Post on 07-Sep-2018
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
D. Markovic / Slide 3
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
3 Paulo Urriza - Qualifying Exam
D. Markovic / Slide 4
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
D. Markovic / Slide 5
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 5
D. Markovic / Slide 6
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
Paulo Urriza - Qualifying Exam 6
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
D. Markovic / Slide 7
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
Paulo Urriza - Qualifying Exam 7
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
D. Markovic / Slide 8
Challenges in CR/PU Coexistence
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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
D. Markovic / Slide 9
Transmitter Identification
9 Paulo Urriza - Qualifying Exam
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
D. Markovic / Slide 10
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
Paulo Urriza - Qualifying Exam 10
D. Markovic / Slide 11
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 11
D. Markovic / Slide 12
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
Paulo Urriza - Qualifying Exam 12
Tx
t
Tx d
f Tx
D. Markovic / Slide 13
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
Paulo Urriza - Qualifying Exam 13
D. Markovic / Slide 14
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
Paulo Urriza - Qualifying Exam 14
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
D. Markovic / Slide 15
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
D. Markovic / Slide 16
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
Paulo Urriza - Qualifying Exam
cluster
Figure: Comparison of Distributed vs. Centralized Weighted Centroid Localization
16
D. Markovic / Slide 17
Automatic Modulation Classification
Paulo Urriza - Qualifying Exam 17
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
D. Markovic / Slide 18
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
Paulo Urriza - Qualifying Exam 18
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
D. Markovic / Slide 19
Existing Single Transmitter Modulation Classification Techniques
Paulo Urriza - Qualifying Exam
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
D. Markovic / Slide 20
Cumulant-based Classifier
Features are higher-order statistics derived from moments [6]
Comparison within a class requires ~150x more samples
Paulo Urriza - Qualifying Exam 20
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)
D. Markovic / Slide 21
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]
Paulo Urriza - Qualifying Exam 21
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)
D. Markovic / Slide 22
Hybrid Classifier Architecture
Paulo Urriza - Qualifying Exam 22
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
D. Markovic / Slide 23
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
Paulo Urriza - Qualifying Exam
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
D. Markovic / Slide 24
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
Paulo Urriza - Qualifying Exam 24
Requires less samples than cumulant and achieves higher classification accuracy at low SNR
Effect of sample size and phase jitter at SNR = 12dB
D. Markovic / Slide 25
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
Paulo Urriza - Qualifying Exam 25
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
D. Markovic / Slide 26
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
Paulo Urriza - Qualifying Exam 26
D. Markovic / Slide 27
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 27
D. Markovic / Slide 28
Multiple Transmitter Identification
Objectives:
– Estimate location
– Estimate transmission parameters ● Modulation Classification
● Symbol Rate and Center Frequency Estimation
Paulo Urriza - Qualifying Exam 28
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
D. Markovic / Slide 29
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
Paulo Urriza - Qualifying Exam 29
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
D. Markovic / Slide 30
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
Paulo Urriza - Qualifying Exam 30
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
D. Markovic / Slide 31
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)
Paulo Urriza - Qualifying Exam 31
D. Markovic / Slide 32
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
Paulo Urriza - Qualifying Exam 32
D. Markovic / Slide 33
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
Paulo Urriza - Qualifying Exam 33
No
rmal
ized
Cyc
lic M
USI
C C
ost
Fu
nct
ion
Cyclic MUSIC
D. Markovic / Slide 34
Future Work
Paulo Urriza - Qualifying Exam 34
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
D. Markovic / Slide 35
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 35
D. Markovic / Slide 36
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.
Paulo Urriza - Qualifying Exam 36
D. Markovic / Slide 37
Proposed Timeline
Paulo Urriza - Qualifying Exam 37
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
D. Markovic / Slide 38
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.
Paulo Urriza - Qualifying Exam 38
D. Markovic / Slide 39
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.
Paulo Urriza - Qualifying Exam 39
D. Markovic / Slide 40
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.
Paulo Urriza - Qualifying Exam 40