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Cognitive Radio Evolution from Agile Platforms to Omniscient Networks: the Road
from Dreams to Prototypes
Charles W. BostianVirginia Tech
Radio Hardware
Awareness
Sensing and
Modeling
Adapting
Evolution and Optimization
Learning
Building and
Retaining Knowledge
Acknowledgements
This project is supported by Award No. 2005-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-0519959. 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).
This work is also supported by Air Force Institute of Technology (AFIT). The views expressed in this article are those of the author and do not reflect the official policy or position of the Air Force, Department of Defense or the U.S. Government.
Center for Wireless Telecommunicationswww.cognitiveradio.wireless.vt.edu
The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.
Defense Advanced Research Projects Agency
Strategic Technology Office
DARPA Order AF89-00
Acknowledgment: The VT Team
An (old) radio guy’s vision of cognitive radio:
A universal transceiver (all modes and all frequencies) capable of discovering radios like itself and working cooperatively to negotiate frequencies, waveforms, and protocols to optimize performance subject to user needs based on the radio’s awareness of its environment and its past experience.
The VT Public Safety Cognitive Radio
• Recognize any P25 Phase 1 waveforms
• Identify known networks
• Interoperate with legacy networks
• Provide a gateway between incompatible networks
•Serve as a repeater when necessary – useful when infrastructure has been destroyed or does not exist.
In developing this prototype, we have solved some hard problems in rapid reconfiguration of a radio platform and in signal recognition and synchronization.
Configure this in real time and operate it.
Find a signal of interest
Cognitive Engine + SDR = Cognitive Radio
The relatively easier part – realization of the cognitive engineGeneral Implementations:
A restricted implementation: the VT Public Safety Cognitive Radio
FCC Worries: Code correctness, insecure memory accesses, tamper resistance.
Off-line unit testing and formal verification plus light-weight yet effective anti-tampering methods to ensure that any module replacement is compliant. Ensures that any replacement of the modules, including over-the-air updates is done by trusted parties.
The harder part – building a “universal” radio platform
The GPP Problem – Latency and Inability to Control Timing
OK for narrowband waveforms with simple timing requirements.
A real problem for wideband waveforms and MACs requiring precise timing.
The solution that we are developing now: A hybrid architecture containing fixed and reconfigurable subsystems.
•Embedded GPP performs cognitive functions and determines radio configuration
•Reconfigurable FPGA and ASICs perform PHY and MAC layer operations
•Accelerators implement application layer functions
System Overview of PSCR (hybrid implementation)
FPGA DSP GPPAnalog RF ADC/DAC
Spectrum Sweeper
SignalClassifier
WaveformRecognition
RFfront-end
PGA ADC DDC
WaveformKnowledge
Base
Case-basedWaveform
Solution Maker
GU
I &
Ce
nte
r Co
ntro
ller
configureFilter Gain Demod
FEC Decoder
De-packet
MAC Carrier Sense Algorithm
MAC Layer Protocol
Filter Gain Mod
FEC Encoder
Packet
RFfront-end
PGA ADC DDC
RFfront-end
PGA DUCDAC
VTSDCSS
Binary Source
Binary Data
RX
TX
My student Ying Wang will demonstrate some of our current spectrum, waveform identification, and radio configuration technology as part of this meeting. She and my student Qinqin Chen invented the system we will demonstrate and many others in our group contributed to the implementation.
What is wrong with this picture?
•One radio platform can’t do it all.•Focuses on interactions of two nodes.•Ignores network issues.•Ignores applications that the radio will run.
The reality:
•Multiple networks
•Multiple protocols
•Multiple applications
•Dynamic Spectrum Access
All this leads to the concept of an application and network driven integrated architecture for a cognitive node
Architecture for An Application and Network Driven Integrated Cognitive Node
Conceived by my student Feng Andrew Ge to capture the overall cognitive radio efforts of our group.
An important part of the implementation: The Universal Cognitive Gateway, dissertation topic of my student Qinqin Chen
Another application: dissertation work of my student Mark Silvius
Dynamic Cellular Cognitive Radio (Ying Wang)
Base station in a infrastructure network
PCN with in the infrastructure network
Wireless Connection to the infrastructure network
Area where the Base station are destroyed
DCCS System
Pilot Symbol from PCN Seeking
Cognitive Mobile Terminal
Register with PCN
Request to turn on PCN, start the
collision avoidance process
Collision Processing
PCN initiation
Cell Adjustment Checking
Backbone conncetion,
Routing Table generation and
updating
IntraCell Management
Broadband Intracell
Communication
Intracell Narrow and Wideband Communication
Universal Classifier and Synchronizer
Backbone Communication
700M Hz Application Scenario Basic Concept
Software Structure
PPCN
PCN
CMT
Contact Information
Charles W. Bostian
Alumni Distinguished Prof.
Virginia Tech
540-231-5096
http://www.cognitiveradio.wireless.vt.edu