1. Bin Le
Bin Le is a PhD candidate in Center for Wireless Telecommunications (CWT) at Virginia Tech, advised by Dr. Charles W. Bostian. Since 2003, Bin joined CWT and conducted the research of direct-conversion receiver (DCR) and started the research in cognitive radio in His research includes radio domain cognition, software defined radio and evolutionary computation. He has diverse publications mainly covering RF engineering, SDR design, signal processing and artificial intelligence. He is also the author of a book chapter of cognitive network support.

2. Recognition and Adaptation in PHY layer
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Recognition and Adaptation in PHY layer
Bin Le
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Center for Wireless Telecommunications
466 Whittemore Hall
Blacksburg, VA 24061
Virginia Tech
04/10/2006

3. Presentation Outline Radio domain cognition Key issues in PHY layer
Cognition cycle
Recognition and adaptation
Key issues in PHY layer
Spectrum utilization
Waveform agility
Hardware support

4. System Model of Cognitive Radio
Meters
Sensing technology
language for environment awareness
Monitor through performance API
Knobs
Encoding of radio operation space
Adaptation through hardware AI
Hardware
Independent
Cognitive Engine
Machine Reasoning
Radio Parameters
“Knobs and Meters”
Sense/Reconfigure
Radio TX
Radio RX
Adaptation
Detect/Sense
Physical Media
Air interface
Functional diagram
Behavioral diagram

5. Cognition Cycle with Two Loops
Environment
Observation
Scenario
Synthesizing
Case identified
Link condition
User/policy
Radio hardware
Knowledge Base
Case report
Reasoning
Case-based
Decision
Making
Apply experience
Success memorized
Bad trail overwritten
Strategy instruction
Radio
Performance
Estimation
Link Configure
Optimization
WSGA
Initialization
Objectives
Constraints

6. Radio Recognition and Adaptation
Wireless environment
Performance API
Transparent
Radio-domain
Adaptation
Decision-making
Learning Core
General AI
Radio-domain specific
Radio-domain
Recognition
Radio platform
Transparent
Radio API

7. PHY Layer in the CR system
Recognition
PHY layer
Adaptation

8. Key Issues at PHY Layer Cognitive spectrum utilization
Sensing reliability (hidden node problem, passive nodes )
Spectrum sharing and performance
Waveform agility
Waveform recognition
Software approach for flexible waveform implementation
Self-optimization of software defined functional architecture
Radio resource management
Hardware performance limitation and trend (ADC, DSP power, etc)

9. Spectrum Utilization
Example of TV bands: Just need better ways of using spectrum
Global spectrum coordination
Better licensing procedure
Better regulations
Improve efficiency of fixed multiplexing
Allow flexible sharing schemes
Better interaction b/t policy and technology
Cognitive spectrum utilization
Smart and flexible transceiver: both hardware and algorithm
Multi-dimensional radio environment knowledge base to enable and support “cognitive” spectral behavior
Proliferate application and services diversity for “prosperous” bandwidth needs

10. Spectrum Awareness and Sharing
The idea of cognitive spectrum utilization couples sensing and adaptation together
Adaptation relies on sensing fidelity
Sensing designed by adaptation needs
Joint design for optimal resource cost
Dynamic spectrum management
Open and dynamic coordination
Overlay/underlay to improve spectrum efficiency
Cognitive spectrum utilization

11. Dynamic spectrum allocation (DSA)
OFDM PHY layer using a/g specs
Ad-hoc node configuration
CSMA/CA vs. DSA using cognitive algorithms
Simplified power control for minimal SNIR

12. Spectrum Underlay DSSS-UWB for spectrum underlay
PN-sequence for multi-user scheme
UWB spectrum is close or below the noise floor of pre-existing narrowband system
External coordination between two service layers is not as critical as in overlay system (using dynamic spectrum allocation scheme)
Spectrum is notched to protect narrow-band channels without much pulse-distortion due to its wide bandwidth

13. UWB-Broadcast Underlay with Notching
BC NW setup
Spectrum probe
Notch and calculate Gp
Compute overall introduced
UWB power in the spectrum
- Probed at one BC Rx node
Calculate SNIR for each
network nodes
Keep inserting UWB nodes
to some cluster size

14. UWB-Broadcast Underlay: Outage vs. Notching
UWB-BC hybrid network
in the underlay zone
UWB txPSD_dB = -130dBW/Hz, notchBW = 20MHz, UWB cluster size = 100, broadcast #channels = 10, notch Loss=30dB
50 iterations
Spectrum at one
BC Rx node after
UWB underlay NW
is established
Both system Pout is reduced when #notches/#channels increases from 0/10 to 10/10

15. Cognitive Spectrum Utilization: MOGA
Simulation Test Conditions
Functions Weights
Minimize spectral occupancy Maximize throughput Interference avoidance
BER BW
Spectral Efficiency
Power
Data Rate
Interference
Chromosome field: Power frequency Pulse Shape Symbol Rate Modulation

16. General Waveform Agility Requirements
Standard-independent waveform parameters
Waveform feature projection and recognition
Adaptive feature extraction
Signal characteristics and channel condition
Hybrid classification approach
Temporal, spectral, and vector space
Open, self-reconfigurable waveform knowledge base

17. Waveform Modulation Features
Temporal statistical features
Statistical moments with different orders
Simple and fast
Spectral statistical features
Power spectral density (PSD) and cyclostationarity features
Huge computation
Vector-space features
Constellation and motion statistics
No additional cost

18. Signal Classification Testbed
SNR=10dB
SNR=50dB
OCON
Initialization
OCON
Training
Training
Algorithm
Configure
OCON_mod1
Generate
modulation
waveform
Waveform
Feature
Extraction
Feature
Set
MAXNET … … …
OCON_modN
Waveform
Load
configure
Feature
Configure
Waveform
Feature
Database
Management
FeatureSet
Output
configure
OCON
Configuration
Database
Management
Feature
Space
Configure

19. Flexible Waveform Implementation
Based on SDR platform
Flexibility for cross-layer optimization
NSF NetS project
Interoperability
NIJ PSCR project
CWT recent NIJ-Anritsu waveform testbed
CWT Gnu-radio testbed

20. Gnu Radio Based SDR Testbed
Neural Network
Classifier
Digital receiver
Power spectral density
Channel BW estimate
IF carrier estimate
Symbol rate estimation
Cyclostationarity
Symbol detection
Digital IF Signal Input
Cyclic features
Artificial
Neural
Network
Modulation
Classifier
USB
2.0
Temporal statistics
Digital modulation features
Vector
Analysis
Analog
modulation
Symbol
constellation
Analog demodulator
Modulation classifier
Symbol
Rate
Modulation
Classifier
clock
Cognitive Radio Receiver
Signal
Input
Multi-rate
Decimator
Blind
Equalization
Baseband
Channel
Filter
Symbol
Detection
demod
Analog
LPF
ADC
Gnu-USRP SDR platform
Frequency
Error
Quadrature
Frequency
Synthesizer
Carrier
Recovery
Symbol
Timing
Phase
estimation
Vector
Analysis
IF carrier
Frequency
Direct
Digital
Synthesizer
DAC

21. PHY-Related Hardware Issues
RF limitation – JTRS challenge
frequency and dynamic range
Flexibility in frequency and bandwidth
Analog-to-digital interface – the neck of SDR
ADC largely sets the digital block boundary
Trend in speed, resolution and power
Software processing capability (speed and flexibility: logic vs. ALU)
Power handling and power efficiency
Substrate limitation: Si vs. GaAs
PA power efficiency improvement
Design trade space and trend projection are most wanted!

22. fs vs. ENOB Performance limitation depends on sampling frequency.
The contribution of each noise source is different at different sampling rates.
A slope close to 1bit/2.3dBsps is shown for sigma-delta ADCs due to noise shaping techniques; in contrast, a slope much closer to thermal-noise boundary (1bit/6dBsps) is shown for SAR ADC group where no noise shaping loop is used.

23. Power Grouped by Structure
Proof over 20 years of power consumption dependency on the ADC structure!

24. P curve fitting

25. Acknowledgement
This work is supported by Award No IJ-CX-K017 awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the author(s) and do not necessarily reflect the views of the Department of Justice.
This material is based upon work supported by the National Science Foundation under Grant No. CNS Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

26. Thank You!
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