Cognitive and Software Radio

Cognitive and Software Radio

Critical Research Issues in SDR and
Cognitive Radio

•
Efficient and flexible SDR hardware

•
Software architectures and waveform
development tools

•
Testing and security of software

•
Sensing technologies

•
Intelligence for Radios

•
Intelligence for Networks

An Open Systems Approach for Rapid
Prototyping Waveforms for SDR

•
Faculty:

J.H. Reed, W.H.
Tranter, R.M. Buehrer, and
C.B. Dietrich

•
Funding:

NSF, SAIC,
Tektronix, TI, ONR

•
Description:

Work is
ongoing in four major areas:

–
Open Source SCA Core
Framework (OSSIE)

–
Rapid Prototyping Tools
for SCA Components and
Waveforms

–
Component and Device
Library

–
Software Defined Radio
Education




A Cognitive Radio Through
Hardware Adaptation

•
Faculty:

P. Athanas

•
Funding:

Harris
Corporation (Melbourne,
FL)

•
Description:

Hardware
adaptation will be
accomplished by sensing
link statistics and multi
-
tasking radio management
functions within the Harris
Morpheus System
-
in
-
a
-
Package SDR.

The architecture of transmitter

and receiver on the Morpheus

software defined radio

Cooperative Game Theory for
Distributed Spectrum Sharing

•
Faculty
: Luiz A. DaSilva,
Allen MacKenzie


•
Description
: We utilize
cooperative game theory
to model situations where
wireless nodes need to
agree on a fair allocation of
existing spectrum

Find out more: J. Suris et al., “Cooperative
Game Theory for Distributed Spectrum
Sharing,” under review (available upon
request), 2006.

Trustworthy Spectrum Sharing in
Software Defined Radio Networks

•
Faculty
:
J.
-
M. Park
, T. Hou,
J. Reed

•
Funding
: NSF

•
Description
: The emergence
of Software Defined Radio
(SDR) technology raises new
security implications. In this
project, we study security
issues that pose the greatest
threat when an adversary is
able to install malicious
software or modify already
installed software on an SDR,
with particular focus on
threats that cannot be
addressed using
cryptographic techniques.

Read more: R. Chen and J.
-
M. Park, “Ensuring
trustworthy spectrum sensing in cognitive radio
networks,” IEEE Workshop on Networking
Technologies for Software Defined Radio
Networks (held in conjunction with IEEE
SECON 2006), Sep. 2006.

Sensing
terminal
Incumbent
signal
transmitter
...
Sensing
terminal
Sensing
terminal
Data collector
(Fusion center)
Data fusion
Final spectrum
sensing result
Distributed Spectrum Sensing
Adversaries
Incumbent Emulation attack
: A
malicious terminal emits signals that
emulate the characteristics of the
incumbent’s signal.
Spectrum Sensing Data Falsification
attack
: A malicious terminal sends
false local spectrum sensing results
to the fusion center.
Local
spectrum
sensing
results
Signals with the
same characteristics
as incumbent signals
False local
spectrum
sensing results
Game
-
theoretic Framework for
Interference Avoidance

•
Faculty
: A. B. MacKenzie,
R. M. Buehrer, J. H. Reed

•
Funding
: ONR, ETRI


•
Description
: We use
game theory models to
investigate and develop
waveform adaptation
schemes for interference
avoidance in distributed
and spectrum sharing
networks.

Read more: R. Menon, A. B. MacKenzie, R. M.
Buehrer and J.H. Reed, “A game
-
theoretic
framework for interference avoidance in ad
-
hoc
networks”, Globecom 2006.

Distributed Spectrum Sensing for
Cognitive Radio Systems

•
Faculty
: Claudio da Silva


•
Description
: This project will
establish detection limits of
distributed
spectrum sensing for
cognitive radio systems. Specific
research objectives are to:

–
design signal processing methods
at the node level,

–
design data fusion techniques,

–
design algorithms for the
transmission of spectrum sensing
information, and

–
evaluate the reliability and
complexity of the spectrum
sensing stage.

Application of Artificial Intelligence
to the Development of Cognitive
Radio engine

•
Faculty
:
J. H. Reed

•
Funding
:
Army Research
Office


•
Description
: we
have

investigated the applicability of
artificial intelligence
algorithms to the development
of cognitive radio engine.

–
Identify the suitability of
the AI techniques for the
various cognitive radio
tasks

–

observing, orienting,
deciding, and learning.

One of the key results is that a robust cognitive
engine relies on the combination of several
artificial intelligence algorithms


Our team is
building a cognitive engine leveraging the
knowledge gathered through this research.


Case
-
based
learning

Genetic
algorithms

HMM, data
mining

Search
-
based
learning

Knowledge
-
based learning

Radio
Environment
Map (REM)

Observations

Cooperative
learning

Parameters
optimization
or tradeoff

Prediction,
plan
ning

Collaborate with
other nodes

Case

Memory

Domain
Knowledge
Base

Neural
network

Situation awareness
and information base

Learning and reasoning

Decision and adaptation

IEEE 802.22 WRAN
–

Cognitive Engine
and Supporting Algorithms

•
Faculty
:
J. H. Reed

•
Funding
:
ETRI


•
Description
: we are
developing
cognitive
engine (CE)
and

supporting algorithms

for
IEEE 802.22 WRAN system.

–
The CE is
capable of
perceiving current
radio
environment,

planning,
learning, and
acting

according to
its

goals

and current radio
environment
.


A typical radio environment for cognitive
WRAN system: WRAN should be aware of
all the local radio activities surrounding
the system so that
it can enable the

coexistence of
primary users and
secondary users
.





집
집
집
집
집
집
집
집
집
WRAN Repeater

TV Station

WRAN

Base Station

집
WRAN

Base Station

집
집
집
집
집
집
집
집
집
집
WRAN CPE

Wireless

MIC

집
집
집
집
TV Station

집
집
집
집
집
집
집
Grade B

Contour of TV station

TV
R
eceiver

Public
S
afety


Radios

IEEE 802.22 WRAN
–

Cognitive Engine
and Supporting Algorithms

•
Cognitive engine

–
Decide, learn, and plan

•
Supporting algorithms

–
Spectrum sensing:
detection and
classification techniques

–
REM
-
enabled cognition

–
Waveform and power
adaptation techniques


HMM Signal Type
1
HMM Signal Type
2
HMM Signal Type N
Choose Maximum
Log Likelihood
Decision
(
Signal
existence
and type
)
Evaluate spectral
coherence function
Extract SCF feature
…
Wide range SNR (
-
9dB ~9dB)
signals are coming and mixed
down IF level
Trained with specific signal type.
For instance, HMM for AM with 9dB
Trained with specific signal type.
For instance, HMM for QPSK with 9dB
[
]
profile( ) max ( )
X
f
C f
a
a
=
1/2
0 0
( )
( ) ( )
2 2
X
X
X X
C
S f
S f S f
a
a
a a
=
é ù
+ -
ê ú
ë û
Case Library

Search Agent

Event

Environm
ent Data

Utility

query

store

Action

Cognitive Engine

Adaptation Algorithm

Detection & Classification

Application of Artificial Intelligence
to the Development of Cognitive
Radio engine

•
Faculty
:
J. H. Reed

•
Funding
:
Army Research
Office


•
Description
: we
have

investigated the applicability of
artificial intelligence
algorithms to the development
of cognitive radio engine.

–
Identify the suitability of
the AI techniques for the
various cognitive radio
tasks

–

observing, orienting,
deciding, and learning.

One of the key results is that a robust cognitive
engine relies on the combination of several
artificial intelligence algorithms


Our team is
building a cognitive engine leveraging the
knowledge gathered through this research.


Case
-
based
learning

Genetic
algorithms

HMM, data
mining

Search
-
based
learning

Knowledge
-
based learning

Radio
Environment
Map (REM)

Observations

Cooperative
learning

Parameters
optimization
or tradeoff

Prediction,
plan
ning

Collaborate with
other nodes

Case

Memory

Domain
Knowledge
Base

Neural
network

Situation awareness
and information base

Learning and reasoning

Decision and adaptation

Cognitive Radio for Public Safety

•
Faculty
: C. W. Bostian,

M. Hsiao, A. B. MacKenzie

•
Funding
: NIJ

•
Description
: We are
developing a public safety
cognitive radio that is
aware of the RF
environment, identifying
activity in public safety
bands, and configures
itself to needed
combinations of waveform
and network parameters.


Read more:
Thomas W. Rondeau, et. al. “Cognitive
Radios in Public Safety and Spectrum Management”
33rd Research Conference on Communications,
Information, and Internet Policy, 2005

Cognitive Engine

•
Faculty
: C. W. Bostian,

S. Ball, M. Hsiao,

A. B. MacKenzie

•
Funding
: NSF

•
Description
: We are
developing a cognitive
engine, a software package
that reads a software
defined radio’s “meters”
and turns its “knobs”
intelligently adapting and
learning from experience in
order to achieve user goals
within operational legal
limits.

Read more:
T.W. Rondeau, B.Le, C.J. Rieser, and
C.W. Bostian, “Cognitive Radios with Genetic
Algorithms; Intelligent Control of Software Defined
Radios,”
Software Defined Radio Forum
, Phoenix, AZ,
Nov. 15
-
18, 2004.


Cognitive Networks

•
Faculty
: Luiz DaSilva, A.
B. MacKenzie

•
Funding
: NSF, DARPA
(pending)


•
Description
: we are
developing
cognitive
networks
, capable of
perceiving current network
conditions and then
planning, learning, and
acting according to end
-
to
-
end goals.


Read more: R. Thomas et al., “Cognitive
networks: adaptation and learning to achieve
end
-
to
-
end performance objectives,” IEEE
Communications Magazine, Dec. 2006

Unlicensed Wide Area Networks Using
Cognitive Radios and Available
Resource Maps

•
Faculty
: Claudio da Silva and Jeff
Reed

•
Funding
: Texas Instruments


•
Description
: we are developing a
new unlicensed wide area network
(UWAN
-
ARM) based on
cognitive
radio

and
available resource maps

that brings together the best
attributes of licensed and
unlicensed technologies into a new
wireless paradigm.

Dynamic Spectrum Sharing

•
Faculty
: R. M. Buehrer, J.
H. Reed

•
Funding
: ONR, ETRI


•
Description
: We have
developed a framework to
investigate and identify
desirable characteristics for
dynamic spectrum sharing
techniques. Desirability is
with respect to impact on
legacy system as well as
capacity of SS network.

Read more: R. Menon, R. M. Buehrer and J. H.
, “Outage probability based comparison of
underlay and overlay spectrum sharing
techniques,” IEEE DySPAN 2005, pp. 101
-
109.

Application of Game Theory to the
Analysis and Design of MANETs

•
Faculty
: J. Reed, R. Gilles,
L. A. DaSilva, A. B.
MacKenzie

•
Funding
: ONR, NSF


•
Description
: We are
developing techniques for
analyzing and designing
MANET and cognitive radio
algorithms in a network
setting.


More information at
www.mprg.org/gametheory

19

Rapid Prototyping for SCA
Development

•
Faculty
: Cameron Patterson

•
Description
: We are
working with BAE, The
Mathworks, and Zeligsoft to
investigate a model
-
based
design flow for SCA radios.
Simulink

and
Component
Enabler

are used to build
models that are linked with
glue code and implemented
in an SCA environment.

CORBA

CORBA

SCA Skeleton

Simulink

SCA Component

Simulink

Glue

Glue