# Presentation: 016399

## COMPARISON AND SIMULATION OF DIGITAL MODULATION RECOGNITION ALGORITHMS

- U.S. ARMY CECOM RDEC
- Intelligence and Information Warfare Directorate
- Fort Monmouth, New Jersey

**COMPARISON AND SIMULATION OF DIGITAL MODULATION RECOGNITION ALGORITHMS**

## Automated Signal Exploitation and Modulation Recognition

- signal

- center frequency

**Cooperative Communication**

**Non-cooperative Communication**

- symbol rate

- signal

**?**

**?**

- Signal processing Statistical estimation

- pulse shaping

**?**

- Demodulated signal

- Modulation recognition

- modulation type

**?**

**Automated Signal Exploitation and Modulation Recognition**

## 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**

## Center frequency offset

- A PSK8 signal

**Center frequency offset**

## 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

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

- After re-sampling

- Before re-sampling

**Pulse Timing**

## 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

**Channel Distortion**

## 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.

**More than a hundred publications on modulation recognition.**
**Many GOTS and COTS products available for various applications.**

**Literature Search**

## Modulation feature extraction
I-Q Analysis
Zero-crossing 
Power-law
Modulation classification
Pattern recognition
Maximum likelihood

**I-Q Analysis**
**Zero-crossing **
**Power-law**
**Modulation classification**
**Pattern recognition**
**Maximum likelihood**

**Some Well-known Approaches**

## Deferent modulation type
Deferent assumption
Deferent signal environment
Deferent specification

**Deferent assumption**
**Deferent signal environment**
**Deferent specification**

**Problems in Algorithm Comparison**

- PSK2

- PSK8

- PSK4

- FSK8

- FSK4

- FSK2

- ASK4

- ASK8

- ASK2

- 32QAM

- CW

- UNKNOWN

- 64QAM

- 16QAM

- MSK

- PSK

- PSK

- FSK

## Based on signal parameter variances and variance thresholds

- Azzouz and Nandi’s Method

## Phase Variance is affected by the Center Frequency Offset

- BPSK with residual carrier

- after phase tracking and compensation

- unwrapped phase tracking

- phase tracking

## Phase Variance is Affected by Pulse Shaping (/4 DQPSK)

## Phase Variance is Affected by Phase Wrap

## 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

- Based on delta-phase and histogram correlation

## Limitation in Liedtke’s Method 

**Manual tuning of center frequency**
**Manual tuning of the time-recovery band pass filter**
**Limited in modulation types**

## Based on zero-crossing phase, and delta phase histogram

**Zero-crossing Detection Method**

## Not robust to multiple frequencies
Need high SNR
Need high sampling rate

**Need high SNR**
**Need high sampling rate**

**Limitation in Zero-Crossing Method**

## Based on power spectrum analysis

**Power-Law Classification Method**

## Need very high sampling rate
Affected by pulse shape
Limited in modulation types
Noise is amplified with high order

**Affected by pulse shape**
**Limited in modulation types**
**Noise is amplified with high order**

**Limitation to Power-Law Method**

## Modified Azzouz and Nandi

**Modified Azzouz and Nandi**

## Center Frequency Offset Correction

**      **Before            BPSK            After

- 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** **

- symbol *x*

- Estimated phase

**Center Frequency Offset Correction**

- Method 1:

- Method 2:	  CFO = angle( (yky*k-1)fs/2/	k = 1, 2, ..., N
- yk is a sample at k  	fs is the sampling frequency

## Modified Liedtke

- IF

## 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 

**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**** **

**Summary**