Dyspan Sdr Cr Tutorial 10 25 Rev02

Understanding the Issues in Software Defined Cognitive Radio Jeffrey H. Reed Charles W. Bostian Virginia Tech Bradley Dept. of Electrical and Computer Engineering

Comment Slide – Delete Before Submitting Following section presented by Reed

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

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

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

Software Defined Radio Levels (1/2)
Highest Level of Reconfigurablity
Completely flexible modulation format, protocols and user functions
Flexible bandwidths and center frequency, i.e., RF front end is also configurable
Adapts to different network and air interfaces
Open architecture for expansion and modifications

Software Defined Radio Levels (2/2)
Lowest Level of Reconfigurability
Radio not easily changed
Preset signal bandwidth and center frequency
Few and preset modulation formats, protocols, and user functions

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

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

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

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
Conventional
Radio
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

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

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

Comment Slide – Delete Before Submitting Following section presented by Bostian

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

What is a Cognitive Radio? 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.

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

1) Access to spectrum (finding an open frequency and using it) Cognitive radios are a powerful tool for solving two major problems:

2) Interoperability (talking to legacy radios using a variety of incompatible waveforms) Cognitive radios are a powerful tool for solving two major problems:

Levels of Radio Functionality Adapted From Table 4-1Mitola, “ Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio, ” PhD Dissertation Royal Institute of Technology, Sweden, May 2000. Level Capability Comments 0 Pre-programmed A software radio 1 Goal Driven Chooses Waveform According to Goal. Requires Environment Awareness. 2 Context Awareness Knowledge of What the User is Trying to Do 3 Radio Aware Knowledge of Radio and Network Components, Environment Models 4 Capable of Planning Analyze Situation (Level 2& 3) to Determine Goals (QoS, power), Follows Prescribed Plans 5 Conducts Negotiations Settle on a Plan with Another Radio 6 Learns Environment Autonomously Determines Structure of Environment 7 Adapts Plans Generates New Goals 8 Adapts Protocols Proposes and Negotiates New Protocols

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 Resources Initiate Processes Negotiate Protocols Orient Infer from Context Select Alternate Goals Plan Normal Immediate Learn New States Observe Outside World Decide Act User Driven (Buttons) Autonomous Infer from Radio Model States Generate “Best” Waveform Establish Priority Parse Stimuli Pre-process Cognitive radio Cognition Cycle

Level
0 SDR
1 Goal Driven
2 Context Aware
3 Radio Aware
4 Planning
5 Negotiating
6 Learns Environment
7 Adapts Plans
8 Adapts Protocols
Relationship between the Cognition Cycle and the Levels of Functionality Normal Urgent Select Alternate Goals Establish Priority Negotiate Immediate Negotiate Protocols Generate Alternate Goals Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 1999, pp 3-10. Determine “Best” Known Waveform Generate “Best” Waveform Allocate Resources Initiate Processes Orient Infer from Context Parse Stimuli Pre-process Plan Normal Learn New States Observe Outside World Decide Act User Driven (Buttons) Autonomous Determine “Best” Plan Infer from Radio Model States

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

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

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

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

Types of Software Defined Cognitive Radios
Policy-Based Radio
Reconfigurable Radio
Cognitive Radio

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”

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

Comment Slide – Delete Before Submitting Following section presented by Reed Needs more work on example SDR architectures

Radio Architecture Rx Tx RF Signal Amplify Mixer Filter Amplify Mixer Filter IF Signal Baseband Signal Superhetrodyne RF Signal Amplify Mixer Filter Analog To Digital Converter IF Signal Digital Signal Processing Software Defined Radio

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

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

Implementing a SDR with the GNU Radio
USRP - Universal Software
Radio Peripheral
Courtesy of http://www.gnu.org/software/gnuradio/doc/exploring-gnuradio.html GNU Radio S/W available at www.gnuradio.org GNU Radio software - core s/w - user made s/w

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

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

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

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

Flexible RF
RF is one of the main limiting factors on system design
Places 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

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.

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

ADCs Getting Better Exponentially
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
B bits f s sample rate Bin Lee, Tom Rondeau, Jeff Reed, Charles Bostian, “Past, Present, and Future of ADCs,” submitted to IEEE Signal Processing Magazine, August 2004

ADC: Improving Even When Considering Power
Power-to-sampling-speed ratio favors less number of comparators
The choice in selecting an ADC is tied to application requirement
P diss 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

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

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

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

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

What kind of software is needed? (1/4)
Something to manage hardware
Configure 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

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

Some way of structuring the waveforms
Standardized 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

Something to actually “run” waveforms
Install functional software in appropriate core
Generate 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

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 operation Application Port

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
Non-secure Secure

Is the SCA Suitable for Commercial Implementations?
Maybe
No
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

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

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

Knobs and Meters Sample tabulation of knobs and meters by layer (adapted from Prof. Huseyin Arslan) Layer Meters (observable parameters) Knobs (writable parameters) MAC Frame error rate Data rate Source coding Channel coding rate and type Frame size and type Interleaving details Channel/slot/code allocation Duplexing Multiple access Encryption PHY Bit error rate SINR Received signal power Noise power Interference power Power consumption Fading statistics Doppler spread Delay spread Angle of Arrival Transmitter power Spreading type and code Modulation type Modulation index Pulse shaping Symbol rate Carrier frequency Dynamic range Equalization Antenna directivity Other Computational power Battery Life CPU Frequency scaling

The VT Cognitive Engine
Simple Concept
Radio Parameters “ Knobs and Meters” Channel Statistics Cognitive Engine Radio RX Radio TX

The VT Cognitive Engine
Simple Concept
Radio TX Channel Statistics Cognitive Engine Radio RX “ Meters” “ Old Knobs Settings” “ Old Knobs Settings” Radio Parameters “ Knobs and Meters” “ Optimized Solution” “ New Settings” “ New Settings”

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

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

Software Architecture - Theory

Software Architecture – Limited Functionality

Software Architecture: Full Functionality

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

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.

Some Approaches to Signal Classification
Cyclic Spectrum Analysis
Statistical characterization of signal parameters
Eigenstructure techniques
Model-based approaches

Analyzing Performance in a Cognitive Radio

Comment Slide – Delete Before Submitting Following section presented by Reed Needs more work on example SDR architectures

Analyzing the Performance of a Network of Cognitive Radios

Ways of Analyzing Performance
For the Cognitive Radio
QOS, 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 Approach
See www.mprg.org/people/gametheory/index.shtml

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

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

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

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

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)

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

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.

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

Steady state characterization
Steady state optimality
Convergence
Stability
Scalability
Key Issues in Analysis 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? a 1 a 2 NE1 NE2 NE3 a 1 a 2 NE1 NE2 NE3 a 1 a 2 NE1 NE2 NE3 a 1 a 2 NE1 NE2 NE3 a 3

Cognitive Radio, Spectrum Policy, and Regulation

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
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
Deals with situations not planned at the initial time of learning to drive
They behave almost exactly the same!!!

“ 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 AWARE Precautions 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”

“ 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

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.

Vehicle Following Distances for Car Drivers ➟ Time needed to vacate channel after primary user (re-) appears for Cognitive Radios Vehicle Following Distances: TWO-SECOND RULE: Use the two-second rule to determine a safe following distance. A traffic model analogy (cont.)

A traffic model analogy (cont.) SPEED LIMIT for car driver ➟ Interference Level Limit (e.g. Max. Allowed Interference Temperature) for Cognitive Radio

City Map for Car Drivers ➟ Radio Environment Map (REM) for Cognitive Radios Learning from “Traffic model analogy” for the development of Cognitive Radio… REM Time (or duration) Location (x, y, z), Type of radio environment Local Policy Profile of primary users Profile of interference Max. allowed Interference Level

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

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!

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 dB  V/m From F. Jondral, “ SPECTRUM POOLING - An Efficient Strategy for Radio Resource Sharing, ” Blacksburg (VA), June 8, 200 4.

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

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 adaptation
Harder to keep tight control!

Regulatory Trends
Proceedings 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

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

DARPA XG Program
XG is trying to Develop the Technology and System Concepts to Dynamically Access Available Spectrum
Source: DARPA XG Program

Spectrum Policy Language Design Actors and Roles Source: BBN Technologies Solutions LLC Area that needs improvements! Spectrum Policy Policy Administrator (e.g. FCC, NTIA) XG System Spectrum Opportunities Awareness via XG Protocols and Sensing query Language Design Knowledge Core Language Model and Representation Policy Language Designer (e.g. BBN/XG Program) Policy Editing and Verification Tools design Machine Readable Policy Instances Policy Repository encode publish Policy Repository

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.

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.

Proposed Approach Policy Engine Cognitive Engine Applications Bios/OS

Example of a Possible Cognitive Radio Application

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.

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

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

Note Daily Drive Home at 5:30 (GPS Aided) Recall Brief Coverage Hole 1. Observe and Analyze Situation 2. Evaluate Alternatives Do Nothing Increase Coding Gain Increase Transmit Power Vertical Handoff Decrease Call Drop Threshold 4. Adapt Network 3. Signal Base Station Request Decrease In Call Drop Threshold CR in a Cellular System

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

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

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. Berkeley
Univ. of Cambridge
Univ. of Col.
Univ. of MD
Univ. of Pittsburg
Univ. of Toronto
Univ. of Warwick
Universitaet Karlsruhe
University of Piraeus
Virginia Tech

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 program is based on the XG Vision rfc, available online: http://www.darpa.mil/ato/programs/xg/

DARPA neXt Generation Program: Research Goals
Development of technologies that enable spectrum agility
Sensing and characterization of the (RF-) environment
Identification of unused spectrum ("opportunities")
Allocation and exploitation of opportunities
Development of standards for a software based policy regime to enable policy agility
explained in more detail on the next slides

DARPA neXt Generation Program: Concepts of Policy Agility (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
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
Software based
Spectrum use policies have to be machine understandable
Policy constraints can be implemented "on-the-fly" via software downloads

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

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

DARPA neXt Generation Program: Promises
Flexible radio operation due to spectrum agility
Simplified user control of XG systems
System operation can be controlled in terms of behavior
No need for technological details
Facilitated policy design
Constraints can be tailored to national or institutional needs in terms of behaviors
No need for technological details
Eased wireless device accreditation
Traceability provides a means for an easy testing procedure of behaviors against policies
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)

E 2 R Research in Europe
E 2 R = 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

E2R Participants 1/2
Academic 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/

E2R Participants 2/2
Manufacturer 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

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

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
Benefits
Increases 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

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

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:start And http://www.mprg.org

CR Test-bed under development Neighbor WLANs Ethernet Actions Cordless Phone Bluetooth MWOL Tektronix TDS694C: Digital Real-time Oscilloscope Tektronix RSA3408A: Real-Time Spectrum Analyzer Distributed Measurement Collaborative Processing Observations Analysis and decision REM online updating TV station

The Future of Cognitive Radio

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

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

IEEE Project 1900 (P1900)
The IEEE P1900 Standards Group was established in 1Q 2005 jointly by the IEEE Communications Society (ComSoc) and the IEEE Electromagnetic Compatibility (EMC) Society .
The objective of this effort is to develop supporting standards related to new technologies and techniques being developed for next generation radio and advanced spectrum management.

IEEE P1900.1 Working Group :
Objective document: “Standard Terms, Definitions and Concepts for Spectrum Management, Policy Defined Radio, Adaptive Radio and Software Defined Radio.”
Purpose: This document will facilitate the development of these technologies by clarifying the terminology and how these technologies relate to each other.

Objective document: “Recommended Practice for the Analysis of In-Band and Adjacent Band Interference and Coexistence Between Radio Systems.”
Purpose: T his standard will provide guidance for the analysis of coexistence and interference between various radio services.

Objective document : “Recommended Practice for Conformance Evaluation of Software Defined Radio (SDR) Software Modules.”
Purpose : This recommended practice will provide guidance for validity analysis of proposed SDR terminal software prior to physical programming and activation of SDR terminal components.

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.)

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

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

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 user Early cognition Mid-level cognition Cognitive radios 2005 2007 2010 Adaptive spectrum allocation

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

Alan Kay, Author

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

Jeffrey H. Reed
Contact Information:
[email_address]
Electrical and Computer Engineering MPRG 432 Durham Hall Blacksburg, VA 24061 (540) 231-2972

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

Charles W. Bostian
Contact Information:
[email_address]
Electrical and Computer Engineering Virginia Tech, Mail Code 0111 Blacksburg, VA 24061-0111 (540) 231-5096

Backup Slides

Hardware Blocks Software Modules

Example: Simple AM Transmitter (1/2)
Building Blocks
All Blocks are each defined as objects
“ Amp” - Gain Stage “ m” - Message Signal “ mix” - Multiplication Stage “ LO” - Local Oscillator “ FIR” - Filter Stage X ~ Amp m FIR

Example: Simple AM Transmitter (2/2)
Connecting Building Blocks
+ 1 Amp µ X ~ FIR m H/W Interface
The arrow is an object that connects the flow graph

Dyspan Sdr Cr Tutorial 10 25 Rev02

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Dyspan Sdr Cr Tutorial 10 25 Rev02 — Presentation Transcript