Dyspan Sdr Cr Tutorial 10 25 Rev02

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

4

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:

Software Defined Radio – Basic Concepts and Relationship to Cognitive Radio

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7

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

Cognitive Radio Concepts

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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”

Technology Challenges in SDR

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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

40

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

43

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

44

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

46

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

49

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

50

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

51

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

52

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

53


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

54


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

55


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

56

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

57

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

58

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

59

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

60

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

Cognitive Radio Implementation

62



63

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)

64

Radio Parameters“Knobs and Meters”

The VT Cognitive Engine

Simple Concept

Channel Statistics

Cognitive Engine

Radio RXRadio TX

65

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”

66

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

67

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

68

Software Architecture - Theory

Radio Hardware

Awareness

Sensing and Modeling

AdaptingEvolution and Optimization

Learning

Building and retaining

Knowledge

69

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

70

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

71

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


Policy Domain


Security

User data securitySystem/Network security

X86/UnixTerminal

Modeling System

User Model

Policy Model

RadioChannel Probe

72

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

73

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.

74

Some Approaches to Signal Classification

Cyclic Spectrum Analysis Statistical characterization of signal

parameters Eigenstructure techniques Model-based approaches

Analyzing Performance in a Cognitive Radio

76



Needs more work on example SDR architectures

77

Analyzing the Performance of a Network of Cognitive Radios

78

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

79

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

80

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

81

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

82

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

83

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)

84

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

85

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.

86

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

87

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

Cognitive Radio, Spectrum Policy, and Regulation

89



90

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!!!

91

“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”

92

“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

93

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.

94

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.)

95

A traffic model analogy (cont.)

SPEED LIMIT for car driver

➟ Interference Level Limit (e.g. Max. Allowed Interference Temperature)

for Cognitive Radio

96

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

97

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

98

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!

99

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.

100

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

101

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!

102

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

103

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

104

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!

106

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.

107

• 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.

108

Bios/OS

Proposed Approach

RadioChannel Probe

Policy EnginePolicy Engine

Cognitive EngineCognitive Engine

ApplicationsApplications

Example of a Possible Cognitive Radio Application

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111

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.

112

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

113

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

114

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

116

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

Current Research Efforts in Cognitive Radio

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119

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

121

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/

122

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

123

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

124

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

125

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)

128

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

129

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/

130

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

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

Rutgers Winlab

134

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

Virginia Tech’s CWT

136

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

Virginia Tech’s MPRG

138

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

139

CR Test-bed under development

AP (Data Collection Node)



InterferenceDetection,

Classification,Location

OSSIE Framework

ArbitraryWaveformGenerator




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

The Future of Cognitive Radio

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142

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

143

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

144

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.

145

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.

146

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.

147

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.

148

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.)

149

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

150

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

151

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

152

Just Remember This...

“The best way to predict the future is to invent it.”

Alan Kay, Author

153

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

154

Jeffrey H. Reed

Contact Information:

[email protected]

Electrical and Computer EngineeringMPRG432 Durham HallBlacksburg, VA 24061(540) 231-2972

155

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

156

Charles W. Bostian

Contact Information:

[email protected]

Electrical and Computer EngineeringVirginia Tech, Mail Code 0111

Blacksburg, VA 24061-0111 (540) 231-5096

157

Backup Slides

158

Hardware Blocks

Software Modules

159

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

160

Example: Simple AM Transmitter (2/2)

Connecting Building Blocks

+ 1Amp µX

~

FIR mH/WInterface

•The arrow is an object that connects the flow graph

Dyspan Sdr Cr Tutorial 10 25 Rev02

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