term

Showed that impact of IMD is dependent on blockers’ modulation

Proposed a modulation-aware IMD compensation based on demodulation / remodulation + sample-by-sample subtraction

35


Ph.D. Thesis Contributions

Analyzed the practical challenges in wideband spectrum sensing and modulation classification

Proposed energy efficient algorithmic solutions that are robust to the considered impairments

Future work: analyze additional wideband challenges such as– High sampling rates– Mixer nonlinearities, I/Q mismatch– Blockers suppression through beamforming– Modulation classification of overlapped signals

36

Thank you very much!Questions?

Acknowledgments

DARPA CLASIC ProgramMy advisor & all faculty on my committee

Lab mates, most importantly Paulo & Fang-Li


Blind Estimation of Receiver IIP3

Objective function given by

Resulting IIP3 offset is 0.15 dB

38

Wideband LO

ADCyW[n]

BPF

AGC

BPF @ fIF

y[n]LNA

RF Front-end DSP

z[n]^

ComputeBPF @ f1

BPF @ f2

f1 f2 fIF=2f2-f1

fIF=2f2-f1

z[n]^

z1[n]~

z2[n]~

z1[n]~ z2[n]~

y[n]

z2[n]2~z1[n]*~

LMS Adaptive Algorithm β3

^

Estim

ation

of β

3 Par

amet

er

0

-6000

-4000

-2000

2 4 6 8 10Iterations

-8000

1000

Actual β3 Q branchI branch

x104


Proposed Architecture for Uncompensated Detectors

This architecture makes sure that the uncompensated detectors are operating at the right point on the ROC point

39

Wideband LO

ADC

LPF

AGCLNA

RF Front-end

Adaptively Estimate β3

Estimate Blocker Power

Estimate Blockers’ Modulation

Estimate Noise Power

Uncompensated Detector

Estim

ate

PDF

unde

r H0

+ Ch

oose

Thr

esho

ld

CAC Computation

Sens

ing

Resu

lt

<>|Rα(ν)| γ

BPF


Published Articles Journal articles- E. Rebeiz, A. Shahed, M. Valkama, D. Cabric, Spectrum Sensing under RF Non-Linearities: Theoretical Analysis and Compensation Algorithm, in preparation to submission to IEEE Transactions on Signal Processing- E. Rebeiz, P. Urriza, D. Cabric, Optimizing Wideband Cyclostationary Spectrum Sensing under Receiver Impairments, in IEEE Transactions on Signal Processing, 2013- E. Rebeiz, F. Yuan, P. Urriza, D. Markovic, D. Cabric, Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks, in IEEE Transactions on Circuits and Systems I, 2013- P. Urriza, E. Rebeiz, P. Pawełczak, D. Čabrić, Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions, IEEE Communications Letters, 2011- P. Urriza, E. Rebeiz, D. Cabric, Multiple Antenna Cyclostationary Spectrum Sensing Based on the Cyclic Correlation Significance Test, in IEEE Journal on Selected Areas in Communications, 2013- P. Sofotasios, E. Rebeiz, L. Zhang, T. Tsiftsis, S. Freear, D. Cabric, Energy Detection-Based Spectrum Sensing over Generalized and Extreme Fading Channels, in IEEE Trans. Vehicular Technology, 2013- P. Urriza, E. Rebeiz, D. Cabric, Optimal Discriminant Functions Based On Sampled Distribution Distance for Modulation Classification, accepted for publication in IEEE Communications Letters, 2013 Selected Conference articles- E. Rebeiz, A. Shahed, M. Valkama, and D. Cabric, Suppressing RF Front-End Nonlinearities in Wideband Spectrum Sensing, in Proc. IEEE CROWNCOM, 2013- E. Rebeiz, P. Urriza, D. Cabric, Experimental Analysis of Cyclostationary Detectors Under Cyclic Frequency Offsets, in Proc. IEEE Asilomar Conference on Signals, Systems and Computers,2012- E. Rebeiz, V. Jain and D. Cabric, Cyclostationary-Based Low Complexity Wideband Spectrum Sensing using Compressive Sampling, in Proc. IEEE ICC, 2012- E. Rebeiz, D. Cabric, Blind Modulation Classification Based on Spectral Correlation and Its Robustness to Timing Mismatch, in Proc. IEEE MILCOM, 2011- E. Rebeiz, D. Cabric, Low Complexity Feature-based Modulation Classifier and its Non-Asymptotic Analysis, in Proc. IEEE GLOBECOM, 2011

40


Theoretical Derivation of IMD Impact on Sensing Performance

41


Maintaining a Constant False Alarm Rate

Typical ACI scenarios only require estimation of blocker strength

Setting the detection threshold requires– Knowledge of modulation of the blockers– Blockers’ power and the noise power– Accurate knowledge of parameter

42

f (Hz)

PSD


Design Strategy of Compensation Algorithm

We model the imperfections as – , for

Residual IMD term is given by Objective is to achieve

43

Blocker Uncertainty (dB)

IIP3

Unc

erta

inty

(dB)

-1.5-1-0.500.511.52

-2 -1.6 -1.2 -0.8 -0.4 0 0.4 0.8 1.2 1.6 2

1

0.75

0.5

0.25

0

-0.25

-0.5

-0.75

-1

Objective is met

Example of feasible region under SIR = -65 dB

Documents

Wideband Cyclostationary Spectrum Sensing and Modulation Classification