cognitive radio

Cognitive Radio Technologies, 2007

1Cognitive RadioTechnologiesCCognitiveognitive RRadioadioTTechnologiesechnologies

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Jeff [email protected]@crtwireless.com(540) 231 2972

James [email protected](540) 230-6012www.crtwireless.com

General DynamicsApril 9, 2007

Cognitive Radio

mailto:[email protected]



http://www.crtwireless.com/


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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: [email protected]


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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: [email protected]

Cognitive RadioTechnologiesCCognitiveognitive RRadioadioTTechnologiesechnologies

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Overview of Presentation Material (1/2)Presenter Material

Reed 1.5 hrs0830-1000

1.Introducing Cognitive Radio 1.1 What is a Cognitive Radio?1.2 Relationship between CR and SDR1.3 Typical Commercial CR Applications1.4 How does CR Relate to WANN and future military networks?1.5 Overview of Implementation Approaches1.6 Overview of Networking Approaches

2. Implementing a Cognitive Radio 2.1Architectural Approaches

Break~20min1000-1020

Break

Neel ~ 1.5 hrs1020-11:50

2.2 Observing the Environment 2.2.1 Autonomous Sensing 2.2.2 Collaborative Sensing 2.2.3 Radio Environment Maps and Observation Databases2.3 Recognizing Patterns 2.3.1 Neural Nets 2.3.2 Hidden Markov Model 2.3.3 Ontological Reasoning2.4 Making Decisions 2.4.1 Common Heuristic Approaches 2.4.2 Case-based Reasoning


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Overview of Presentation Material (2/2)Presenter Material

Lunch ~ 40min1150-1230 Lunch Break

Reed ~ 1 hr1230-1330

2.4 Helping a Machine Learn2.5 Representing Information2.6 Current Implementations including VT’s Prototypes

Neel ~ 1.0 hrs1330-1430

3. Networking Cognitive Radios3.1 The Interactive Problem3.2 The Role of Policy in Networked Cognitive Radios

Break ~ 20min1430-1450

Break

Neel ~ 0.5 hrs1450-1520

3.3 Approaches to Designing Well-behaved Cognitive Radio Networks 3.4 Emerging Standards

Reed ~ 0.6 hrs1520-1600

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 ArchServices

Waveform Software

Co

ntr

ol

Pla

ne


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Definer

Adapts (Intelligently)

Autonom

ous

Can sense

Environm

ent

Transm

itter

Receiver

“Aw

are” Environm

ent

Goal D

riven

Learn the E

nvironment

“Aw

are” Capabilities

Negotiate W

aveforms

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


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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


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Level Capability Comments

0 Pre-programmed A software radio

1 Goal DrivenChooses Waveform According to Goal. Requires Environment Awareness.

2 Context Awareness Knowledge of What the User is Trying to Do

3 Radio AwareKnowledge of Radio and Network Components, Environment Models

4 Capable of PlanningAnalyze Situation (Level 2& 3) to Determine Goals (QoS, power), Follows Prescribed Plans

5 Conducts Negotiations Settle on a Plan with Another Radio

6 Learns EnvironmentAutonomously Determines Structure of Environment

7 Adapts Plans Generates New Goals

8 Adapts Protocols Proposes and Negotiates New ProtocolsAdapted 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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NormalUrgent

Level0 SDR1 Goal Driven2 Context Aware3 Radio Aware4 Planning5 Negotiating6 Learns

Environment7 Adapts Plans8 Adapts Protocols

Allocate ResourcesInitiate Processes

OrientInfer from Context

Parse Stimuli

Pre-processSelect Alternate

GoalsEstablish Priority

PlanNormal

Negotiate

Immediate

LearnNewStates

Negotiate Protocols

Generate AlternateGoals

Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 1999, pp 3-10.

Observe

OutsideWorld

Decide

Act

User Driven(Buttons)

Autonomous Determine “Best” Plan

Infer from Radio Model

States

Determine “Best” Known WaveformGenerate “Best” Waveform

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 ResourcesInitiate Processes

Negotiate Protocols

OrientInfer from Context

Select AlternateGoals

Plan

Normal

Immediate

LearnNew

States

Observe

OutsideWorld

Decide

Act

User Driven(Buttons)Autonomous

Infer from Radio Model

StatesGenerate “Best” Waveform

Establish Priority

Parse Stimuli

Pre-process

Cognition cycle

Conceptual Operation[Mitola_99]


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Relationship Between SDR and CRCognitive 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

ConventionalRadio

• Supports a fixed number of systems

• Reconfigurability decided at the time of design

• May support multiple services, but chosen at the time of design

Cognitive Radio• Can create new

waveforms on its own

• Can negotiate new interfaces

• Adjusts operations to meet the QoS required by the application for the signal environment

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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RandomAccess

TDMAPrimary 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 PoorSignal 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


First Hop


First Hop


First Hop


First Hop


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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• 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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• 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– 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

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:

– Radio Knowledge Representation Language

– XML– Web-based Ontology

Language (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

OutsideWorld


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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 E2R 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)

cognitive radio

Documents

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radio engineeringieee

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networking cognitive