james neel [email protected] april 25-26, 2007

1© Cognitive Radio Technologies, 2007

Analysis and Design of Cognitive Radio Networks

and Distributed Radio Resource Management Algorithms

James [email protected] 25-26, 2007

CRTCognitive Radio Technologies

CRTCRTCognitive Radio Technologies


Small business officially incorporated in Feb 2007 to commercialize cognitive radio researchEmail: [email protected]

[email protected]: crtwireless.comTel: 540-230-6012 Mailing Address:

Cognitive Radio Technologies, LLC147 Mill Ridge Rd, Suite 119Lynchburg, VA 24502

CRT Information

mailto:[email protected]

mailto:[email protected]


0 50 100 150 200 250 300

40

60

80

100

120

140

Cha

nnel

0 50 100 150 200 250 300-90

-80

-70

-60

I i(f) (d

Bm

)

0 50 100 150 200 250 300-80

-75

-70

-65

-60

-55

iteration

(f)

(dB

m)

CRT’s Strengths Analysis of networked cognitive

radio algorithms (game theory) Design of low complexity, low

overhead (scalable), convergent and stable cognitive radio algorithms

– Infrastructure, mesh, and ad-hoc networks

– DFS, TPC, AIA, beamforming, routing, topology formation

Net Interference

Average LinkInterference

FrequencyAdjustments

Statistics from yet to be published DFS algorithm for ad-hoc networks

P2P 802.11a links in 0.5kmx0.5km area(discussed in tomorrow’s slides)

0 10 20 30 40 50 60 70 80 90 100-90

-85

-80

-75

-70

-65

-60

-55

-50

-45

Ste

ady-

stat

e In

terfe

renc

e le

vels

(dB

m)

Number Links

Typical Worst Case Without AlgorithmAverage Without AlgorithmTypical Worst Case With AlgorithmAverage With AlgorithmColission Threshold


Selected Publications (why selected)

(First paper to propose application of game theory to cognitive radio networks)

– J. Neel, R. Buehrer, J. Reed, and R. Gilles, “Game Theoretic Analysis of a Network of Cognitive Radios,” Midwest Symposium on Circuits and Systems 2002

(First paper to use game theory to examine convergence properties of cognitive radio networks)

– J. Neel, J. Reed, and R. Gilles, “Convergence of Cognitive Radio Networks,” Wireless Communications and Networking Conference 2004, March 21-25, 2004, vol. 4, pp. 2250-2255.

(Outstanding Paper Award)– J. Neel, J. Reed, R. Gilles, “Game Models for Cognitive Radio

Analysis,” SDR Forum 2004 Technical Conference, Nov. 2004, paper # 01.5-05.

(Outstanding Paper Award)– J. Neel, J. Reed, R. Gilles, “The Role of Game Theory in the Analysis

of Software Radio Networks,” SDR Forum Technical Conference, San Diego Nov. 11-12, 2002, paper # 04.3-001.


Selected Publications (why selected) (Textbook chapter closely related to today’s presentation)

– J. Neel. J. Reed, A. MacKenzie, Cognitive Radio Network Performance Analysis in Cognitive Radio Technology, B. Fette, ed., Elsevier August 2006.

(Shorter upcoming tutorial across the pond)– J. Neel, “Game Theory in the Analysis and Design of Cognitive

Radio Networks,” DySPAN07 April 17, 2007,. (Stuff beyond Cognitive Radio Networks)

– W. Tranter, J. Neel, and C. Anderson, Simulation of Ultra Wideband Communication Systems, in An Introduction to Ultra Wideband Communication Systems, Prentice Hall 2005.

– J. Neel and J. Reed. Case Studies in Software Radio Design, in Jeffrey H. Reed. Software Radios: A Modern Approach to Radio Engineering, Prentice Hall 2002.

– J. Reed, J. Neel and S. Sachindar, Analog to Digital and Digital to Analog Conversion, in Jeffrey H. Reed, Software Radios: A Modern Approach to Radio Engineering, Prentice Hall 2002.


Tutorial Background

Most material from my three week defense– Very understanding committee– Dissertation online @

http://scholar.lib.vt.edu/theses/available/etd-12082006-141855/

– Original defense slides @ http://www.mprg.org/people/gametheory/Meetings.shtml

Other material from training short course I gave in summer 2003

– http://www.mprg.org/people/gametheory/Class.shtml Some material is new

– Consider presentation subject to existing NDAs



http://www.mprg.org/people/gametheory/Meetings.shtml

http://www.mprg.org/people/gametheory/Class.shtml


Research in a nutshell Hypothesis: Applying game theory and game models

(potential and supermodular) to the analysis of cognitive radio interactions

– Provides a natural method for modeling cognitive radio interactions

– Significantly speeds up and simplifies the analysis process (can be performed at the undergraduate level – Senior EE)

– Permits analysis without well defined decision processes (only the goals are needed)

– Can be supplemented with traditional analysis techniques– Can provides valuable insights into how to design cognitive

radio decision processes– Has wide applicability


Research in a nutshell

Focus areas:– Formalizing connection between game theory and

cognitive radio– Collecting relevant game model analytic results – Filling in the gaps in the models

Model identification (potential games) Convergence Stability

– Formalizing application methodology– Developing applications


Presentation Overview

x

y

Cognitive RadioBasic FunctionalityApplicationsClassifications

Modeling Cognitive Radio BehaviorMotivating ProblemsAnalysis ObjectivesBasic Models

“Traditional” Analysis InsightsEvolution FunctionsContraction Mappings


Presentation Overview

Game TheoryNormal Form gamesMixed StrategiesExtensive Form GamesRepeated Games

Special Game ModelsSupermodular gamesPotential games

AlgorithmsAd-hoc power controlSensor network formationInterference Reducing NetworksInfrastructure DFSAd-hoc DFSJoint algorithms


Approximate Timeline

08:00-08.30 Introduction08:00-10:00 Special Models08:30-09:30 Cognitive Radio

09:30-10:30 Models of CR Behavior 10:15-12:00 Algorithms

10:45-12:00 Traditional Analysis13:00-15:00 Game Theory 13:00-

17:00 Discussion13:15-17:00 Game Theory

Day 1 (April 25th) Day 2 (April 26th)


Cognitive Radio Concepts

How does a radio come to be “cognitive”?


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

Con

trol

Pla

ne


Defining Cognitive Radio is Surprisingly Difficult

[Mitola 1999] coined the term to refer to – “A radio that employs model based reasoning to achieve a

specified level of competence in radio-related domains.” [Haykin 2005]

– “An intelligent wireless communication system that is aware of its surrounding environment (i.e., outside world), and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g., transmit-power, carrier-frequency, and modulation strategy) in real-time, with two primary objectives in mind:

highly reliable communications whenever and wherever needed; efficient utilization of the radio spectrum.


Regulatory FCC

– “A radio that can change its transmitter parameters based on interaction with the environment in which it operates.”

NTIA– “A radio or system that senses its operational

electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify system operation, such as maximize throughput, mitigate interference, facilitate interoperability, and access secondary markets.”

ITU (Wp8A) – “A radio or system that senses and is aware of its

operational environment and can dynamically and autonomously adjust its radio operating parameters accordingly.”


SDR Forum Definitions Cognitive Radio Working Group

– “A radio that has, in some sense, (1) awareness of changes in its environment and (2) in response to these changes adapts its operating characteristics in some way to improve its performance or to minimize a loss in performance.”

Cognitive Radio Special Interest Group– “An adaptive, multi-dimensionally aware, autonomous radio

(system) that learns from its experiences to reason, plan, and decide future actions to meet user needs.”


IEEE

IEEE USA– “A radio frequency transmitter/receiver that is designed to

intelligently detect whether a particular segment of the radio spectrum is currently in use, and to jump into (and out of, as necessary) the temporarily-unused spectrum very rapidly, without interfering with the transmissions of other authorized users.”

IEEE 1900.1 – “A type of radio that can sense and autonomously reason about its

environment and adapt accordingly. This radio could employ knowledge representation, automated reasoning and machine learning mechanisms in establishing, conducting, or terminating communication or networking functions with other radios. Cognitive radios can be trained to dynamically and autonomously adjust its operating parameters.”


Virginia Tech Cognitive Radio Working Group

“An adaptive radio that is capable of the following:a) awareness of its environment and its own capabilities, b) goal driven autonomous operation, c) understanding or learning how its actions impact its goal, d) recalling and correlating past actions, environments, and performance.”


Definer

Adapts (Intelligently)

Autonom

ous

Can sense

Environm

ent

Transmitter

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


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


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


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 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


NormalUrgent

Level0 SDR1 Goal Driven2 Context Aware3 Radio Aware4 Planning5 Negotiating6 Learns Environment7 Adapts Plans8 Adapts Protocols

Allocate ResourcesInitiate Processes

OrientInfer from Context

Parse StimuliPre-process

Select AlternateGoals

Establish 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


OODA Loop: (continuously) Observe outside world Orient to infer meaning of

observations Adjust waveform as needed

to achieve goal Implement processes needed to

change waveformOther 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

StatesObserve

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]


A radio whose operation/ adaptations are governed by a set of rules

Almost necessarily coupled with cognitive radio

Allows flexibility for setting spectral policy to satisfy regional considerations

Policies

Policy-Based Radio

frequency

mask


Cognitive Radio Applications


Strong Artificial Intelligence

Concept: Make a machine aware (conscious) of its environment and self aware

• A complete failure (probably a good thing)


Weak Artificial Intelligence Concept: Develop powerful (but limited) algorithms

that intelligently respond to sensory stimuli Applications

– Machine Translation– Voice Recognition– Intrusion Detection– Computer Vision– Music Composition


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 engine

– 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.


Weak/Procedural Cognitive Radios 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)

– A function of the fuzzy definition Implementations:

– CWT Genetic Algorithm Radio– MPRG Neural Net Radio– Multi-dimensional hill climbing DoD LTS (Clancy)– Grambling Genetic Algorithm (Grambling)– Simulated Annealing/GA (Twente University)– Existing RRM Algorithms?


Strong/Ontological Radios

Radio’s adaptations determined by some reasoning engine which is guided by its ontological knowledge base (which is informed by observations)

Proposed Implementations:– CR One Model based reasoning

(Mitola) – Prolog reasoning engine (Kokar)– Policy reasoning (DARPA xG)


Intelligent 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


Security

User Model

Policy Model

WSG

A

Evolver

|(Simulated Meters) – (Actual Meters)| Simulated Meters

Actual Meters

Cognitive System Module

Cognitive System ControllerChob

Uob

Reg

Knowledge BaseShort Term MemoryLong Term Memory

WSGA Parameter SetRegulatory Information

Initial ChromosomesWSGA Parameters

Objectives and weights

System Chromosome

}max{}max{

UUU

CHCHCH

USDUSD

Decision Maker

CE

-user interface

User DomainUser preference

Local service facility

Policy DomainUser preference

Local service facility

SecurityUser data security

System/Network security

X86/UnixTerminal

Radio-domain cognitionRadio

Resource Monitor

Performance API Hardware/platform API

Radio

Radio Performance

Monitor

WMS

CE-Radio Interface

Search SpaceConfig

ChannelIdentifier

WaveformRecognizer

ObservationOrientation

Action

Example Architecture from CWT

Decision

Learning

Models


DFS in 802.16h Drafts of 802.16h

defined a generic DFS algorithm which implements observation, decision, action, and learning processes

Very simple implementation

Modified from Figure h1 IEEE 802.16h-06/010 Draft IEEE Standard for Local and metropolitan area networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Amendment for Improved Coexistence Mechanisms for License-Exempt Operation, 2006-03-29

Channel AvailabilityCheck on next channel

Available?

Choose Different Channel

Log of Channel Availability

Stop Transmission

Detection?

Select and change to new available channel in a defined time with a max. transmission time

In service monitoring of operating channel

Channel unavailable for Channel Exclusion time

Available?

Background In service monitoring (on non-

operational channels)

Service in function

No

No

No

Yes

Yes

Start Channel Exclusion timer

Yes

Learning

Observation

Decision, Action

Decision, Action

Observation


Explicitly opened up Japanese spectrum for 5 GHz operation

Part of larger effort to force equipment to operate based on geographic region, i.e., the local policy

Lower Upper

U.S. 2.402 2.48Europe 2.402 2.48Japan 2.473 2.495Spain 2.447 2.473France 2.448 2.482

2.4 GHz

USUNII Low 5.15 – 5.25 (4) 50 mWUNII Middle 5.25 – 5.35 (4) 250 mWUNII Upper 5.725-5.825 (4) 1 W5.47 – 5.725 GHz released in Nov 2003

Europe5.15-5.35 200 mW5.47-5.725 1 W

Japan4.9-5.0915.15-5.25 (10 mW/MHz) unlicensed

5 GHz

802.11j – Policy Based Radio


Enhances QoS for Voice over Wireless IP (aka Voice over WiFi ) and streaming multimedia

Anticipated changes– Enhanced Distributed Coordination Function (EDCF)

Shorter random backoffs for higher priority traffic– Hybrid coordination function (orientation)

Defines traffic classes In contention free periods, access point controls medium

access (observation) Stations report to access info on queue size. (Distributed

sensing)

802.11e – Almost Cognitive


Dynamic Frequency Selection (DFS)– Avoid radars

Listens and discontinues use of a channel if a radar is present

– Uniform channel utilization Transmit Power Control (TPC)

– Interference reduction– Range control– Power consumption Savings– Bounded by local regulatory

conditions

802.11h – Unintentionally Cognitive


Observe– Must estimate channel characteristics (TPC)– Must measure spectrum (DFS)

Orientationa) Radar present? b) In band with satellite??c) Bad channel?d) Other WLANs?

Decision – Change frequency– Change power– Nothing

Action Implement decision

Learn– Not in standard, but most implementations should learn the environment to

address intermittent signals

Outside World

Observe

OrientDecide

Act

Learn

802.11h: A simple cognitive radio


802.16h Improved Coexistence

Mechanisms for License-Exempt Operation

Basically, a cognitive radio standard

Incorporates many of the hot topics in cognitive radio

– Token based negotiation– Interference avoidance– Network collaboration– RRM databases

Coexistence with non 802.16h systems

– Regular quiet times for other systems to transmit

From: M. Goldhamer, “Main concepts of IEEE P802.16h / D1,” Document Number: IEEE C802.16h-06/121r1, November 13-16, 2006.


General Cognitive Radio Policies in 802.16h

Must detect and avoid radar and other higher priority systems

All BS synchronized to a GPS clock All BS maintain a radio environment map (not their

name) BS form an interference community to resolve

interference differences All BS attempt to find unoccupied channels first

before negotiating for free spectrum– Separation in frequency, then separation in time


802.11h (“Weak” CR on hardware radios – defined shortly)

Idea: Upgrade control processes to permit use bands 802.11a devices to operate as secondary users to radar and satellites

Dynamic Frequency Selection (DFS)– Avoid radars

Listens and discontinues use of a channel if a radar is present

– Uniform channel utilization Transmit Power Control (TPC)

– Interference reduction– Range control– Power consumption Savings– Bounded by local regulatory

conditions


IEEE 802.22 – Planned Cognition Wireless Regional Area Networks

(WRAN)– Aimed at bringing broadband access in

rural and remote areas– Takes advantage of better propagation

characteristics at VHF and low-UHF– Takes advantage of unused TV

channels that exist in these sparsely populated areas (Opportunistic spectrum usage)

802.22 specifications– TDD OFDMA PHY– DFS, sectorization, TPC– Policies and procedures for operation in

the VHF/UHF TV Bands between 54 MHz and 862 MHz

– Target spectral efficiency: 3 bps/Hz– Point-to-multipoint system– 100 km coverage radius

Devices– Base Station (BS)– Customer Premise Equipment

(CPE) Master/Slave relation

– BS is master– CPE slave

Max Transmit CPE 4W


802.22: Cognitive Aspects Observation

– Aided by distributed sensing (subscriber units return data to base)– Digital TV: -116 dBm over a 6 MHz channel– Analog TV: -94 dBm at the peak of the NTSC (National Television

System Committee) picture carrier– Wireless microphone: -107 dBm in a 200 kHz bandwidth.– Possibly aided by spectrum usage tables

Orientation– Infer type of signals that are present

Decision– Frequencies, modulations, power levels, antenna choice (omni and

directional) Policies

– 4 W Effective Isotropic Radiated Power (EIRP)– Spectral masks, channel vacation times


The Interaction Problem

Outside world is determined by the interaction of numerous cognitive radios

Adaptations spawn adaptations

OutsideWorld


Dynamic Spectrum Access Pitfall Suppose

– g31>g21; g12>g32 ;

g23>g13

Without loss of generality

– g31, g12, g23 = 1– g21, g32, g13 = 0.5

Infinite Loop!– 4,5,1,3,2,6,4,…

Chan. (0,0,0) (0,0,1) (0,1,0) (0,1,1) (1,0,0) (1,0,1) (1,1,0) (1,1,1)

Interf. (1.5,1.5,1.5) (0.5,1,0) (1,0,0.5) (0,0.5,1) (0,0.5,1) (1,0,0.5) (0.5,1,0) (1.5,1.5,1.5)

Interference Characterization

0 1 2 3 4 5 6 7

1

2

3


Implications In one out every four deployments, the

example system will enter into an infinite loop

As network scales, probability of entering an infinite loop goes to 1:– 2 channels– k channels

Even for apparently simple algorithms, ensuring convergence and stability will be nontrivial

31 3/ 4 nCp loop 111 1 2 n kCkp loop


Locally optimal decisions that lead to globally undesirable networks

Scenario: Distributed SINR maximizing power control in a single cluster

For each link, it is desirable to increase transmit power in response to increased interference

Steady state of network is all nodes transmitting at maximum power

Power

SINR

Insufficient to consider only a single link, must consider interaction


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 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


1. Steady state characterization

2. Steady state optimality3. Convergence4. Stability/Noise5. Scalability

a1

a2

NE1

NE2

NE3

a1

a2

NE1

NE2

NE3

a1

a2

NE1

NE2

NE3

a1

a2

NE1

NE2

NE3

a3

Steady State Characterization Is it possible to predict behavior in the system? How many different outcomes are possible?

Optimality Are these outcomes desirable? Do these outcomes maximize the system target parameters?

Convergence How do initial conditions impact the system steady state? What processes will lead to steady state conditions? How long does it take to reach the steady state?

Stability/Noise How do system variations/noise impact the system? Do the steady states change with small variations/noise? Is convergence affected by system variations/noise?

Scalability As the number of devices increases, How is the system impacted? Do previously optimal steady states remain optimal?

Network Analysis Objectives

(Radio 1’s available actions)

(Rad

io 2

’s a

vaila

ble

actio

ns)

focu s


Modeling


General Comments on Analyzing Cognition Cycle

Level0 SDR1 Goal Driven2 Context Aware3 Radio Aware4 Planning5 Negotiating6 Learns Environment7 Adapts Plans8 Adapts Protocols

0. No - not a CR1. OK2. OK3. OK4. Probably5. Ok6. Ok (might even simplify)7. No – unconstrained

problem8. No – unconstrained

problem

Focus of this work


Why focus on OODA loop, i.e., why exclude other levels?

OODA loop is implemented now (possibly just ODA loop as little work on context awareness)

Changing plans – Over short intervals plans

don’t change – Messy in the general case

(work could easily accommodate better response equivalent goals)

Negotiating – Could be analyzed, but

protocols fuzzy– General case left for future

work

Learning environment– Implies improving

observations/orientation. Over short intervals can be assumed away

– Left for future work Creation of new actions,

new goals, new decision rules makes analysis impossible

– Akin to solving a system of unknown functions of unknown variables

– Most of this learning is supposed to occur during “sleep” modes

Won’t be observed during operation


General Model (Focus on OODA Loop Interactions)

Cognitive Radios Set N Particular radios, i, j

OutsideWorld



Actions Different radios may

have different capabilities

May be constrained by policy

Should specify each radio’s available actions to account for variations

Actions for radio i – Ai

Act



Decision Rules Maps observations

to actions– ui:OAi

Intelligence implies that these actions further the radio’s goal

– ui:O The many different

ways of doing this merit further discussion

Decide

Implies very simple, deterministic function,

e.g., standard interference function


Modeling Interactions (1/3)

Radio 2Actions

Radio 1Actions

Action Space

u2u1

Decision Rules

Decision Rules

Outcome Space

:f A OInformed by Communications Theory

1 2ˆ ˆ, 1 1̂u 2 2ˆu


Modeling Interactions (2/3) Radios implement actions, but observe outcomes. Sometimes the mapping between outcomes and

actions is one-to-one implying f is invertible. In this case, we can express goals and decision

rules as functions of action space.– Simplifies analysis

One-to-one assumption invalid in presence of noise.


Modeling Interactions (3/3) When decisions are made

also matters and different radios will likely make decisions at different time

Tj – when radio j makes its adaptations

– Generally assumed to be an infinite set

– Assumed to occur at discrete time

Consistent with DSP implementation

T=T1T2Tn

t T

Decision timing classes Synchronous

– All at once Round-robin

– One at a time in order– Used in a lot of analysis

Random– One at a time in no order

Asynchronous– Random subset at a time– Least overhead for a

network


Cognitive Radio Network Modeling Summary

Radios Actions for each radio Observed Outcome

Space Goals Decision Rules Timing

i,j N, |N| = n A=A1A2An

O

uj:O (uj:A) dj:OAi (dj:A Ai) T=T1T2Tn


DFS Example

Two radios Two common channels

– Implies 4 element action space Both try to maximize Signal-to-Interference

Ratio Alternate adaptations


Questions?

james neel [email protected] april 25-26, 2007

Documents