adaptive demodulation techniques for next … demodulation techniques for next generation software...
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Adaptive Demodulation Techniques for Next
Generation Software Defined Radios
U.S. Army RDECOM Communication-Electronics RD&E Center
Fort Monmouth, NJ 07703, USA
Contents
IntroductionModulation classification overviewResearch on commercial applicationsChallenges
Modulation Classifier
From: http://www.ottawa.drdc-rddc.gc.ca
………………………
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signal
Center Frequency Estimation
Demodulated signal
Modulation Recognition
What is Automatic Modulation Classification ?
BW Estimation SNR
Estimation
4
0
5 67 8 9
1 2 3
Phase Estimation
Filter
Statistical Estimation
IF Demodulation Filter
LO
A/DDemodulation Demodulation
Automatic Classification
Channel Equalizer
Channel EstimationSymbol Rate
Estimation
A non-cooperative communication technique which uses statistical methods to estimate the signal modulation types
Analog Digital
PSK/QAM Preprocessing
FSK/MSK Preprocessing
Estimation Confidence
Rating Failure
Modulation Scheme Classification Confidence Modulation Parameters
Analog Preprocessing
Classification Decision
Unknown Type
Coarse Modulation Estimation
FSK/MSK Modulation Estimation
PSK/QAM Modulation Estimation
Analog Modulation Estimation
Preprocessed IF
PSK/QAM Feature
Extraction
FSK/MSK Feature
Extraction
Analog Feature
Extraction
SNR Estimation
Templates Building
Modulation ClassificationA non-cooperative communication technique which uses statistical methods to estimate the modulation type of a unknown signal
SDR Applications (1)
4 05 678 91 2 34
05 678 91 2 3
4 05 678 91 2 3
4
0
5 67 8 9
1 2 3
4
0
5 67 8 9
1 2 3
Overcome channel fading Monitor communication spectrumRemove co-channel interferences
SDR Applications (2)Deep Space Communication
4 05 678 91 2 3
4
0
5 67 8 9
1 2 3
4
0
5 67 8 9
1 2 3
Reduce the scheduling and configuration burdens of communications
Modulation Classification Overview
Feature Extraction: Amplitude, Differential Phase, and Frequency
I
/
( )2
( )2
+
tan-1
Amplitude
IF
sqrt
timing circuit
Q
tΔΔ2φ
TΔΔφ
Freq.
Delta phase
BPFbaud rate detector
hist
ogra
m
CW PSK2 PSK4 PSK8 ASK2 FSK2
templates
reco
gniti
on tr
ee
STD
•Input: IF•Feather: Amplitude, phase, diff phase, frequency•Statistics: histogram, STD•Classifier: max correlation, decision tree•Reference: Liedtke 1984
corre
latio
n
Higher-order Transform of Constellations
The 4th order constellations
V29-8 and V29-16 constellations
QPSK
PSK8 PSK16
QAM16 QAM64
QAM32 QAM4-12 QAM16-16
QAM44-20c2
0 c2
1 c4
0 c4
1 c4
2 c6
0 c6
1 c6
2 c6
3 c8
0 c8
1 c8
2 c8
3 c8
4
Power-law:
Moment:
Cumulant:
Higher-order Statistical Features
∏ ∑= = ⎭
⎬⎫
⎩⎨⎧
=K
k
M
jk
ij
iKi
i
rpM
rHG1 1
)( )(1)|(
∑=
=K
k
krm1
440 )(
Mkr
4th order transformation of QPSK
4th order transformation of QAM16
)(4 kr
4th order dominant
points
1st order2nd
orde
r
2ndor
der
Q
I
2204040 3mmC −=
Cumulants vs. SNRs
Cyclic Spectral Analysis
∫−−
∞→+=
2/
2/
2),(1lim)( **
T
T
atjxxT
axx
dtettRT
R πττ
Theoretical spectrum correlation magnitude Gardner and Spooner 1992
•Input: IF•Features: cycle frequencies•Reference: Menguc, 2004
∫∞
∞−
−= ττ τπ deRfS fjaxx
axx
2)()( **
{ })()(),( ** ττ +=+ txtxEttR
xx
Decision Templates
Baseband
Cycle Freq
Cyclic autocorrelation
Spectrum correlation density
Time varying autocorrelation
Feature Classification: Maximum Likelihood (ALRT)
))(|( krHl QPSK
))(|( krHl BPSK
))(|( 8 krHl PSK
MAXBaseband Modulation Scheme
unknown
8PSK
QPSK
BPSK
PDFBPSK
PDFQPSK
PDF8PSK
∏=
K
k 1
(.)
∏=
K
k 1
(.)
∏=
K
k 1
(.)
• Input: baseband• Feature: Complex envelop• Classifier: maximum likelihood• References: Polydoros and
Kim 1995
Templates
Feature Classification: Histogram Correlation
∏ ∑= = ⎭
⎬⎫
⎩⎨⎧
=K
k
M
jk
ij
iKi
i
rpM
rHG1 1
)( )(1)|(
⎪⎭
⎪⎬⎫
⎪⎩
⎪⎨⎧ −−= 2
2)(
2)(
2
)(exp
21)(
σπσ
jbrrp
ik
ki
jALRT
HIST
∑=
iM
jk
ij
i
rpM 1
)( )(1
Quantize
∑=
iM
jk
ij
i
rpM 1
)( )(1
)()(k
ij rp
)( iqD
)( iqp
)( iqp
∑∑∑∑== Ω∈=
←==Q
k
iq
iq
Q
q kk
iK
kk
iKi DprprprHL
q 1
)()(
1
)(
1
)( log)(log)(log)|(
• Input: baseband/IF• Feature: frequency/diff phase• Classifier: max correlation• References: Liedtke 1984
Research on Commercial Applications
Maintain a constent BER by varying modulation schemesModulation schemes: QPSK, 16QAM, and 64QAMData frame based modulation recognitionA pilot symbol is used in forward channelReference: Jain, P.; Buehrer, R.M, “Implementation of adaptive modulation on the Sunrise software radio,” The proceedings of the 45th Midwest Symposium on Circuits and Systems, Volume: 3 , 4-7 Aug 2002. Pages:III-405 - III-408
Research on Adaptive Modulation Based on SDR - Cooperative
Slow Flat Fading
ChannelsTransmitter
Feed Back Channel
ReceiverDATA DATA
Pilot Pilot
Pilot Data
Environment limitation and restrictionElimination of the signal overhead informationAttractive for packet data services
Why Applying Non-cooperative Demodulation
Data output
Modulation Recognition
Preprocessing
Choose Demodulator
Modulation Recognition
Air-interface PreprocessingRF
IF
Delayer Demod
Deference Between Military and Commercial Applications
. .SIGINT SDR .
Real time classification demodulationSNR low high Candidates unlimited limited QoS friend / foe packet lossPulse shape unknown knownBandwidth unknown knownBaud rate unknown knownBlindness more less
Assume:Equally likelySymmetrical QAM/PSK
Purpose:Reduce the processing time
Issues:Low utilization of available informationMay need longer data length for randomness
Reduced form constellation
Q
I
Q
I
Constellation for QPSK
Research of Nolan et al. Reduce Form Constellation
Automatically recognize BPSK, QPSK, 8PSK, pi/4QPSK, 16QAM, FSK, MSK, GMSK, AM, FM, CW, and SSB using decision tree for spectrum, variance, and baud detection analysis.
IF
Data output
Transmitter
Adaptive Receiver – Ishii et al.
OSC
RF Modulation Estimation
Demodulation
•Spectrum
•Envelope
•Baud
•Amplitude
•Phase
BPF
Decision tree
Thresholds
Automatically recognize BPSK, QPSK, 8PSK, and 16QAM using amplitude and differential phase variances. Channel gain estimation is discussed (2000).Automatically recognize BPSK, QPSK, and 8PSK using differential phase and maximum likelihood test.
IF Data outDemodulation
OSC
BPFRF
Maximum Likelihood Modulation Estimation
Noise Variance Estimation
Blind Modulation Estimation Umebayshi et al.
Transmitter
Research of Menguc and Jondral (1)Air Interface Identification for SDR
Identify (Verify?)TDMA-GMSKOFDM-PSK/QAMCDMA-QPSK
Base band
Data output
Transmitter
RF
Likelihood Test
Calculate Cyclic
Autocorrelation
•Determine Number of Interfaces
•Estimate Carrier and BW
Preprocessing
Demodulation
Recognize Air Interfaces
Threshold
Research of Menguc and Jondral (2) Magnitude Plot of the Cyclic Autocorrelation
Estimations
GMSK OFDM CDMA
* O. Menguc, “Air interface identification for software radio systems,” Ph.D. Dissertation, University of Fridericiana Karlsruhe, Nov. 30, 2004.
IssuesProcessing speedNeed universal front end
Research of Simon and Divsalar (1)Data Format classification for SDR
∑∑−
=
−
=⎟⎟⎠
⎞⎜⎜⎝
⎛<⎟⎟
⎠
⎞⎜⎜⎝
⎛ 1
0,
0
1
0,
0
22coshln22coshlnbb K
nManchesterk
K
nNRZk r
NPr
NP
large;2ln||small;2/
{)cosh(ln2
xxxx
x−
≅
•Discriminate NRZ and Manchester code
•Reduce complexity
•Extend to non-coherent case
Ln cosh(x) vs x
Use two curves approximate ln cosh(x) in order to simplify the ML computation
small
Research of Simon and Divsalar (2)Reduced Complexity ML Implementation
SNR Estimation
BasebandData
Other Research Results on Modulation Classification Based on SDR
Gu et al. “Channelized receiver platform of SDR based on FPGAs, Proceedings of The 5th IEEE International conference on ASIC, Vol.2, Oct. 2003, pp.840-843.Yang, “An enhanced SOFM method for automatic recognition and identification of digital modulations,” Proceedings of the 2nd
IEEE International Workshop on Electronic Design, Test and Applications (DELTA’04), Jan. 2004, pp.174-179.Ko et al. “Modulation type classification Method using wavelet transform for adaptive demodulator,” Proceedings of 2004 International Symposium on Intelligent Signal processing and Communication System, Vol.46, Oct. 1995, pp.211-222.Hooftand Darwish, “A reconfigurable software digital radio architecture for electronic signal interception, identification,communication and jamming,” COTS Joural, April 2002, pp.31-35.
Normalization
RF signal
Pulse Timing and Matched Filtering
Local Oscillator
Band Pass Filter
Quantization and Address Mapping
Noise Power Estimation
Look Up Table
Decision Process
Confidence Check
Estimated Modulation Scheme
Carrier and Carrier Phase Tracking
Real-time Data
Output
Choose Demodulator
Delayer Demodulation Process
Adaptive modulation is not only an important information warfare practice but also an effective tool to maximize the data capacity and minimize the transmission error in SDR applications.Automated modulation classification is a solution in handling the non-cooperative communication problem for SDR.Blind estimation of modulation parameters such as center frequency offset, carrier phase, pulse shape, symbol rate, and bandwidth is critical to the robustness of modulation classification.A good modulation classifier should be able to identify modulation scheme fast and robust.
Summary
Future Work
Faster estimatorShorter data lengthLower SNRBetter channel estimationBetter QoS