Overview - Cognitive Radio Technologies

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Feb 23, 2014 (3 years and 5 months ago)

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Cognitive Radio Technologies, 2007

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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

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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

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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

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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

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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



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What is a Cognitive Radio?

Concepts, Definitions



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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



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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



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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

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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

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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



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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



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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]



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Relationship Between SDR
and CR

Cognitive radio
is a revolutionary
evolution of
software radio



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Cognitive Radio & SDR


SDR’s impact on the wireless world is difficult to predict


“But what…is it good for?”


Engineer at the Advanced Computing Systems Division of
IBM, 1968, commenting on the microchip


Some believe SDR is not necessary for cognitive radio


Cognition is a function of higher
-
layer application


Cognitive radio without SDR is limited


Underlying radio should be highly adaptive


Wide QoS range


Better suited to deal with new standards


Resistance to obsolescence


Better suited for cross
-
layer optimization



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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



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How is a Software Radio Different
from Other Radios?
-

Design

Software Radio


Conventional
Radio +


Software
Architecture


Reconfigurability


Provisions for
easy upgrades


Conventional

Radio


Traditional RF
Design


Traditional
Baseband Design

Cognitive Radio


SDR +


Intelligence


Awareness


Learning


Observations



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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



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Typical Cognitive Radio
Applications

What does
cognitive
radio enable?



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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



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Random

Access

TDMA

Primary Signals

Opportunistic Signals

Conceptual example of
opportunistic spectrum utilization



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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



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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



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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.



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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



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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



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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



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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



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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





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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




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Cognitive Radio and Military
Networks

How is the military
planning on using
cognitive radio?



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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



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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



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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



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Overview of Implementation
Approaches

How does the
radio become
cognitive?



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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.



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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



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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



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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



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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



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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



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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
)



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Overview of Cognitive
Networking

What happens when
they leave the lab?



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The Interaction Problem


Outside world is determined by the interaction
of numerous cognitive radios


Adaptations spawn adaptations

Outside

World



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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



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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



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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



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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



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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)