Jeff Reed

1
Cognitive Radio

• Jeff Reed
• reedjh_at_vt.edu
• reedjh_at_crtwireless.com
• [phone]
• James Neel
• James.neel_at_crtwireless.com
• [phone]www.crtwireless.com
• General Dynamics
• April 9, 2007

2
Jeffrey H. Reed

• Director, Wireless _at_ Virginia Tech
• Willis G. Worcester Professor, 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 CR projects from
• ETRI, ONR, ARO, Tektronix
• Email reedjh_at_vt.edu

3
James Neel

• President, Cognitive Radio Technologies, LLC
• PhD, Virginia Tech 2006
• Textbook chapters on
• Cognitive Network Analysis in
• Data Converters in Software Radio A Modern
  Approach to Radio Engineering
• SDR Case Studies in Software Radio A Modern
  Approach to Radio Engineering
• UWB Simulation Methodologies in An Introduction
  to Ultra Wideband Communication Systems
• SDR Forum Paper Awards for 2002, 2004 papers on
  analyzing/designing cognitive radio networks
• Email james.neel_at_crtwireless.com

4
Overview of Presentation Material (1/2)
Presenter Material
Reed 1.5 hrs 0830-1000 Introducing Cognitive Radio 1.1 What is a Cognitive Radio? 1.2 Relationship between CR and SDR 1.3 Typical Commercial CR Applications 1.4 How does CR Relate to WANN and future military networks? 1.5 Overview of Implementation Approaches 1.6 Overview of Networking Approaches 2. Implementing a Cognitive Radio 2.1Architectural Approaches
Break 20min 1000-1020 Break
Neel 1.5 hrs 1020-1150 2.2 Observing the Environment 2.2.1 Autonomous Sensing 2.2.2 Collaborative Sensing 2.2.3 Radio Environment Maps and Observation Databases 2.3 Recognizing Patterns 2.3.1 Neural Nets 2.3.2 Hidden Markov Model 2.3.3 Ontological Reasoning 2.4 Making Decisions 2.4.1 Common Heuristic Approaches 2.4.2 Case-based Reasoning
5
Overview of Presentation Material (2/2)
Presenter Material
Lunch 40min 1150-1230 Lunch Break
Reed 1 hr 1230-1330 2.4 Helping a Machine Learn 2.5 Representing Information 2.6 Current Implementations including VTs Prototypes
Neel 1.0 hrs 1330-1430 3. Networking Cognitive Radios 3.1 The Interactive Problem 3.2 The Role of Policy in Networked Cognitive Radios
Break 20min 1430-1450 Break
Neel 0.5 hrs 1450-1520 3.3 Approaches to Designing Well-behaved Cognitive Radio Networks 3.4 Emerging Standards
Reed 0.6 hrs 1520-1600 4. Summary and Conclusions 4.1 Outstanding Research Issues 4.2 The Opportunities 4.3 Speculation on How the Future May Evolve
6
What is a Cognitive Radio?

• Concepts, Definitions

7
Cognitive Radio Basic Idea

• Software radios permit network or user to control
  the operation of a software radio
• Cognitive radios enhance the control process by
  adding
• Intelligent, autonomous control of the radio
• An ability to sense the environment
• Goal driven operation
• Processes for learning about environmental
  parameters
• Awareness of its environment
• Signals
• Channels
• Awareness of capabilities of the radio
• An ability to negotiate waveforms with other
  radios

Waveform Software
Software Arch Services
Control Plane
OS
Board APIs
Board package (RF, processors)
8
Cognitive Radio Capability Matrix
Definer Adapts (Intelligently) Autonomous Can sense Environment Transmitter Receiver Aware Environment Goal Driven Learn the Environment Aware Capabilities Negotiate Waveforms No interference
FCC ? ? ? ?
Haykin ? ? ? ? ? ? ? ?
IEEE 1900.1 ? ? ? ? ?
IEEE USA ? ? ? ? ? ? ?
ITU-R ? ? ? ? ? ?
Mitola ? ? ? ? ? ? ? ? ? ?
NTIA ? ? ? ? ? ? ?
SDRF CRWG ? ? ? ? ? ?
SDRF SIG ? ? ? ? ? ? ? ? ?
VT CRWG ? ? ? ? ? ? ? ? ?
9
Why So Many Definitions?

• People want cognitive radio to be something
  completely different
• Wary of setting the hype bar too low
• Cognitive radio evolves existing capabilities
• Like software radio, benefit comes from the
  paradigm shift in designing radios
• Focus lost on implementation
• Wary of setting the hype bar too high
• Cognitive is a very value-laden term in the AI
  community
• Will the radio be conscious?
• Too much focus on applications
• Core capability radio adapts in response
  changing operating conditions based on
  observations and/or experience
• Conceptually, cognitive radio is a magic box

10
Used cognitive radio definition

• A cognitive radio is a radio whose control
  processes permit the radio to leverage
  situational knowledge and intelligent processing
  to autonomously adapt towards some goal.
• Intelligence as defined by American Heritage_00
  as The capacity to acquire and apply knowledge,
  especially toward a purposeful goal.
• To eliminate some of the mess, I would love to
  just call cognitive radio, intelligent radio,
  i.e.,
• a radio with the capacity to acquire and apply
  knowledge especially toward a purposeful goal

11
Levels of Cognitive Radio Functionality
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
Adapted From Table 4-1Mitola, Cognitive Radio
An Integrated Agent Architecture for Software
Defined Radio, PhD Dissertation Royal Institute
of Technology, Sweden, May 2000.
12
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

Select Alternate Goals
Generate Alternate Goals
Establish Priority
Immediate
Normal
Urgent
Determine Best Known Waveform
Generate Best Waveform
Negotiate
Negotiate Protocols
Adapted From Mitola, Cognitive Radio for
Flexible Mobile Multimedia Communications , IEEE
Mobile Multimedia Conference, 1999, pp 3-10.
13
Conceptual Operation
Cognition cycle
Mitola_99

• OODA Loop (continuously)
• Observe outside world
• Orient to infer meaning of observations
• Adjust waveform as needed to achieve goal
• Implement processes needed to change waveform
• Other processes (as needed)
• Adjust goals (Plan)
• Learn about the outside world, needs of user,

Infer from Context
Orient
Infer from Radio Model
Establish Priority
Normal
Pre-process
Select Alternate Goals
Parse Stimuli
Plan
Urgent
Immediate
Learn
Observe
New States
Decide
States
User Driven (Buttons)
Generate Best Waveform
Autonomous
Outside World
Act
Allocate Resources Initiate Processes Negotiate
Protocols
14
Relationship Between SDR and CR

• Cognitive radio is a revolutionary evolution of
  software radio

15
Cognitive Radio SDR

• SDRs impact on the wireless world is difficult
  to predict
• But whatis 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

16
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

17
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

18
How is a Software Radio Different from Other
Radios? - Upgrade Cycle

• Cognitive Radio
• SDR upgrade mechanisms
• Internal upgrades
• Collaborative upgrades

• 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

19
Typical Cognitive Radio Applications

• What does cognitive radio enable?

20
Bandwidth isnt scarce, its underutilized

• Measurements averaged over six locations
• Riverbend Park, Great Falls, VA,
• Tysons Corner, VA,
• NSF Roof, Arlington, VA,
• New York City, NY
• NRAO, Greenbank, WV,
• SSC Roof, Vienna, VA
• 25 occupancy at peak

Modified from Figure 1 in Published August 15,
2005 M. McHenry in NSF Spectrum Occupancy
Measurements Project Summary, Aug 15, 2005.
Available online http//www.sharedspectrum.com/?s
ectionnsf_measurements
21
Conceptual example of opportunistic spectrum
utilization
22
Cognitive radio permits the deployment of cheaper
radios

• RF components are expensive
• Cheaper analog implies more
• spurs and out-of-band emissions
• Processing is cheap and getting cheaper
• Cognitive radios will adapt around spurs (just
  another interference source) or teach the radio
  to reduce the spurs
• Better radios results in still more available
  spectrum as the need arises.
• Likely able to exploit SDR

23
Improved Link Reliability

• 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, form an ad-hoc
  network
• Radio learns best course of action from situation

Signal Quality
Good
Transitional
Poor

• Can aid cellular system
• Inform system other radios of identified gaps

24
Automated Interoperability

• Basic SDR idea
• Use a SDR as a gateway to translate between
  different radios
• Problems
• Which devices are present?
• Which links to support?
• With SDR some network administrator must answer
  these questions
• Basic CR idea
• Let the cognitive radio observe and learn from
  its environment in an automated fashion.

25
Spectrum Trading

• Underutilized spectrum can be sold to support a
  high demand service
• Currently done in Britain
• Permitted in US among public safety users
• Currently has a very long time scale (months)
• Faster spectrum trading could permit for
  significant increases in available bandwidth
• How to recognize need and availability of
  additional spectrum?
• Environment context awareness memory

26
Collaborative Radio

• A radio that leverages the services of other
  radios to further its goals or the goals of the
  networks.
• Cognitive radio enables the collaboration process
• Identify potential collaborators
• Implies observations processes
• Classes of collaboration
• Distributed processing
• Distributed sensing

27
Cooperative Antenna Arrays

• Concept
• Leverage other radios to effect an antenna array
• Applications
• Extended vehicular coverage
• Backbone comm. for mesh networks
• Range extension with cheaper devices
• Issues
• Timing, mobility
• Coordination
• Overhead

Cooperative MIMO
Second Hop
First Hop
First Hop
First Hop
First Hop
First Hop
First Hop
Relay cluster
Relay cluster
Relay cluster
Relay cluster
Relay cluster
Relay cluster
Destination Cluster
Source Cluster
Source Cluster
Source Cluster
Source Cluster
Source Cluster
Source Cluster
Transmit Diversity
destination
source
28
Other Opportunities for Collaborative Radio (1/3)

• Distributed processing
• Exploit different capabilities on different
  devices
• Maybe even for waveform processing
• Bring extra computational power to bear on
  critical problems
• Useful for most collaborative problems

• Collaborative sensing
• Extend detection range by including observations
  of other radios
• Hidden node mitigation
• Improve estimation statistics by incorporating
  more independent observations
• Immediate applicability in 802.22, likely useful
  in future adaptive standards

29
Other Opportunities for Collaborative Radio (2/3)

• Improved localization
• Application of collaborative sensing
• Security
• Friend finders

• Reduced contention MACs
• Collaborative scheduling algorithms to reduce
  collisions
• Perhaps of most value to 802.11
• Some scheduling included in 802.11e

30
Other Opportunities for Collaborative Radio (3/3)

• Distributed mapping
• Gather information relevant to specific locations
  from mobiles and arrange into useful maps
• Coverage maps
• Collect and integrate signal strength information
  from mobiles
• If holes are identified and fixed, should be a
  service differentiator
• Congestion maps
• Density of mobiles should correlate with traffic
  (as in automobile) congestion
• Customers may be willing to pay for real time
  traffic information

• Theft detection
• Devices can learn which other devices they tend
  to operate in proximity of and unexpected
  combinations could serve as a security flag (like
  flagging unexpected uses of credit cards)
• Examples
• Car components that expect to see certain mobiles
  in the car
• Laptops that expect to operate with specific
  mobiles nearby

31
Cognitive Radio and Military Networks

• How is the military planning on using cognitive
  radio?

32
Drivers in Commercial and Military Networks

• Many of the same commercial applications also
  apply to military networks
• Opportunistic spectrum utilization
• Improved link reliability
• Automated interoperability
• Cheaper radios
• Collaborative networks
• Military has much greater need for advanced
  networking techniques
• MANETs and infrastructure-less networks
• Disruption tolerant
• Dynamic distribution of services
• Energy constrained devices
• Goal is to intelligently adapt device, link, and
  network parameters to help achieve mission
  objectives

From P. Marshall, WNaN Adaptive Network
Development (WAND) BAA 07-07 Proposers Day, Feb
27, 2007
33
Wireless Network after Next (WNaN)
Program Organization
Reliability through frequency and path diversity
Intelligent agent cross-layer optimization
Figures from P. Marshall, WNaN Adaptive Network
Development (WAND) BAA 07-07 Proposers Day, Feb
27, 2007
34
DARPAs WNAN Program
WNaN Protocol Stack

• Objectives
• Reduced cost via intelligent adaptation
• Greater node density
• Gains in throughput/scalability
• Leveraged programs
• Control Based MANET low overhead protocols
• Microsystems Technology Office RFMEMS, Hermit,
  ASP
• xG opportunistic use of spectrum
• Mobile Network MIMO - MIMO Wideband Network
  Waveform
• Connectionless Networks rapid link acquisition
• Disruption Tolerant Networks (DTN) network
  layer protocols

CBMANET
Optimizing
Topology
CBMANET
WNaN
CBMANET
Network
WNaN
MAC
xG
MIMO (MNM)
COTS
Physical
MEMS (MTO)
Other programs
WNaN program
Legend
35
Overview of Implementation Approaches

• How does the radio become cognitive?

36
Implementation Classes

• Weak cognitive radio
• Radios adaptations determined by hard coded
  algorithms and informed by observations
• Many may not consider this to be cognitive (see
  discussion related to Fig 6 in 1900.1 draft)

• Strong cognitive radio
• Radios adaptations determined by conscious
  reasoning
• Closest approximation is the ontology reasoning
  cognitive radios

• In general, strong cognitive radios have
  potential to achieve both much better and much
  worse behavior in a network, but may not be
  realizable.

37
Brilliant Algorithms and Cognitive Engines

• Most research focuses on development of
  algorithms for
• Observation
• Decision processes
• Learning
• Policy
• Context Awareness
• Some complete OODA loop algorithms
• In general different algorithms will perform
  better in different situations

• Cognitive engine can be viewed as a software
  architecture
• Provides structure for incorporating and
  interfacing different algorithms
• Mechanism for sharing information across
  algorithms
• No current implementation standard

38
Observation Sources
39
Orientation Processes

• Gives radio significance of observations
• Does multipath profile correspond to a known
  location?
• Really just hypotheses testing
• Algorithms
• Data mining
• Hidden Markov Models
• Neural Nets
• Fuzzy Logic
• Ontological Reasoning

40
Decision Processes

• Purpose Map what radio believes about network
  state to an adaptation
• Guided by radio goal and constrained by policy
• May be supplemented with model of real world
• Common algorithms (mostly heuristics)
• Genetic algorithms
• Simulated annealing
• Local search
• Case based reasoning

41
Learning Processes

• Informs radio when situation is not like one its
  seen before or if situation does not correspond
  to any known situation
• Logically, just an extension to the orientation
  process with
• a none of the above option
• Increase number of hypotheses after none of the
  above
• Refine hypotheses and models
• Algorithms
• Data mining
• Hidden Markov Models
• Neural Nets
• Fuzzy Logic
• Ontological Reasoning
• Case based learning
• Bayesian learning
• Other proposed learning tasks
• New actions, new decision rules, new channel
  models, new goals, new internal algorithms

42
Knowledge Representation

• Issue
• How are radios aware of their environment and
  how do they learn from each other?
• Technical refinement
• Thinking implies some language for thought.
• Proposed languages
• Radio Knowledge Representation Language
• XML
• Web-based Ontology Language (OWL)

43
Overview of Cognitive Networking

• What happens when they leave the lab?

44
The Interaction Problem

• Outside world is determined by the interaction of
  numerous cognitive radios
• Adaptations spawn adaptations

45
Potential Problems with Networked Cognitive Radios

• Distributed
• Infinite recursions
• Instability (chaos)
• Vicious cycles
• Adaptation collisions
• Equitable distribution of resources
• Byzantine failure
• Information distribution

• Centralized
• Signaling Overhead
• Complexity
• Responsiveness
• Single point of failure

46
Implications

• Best of All Possible Worlds
• Low complexity distributed algorithms with low
  anarchy factors
• Reality implies mix of methods
• Hodgepodge of mixed solutions
• Policy bounds the price of anarchy
• Utility adjustments align distributed solution
  with centralized solution
• Market methods sometimes distributed, sometimes
  centralized
• Punishment sometimes centralized, sometimes
  distributed, sometimes both
• Radio environment maps centralized information
  for distributed decision processes
• Fully distributed
• Potential game design really, the Panglossian
  solution, but only applies to particular problems

47
Cognitive Networks

• Rather than having intelligence reside in a
  single device, intelligence can reside in the
  network
• Effectively the same as a centralized approach
• Gives greater scope to the available adaptations
• Topology, routing
• Conceptually permits adaptation of core and edge
  devices
• Can be combined with cognitive radio for mix of
  capabilities
• Focus of E2R program

R. Thomas et al., Cognitive networks adaptation
and learning to achieve end-to-end performance
objectives, IEEE Communications Magazine, Dec.
2006
48
Emerging Commercial Implementations

• Dynamic Frequency Selection
• 802.11h
• 802.11y
• 802.11 for TV bands?
• Distributed Collaboration
• 802.16h

• Collaborative Sensing
• 802.22
• Radio Resource Maps
• 802.16h
• 802.11y
• Policy radios
• 802.11e
• 802.11j

49
Summary

• Cognitive radio evolves the software radio
  concept to permit intelligent autonomous
  adaptation of radio parameters
• Significant variation in definitions of
  cognitive radio
• Question of how cognitive the radio is
• Numerous new applications enabled
• Opportunistic spectrum utilization, collaborative
  radio, link reliability, advanced network
  structures
• Differing implementation approaches
• Many applications implementable with simple
  algorithms
• Greater flexibility achievable with a cognitive
  engine approach

• Many objectives will require development of a
  cognitive language
• In a network, adaptations of cognitive radios
  interact
• Interaction can be mitigated with policy,
  punishment, cost adjustments, centralization or
  potential games
• Commercial implementations starting to appear
• 802.22, 802.11h,y, 802.16h
• And may have been around for a while (cordless
  phones with DFS)