dyspan sdr cr tutorial 10 25 rev02
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Understanding the Issues in Software Defined Cognitive Radio
Jeffrey H. Reed
Charles W. BostianVirginia Tech
Bradley Dept. of Electrical and Computer Engineering
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Comment Slide – Delete Before Submitting
Following section presented by Reed
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What You Will Learn Basic Concepts of Software Defined Radio
(SDR) Basic Concepts of Cognitive Radio (CR) and its
relationship to SDR. How Cognitive Radios are Implemented Analyzing Cognitive Radio Behavior and
Performance Regulatory Issues in Cognitive Radio
Deployment Cognitive Radio Applications in Interoperability
and Spectrum Access Current Research Issues
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Acknowledgements
Albrecht Johannes Fehske
Thomas Rondeau Bin Le James Neel David Scaperoth
Kyouwoong Kim David Maldonado Lizdabel Morales Youping Zhao Joseph Gaeddert
Students that contributed to this presentation:
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Software Defined Radio – Basic Concepts and Relationship to Cognitive Radio
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Comment Slide – Delete Before Submitting
Following section presented by Reed
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Software Defined Radio (SDR)
Termed coined by Mitola in 1992 Radio’s physical layer behavior is primarily
defined in software Accepts fully programmable traffic & control
information Supports broad range of frequencies, air
interfaces, and application software Changes its initial configuration to satisfy user
requirements
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Software Defined Radio Levels (1/2)
Highest Level of ReconfigurablityCompletely flexible modulation format,
protocols and user functionsFlexible bandwidths and center frequency,
i.e., RF front end is also configurableAdapts to different network and air interfacesOpen architecture for expansion and
modifications
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Software Defined Radio Levels (2/2)
Lowest Level of ReconfigurabilityRadio not easily changedPreset signal bandwidth and center
frequencyFew and preset modulation formats,
protocols, and user functions
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Advantages of SDR Reduced content of expensive custom silicon Reduce parts inventory Ride declining prices in computing
components DSP can compensate for imperfections in RF
components, allowing cheaper components to be used
Open architecture allows multiple vendors Maintainability enhanced
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Drawbacks of SDR Power consumption (at least for now) Security Cost Software reliability Keeping up with higher data rates Fear of the unknown Both subscriber and base units should
be SDR for maximum benefit
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Applications for SDR Military
Full Connectivity Sensor Networks Better Performance
Commercial Lower Cost – subscriber units Lower Cost – base unit Lower Cost – network Better performance
Regulatory Stretch expensive spectrum Build in innovation mechanisms
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How is a Software Radio Different from Other Radios? - Application
Software Radio Dynamically
support multiple variable systems, protocols and interfaces
Interface with diverse systems
Provide a wide range of services with variable QoS
ConventionalRadio
Supports a fixed number of systems
Reconfigurability decided at the time of design
May support multiple services, but chosen at the time of design
Cognitive Radio Can create new
waveforms on its own
Can negotiate new interfaces
Adjusts operations to meet the QoS required by the application for the signal environment
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How is a Software Radio Different from Other Radios?- Design
Software Radio Conventional
Radio + Software
Architecture Reconfigurability Provisions for
easy upgrades
Conventional
Radio Traditional RF
Design Traditional
Baseband Design
Cognitive Radio SDR + Intelligence Awareness Learning Observations
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How is a Software Radio Different from Other Radios? - Upgrade Cycle
Software Radio Ideally software
radios could be “future proof”
Many different external upgrade mechanisms Over-the-Air
(OTA)
Conventional Radio
Cannot be made “future proof”
Typically radios are not upgradeable
Cognitive Radio SDR upgrade
mechanisms Internal upgrades Collaborative
upgrades
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Cognitive Radio Concepts
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Comment Slide – Delete Before Submitting
Following section presented by Bostian
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Cognitive Radio
Term coined by Mitola in 1999 Mitola’s definition:
Software radio that is aware of its environment and its capabilities Alters its physical layer behavior Capable of following complex adaptation strategies
“A radio or system that senses, and is aware of, its operational environment and can dynamically and autonomously adjust its radio operating parameters accordingly”
Learns from previous experiences Deals with situations not planned at the initial time of
design
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Adaptive radios can adjust
themselves to accommodate anticipated events
Fixed radios are set by their
operators
Cognitive radios can sense their
environment and learn how to adapt
Beyond adaptive radios, cognitive radios can handle unanticipated channels and events.
Cognitive radios require:• Sensing• Adaptation• Learning
Cognitive radios intelligently optimize their own performance in response to user requests and in conformity with FCC rules.
What is a Cognitive Radio?
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Cognitive radios are machines that sense their environment (the radio spectrum) and respond intelligently to it.
Like animals and people they
• seek their own kind (other radios with which they want to communicate)
• avoid or outwit enemies (interfering radios)
• find a place to live (usable spectrum)
• conform to the etiquette of their society (the Federal Communications Commission)
• make a living (deliver the services that their user wants)
• deal with entirely new situations and learn from experience
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1) Access to spectrum (finding an open frequency and using it)
Cognitive radios are a powerful tool for solving two major problems:
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2) Interoperability (talking to legacy radios using a variety of incompatible waveforms)
Cognitive radios are a powerful tool for solving two major problems:
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Levels of Radio FunctionalityLevel Capability Comments
0 Pre-programmed A software radio
1 Goal DrivenChooses Waveform According to Goal. Requires Environment Awareness.
2 Context Awareness Knowledge of What the User is Trying to Do
3 Radio AwareKnowledge of Radio and Network Components, Environment Models
4 Capable of PlanningAnalyze Situation (Level 2& 3) to Determine Goals (QoS, power), Follows Prescribed Plans
5 Conducts Negotiations Settle on a Plan with Another Radio
6 Learns EnvironmentAutonomously Determines Structure of Environment
7 Adapts Plans Generates New Goals
8 Adapts Protocols Proposes and Negotiates New Protocols
Adapted From Table 4-1Mitola, “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,” PhD Dissertation Royal Institute of Technology, Sweden, May 2000.
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What is a cognitive radio?
An enhancement on the traditional software radio concept wherein the radio is aware of its environment and its capabilities, is able to independently alter its physical layer behavior, and is capable of following complex adaptation strategies.
Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 1999, pp 3-10.
Urgent
Allocate ResourcesInitiate Processes
Negotiate Protocols
OrientInfer from Context
Select AlternateGoals
Plan
Normal
Immediate
LearnNew
States
Observe
OutsideWorld
Decide
Act
User Driven(Buttons)Autonomous
Infer from Radio Model
StatesGenerate “Best” Waveform
Establish Priority
Parse Stimuli
Pre-process
Cognitive radio Cognition Cycle
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NormalUrgent
Level0 SDR1 Goal Driven2 Context Aware3 Radio Aware4 Planning5 Negotiating6 Learns
Environment7 Adapts Plans8 Adapts Protocols
Allocate ResourcesInitiate Processes
OrientInfer from Context
Parse Stimuli
Pre-processSelect Alternate
GoalsEstablish Priority
PlanNormal
Negotiate
Immediate
LearnNewStates
Negotiate Protocols
Generate AlternateGoals
Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 1999, pp 3-10.
Observe
OutsideWorld
Decide
Act
User Driven(Buttons)
Autonomous Determine “Best” Plan
Infer from Radio Model
States
Determine “Best” Known WaveformGenerate “Best” Waveform
Relationship between the Cognition Cycle and the Levels of Functionality
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FCC Motivation for Cognitive Radio
Currently the FCC is refarming licensed bands such as the TV Bands
Long-term vision Eliminate rigid, coarse spectrum allocations Switch to demand-based approach
Improve relative spectral efficiency
Need new protocols for Supporting long-term vision of the FCC Inter-network interference avoidance Maximizing utilization of available bandwidth
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Cognitive Radio Advantages All the software radio benefits Improved link performance
Adapt away from bad channels Increase data rate on good channels
Improved spectrum utilization Fill in unused spectrum Move away from over occupied spectrum
New business propositions High speed internet in rural areas High data rate application networks (e.g., Video-conferencing)
Significant interest from FCC, DoD Possible use in TV band refarming
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Cognitive Radio Drawbacks
All the software radio drawbacks Significant research to realize
Information collection and modeling Decision processes Learning processes Hardware support
Regulatory concerns Loss of control Fear of undesirable adaptations
Need some way to ensure adaptations yield desirable networks
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Cognitive Radio & SDR SDR’s impact on the wireless world is difficult to predict
“But what…is it good for?” Engineer at the Advanced Computing Systems Division of IBM,
1968, commenting on the microchip Some believe SDR is not necessary for cognitive radio
Cognition is a function of higher-layer application Cognitive radio without SDR is limited
Underlying radio should be highly adaptive Wide QoS range Better suited to deal with new standards
Resistance to obsolescence
Better suited for cross-layer optimization
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Types of Software Defined Cognitive Radios
Policy-Based Radio Reconfigurable Radio Cognitive Radio
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Policy-based Radio A radio that is governed by a predetermined set
of rules for choosing between different predefined waveforms
The definition and implementation of these rules can be: during the manufacturing process during configuration of a device by the user; during over-the-air provisioning; and/or by over-the-air control
Analogous to rules of what to order from a menu “I’ll have GSM today”
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Reconfigurable Radio
A radio whose hardware functionality can be changed under software control
Reconfiguration control of such radios may involve any element of the communication network
Analogous to rules of what to order from a menu and permit substitutions to the order“I’ll have GSM today with the 802.11 FEC”
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Technology Challenges in SDR
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Comment Slide – Delete Before Submitting
Following section presented by Reed
Needs more work on example SDR architectures
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Radio Architecture
RxTx
RF Signal Amplify
MixerFilter
AmplifyMixerFilter
IF Signal
Baseband Signal
Superhetrodyne
RF Signal Amplify
MixerFilter
AnalogTo DigitalConverter
IF Signal Digital
Signal Processing
Software Defined Radio
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Behind the Converters: The Software Architecture
Nature of Architecture Depends on Applications: Commercial vs. Military
Benefits of a Good Architecture Clear way to implement system Reuse --- modularity Quality control and testing Portability – one radio to another Upgradability Outsourcing/managing development Language independence More potential for Over-the-Air Programming Standardized interfaces
Middleware-based architectures are commonly used
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Example SDR: GNU Radio
What is GNU Radio?GNU Radio is a set of S/W signal processing
building blocks that allow users to create their own S/W radio
Why GNU Radio? Attempts to solve the complexity issues of both
H/W and S/W of SDR Modular (use with most any GPP)
S/W used on Windows, Linux, Mac
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Implementing a SDR with the GNU Radio
USRP - Universal Software Radio Peripheral
GNU Radio software - core s/w - user made s/w
Courtesy of http://www.gnu.org/software/gnuradio/doc/exploring-gnuradio.html
GNU Radio S/W available at www.gnuradio.org
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USRP 4 ADC’s: •12bits per second, 64MSps,
•Analog Input BW over 200Mhz
4 DAC’s•14bits per second, 128MSps
Receive Channel RF Interface
Transmit Channel RF Interface
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Challenges in SDR Design Hardware
Significant effort in computing HW Advance DSP Designs Flexible RF and antennas Flexible ADCs Tradeoff of performance and flexibility
Software Integration of components into single design flow Tradeoff of performance and flexibility
Testing and validation FCC hardware/software certification Smoothing of design cycle
Reduce overall time-to-market
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Technology Challenges of SDR Technology in SDR partitioned into three basic
pieces Hardware
Physical devices on which processing is performed or interface to the “real world”
Software Glue holding together system
Network Functionality and ultimate value to the end-user
Advances needed in all three arenas
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Hardware Significant effort to date in computing HW
Non-traditional computing platforms Advanced DSP designs High data rate FEC remains problematic
Emphasis on computing HW alone can be myopic Other critical areas that require significant further
work Flexible (or software controlled) RF Flexible ADC Antennas
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Flexible RF RF is one of the main limiting factors on
system designPlaces fundamental limits on the signal
characteristics BW, SNR, linearity
Truly flexible SDR requires flexible RF Difficult task
RF is fundamentally analog and requires different a different approach for the management of attributes
One method for achieving this is through the use of MEMS
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MEMS (Micro Electro Mechanical Systems) Designs for RF Front Ends
Tunable antenna with narrow fixed bandwidth
Patch antenna connected by RF switches
E-tenna’s Reconfigurable Antenna
Idealized MEMs RF Front-end for a Software Radio
Use MEMS filter banks to create tunable RF filters
J.H. Reed, Software Radio: A Modern Approach to Radio Design, Prentice-Hall 2002.
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ADC Challenges
ADC is the bound between analog and digital world
SDR requires the tuning of ADC characteristics Number of bits
Support adequate SNR and dynamic range
Sampling rate Prevent over-sampling (waste power)
ADC technology trends are not necessarily compatible with these needs
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0.E+00
5.E+09
1.E+10
2.E+10
2.E+10
3.E+10
3.E+10
4.E+10
4.E+10
5.E+10
5.E+10
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
Year
P
Flash
Folding
Half-Flash
Pipelined
SAR
Sigma-Delta
Unknown
SAR Regression
Sigma-Delta Regression
Unknown Regression
Total Regression`
2B sP f B bitsfs sample rate
ADCs Getting Better Exponentially
Bin Lee, Tom Rondeau, Jeff Reed, Charles Bostian, “Past, Present, and Future of ADCs,” submitted to IEEE Signal Processing Magazine, August 2004
1994 ~ 2004 a leap of Analog to Digital Converter (ADC) technology Regression curve fit shows exponential increasing trends Trends are quite different for different ADC structures
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ADC: Improving Even When Considering Power
2B s
diss
fF
P
Power-to-sampling-speed ratio favors less number of comparators The choice in selecting an ADC is tied to application requirement
0
1E+11
2E+11
3E+11
4E+11
5E+11
6E+11
7E+11
8E+11
9E+11
1E+12
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
Year
F
Flash
Folding
Half-Flash
Pipelined
SAR
Sigma-Delta
Unknown
SAR Regression
Flash Regression
Sigma-Delta Regression
Unknown Regression
Total Regression
Pdiss is power dissipation
Bin Lee, Tom Rondeau, Jeff Reed, Charles Bostian, “Past, Present, and Future of ADCs,” submitted to IEEE Signal Processing Magazine, August 2004
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Integration of Hardware
DSP share traits with GPP Similar programming methods Similar computing concepts
Even though implementation may be wildly different
FPGA and CCM do not share these traits with GPP Completely different programming paradigm Portability is an extremely difficult problem
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Software Operating Environment Standardized structure for the management of
HW and SW components SCA
Technology to date has been largely derived from existing PC paradigm GPP-centric structure SCA 3.0 Hardware Supplement is an attempt to
rectify this problem Several challenges remain
Power management Integration of HW into structure
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Software Architectures “The sheer ease with which we can produce a superficial
image often leads to creative disaster.” Ansel Adams [1902-1984], American artist (photography) Poor architectural design is leads to significant inefficiencies
Architectures provide multiple benefits Clear way to implement system
Generally component-based Software or hardware components
Standardized interfaces Standard technology interface
Common technology like middleware Standard semantic -- API
Architectures becoming more prominent Software Communications Architecture (SCA) $14B to $27B for SCA radio work by DoD Cluster 5 contract up to $1B for embedded & handheld prototypes Maintain awareness of activity: big money for SDR
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So How Do You Make a Software Radio? You have some hardware
And you want to run some waveforms GSM, IS-95, or some other technology that the
hardware is powerful enough to support
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What kind of software is needed? (1/4)
Something to manage hardwareConfigure associated devices
Set devices to known state i.e.: Make sure NCO is available and ready
Initialize cores Make sure programmable devices are ready
Set memory pointers in DSP Set FPGA to known state
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What kind of software is needed? (2/4)
Some standardized way of storing relevant information More than just short-term memory
Store configuration files Store last state of the machine Store user-defined attributes
Identity Permissions
Store functional software Should be able to map any kind of storage device to
this Dynamic RAM, hard drive, FLASH, other
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What kind of software is needed? (3/4)
Some way of structuring the waveformsStandardized way of structuring “applications”
so that the radio can “run” them In a Windows machine, these are .exe files
It has to be generic enough for it to fit well with machines other than GPPs
Needs to be able to interface with functional software
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What kind of software is needed? (4/4)
Something to actually “run” waveforms Install functional software in appropriate coreGenerate a start event
Something to keep track of what is available and what can and cannot be installed Ideally, this will bind the whole thing together
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Fundamental Composition of the SCA
Keep track of HW in the system
Store working environment, bit images, properties, etc.
Boot up and maintain HW
Keep track of what’s there (installed)
Manage collection of resources to create waveform
Capabilities e.g.,Start and stop, test, describe
Connections between resources
Device Manager
FileSystem Manager
Devices
Domain Manager
Application Factory
Resoruces
Manage waveform operationApplication
Port
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Software Communications Architecture (SCA)
Processor-centric structure Standardized interface for
components Seamless handling of HW
and SW Open-source
implementations available OSSIE
C++ by MPRG SCARI
Java by Communications Research Centre
O S
C O R BA
ID L
R edB lack
M anagem entO bjects
F ileSystem
C onfigurationF iles
Softw are
H ardw areH ardw are
Softw are
API
APIAPIAPIN on-C O R BA
Softw are(Legacy)
C O R BAAdapter
N on-C O R BASoftw are(Legacy)
C O R BAAdapter
API
Trans.Security
SecurityBoundary
Non-secure Secure
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Is the SCA Suitable for Commercial Implementations?
MaybeNo
Current version is GPP-centric, hence heavy Irrelevant capabilities decrease its effectiveness Focus on waveform portability has limited appeal Static nature not well suited for cognitive radio No provisions for power management
Yes Basic architectural principles are sound SCA 3.0 is a first step in dealing with GPP-centric
communications within the radio Significant momentum ($$$ and time) within defense industry Being adopted by several other nations’ defense establishments
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Summary of Trends SDR need is driven by two principal factors
New applications Cognitive radio, collaborative radio & advanced roaming
Increased number of protocols to support Potential cost reductions
ADC is no longer the key bottleneck Flexible RF products starting to come to market Software architecture critical
Additional technology supporting architectural approach available Reconfigurable hardware needed
General-purpose hardware approach is likely to be unable to keep up with wireless bandwidth growth
Component-based reconfigurable hardware architectures present powerful solution
Multi-core processors show promise
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SDR Market Today Military
JTRS program created multi-billion dollar SDR market DARPA neXt Generation (XG) Communications
project International derivatives of JTRS/SCA (EU, Canada,
etc) Commercial
Digital RF processors (TI Bluetooth and GSM) Multi-standard basestation implementations (Vanu) SDR handsets probably within 3 years as low power
processors become available Regulatory
Recent FCC directive to ensure code and RF compatibility
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Cognitive Radio Implementation
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Comment Slide – Delete Before Submitting
Following section presented by Bostian
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Knobs and MetersLayer Meters
(observable parameters)Knobs
(writable parameters)MAC Frame error rate
Data rateSource codingChannel coding rate and typeFrame size and typeInterleaving detailsChannel/slot/code allocationDuplexingMultiple accessEncryption
PHY Bit error rateSINRReceived signal powerNoise powerInterference powerPower consumptionFading statisticsDoppler spreadDelay spreadAngle of Arrival
Transmitter powerSpreading type and codeModulation typeModulation indexPulse shapingSymbol rateCarrier frequencyDynamic rangeEqualizationAntenna directivity
Other Computational powerBattery Life
CPU Frequency scaling
Sample tabulation of knobs and meters by layer (adapted from Prof. Huseyin Arslan)
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Radio Parameters“Knobs and Meters”
The VT Cognitive Engine
Simple Concept
Channel Statistics
Cognitive Engine
Radio RXRadio TX
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Radio TX
The VT Cognitive Engine
Simple Concept
Channel Statistics
Cognitive Engine
Radio RX
“Meters”“Old KnobsSettings”
“Old KnobsSettings”
Radio Parameters“Knobs and Meters”
“Optimized Solution”“New Settings”“New Settings”
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The VT Tiered Approach to Cognition Modeling System
Take in surrounding radio environment and user/network requirements
Remember models and apply Case-based Decision Theory to determine best course of action to take
Use Genetic Algorithms to update and optimize the new radio parameters
Monitor feedback from radio to understand system performance Penalize knowledge base for poor
performance
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The Cognitive Engine “Intelligent agent” that manages cognition tasks
in a Cognitive Radio Independent entity that oversees cognitive
operations Ideal Characteristics:
Intelligence (Accurate decisions) Reliability (Consistent decisions) Awareness (Informed decisions) Adaptability (Situation dependent decisions) Efficiency (Low overhead decisions) Excellent QoS (Good decisions)
Tradeoffs exist between these characteristics
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Software Architecture - Theory
Radio Hardware
Awareness
Sensing and Modeling
AdaptingEvolution and Optimization
Learning
Building and retaining
Knowledge
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Software Architecture - Theory
EnvironmentObservation
Link condition
User/policy
Radio hardware
ScenarioSynthesizing Case identified
Case-basedDecisionMaking
Case reportKnowledge Base
Reasoning
Apply experienceStrategy instruction
Link ConfigureOptimization
WSGAInitializationObjectivesConstraints
PerformanceEstimation
Bad trail overwrittenSuccess memorized
Radio Hardware
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Software Architecture – Limited Functionality
CE-Radio Interface
WMS
Security
Sel
ecto
r
API
Cognitive System Module
Cognitive System Controller
wavfrm
Policy
Sec
Knowledge BaseShort Term MemoryLong Term Memory
Decision Maker
CE
-use
r in
terf
ace
Policy Domain
User preferenceLocal service facility
Security
User data securitySystem/Network security
Modeling System
Policy Model
Radio
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Software Architecture: Full Functionality
CE-Radio Interface
WMS
Security
WS
GA
Evolver
API
Resource Monitor
|(Simulated Meters) – (Actual Meters)|Simulated Meters
Actual Meters
Radio
Re
sou
rce
s
Cognitive System Module
Cognitive System Controller
Chob
Uob
Reg
Knowledge BaseShort Term MemoryLong Term Memory
WSGA Parameter SetRegulatory Information
Initial ChromosomesWSGA Parameters
Objectives and weights
System Chromosome
}max{}max{
UUU
CHCHCH
USDUSD
Decision Maker
CE
-use
r in
terf
ace
User Domain
User preferenceLocal service facility
Policy Domain
User preferenceLocal service facility
Security
User data securitySystem/Network security
X86/UnixTerminal
Modeling System
User Model
Policy Model
RadioChannel Probe
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Some Approaches to Cognitive Engine Genetic Algorithms Markov Models Neural Nets Expert Systems and Natural Language
Processing Fuzzy Logic
Open issue on what are the appropriate cognitive engine techniques
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GA’s and biological metaphor
The WSGA uses a genetic algorithm, which operates on chromosomes.
The genes of the chromosome represent the traits of the radio (frequency, modulation, bandwidth, coding, etc.).
The WSGA creatively analyzes the information from the CSM to create a new radio chromosome.
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Some Approaches to Signal Classification
Cyclic Spectrum Analysis Statistical characterization of signal
parameters Eigenstructure techniques Model-based approaches
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Analyzing Performance in a Cognitive Radio
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Comment Slide – Delete Before Submitting
Following section presented by Reed
Needs more work on example SDR architectures
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Analyzing the Performance of a Network of Cognitive Radios
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Ways of Analyzing Performance
For the Cognitive RadioQOS, Detection of Primary Users (PU), SW
Platform, QOS of PU, Position Location For the network of Cognitive Radios
Quantifying the impact of the use of CR in a network
Game Theoretic ApproachSee www.mprg.org/people/gametheory/index.shtml
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Cognitive Radio Performance Evaluation: QoS Parameters
Data throughput Latency Voice quality Video quality
These depend on link performance measures: PHY Layer, e.g.:
Bit error rate (BER) Signal to noise ratio (SIR) Signal to interference and noise
ratio (SINR) Received signal strength
MAC, network-layer, e.g.: Frame error rate (FER) Packet error rate Routing table change rate
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Cognitive Radio Performance Evaluation: Detection of Primary Users
Probability of detection (PoD) as a function of: number of observed symbols SNR Number of signals present (primary and secondary) Level of cooperation, e.g., number of devices (CRs)
needed to achieve a given PoD (see next slide)
Probability of false alarm same parameters as PoD
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Cognitive Radio Performance Evaluation: Underlying Software Radio Platform
Number of supported waveforms
Processing power (mips, flops, #gates)
Waveform-code reusability and portability Reusable: the same code
can be used in principle in a different SDR platform
Portable: instantaneous plug and play
Delay for loading unloading waveforms
RF front-end: Frequency range, Dynamic
range, Sampling frequency, Sensitivity, Selectivity, Stability, Spurious response
Power consumption Size, Weight, Cost
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Cognitive Radio Performance Evaluation: Position Location Main perfromance measures for position location service:
Precision and Availability Different technologies provide different quality of position location
services: Assisted GPS (AGPS)
performance degrades significantly when no clear view of sky (indoors, urban canyons)
works best in rural areas (no shadowing) Network based services
accuracy in general lower than AGPS works best with many base stations present (populated areas) performance doesn't degrade indoors
Hybrid services Combines advantages of both approaches AGPS whenever possible, if not available switch to network based service
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Cognitive Radio Performance Evaluation: Primary users' QoS
Time needed to vacate channel after primary user (re-) appears
Negative impacts: Increased SINR, BER, FER, … results in: Decreased:
Data throughput Latency Voice quality Video quality
Increased Call drop rate (cell phone networks) Handover failure (cell phone networks)
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Dynamic cognitive radios in a network
Dynamic benefits Improved spectrum utilization Improve QoS
Many decisions may have to be localized Distributed behavior
Adaptations of one radio can impact adaptations of others Interactive decisions Locally optimal decisions may
be globally undesirable
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Locally optimal decisions that lead to globally undesirable networks
Scenario: Distributed SINR maximizing power control in a single cluster
For each link, it is desirable to increase transmit power in response to increased interference
Steady state of network is all nodes transmitting at maximum power
Power
SINR
Need way to analyze networks with interactive decisions.Game theory can help.
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What is a game? A game is a model (mathematical representation) of
an interactive decision process. Its purpose is to create a formal framework that
captures the process’s relevant information in such a way that is suitable for analysis.
Different situations indicate the use of different game models.
Identification of the type of game played by the cognitive radios provides insights into performance
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1. Steady state characterization
2. Steady state optimality
3. Convergence4. Stability5. Scalability a1
a2
NE1
NE2
NE3
a1
a2
NE1
NE2
NE3
a1
a2
NE1
NE2
NE3
a1
a2
NE1
NE2
NE3
a3
Steady State Characterization Is it possible to predict behavior in the system? How many different outcomes are possible?
Optimality Are these outcomes desirable? Do these outcomes maximize the system target parameters?
Convergence How do initial conditions impact the system steady state? What processes will lead to steady state conditions? How long does it take to reach the steady state?
Stability How does system variations impact the system? Do the steady states change? Is convergence affected?
Scalability As the number of devices increases, How is the system impacted? Do previously optimal steady states remain optimal?
Key Issues in Analysis
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Cognitive Radio, Spectrum Policy, and Regulation
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Comment Slide – Delete Before Submitting
Following section presented by Reed
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An Analogy between a Cognitive Radio and a Car Driver
Cognitive Radio’s capabilities: Senses, and is aware of, its
operational environment and its capabilities
Can dynamically and autonomously adjust its radio operating parameters accordingly
Learns from previous experiences
Deals with situations not planned at the initial time of design
Car Driver’s capabilities: Senses, and is aware of, its
operational environment and its capabilities
Can dynamically and autonomously adjust the driving operation accordingly
Learns from previous experiences
Deals with situations not planned at the initial time of learning to drive
They behave almost
exactly the same!!!
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“Rules of the Road” ➟“Rules of the Cognitive Radio”
POLICY AWARE Primary User has higher priority over Secondary users
Radio emission may be prohibited at certain location or for certain type of radio
LOCATION AWAREPrecautions for certain areas, such as hospital, airplane, gas station, etc, where RF emission is highly restricted
Parking Zone
*Source of some pictures in this section: “California Drivers Handbook 2005”; “Illinois Rules of the Road 2004”
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“Rules of the Road”-inspired CR Philosophy and Etiquette Insights from “Traffic Model Analogy”
TRAFFIC Scheduling
Various traffic schedule methods and duplex methods for efficient and fair sharing of congested unlicensed spectrum
TDD vs. FDD ➟
Dynamic Uplink/Downlink transmission in TDD mode
Spectrum pooling is encouraged
Traffic Law Spectrum Regulations➟
Management by both Punishment and Encouragement
FDD mode operation with paired spectrum
$ fine
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A traffic model analogy – Common Issues
It is critical that everyone drives sensibly or defensively
➟ Every CR should be aware of Hidden Node problems
Hidden Node Problem
A and C are unaware of their interference at B, due to A, C cannot hear each other.
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Vehicle Following Distances: TWO-SECOND RULE:Use the two-second rule to determine a safe following distance.
Vehicle Following Distances for Car Drivers
➟ Time needed to vacate channel after primary user (re-) appears for Cognitive Radios
A traffic model analogy (cont.)
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A traffic model analogy (cont.)
SPEED LIMIT for car driver
➟ Interference Level Limit (e.g. Max. Allowed Interference Temperature)
for Cognitive Radio
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City Map for Car Drivers
➟ Radio Environment Map (REM) for Cognitive Radios
Learning from “Traffic model analogy” for the development of Cognitive Radio…
REMREM
Time (or duration)
Location (x, y, z),
Type of radio environment
Local Policy
Profile of primary users
Profile of interference
Max. allowed Interference Level
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Learning from “Traffic model analogy” for the development of Cognitive Radio…(cont.)
Regular conformance check against
regulations
Language and Etiquette for CR for
Signaling and Negotiation
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Spectrum Policy Challenges The spectrum is already allocated
True spectrum scarcity on urban areas (ISM band)
We need to deal with existing standards The standards are embedded in the hardware!
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Spectrum Utilization Spectrum utilization is quite low in many bands Concept:
Have radios (or networks) identify spectrum opportunities at run-time Transparently (to legacy systems) fill in the gaps (time, frequency, space)
Considered Bands ISM Public Safety TV (UHF)
Lichtenau (Germany), September 2001
dBV
/m
From F. Jondral, “SPECTRUM POOLING - An Efficient Strategy for Radio Resource Sharing,” Blacksburg (VA), June 8, 2004.
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Spectrum Occupancy Study
Spectrum occupancy in each band averaged over six locations
(Riverbend Park, Great Falls, VA, Tysons Corner, VA, NSF Roof,
Arlington, VA, New York City, NRAO, Greenbank, WV,
SSC Roof, Vienna, VA) [
Source: FCC NPRM 03-0322. http://hraunfoss.fcc.gov/edocs_public/attachmatch/FCC-03-322A1.pdf
Results from Shared Spectrum Co. and Univ. of Kansas
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Regulatory Trends In an effort to improve radio spectrum
management and promote a more efficient use of it, the regulatory bodies are trying to adopt a new spectrum access model.
This represents a paradigm shift from hardware-embedded policy implementation to dynamic software-based adaptationHarder to keep tight control!
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Regulatory TrendsProceedings that are the Key Drivers: Receiver Standards
ET Docket No. 03-65 NOI Interference Temperature
ET Docket 03-237 NPRM/NOI Cognitive Radio
ET Docket No. 03-108 NPRM License-exempt Operation in the TV Broadcast Bands
ET Docket No. 04-186 Additional Spectrum for License-exempt devices below
900 MHz and in the 3 GHz Band ET Docket No. 02-380
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Policy Engine Approach PE needs to provide limiting operational
parameters Interpret policy automatically Act dynamically in response to the operating
environment PE needs to authenticate the policy It will require an extremely efficient policy format
It must handle the complexity of current policy without presenting a significant load to the CE
The goal is to limit the search space before looking for a solution Rely on CE to do the reasoning about spectrum
sharing
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DARPA XG Program XG is trying to Develop the Technology
and System Concepts to Dynamically Access Available Spectrum
React
Formulate Best Course of Action
ReactReact
Formulate Best Formulate Best Course of ActionCourse of Action
Adapt
Transition network to new emission plan
AdaptAdapt
Transition Transition network to new network to new emission plan emission plan
Characterize
Rapid waveform determination
CharacterizeCharacterize
Rapid waveform Rapid waveform determinationdetermination
Sense
Real time, Low-power, wideband
monitoring
SenseSense
Real time, LowReal time, Low--power, wideband power, wideband
monitoringmonitoring
AutonomousAutonomousDynamic Dynamic SpectrumSpectrumUtilizationUtilization
Source: DARPA XG Program
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Spectrum Policy Language Design
SpectrumPolicy
PolicyAdministrator
(e.g. FCC, NTIA)
XG System
SpectrumOpportunities
Awareness via XG Protocols and Sensing
query
LanguageDesign
Knowledge
Core LanguageModel and
Representation
Policy LanguageDesigner
(e.g. BBN/XG Program)
Policy Editingand Verification
Tools
design
MachineReadable
Policy Instances
PolicyRepository
encode
publish
Actors and RolesActors and Roles
Source: BBN Technologies Solutions LLC
PolicyRepository
Area that needs Area that needs improvements!improvements!
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The BIG Question: FCC Certification
• At all costs, the FCC must avoid “an epidemic situation in the unlicensed area.”
• FCC likes to operate from “established engineering practices.” The SDR and CR communities must defined these.
•Open source radios are a particular problem because their operating parameters are not necessarily bounded.
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• People seeking certification must explain how their software will respect parameter limits specified in FCC rules.
• Submitted software must be accompanied by flow charts, code, and an explanation of how it works.
• Software certification should not be more difficult to achieve than hardware certification.
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Bios/OS
Proposed Approach
RadioChannel Probe
Policy EnginePolicy Engine
Cognitive EngineCognitive Engine
ApplicationsApplications
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Example of a Possible Cognitive Radio Application
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Comment Slide – Delete Before Submitting
Following section presented by Reed
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How can CR improve Spectrum Utilization? Allocate the frequency usage in a network. Assist secondary markets with frequency use,
implemented by mutual agreements. Negotiate frequency use between users. Provide automated frequency coordination. Enable unlicensed users when spectrum not in
use. Overcome incompatibilities among existing
communication services.
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How can CR improve Network Management Efficiency? Present Practice characterizes service demand in a
network statistically By using cognitive radio, time-space characterization of
demand is possible Cognitive Radio
Learns plans of the user to move and use wireless resources Expresses its plans to the network reducing uncertainty about
future demand The network can use its resources more efficiently
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How can a CR Enhance Service Delivery?
Wireless Communications in general and cognitive radio in particular have great potential to generate personal user information For example: actual position, native language, habits,
travel, etc.
Enhanced services can be provided using this information
CR interacts with the network on user’s behalf
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Note Daily Drive Home at 5:30(GPS Aided)Recall Brief Coverage Hole
1. Observe and Analyze Situation
2. Evaluate Alternatives
Do NothingIncrease Coding GainIncrease Transmit PowerVertical HandoffDecrease Call Drop Threshold
4. Adapt Network
3. Signal Base Station
Request Decrease In Call Drop Threshold
CR in a Cellular System
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Example of Cognitive Radio in Cellular Environment
Cognitive radio is aware of areas with a bad signal
Can learn the location of the bad signal Has “insight”
Radio takes action to compensate for loss of signal Actions available:
Power, bandwidth, coding, channel
Radio learns best course of action from situation
G ood signal
Transition in signal
Bad signal
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Supplements Cellular System Cellular systems are plagued with coverage
gaps Cognitive radio can enhance coverage around
these gaps by: Learning the areas of coverage gaps Learning the best PHY layer parameters Taking action prior to getting to the area Sharing this knowledge with other cell phones
Coverage gaps are found very rapidly
Alert cellular system of gap, so provider can remedy situation
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Current Research Efforts in Cognitive Radio
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Comment Slide – Delete Before Submitting
Following section presented by Reed
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Universities Participating at Dyspan
Bar-Ilang Univ. Georgia Tech Mich. State Univ. Michigan Tech MIT Northwestern Univ. Ohio Univ. Rutgers Univ. RWTH Aachen Univ. Stanford Univ.
Univ. of Calif. BerkeleyUniv. of CambridgeUniv. of Col.Univ. of MDUniv. of PittsburgUniv. of TorontoUniv. of WarwickUniversitaet KarlsruheUniversity of PiraeusVirginia Tech
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DARPA
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DARPA neXt Generation Program - Motivation Problems:
Spectrum Scarcity Spectral resources are not fully exploited Opportunities exist in space, time, frequency Current static spectrum allocation prevents efficient spectrum utilization
Deployment difficulty Different policy regimes in different countries Deployment of communication networks tedious Of particular interest in military applications
Proposed solution: Complement static spectrum allocation with "Opportunistic spectrum access"
Primary users Licensed Priority to use allocated spectrum Guaranteed QoS
Secondary users Non-licensed Can allocate unused spectrum among themselves Have to vacate bands if required by primaries
Unless otherwise stated, all the information in this description of the DARPA XG programis based on the XG Vision rfc, available online: http://www.darpa.mil/ato/programs/xg/
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DARPA neXt Generation Program: Research Goals
1. Development of technologies that enable spectrum agility Sensing and characterization of the (RF-)
environment Identification of unused spectrum ("opportunities") Allocation and exploitation of opportunities
2. Development of standards for a software based policy regime to enable policy agility explained in more detail on the next slides
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DARPA neXt Generation Program: Concepts of Policy Agility (1)1. Decoupling of policies from implementation
Define abstract behaviors, e.g., "Channel can be vacated within t sec."
Policies implement (dictate) behaviors Protocols instantiate behaviors
2. Traceability All behaviors must be traceable to policies:
Each operational mode a device is capable of is tied to a specific policy which allows it
3. Software based Spectrum use policies have to be machine understandable Policy constraints can be implemented "on-the-fly" via software
downloads
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DARPA neXt Generation Program: Concepts of Policy Agility (2)
Figure drawn from XG Vision RFC
Decoupling policies, behaviors, and protocols: Separating what needs to be done from how it is implemented
The framework's four key components
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DARPA neXt Generation Program: Concepts of Policy Agility (3)
Machine understandable policies will enable software downloads "on-the-fly"
Figure drawn from XG Vision RFC
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DARPA neXt Generation Program: Promises1. Flexible radio operation due to spectrum agility2. Simplified user control of XG systems
System operation can be controlled in terms of behavior No need for technological details
3. Facilitated policy design Constraints can be tailored to national or institutional needs in terms of
behaviors No need for technological details
4. Eased wireless device accreditation Traceability provides a means for an easy testing procedure of behaviors
against policies5. Broad and future proof standard
Will be designed to be applicable to a broad range of radios Future proof design will enable extension of the standard Framework character: different technological solutions (protocols) can be
accomodated to perform a particular task (sensing, identification, allocation)
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E2R
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E2R Research in Europe
E2R = End-to-End Reconfigurability Efficient, advanced & flexible end-user service
provision Tailoring of application and service provision to user
preferences and profile Efficient spectrum, radio and equipment resources
utilization Enabling technologies for flexible spectrum resources
Multi-standard platforms A single hardware platform shared dynamically amongst
multiple applications
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E2R Participants 1/2Academic Partners Eurecom: Institut Eurecom I2R KCL:Centre for Telecommunications Research (CTR) - King's College London UoA: University of Athens TUD: Dresden University UoKarlsruhe: University of Karlsruhe, Communications Engineering Lab UPRC: University of Piraeus Research Center UNIS: University of Surrey
Operator R&D Partners DoCoMo: DoCoMo Communications Laboratories Europe GmbH FT: France Telecom R&D TILAB: Telecom Italia S.p.A. TID: Telefonica I+D
Source http://e2r.motlabs.com/
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E2R Participants 2/2Manufacturer Partners MOTO: Motorola Labs ACP: Advanced Circuit Pursuit AG ASEL: Alcatel SEL DICE: Danube Integrated Circuit Engineering Nokia: Nokia GmbH PMDL: Panasonic UK PEL: Panasonic European Laboratories GmbH SM: Siemens Germany SMC: Siemens Mobile Communications SpA THC: Thales Communications TRL: Toshiba Research Europe Limited MIL: Motorola Israel Ltd
Regulator partners DiGITIP UPC: UPC RegTP
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Berkeley Wireless Research Center
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Berkeley Wireless Research Center• Designing a cognitive radio to improve spectrum utilization• Radio searches for feasible region and optimal waveform for
transmission (environment sensing)• Avoiding of Interference with primary spectrum users by:
- Measuring spectrum usage in time, frequency, and space- Having statistical traffic models of primary spetrum users
• A cognitive radio test bed is currently being built
•From R.W. Brodersen, A. Wolisz, D. Cabric, S. M. Mishra, D. Willkomm "Corvus: A Cognitive Radio Aproach For Usage of Virtual Unlicensed Spectrum", July 29th 2004
• The six system functions are split between physical and data link layer
• Two control channels:-UCC for group management (group announcement)
-GCC used only by members of a certain group
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Rutgers Winlab
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WINLAB Rutgers University
• Design of info-stations for emergency and disaster relief applications
• Use of customized commercially available hardware, e.g. 802.11 wireless
From: http://www.winlab.rutgers.edu/pub/docs/focus/Infostations.html
BenefitsIncreases the total information available for rescue workers
tailors the information with regard to specific needs and available bandwidth
coordinates communication of different rescue groups at one site
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Virginia Tech’s CWT
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National Science Foundation Grant CNS-0519959 “An Enabling Technology for Wireless Networks – the VT Cognitive Engine”
National Institute of Justice Grant 2005-IJ-CX-K017 “A Prototype Public Safety Cognitive Radio for Universal Interoperability.”
•Develop and test a prototype system for using cognitive techniques to allow WiFi-like unlicensed operation in unoccupied TV channels.
•Investigate the behavior of networks containing both legacy radios and cognitive radios that can interoperate with them.
• Build a prototype cognitive radio that can recognize and interoperate with three commonly used and mutually incompatible public safety waveform standards
http://support.mprg.org/dokuwiki/doku.php?id=cognitive_radio:start
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Virginia Tech’s MPRG
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Some SDR and Cognitive Radio Research at VT SCA core framework
Open source effort Role of DSPs Power Management Integration of testing
into the framework Rapid prototyping
tools Smart antennas
Smart antenna API Networking
performance Experimental MIMO
systems
Cooperative radios Distributed MIMO Distributed Applications
Cognitive radio networks Game theory analysis of
cognitive networks Learning Techniques
Test Beds UWB SDR Low Power SCA Distributed PCs Public Safety Radio Demo
Keep up to date at http://support.mprg.org/dokuwiki/doku.php?id=cognitive_radio:startAnd http://www.mprg.org
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CR Test-bed under development
AP (Data Collection Node)
AP (Data Collection Node)
AP (Data Collection Node)
InterferenceDetection,
Classification,Location
OSSIE Framework
ArbitraryWaveformGenerator
AP (Data Collection Node)
AP (Data Collection Node)
AP (Data Collection Node)
InterferenceDetection,
Classification,Location
OSSIE Framework
ArbitraryWaveformGenerator
Neighbor
WLANs
Ethernet
Actions
Cordless Phone Bluetooth
MWOL
Tektronix TDS694C: Digital Real-time Oscilloscope
Tektronix RSA3408A: Real-Time Spectrum Analyzer
Distributed MeasurementDistributed Measurement
Collaborative ProcessingCollaborative ProcessingObservations
Analysis and decision
REM online updating
TV station
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The Future of Cognitive Radio
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Comment Slide – Delete Before Submitting
Following section presented by Bostian
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Public Safety - Interoperability
Focus on multi-agency interoperability since 9/11/2001 Cognitive radio technology can improve interoperability
by enabling devices to bridge communications between jurisdictions using different frequencies and modulation formats.
Such interoperability is crucial to enabling public safety agencies to do their jobs.
Example: National Public Safety Telecommunications Council (NPSTC) supported by U.S. DOJ’s AGILE Program
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IEEE 802.22
WRAN system based on 802.22 will make use of unused TV broadcast channels
Interoperable air interface for use in spectrum allocated to TV Broadcast Service
Allows Point to Multi-point Wireless Regional Area Networks (WRANS)
Supports a wide range of services Data, voice and video Residential, Small and Medium Enterprises Small Office/Home Office (SOHO) locations
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IEEE Project 1900 (P1900) The IEEE P1900 Standards Group was established in The IEEE P1900 Standards Group was established in
1Q 2005 jointly by the IEEE 1Q 2005 jointly by the IEEE Communications Communications SocietySociety (ComSoc) and the IEEE (ComSoc) and the IEEE Electromagnetic Electromagnetic Compatibility (EMC) SocietyCompatibility (EMC) Society..
The objective of this effort is to develop supporting The objective of this effort is to develop supporting standards related to new technologies and techniques standards related to new technologies and techniques being developed for next generation radio and being developed for next generation radio and advanced spectrum management.advanced spectrum management.
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IEEE P1900.1 Working GroupIEEE P1900.1 Working Group:: Objective document:Objective document: “Standard Terms, “Standard Terms,
Definitions and Concepts for Spectrum Definitions and Concepts for Spectrum Management, Policy Defined Radio, Adaptive Management, Policy Defined Radio, Adaptive Radio and Software Defined Radio.” Radio and Software Defined Radio.”
Purpose:Purpose: This document will facilitate the This document will facilitate the development of these technologies by development of these technologies by clarifying the terminology and how these clarifying the terminology and how these
technologies relate to each other.technologies relate to each other.
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IEEE P1900.2 Working GroupIEEE P1900.2 Working Group:: Objective document:Objective document: “Recommended “Recommended
Practice for the Analysis of In-Band and Practice for the Analysis of In-Band and Adjacent Band Interference and Coexistence Adjacent Band Interference and Coexistence Between Radio Systems.”Between Radio Systems.”
Purpose:Purpose: TThis standard will provide his standard will provide guidance for the analysis of coexistence and guidance for the analysis of coexistence and interference between various radio services. interference between various radio services.
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IEEE P1900.3 Working GroupIEEE P1900.3 Working Group:: Objective documentObjective document: “Recommended Practice : “Recommended Practice
for Conformance Evaluation of Software for Conformance Evaluation of Software Defined Radio (SDR) Software Modules.”Defined Radio (SDR) Software Modules.”
PurposePurpose: This recommended practice will : This recommended practice will provide guidance for validity analysis of provide guidance for validity analysis of proposed SDR terminal software prior to proposed SDR terminal software prior to physical programming and activation of SDR physical programming and activation of SDR terminal components. terminal components.
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IEEE 802.11h 802.11h helps WLANs share spectrum How?
801.11h implements two methods to help spectrum sharing:
Dynamic Frequency Selection (DFS) Transmission Power Control (TPC)
DFS is used to select the appropriate spectrum for WLAN
TPC is used to manage WLAN networks and stations for Reduction of interference, Range control (setting borders for WLAN), and Reduction of power consumption (beneficial in laptop use e.g.)
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IEEE 802.15.3a
Multiband OFDM for Personal Area Network Wireless USB2.0 (480Mbps) at 5 meters distances
Cognitive Radio - Plausible Application to UWB Regulation Very fast spectrum sculpting via OFDM technology
with wide bandwidth 528MHz QoS Support
QoS can be supported by controlling the number of sub-carriers
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Hurdles in CR
FCC Development Policies The process and rules
governing how frequencies and waveforms are selected and approved for use by cognitive equipment must be addressed.
Software Flexibility Interface with policy updates
Real-life functionality CR devices are smart enough
to understand user request and surrounding environments
Network availability for CR Network needs to announce
their availability to CR Flexible or Reconfigurable
Hardware Requires a language and
protocols for initial interfacing with software and validation for existing devices as policies change across time and space
Software Architectures More dynamic than SCA
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Predictions for Future Evolution
Time
SDR with high ASIC content
Re-programmable
for fixed number of systems
Factory reprogrammable
Increased use of
reconfigurable hardware
Limited reconfiguration
by userEarly cognition
Mid-level cognition
Cognitive radios
2005 2007 2010
Adaptive spectrum allocation
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Just Remember This...
“The best way to predict the future is to invent it.”
Alan Kay, Author
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Jeffrey H. Reed Willis G. Worcester Professor of ECE and Deputy
Director, Mobile and Portable Radio Research Group (MPRG)
Authored book, Software Radio: A Modern Approach to Radio Engineering
IEEE Fellow for Software Radio, Communications Signal Processing and Education
Industry Achievement Award from the SDR Forum Highly published. Co-authored – 2 books, edited – 7
books. Previous and Ongoing SDR projects from
DARPA, Texas Instruments, ONR, Mercury, Samsung, NSF, General Dynamics and Tektronix
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Jeffrey H. Reed
Contact Information:
Electrical and Computer EngineeringMPRG432 Durham HallBlacksburg, VA 24061(540) 231-2972
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Charles W. Bostian Alumni Distinguished Professor of ECE and
Director, Center for Wireless Telecommunications
Co-author of John Wiley texts Solid State Radio Engineering and Satellite Communications.
IEEE Fellow for contributions to and leadership in the understanding of satellite path radio wave propagation.
Award winning teacher Previous and Ongoing CR projects from National
Science Foundation, National Institute of Justice
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Charles W. Bostian
Contact Information:
Electrical and Computer EngineeringVirginia Tech, Mail Code 0111
Blacksburg, VA 24061-0111 (540) 231-5096
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Backup Slides
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Hardware Blocks
Software Modules
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Example: Simple AM Transmitter (1/2)Building Blocks
•All Blocks are each defined as objects
X
~Amp
m
FIR
“Amp” - Gain Stage
“m” - Message Signal
“mix” - Multiplication Stage
“LO” - Local Oscillator
“FIR” - Filter Stage
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Example: Simple AM Transmitter (2/2)
Connecting Building Blocks
+ 1Amp µX
~
FIR mH/WInterface
•The arrow is an object that connects the flow graph