Cognitive Radio Evolution from Agile
Platforms to Omniscient Networks: the Road
from Dreams to Prototypes
Learning
Awareness
Radio
Hardware
Sensing
and
Modeling
Building
and
Retaining
Knowledge
Adapting
Evolution and Optimization
Charles W. Bostian
Virginia Tech
bostian@vt.edu
Acknowledgements
This project is supported by Award No. 2005-IJCX-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 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.
This material is based upon work supported by the
National Science Foundation under Grant No. CNS0519959. 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).
Defense Advanced Research Projects Agency
Strategic Technology Office
DARPA Order AF89-00
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.
Center for Wireless Telecommunications
www.cognitiveradio.wireless.vt.edu
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.
Find a
signal of
interest
Configure this in
real time and
operate it.
Cognitive Engine + SDR = Cognitive Radio
The relatively easier part – realization of the cognitive engine
General 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 antitampering 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)
ADC/DAC
Analog RF
FPGA
DSP
GPP
RF
front-end
PGA
ADC
DDC
Spectrum
Sweeper
Signal
Classifier
Waveform
Knowledge
Base
RX
RF
front-end
PGA
Waveform
Recognition
ADC
DDC
Filter
Gain
Case-based
Waveform
Solution Maker
Demod
FEC
Decoder
MAC Layer Protocol
De-packet
Binary Data
Packet
Binary Source
MAC Carrier Sense Algorithm
TX
RF
front-end
PGA
FEC
Encoder
DAC
DUC
Filter
Gain
Mod
GUI &
Center Controller
VTSDCSS
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)
Basic Concept
700M Hz Application Scenario
PCN
PPCN
CMT
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
Software Structure
Pilot
Symbol
from PCN
Seeking
Request to turn on
PCN, start the
collision avoidance
process
PCN initiation
IntraCell
Management
Intracell Narrow
and Wideband
Communication
Cognitive Mobile
Terminal
Register with
PCN
Collision
Processing
Cell Adjustment
Checking
Backbone
conncetion,
Routing Table
generation and
updating
Broadband
Intracell
Communication
Backbone
Communication
Universal
Classifier and
Synchronizer
Contact Information
Charles W. Bostian
Alumni Distinguished Prof.
Virginia Tech
bostian@vt.edu
540-231-5096
http://www.cognitiveradio.wireless.vt.edu