advanced spectrum sensing for multiple transmitter ... · algorithm for primary user localization...

Advanced Spectrum Sensing for

Multiple Transmitter Identification

Paulo Urriza Advisor: Prof. Danijela Čabrić

UCLA Electrical Engineering Department

7 September 2011

Qualifying Examination

D. Markovic / Slide 2

Biography

Incoming 3rd Year Ph.D. Student

– Advisor: Prof. Danijela Čabrid

Master of Science (August 2009)

– Advisor: Prof. Joel Joseph S. Marciano Jr.

– University of the Philippines Diliman, Philippines

Bachelor of Science (April 2007)

– University of the Philippines Diliman, Philippines

2 Paulo Urriza - Qualifying Exam


Publications

Conferences [1] J. Wang, P. Urriza, Y. Han, D. Čabrid, “Performance Analysis of Weighted Centroid Algorithm for Primary User Localization in Cognitive Radio Networks”, in Proc. Asilomar Conference on Signals, Systems, and Computers. 7-10 Nov. 2010, Pacific Grove, CA, USA

[2] P. Urriza, E. Rebeiz, D. Čabrid, “Hardware Implementation of Distribution Distance-based Modulation Level Classification”, in Proc. Asilomar Conference on Signals, Systems, and Computers. 6-9 Nov. 2011, Pacific Grove, CA, USA

Journals and Letters [3] P. Urriza, E. Rebeiz, P. Pawełczak, D. Čabrid, “Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions”, IEEE Communications Letters, vol.15, no.5, pp.476-478, May 2011

[4] J. Wang, P. Urriza, Y. Han, D. Čabrid, “Weighted Centroid Algorithm for Estimating Primary User Location: Theoretical Analysis and Distributed Implementation”, accepted for publication to IEEE Transaction on Wireless Communications



Outline of this talk

Motivations for Transmitter Identification

– Challenges in Coexistence

– Advanced Spectrum Sensing

Transmitter Identification Techniques

– Single Transmitter Scenarios ● Passive Localization

● Low-Complexity Modulation Classification

– Multiple/Co-channel Transmitter Scenarios ● Joint Classification and Localization

Conclusion

– Contributions

– Proposed Timeline

Paulo Urriza - Qualifying Exam 4










Conclusion

– Contributions




The Problem of Spectrum Scarcity

Growing demands for spectral resources

– From voice-only to multimedia content

– Rapid increase in the number of wireless devices

Available spectrum looks scarce by static spectrum allocation


Measurements show the allocated spectrum is vastly underutilized

“Chicago spectrum occupancy measurements & analysis and a long-term studies proposal” McHenry et. al. 2006 Spectrum allocation from NTIA


Potential solution: Cognitive Radio (CR)

Improves spectral efficiency by exploiting temporal and spatial spectrum holes left by Primary Users (PU)

Now a wireless access standard: IEEE 802.22 Wireless Regional Area Networks (WRANs) as of July 1, 2011 [1]

– Rural wireless broadband

– Exploits TV white-space

Two Key Steps in CR:

– Exploration of RF environment

– Exploitation of available

spectrum holes


Figure from Haykin, 2005, “Cognitive Radio: Brain Empowered Wireless Communications”

RadioEnvironment

(Outside world)

Radio-Scene

analysis

Channel-stateAnd Prediction

Powercontrol andSpectrum

Mgmt.

RFstimuli

Action:transmitted

signalSpectrum Holes

Noise-floor statisticsTraffic Statistics

QuantizedChannel capacity

InterferenceTemperature

Transmitter Receiver


Challenges in CR/PU Coexistence


System Level Architecture: Coexisting with both Licensed and Unlicensed Users in TV Whitespace

CR 1

CR 2Low-power

Licensed Users

TV Broadcast

Coexisting in the TV Band

802.22BS

CPE 1

CPE 2

CPE 3

Secondary Users

– CPE = Consumer Premises Equipment

– BS = Base Station

Interference constraints Requires very sensitive and reliable detection and identification

Heterogeneous networks Ability to distinguish between PUs

Non-cooperative Blind/Asynchronous Timing

Temporal variation Fast sensing times


Transmitter Identification


The more we know about the PUs (and other SUs) the better we can adapt our strategies for dynamic spectrum access.

Traditional Spectrum Sensing – is the PU present or absent?

Identification:

– Distinguish between

active transmitters

– Finding transmit

parameters: ● Modulation

● Center frequency

● Symbol rate

● Location

Detection(Spectrum Sensing)

Parameter Estimation(fc, Rs, BW, α)

Localization and Tracking

ModulationClassification

Identification of Active TransmittersW

ire

less

Tra

ffic

Se

nsi

ng

MAC/Routing Protocols UtilizingWireless Traffic Sensing

MAC-layer Classification

Traffic Estimation

Traffic Prediction

TrafficCharacterization

Advanced Spectrum Sensing System

this work


Objective

Practical, hardware implementable algorithms and architectures for identifying transmitters through:

– Location

– Transmit Parameters

General specifications:

– Algorithms should perform in real time

– Low complexity/energy efficient algorithms are key to applicability of cognitive radio in mobile devices

– Multiple or Single transmitter

– Only passive measurements can be used in identification











Conclusion

– Contributions




Transmitters can be orthogonalized in special cases

– Spectrally Orthogonal

– Temporally Orthogonal

– Spatially Orthogonal

Can be done in 2 independent steps for single transmitter:

– Localization

– Transmitter Parameter Estimation

Applicability of Single Transmitter Scenario


Tx

t

Tx d

f Tx


Existing Localization Techniques

Types according to required measurements

– Range-based – estimate distance to target first [2] ● Lateration, Min-Max (Intersection)

– Range-free – no distance required, cheaper and more robust ● Centroid (CL), Weighted Centroid (WCL), DV-Hop

Two main types according to target cooperation

– Cooperative Localization ● Time-of-Arrival (TOA), Time-Delay-of-Arrival (TDOA)

– Non-interactive Localization ● Received Signal Strength/RSS-only (WCL, etc.)

● Bearing-only (Angle-of-Arrival only)

CR Localization is Range-free and Non-interactive



Weighted Centroid Localization for CR

What is WCL?

Why WCL?

– Low computational complexity

– Robustness to propagation environment

WCL has also been used in WSN scenario

– Localize through beacons (cooperative)

– Power efficiency is more an issue in CR

Objective: Analysis of WCL with various impairments in order to recommend design guidelines for its effective deployment


PU

SU

R

Li=(Lx,Ly)

𝐋 𝑝 = 𝑃𝑖 − 𝑃𝑚𝑖𝑛 𝐋𝑖

𝑁𝑖=1

𝑃𝑖 − 𝑃𝑚𝑖𝑛𝑁𝑖=1

J. Wang, P. Urriza, Y. Han, D. Čabrid, “Performance Analysis of Weighted Centroid Algorithm for Primary User Localization in Cognitive Radio Networks”, in Proc. Asilomar CSSC. 7-10 Nov. 2010, Pacific Grove, CA, USA

J. Wang, P. Urriza, Y. Han, D. Čabrid, “Weighted Centroid Algorithm for Estimating Primary User Location: Theoretical Analysis and Distributed Implementation”, accepted to IEEE Transaction on Wireless Communications. 29 June 2011


Performance Analysis of WCL

Investigated various impairments: Correlated shadowing, Irregularity in transmission range (DoI), Randomness/distance in node-placement, Errors in self-localization

Paulo Urriza - Qualifying Exam

Without Correlation

With Correlation

Figure: Comparison of Simulation vs. Analytical Accuracy of Weighted Centroid Localization

15


Distributed Implementation of WCL

Designed a practical, cluster-based implementation of WCL and analyzed:

– Communication overhead

– Computation burden

– Average transmit power

Algorithm uses cluster gradients to reduce the number of messages

However, the WCL approach is not applicable to co-channel transmitters


cluster

Figure: Comparison of Distributed vs. Centralized Weighted Centroid Localization

16


Automatic Modulation Classification


Preprocessing Tasks – band segmentation, sampling, filtering

– 𝑓𝑐 - center freq. (periodogram, FFT(𝑥2 𝑛 ) [3])

– 𝑅𝑏 - symbol rate (wavelet transform [4])

Objective of this work

– Develop low complexity algorithms for modulation classification for use in transmitter identification

Modulator Channel +

InterferenceInput Symbols

+

Receivernoise

Preprocessor Demodulator

Classification algorithm

Output Symbols

Modulationformat

Receiver

Modulation Classifier General System Model


Signal Model for Modulation Classfication

Classes of Modulation Techniques

– Single Carrier (Narrowband Techniques)

– Wideband (Spread Spectrum, OFDM, etc.)

Single Carrier Modulation After Pre-processing

– Symbols: 𝐫 ≜ 𝑟1, 𝑟2, ⋯ , 𝑟𝑀 drawn from 1 of 𝐾 constellations

– Baseband complex envelope:

– In this work we focus on the ff. classes: ● ASK, PSK, QAM, MSK


𝑦 𝑛 = 𝐴𝑒𝑗2𝜋𝑓0𝑇𝑛+𝑗𝜃𝑛 𝑟𝑙𝑕 𝑛𝑇 − 𝑙𝑇 + 𝜖𝑇𝑇 + 𝑔 𝑛

𝑀

𝑙=1

Frequency offset

Timing errors

0 ≤ 𝜖𝑇 < 1

Additive noise

sequence

Phase Jitter

Symbol Period

Residual channel effects

Scaling Factor


Existing Single Transmitter Modulation Classification Techniques


Modulation classification algorithms [5]

– Likelihood-based (LB) ● Based on a likelihood ratio hypothesis test

● Uses PDF of received signal under certain assumptions

● Optimal in the Bayesian sense but high complexity

– Feature-based (FB) ● Selected features that distinguish each class are observed

● Usually simpler to implement

– Examples: ● Maximum Likelihood (ML)

● Likelihood Ratio Test (ALRT,GLRT,HLRT)

● Goodness-of-Fit (GoF) Test

● Cumulant

● Spectral Correlation

19

Feature Based

Likelihood Based


Cumulant-based Classifier

Features are higher-order statistics derived from moments [6]

Comparison within a class requires ~150x more samples


Comparison

# samples for

𝑷𝒄 = 0.95

(noise-free)

BPSK vs. 4-PAM 96

4-PAM vs. 16-QAM 88

16-QAM vs. 64-QAM 14,833

𝐶 42 =𝐸 𝑦 4 − 𝐸 𝑦2 2 − 2𝐸2 𝑦 2

𝐸 𝑦 2 − 𝜎2 2

Example: Normalized 4th-Order Zero-lag Cumulant

Constellation 𝐶 42

BPSK -2.0000

4-PAM -1.3600

PSK -1.0000

16-QAM -0.6800

64-QAM -0.6191

Best used as preliminary classifier (BPSK vs PSK vs

PAM vs QAM)


Goodness-of-Fit (GoF)-based Classifier

Procedure for GoF Classification

1. Calculate CDF, 𝐹0 𝑧 , of chosen feature for all constellations

2. Measure empirical CDF of

received signal’s feature

3. Calculate the GoF statistic

which is a measure of distribution distance to 𝐹0 𝑧

4. Choose constellation with the minimum statistic

Kolmogorov-Smirnov (KS) Test [7]


𝐹 1 𝑧 ≜1

𝑁 𝐈 𝑧𝑛 ≤ 𝑧

𝑁

𝑛=1

𝐷 = max1≤𝑛≤𝑁

𝐹 1 𝑧𝑛 − 𝐹0 𝑧𝑛

empirical cdf

Best used as to distinguish within a class (i.e. {16, 64, 256}-QAM)


Hybrid Classifier Architecture


Cumulant classifier is used to identify the general modulation class (ASK, PSK, QAM, MSK).

– Selects the feature used by the GoF-test

Using the specific feature tailored for the chosen class, the GoF test identifies the subclass (i.e. 16-QAM vs. 64-QAM)

– Possible features include: ● 𝑦 𝑛 for ASK

● ∠𝑦(𝑛) for PSK

● *𝑅𝑒 𝑦 𝑛 , 𝐼𝑚,𝑦(𝑛)-+ for QAM

PreprocessingFrom ADC

CumulantClassifier

FeatureExtraction

y(n)GoF

Classifier

Classification Result

Class

Level

Reduces: • # of samples • computational

complexity for classification


Novel classifier aimed at subclasses (based on GoF)

– Pre-calculates the points at which max 𝐷 is expected to occur

– Also experimented with other GoF tests such as Kuiper test

Reduced-Complexity KS and Kuiper (rcK/rcKS) Tests

0.0

0.2

0.4

0.6

0.8

1.0

-1.5 -0.9 -0.3 0.3 0.9 1.5

Maximumdeviationused as

Test Points

4-QAM

16-QAM

Amplitude

CD

F

t1 t2


How rcK and rcKS classification works

23

Better than KS

P. Urriza, E. Rebeiz, P. Pawełczak, D. Čabrid, “Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions”, IEEE Communications Letters , vol.15, no.5, pp.476-478, May 2011


Reduced Complexity Level Classifier

Compared complexity and memory use against Cumulants and KS

– No multiplications; only a bank of comparators

– Analytical expression for 𝑃𝑐 SNR


Requires less samples than cumulant and achieves higher classification accuracy at low SNR

Effect of sample size and phase jitter at SNR = 12dB


Hardware Implementation in CR Testbed

CR Testbed

– BEE2 platform: 5 high performance Xilinx FPGAs

– Front End Baseband ● 2x 12bit ADCs at 64 MS/s

● 2x 14bit DACs at 128 MS/s

– Analog front-end radio

Additional HW Challenges

– Pre-processing ● Noise estimation

● Timing offset

● Rotation of constellation


rcK/rcKS Classifier BEE2 Board

RF Front-End 1

RF Front-End 2

Transmitter Hardware Emulation Setup

( )2

( )2

CORDICProcessor |y(n)|

Concatenate

Re{y(n)}

Thre

sho

ldC

lass

ifie

r

Cum. Database

Modulation Class

CDF Database

<

<

Count &Classify Modulation

Level

Feature Extraction

Cumulant Classifier

Reduced Complexity Kuiper Classifier

y(n)

( )2

Proposed Architecture


Future Work on Single Transmitter Modulation Classification

Non-coherent Classifier based on cumulant and rcK

– Blind time synchronization

– Performance analysis of Non-coherent rcK

OFDM Classification

– PHY technique used in most modern standards such as LTE, WiMax, 802.11b/g/n, and 802.22 (CR)

– Application of rcK in subcarrier modulation classification

Simulation / Hardware Verification of Full Single Transmitter Classifier











Conclusion

– Contributions




Multiple Transmitter Identification

Objectives:

– Estimate location

– Estimate transmission parameters ● Modulation Classification

● Symbol Rate and Center Frequency Estimation


Primary Users:

– 𝑁 ≥ 1 PUs present

– 𝑀 ≥ 1 SUs present

– Transmits in entire sensing time (perfect detection)

– Can’t be orthogonalized in time/frequency/space

Network Model

SU-2

PU-1

PU-3

PU-2

Fusion Center

SU-1

SU-3

Secondary User

Primary User


Separability of QAM, BPSK, and MSK in SCF domain

Existing Algorithm for Co-channel Modulation Classification

Spectral Correlation Function (SCF)

– Uses cyclostationary properties of most man-made signals

– Gardner ’87 [10]

𝑆 𝑥 𝛼, 𝑓 =1

𝑁𝑀 𝑋𝑘 𝑓 −

𝛼

2𝑋𝑘

∗ 𝑓 +𝛼

2

𝑀

𝑘=1

𝑋𝑘 𝑓 = 𝐹𝐹𝑇 𝑥 𝑛


Modulation Peaks at 𝜶, 𝒇

BPSK 1

𝑇, 𝑓𝑐 , 2𝑓𝑐 , 0 , 2𝑓𝑐 ±

1

𝑇, 0

MSK 1

𝑇, 𝑓𝑐 , 2𝑓𝑐 ±

1

2𝑇, 0

QAM 1

𝑇, 𝑓𝑐

AM 2𝑓𝑐 , 0

– Distinct cyclic features

– Additive for uncorrelated signals

– Problem: data association


Existing Algorithm for Co-channel Source Localization

Angle-of-Arrival (AoA) based

– Fusion of angle measurements from SUs with known locations

– Preferred method for non-cooperative targets

– AoA can be estimated w/o cooperation using: ● Covariance-based (MUSIC, ESPRIT)

● Directional antennas

Challenges

– # of antennas > # of sources

– Problem: data association


SU2

PU1

Ghost Node

SU1 PU2

SU3

The data association problem causes a “ghost node” (Reed [c])

AoA Fusion with multiple targets is very complex due to data association


Importance of Cyclic Frequency

Spooner ’95 [12] – first to specifically address the co-channel case

– Based on cyclic cumulants which are ther Fourier components of the 𝑛th order cumulant (𝛼 known)

Duval ‘08 [16] – Multi-source localization and classification

– Conventional MUSIC in determining: # of PUs, location of PUs

– Only single-carrier vs. multi-carrier (i.e. OFDM) classification in their architecture using cyclic cumulants

AoA estimation based on MUSIC but with 𝛼-selectivity proposed by Schell et.al. [17] in 1989 called Cyclic MUSIC

Modulation classification and AoA estimation of co-channel signals are fundamentally linked by the Cyclic Frequencies (𝛼)



Joint Localization and Classification

Problem Statement

Develop algorithms and architecture for joint localization and RF transmit parameter estimation (modulation class, center frequency, symbol rate) that exploits cyclic frequency

Advantages of Joint Approach

– Most signal processing blocks shared

– Pre-association: data fusion complexity is reduced

– Less sensors required to eliminate ghost nodes [16]

Research Challenges

– No prior knowledge of 𝛼’s

– Calculation of the SCF is very computationally demanding



Solving the Data Association Problem

1. Estimate cycle frequencies (𝛼’s)

● Can be done without SCF through 𝐹𝐹𝑇 𝑥 𝑛 𝑥∗ 𝑛

2. Make initial association of 𝛼’s to 1 of 𝑁 transmitters

● Based on allowed 𝛼 groupings

3. Using initial association, use Cyclic MUSIC on each possible transmitter

4. Check association of any ambiguous 𝛼’s using AoA


No

rmal

ized

Cyc

lic M

USI

C C

ost

Fu

nct

ion

Cyclic MUSIC


Future Work


Algorithm Development:

– Cycle Frequency estimation / selection algorithm ● Key to reducing the complexity due to cyclic covariance

● Avoids the calculation of the entire SCF

● Small estimation errors in alpha have a big effect on the resulting modulation and AoA

– Modulation classifier for overlapped signals

Performance Evaluation:

– Analysis of classification accuracy (with 𝛼 estimation errors)

– Comparison with Duval’s method

– Complexity comparison with purely data-association approach

Implementation of proposed architecture hardware platform










Conclusion

– Contributions




Expected Contributions

Algorithms and architecture for performing Transmitter identification for both the single and multiple transmitter scenario.

A hybrid modulation classification algorithm for digital modulations both single and multi-carrier

Performance evaluation and analysis of Weighted Centroid Localization algorithm including a distributed implementation.

An algorithm for joint modulation classification and localization applicable to the multiple/co-channel transmitter scenario.



Proposed Timeline


Tasks 09/11 12/11 03/12 06/12 09/12 12/12 03/13 06/13Hardware Implementation of

Kuiper Classifier

Hybrid Classifier Design and

Evaluation

Hardware Implementation of

Complete Hybrid Classifier

Write Journal # 1

Development of Joint

Localization and Modulation

Write Journal # 2

Thesis Writing

Time


References - I

[1] Standard for Wireless Regional Area Networks Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands, IEEE Std. P802.22. July 2011

[2] K. Langendoen and N. Reijers, “Distributed localization in wireless sensor networks: A quantitative comparison,” Computer Networks, vol. 43, no. 4, pp. 499–518, Nov. 2003.

[3] Z. Yu, Y. Shi, W. Su, “A Blind Carrier Frequency Estimation Algorithm for Digitally Modulated Signals”, in Proc. IEEE MILCOM, 2004

[4] Chan, Y.T.; Plews, J.W.; Ho, K.C.; , "Symbol rate estimation by the wavelet transform," Circuits and Systems, 1997. ISCAS '97

[5] O. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “Survey of automatic modulation classification techniques: classical approaches and new trends,” , IET Communications, vol. 1, no. 2, pp. 137 –156, Apr. 2007.

[6] A. Swami and B. M. Sadler, “Hierarchical digital modulation classification using Cumulants,” IEEE Trans. Commun., vol. 48, no. 3, pp. 416–429, Mar. 2000.

[7] F. Wang and X. Wang, “Fast and robust modulation classification via Kolmogorov-Smirnov test,” IEEE Trans. Wireless Commun., vol. 58, no. 8, pp. 2324–2332, Aug. 2010.

[8] Y. Yang and C.H. Liu, “An asymptotic optimal algorithm for modulation classification,” IEEE Commun. Lett., vol. 2, no. 5, pp. 117 -119, May 1998.



References - II

[9] S. Shi and Y. Karasawa, “Noncoherent Maximum Likelihood Classification of Quadrature Amplitude Modulation Constellations: Simplification, Analysis, and Extension,” IEEE Trans. Wireless Commun., vol. 10, no. 4, pp. 1312 -1322, April 2010.

[10] W. Gardner, W. Brown, and C.-K. Chen, “Spectral correlation of modulated signals: Digital modulation,” IEEE Trans. Commun., vol. 35, no. 6, pp. 595–601, Jun. 1987.

[11] Kim, Y. & Weber, “C. Generalized single cycle classifier with applications to SQPSK vs. 2kPSK Military Communications Conference, “ IEEE MILCOM '89.

[12] Spooner, C. M. “Classification of co-channel communication signals using cyclic cumulants” Asilomar Conference on Signals, Systems, and Computers, 1995

[13] Haitao, F.; Qun, W. & Rong, S. Modulation Classification Based on Cyclic Spectral Features for Co-Channel Time-Frequency Overlapped Two-Signal Pacific-Asia Conference on Circuits, Communications and Systems. PACCS '09

[14] A. Bishop and P. Pathirana, “Localization of emitters via the intersection of bearing lines: A ghost elimination approach,” Vehicular Technology, IEEE Transactions on, vol. 56, no. 5, pp. 3106 –3110, sept. 2007.

[15] J. D. Reed, C. R. C. M. da Silva, and R. M. Buehrer, “Multiple-source localization using line-of-bearing measurements: Approaches to the data association problem,” in Proc. IEEE MILCOM, Nov. 17–19 2008.



References - III

[16] O. Duval, A. Punchihewa, F. Gagnon, C. Despins, and V. K. Bhargava, “Blind multisources detection and localization for cognitive radio,” in Proc. IEEE GLOBECOM, Nov. 30–Dec. 4 2008.

[17] S. V. Schell, R. A. Calabretta, W. A. Gardner, and B. G. Agee, “Cyclic music algorithms for signal-selective direction estimation,” in Proc. IEEE ICASSP, May 23–26, 1989.


Thank you very much!

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