Download - Area 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 Morpheussoftware 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.
Sensingterminal
Incumbentsignal
transmitter
...
Sensingterminal
Sensingterminal
Data collector(Fusion center)
Data fusion Final spectrumsensing result
Distributed Spectrum Sensing
Adversaries
Incumbent Emulation attack: Amalicious terminal emits signals thatemulate the characteristics of theincumbent’s signal.
Spectrum Sensing Data Falsificationattack: A malicious terminal sendsfalse local spectrum sensing resultsto the fusion center.
Localspectrumsensingresults
Signals with thesame characteristicsas incumbent signals
False localspectrum
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.
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.
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 ( )Xf
C faa =
1/ 20 0
( )( ) ( )2 2
X
X
X X
CS f
S f S f
a
a
a a
=
é ù+ -ê úë û
Case Library
Search Agent
Event
Environment Data
Utility
querystore
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.
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
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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
SimulinkGlueGlue