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

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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

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

SimulinkGlueGlue