Overview - Cognitive Radio Technologies



Cognitive Radio Technologies, 2007

1

C
ognitive
R
adio
T
echnologies
C
C
ognitive
ognitive
R
R
adio
adio
T
T
echnologies
echnologies
CRT
CRT
CRT
Jeff Reed

reedjh@vt.edu

reedjh@crtwireless.com

(540) 231 2972


James Neel

James.neel@crtwireless.com

(540) 230
-
6012

www.crtwireless.com


General Dynamics

April 9, 2007

Cognitive Radio



Cognitive Radio Technologies, 2007

2

Jeffrey H. Reed

•
Director, Wireless @ 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@vt.edu



Cognitive Radio Technologies, 2007

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@crtwireless.com

C
ognitive
R
adio
T
echnologies
C
C
ognitive
ognitive
R
R
adio
adio
T
T
echnologies
echnologies
CRT
CRT
CRT


Cognitive Radio Technologies, 2007

4

Overview of Presentation
Material (1/2)

Presenter

Material

Reed

1.5 hrs

0830
-
1000

1.
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
-
11:50

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



Cognitive Radio Technologies, 2007

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 VT’s 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



Cognitive Radio Technologies, 2007

6

What is a Cognitive Radio?

Concepts, Definitions



Cognitive Radio Technologies, 2007

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

Board package

(RF, processors)

Board APIs

OS

Software Arch

Services

Waveform Software

Control Plane



Cognitive Radio Technologies, 2007

8

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



















Cognitive Radio Capability
Matrix



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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




Cognitive Radio Technologies, 2007

11

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.

Levels of Cognitive Radio
Functionality



Cognitive Radio Technologies, 2007

12

Normal

Urgent

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

Allocate Resources

Initiate Processes

Orient

Infer from Context

Parse Stimuli

Pre
-
process

Select Alternate

Goals

Establish Priority

Plan

Normal

Negotiate

Immediate

Learn

New

States

Negotiate Protocols

Generate Alternate

Goals

Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 199
9,
pp 3
-
10.

Observe

Outside

World

Decide

Act

User Driven

(Buttons)

Autonomous

Determine “Best”

Plan

Infer from Radio Model

States

Determine “Best”

Known Waveform

Generate “Best”

Waveform

Cognition Cycle



Cognitive Radio Technologies, 2007

13

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,…

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

Cognition cycle

Conceptual Operation

[Mitola_99]



Cognitive Radio Technologies, 2007

14

Relationship Between SDR
and CR

Cognitive radio
is a revolutionary
evolution of
software radio



Cognitive Radio Technologies, 2007

15

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



Cognitive Radio Technologies, 2007

16

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?
-

Application



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

18

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

How is a Software Radio Different
from Other Radios?
-

Upgrade Cycle



Cognitive Radio Technologies, 2007

19

Typical Cognitive Radio
Applications

What does
cognitive
radio enable?



Cognitive Radio Technologies, 2007

20

Measurements averaged
over six locations:


1.
Riverbend Park, Great
Falls, VA,

2.
Tysons Corner, VA,

3.
NSF Roof, Arlington, VA,

4.
New York City, NY

5.
NRAO, Greenbank, WV,

6.
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/?section=nsf_measurements

Bandwidth isn’t scarce,

it’s underutilized



Cognitive Radio Technologies, 2007

21

Random

Access

TDMA

Primary Signals

Opportunistic Signals

Conceptual example of
opportunistic spectrum utilization



Cognitive Radio Technologies, 2007

22

•
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

Cognitive radio permits the
deployment of cheaper radios



Cognitive Radio Technologies, 2007

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

Good

Transitional

Poor

Signal Quality



Can aid cellular system



Inform system & other radios of identified gaps



Cognitive Radio Technologies, 2007

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.



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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

source

destination

Transmit Diversity

Cooperative MIMO

Source Cluster

Relay cluster

First Hop

Second Hop

Source Cluster

Relay cluster

First Hop

Source Cluster

Relay cluster

First Hop

Source Cluster

Relay cluster

First Hop

Source Cluster

Relay cluster

First Hop

Source Cluster

Relay cluster

First Hop

Destination Cluster



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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





Cognitive Radio Technologies, 2007

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




Cognitive Radio Technologies, 2007

31

Cognitive Radio and Military
Networks

How is the military
planning on using
cognitive radio?



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

33

Wireless Network after Next
(WNaN)

Figures from: P. Marshall, “WNaN Adaptive Network Development (WAND) BAA 07
-
07 Proposers’ Day”, Feb 27, 2007

Program Organization

Reliability through frequency and path diversity

Intelligent agent cross
-
layer optimization



Cognitive Radio Technologies, 2007

34

DARPA’s WNAN Program

•
Objectives

–
Reduced cost via intelligent
adaptation

–
Greater node density

–
Gains in throughput/scalability

•
Leveraged programs

–
C
ontrol
B
ased
MANET

–

low

overhead protocols

–
M
icrosystems
T
echnology
O
ffice
–

RFMEMS, Hermit, ASP

–
xG
–

opportunistic use of
spectrum

–
M
obile
N
etwork
M
IMO
-

MIMO
Wideband Network Waveform

–
C
onnectionless
N
etworks
–

rapid link acquisition

–
D
isruption
T
olerant
N
etworks
(DTN)
–

network layer protocols

CBMANET

WNaN Protocol Stack

CBMANET

CBMANET

xG

COTS

MEMS (MTO)

WNaN

WNaN

MIMO (MNM)

Physical

MAC

Network

Topology

Optimizing

Other

programs

WNaN

program

Legend



Cognitive Radio Technologies, 2007

35

Overview of Implementation
Approaches

How does the
radio become
cognitive?



Cognitive Radio Technologies, 2007

36

Implementation Classes

•
Weak

cognitive radio

–
Radio’s 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

–
Radio’s 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.



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

38

•
Spectrum information is
provided by the network

•
Spectrum information is
shared by other cognitive
radios

•
Observes user's applications,
incoming/ outgoing data
streams

•
Performs speech analysis

User

•
Passively "listens" to the
spectrum

•
Performs channel quality
estimation

Spectrum

(communication
opportunities)

•
Receives GPS signals to
determine position

•
Parses short
-
range wireless
broadcasts in buildings or
urban areas for mapped
environment

•
Observes the network for e.g.
weather forecast, reported
traffic jams, …etc.

•
Measures
temperature, light
level, humidity, …

Environment

(physical quantities, position,
situations)

Other opportunities to
get information

How the cognitive
radio gets the
information?

Information is about

Observation Sources



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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:

–
R
adio
K
nowledge
R
epresentation
L
anguage

–
XML

–
W
eb
-
based
O
ntology
L
anguage (
OWL
)



Cognitive Radio Technologies, 2007

43

Overview of Cognitive
Networking

What happens when
they leave the lab?



Cognitive Radio Technologies, 2007

44

The Interaction Problem

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

•
Adaptations spawn adaptations

Outside

World



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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 E
2
R program


R. Thomas et al., “Cognitive networks: adaptation and learning to achieve
end
-
to
-
end performance objectives,” IEEE Communications Magazine, Dec.
2006



Cognitive Radio Technologies, 2007

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



Cognitive Radio Technologies, 2007

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)