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COMPARISON AND SIMULATION OF DIGITAL MODULATION RECOGNITION ALGORITHMS. Wei Su and John Kosinski U.S. ARMY CECOM RDEC Intelligence and Information Warfare Directorate Fort Monmouth, New Jersey. Automated Signal Exploitation and Modulation Recognition. signal. Demodulated signal.

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Presentation Transcript
slide1

COMPARISON AND SIMULATION OF DIGITAL MODULATION RECOGNITION ALGORITHMS

Wei Su and John Kosinski

U.S. ARMY CECOM RDEC

Intelligence and Information Warfare Directorate

Fort Monmouth, New Jersey

automated signal exploitation and modulation recognition
Automated Signal Exploitation and Modulation Recognition

signal

Demodulated signal

Cooperative Communication

modulation type

pulse shaping

symbol rate

center frequency

signal

Non-cooperative Communication

?

?

?

?

Modulation recognition

Signal processing Statistical estimation

factors affect the modulation recognition results
Factors Affect the Modulation Recognition Results
  • Residual carrier frequency estimation
  • Carrier frequency and phase tracking
  • Baud rate estimation and pulse timing
  • Pulse shaping recovery
  • Channel distortion
slide4

Center frequency offset

A PSK8 signal

PSK8 signal with phase shift produced by center frequency offset

slide5

Pulse Timing

  • Re-sampling will be necessary if the symbol frequency divided by sample frequency is not an integer
  • Error is introduced in re-sampling

4 samples per symbol

10/3 samples per symbol

Before re-sampling

After re-sampling

slide6

Transmitter

Radio Channel

RF Receiver

Equalizer

Decision Maker

Channel Distortion

Before 16-QAM After

An adaptive filter (lower figure) is used to eliminate the multiple channel distortion in wireless communication. A distorted 16-QAM signal (left figure) is blindly equalized to give a correct output (right figure) before recognition process

slide7

Literature Search

  • A web site search of “modulation recognition” gave 52,500 hits.
  • More than a hundred publications on modulation recognition.
  • Many GOTS and COTS products available for various applications.
slide8

Some Well-known Approaches

Modulation feature extraction

  • I-Q Analysis
  • Zero-crossing
  • Power-law

Modulation classification

  • Pattern recognition
  • Maximum likelihood
problems in algorithm comparison
Problems in Algorithm Comparison
  • Deferent modulation type
  • Deferent assumption
  • Deferent signal environment
  • Deferent specification

MSK

FSK8

CW

FSK4

16QAM

FSK

FSK2

32QAM

PSK

64QAM

PSK8

PSK4

UNKNOWN

PSK

PSK2

ASK8

ASK4

ASK2

slide10

thresholds

CW PSK2PSK4ASK2ASK4FSK2FSK4

frequency phase amplitude

recognition tree

Base Band Signal

s.t.d.

abs

Azzouz and Nandi’s Method

Based on signal parameter variances and variance thresholds

phase variance is affected by the center frequency offset
Phase Variance is affected by the Center Frequency Offset

phase tracking

unwrapped phase tracking

BPSK with residual carrier

after phase tracking and compensation

limitation in azzouz and nandi
Limitation in Azzouz and Nandi
  • Not robust to center frequency offset
  • Not robust to pulse shaping
  • Not robust to phase wrap
  • Thresholds depend on SNR level
  • Restricted in modulation types (2 bits)
liedtke s method

baud rate detector

BPF

timing circuit

templates

Freq.

s.t.d

IF

I

CW PSK2 PSK4 PSK8 ASK2 FSK2

delta phase

/

tan-1

histograms

recognition tree

Q

( )2

sqrt

+

( )2

amplitude

Liedtke’s Method

Based on delta-phase and histogram correlation

limitation in liedtke s method
Limitation in Liedtke’s Method
  • Manual tuning of center frequency
  • Manual tuning of the time-recovery band pass filter
  • Limited in modulation types
slide17

frequency estimation

s.t.d.

PSK2 PSK4 PSK8 FSK2 FSK4 FSK8

recognition tree

phase estimation

delta-phase histogram

IF

zero-crossing counter

histogram templates

templates

Zero-crossing Detection Method

Based on zero-crossing phase, and delta phase histogram

slide18

Limitation in Zero-Crossing Method

  • Not robust to multiple frequencies
  • Need high SNR
  • Need high sampling rate
slide19

peak detection

( )2

FFT

signal

PSK2 PSK4 ASK2 FSK2

recognition tree

( )4

envelope

s.t.d.

Power-Law Classification Method

Based on power spectrum analysis

slide20

Limitation to Power-Law Method

  • Need very high sampling rate
  • Affected by pulse shape
  • Limited in modulation types
  • Noise is amplified with high order
modified azzouz and nandi

frequency

frequency estimation

s.t.d.

abs

phasex2

s.t.d.

I

abs

X2

phase

CW PSK2PSK4PSK8ASK2ASK4FSK2FSK4

track and correct phase

/

tan-1

s.t.d.

down demodulation

IF

recognition tree

abs

( )2

amplitude

+

sqrt

s.t.d.

timing recovery

abs

Q

( )2

adaptive thresholds

Modified Azzouz and Nandi
center frequency offset correction
Center Frequency Offset Correction

Method 1:

Estimated phase

symbol x

The unwanted phase rotation (left figure) is corrected adaptively (right figure) by a modified blind carrier phase tracker (upper figure) so that the feature variation methods can be used robustly for modulation recognition

Before BPSK After

Method 2: CFO = angle( S(yky*k-1)fs/2/p k = 1, 2, …, N

yk is a sample at k fs is the sampling frequency

modified liedtke

frequency

frequency estimation

templates

peak detector

timing circuit

CW PSK2PSK4PSK8ASK2ASK4ASK8FSK2FSK4FSK8

down demodulation

I

delta phase

/

tan-1

variance test

recognition tree

Q

( )2

+

sqrt

( )2

amplitude

Modified Liedtke

IF

slide24

Summary

  • There are many publications but most of them are related to base band symbol recognition
  • Center frequency offset (CFO), pulse shape, timing, and channel fading can be estimated but the estimates will not be perfect
  • Signal type UNKNOWN should be defined in all algorithms
  • A good modulation recognition algorithm should be robust to CFO, timing error, and fading