Cognitive Radio Networks.ppt - UNSW School of Computer ...qianzh/comp562-2011/notes/CRN.pdf · • NTT DoCoMO, Motorola, Nokia ... yA radio can change transmit parameter based on

Wireless RevolutionWireless Revolution

RFID ApplicationsRFID Applications

V iV i

InternetInternet

Wireless EverywhereWireless EverywhereVoiceVoice EmailEmailVariousVarious

Info/MediaInfo/Media New MobileNew Mobile

Wireless EverywhereWireless EverywhereMoving Towards Moving Towards TrillionsTrillions

SMSSMSDevicesDevicesof Wireless Devicesof Wireless Devices

19971997 20072007 FutureFuture

VideoVideoMobile TVMobile TV Sensor Sensor

ApplicationsApplications997997 77 utu eutu e

0.20 Billion Mobile devices

1.8 Billion Mobile devices

Trillion’sWireless devices

Evolvement of DevicesEvolvement of DevicesSingle Radio Multi‐Radio (multi‐card) Cognitive Radio Single Radio Multi Radio (multi card) Cognitive Radio (single chip)

• Wireless Combo 11a/b/g card widely being used • Multi-radio smartphones available at the end of year 2004Multi radio smartphones available at the end of year 2004

• NTT DoCoMO, Motorola, Nokia

• Intel’s Cognitive Radio Chip• All-in-one chipset 802 11b/g and 802 11a• All-in-one chipset 802.11b/g and 802.11a• Single radio chip for: Wifi, Wimax, bluetooth, UWB …

• “XG1TM” by adapt4, inc

One Radio / One Network Multiple Radios / Networks One Single Cognitive Radio

Limited SupplyLimited Supply

Growing DemandGrowing Demand

ChallengesChallenges

h ll b ll l dBy 2020, there will be trillions wireless devicesChallenge for spectrum efficiencyChallenge for network architecture

Future wireless networks need ~100‐1000x increases in density and bit‐rate of radios

Motivates better spectrum coordination methodsMotivates better spectrum coordination methods

Spot shortages of spectrum will occur if present static allocation is continuedallocation is continued

Spectrum Spectrum EverywhereSpectrum, Spectrum, Everywhere, but . . .but . . .

Fixed allocationLittle sharingRigid requirement on how to use

Predicted Spectrum Requirements byPredicted Spectrum Requirements by 2020 on Regional Basis

Region 1 Region 2 Region 3

User Demand

Predicted Total

Already Identified

Net Addition

Already Identified

Net Addition

Already Identified

Net Addition

Setting (MHz) (MHz) (MHz) (MHz) (MHz) (MHz) (MHz)

LOW 1280 693 587 723 557 749 531

HIGH 1720 693 1027 723 997 749 971HIGH 1720 693 1027 723 997 749 971

-- Mobile Industry backing (mib) Terrestrial Spectrum for IMT

S t O t iti i S TiSpectrum Opportunities in Space, Time, & Frequency& Frequency

Spectrum Under UtilizationSpectrum Under‐Utilization

Most spectrum is unused actuallyMost spectrum is unused actually

11

i SDynamic Spectrum AccessS Spectrum users

Primary (PRI) user (with license) uses spectrum when desiredSecondary (SEC) user opportunistically uses remaining spectrumy pp y g p

Goals:maximize spectrum utilization while minimize interference

R d l ( b d l h l )Road analogue (spectrum bands lanes, spectrum users vehicles)

Real RoadSpectrum Road

Open SpectrumPRI SpectrumPRI

SEC

C i i diCognitive RadioC iti di i th k bli t h l f D i Cognitive radio is the key enabling technology for Dynamic Spectrum Access!!C bilit t h th t i t i ti Capability to use or share the spectrum in an opportunistic manner

Band idth har estingBandwidth harvestingDynamic spectrum access techniques allow the CR to operate in the best available channeloperate in the best available channelCR enables the usage of temporally unused spectrum

h l hSpectrum hole or white space

C iti R diCognitive RadioCognitive Radio (CR) Definition

A radio can change transmit parameter based on h hinteraction with the environment

Cognitive CycleSensing, analysis, decision

Particular requirementsSpectrum sensing

Fast and quite sensitive

Frequency agilityCentral frequency, bandwidth

Spectrum Management FrameworkSpectrum Management FrameworkDetermine which portions of the spectrum is available and detect the Select the best available spectrum is available and detect the presence of licensed users when a user operates in a licensed band

channelCoordinate access to this channel with other users

Spectrum DecisionSpectrum

Sh i

Channel CapacityVacate the channel

when a licensed user is detected

SharingSpectrum

holeDecisionRequest

Spectrum Sensing

Spectrum Mobility Primary user

detectionSpectrum

detection (Channel)Characterization

TransmittedSignal

Radio Environment

What is the spectrum usage in China?

Is there any correlation among the spectrum usage?Is there any correlation among the spectrum usage?

Whether we can leverage the spectrum correlation for spectrum usage prediction?

M C d dMeasurement Conducted4 measurement sites

2 in downtown area and 2 in suburban area2 in suburban area

Measurement period From 15:00 Feb 16thFrom 15:00 Feb 16th 2009 to 15:00 Feb 23th 2009

F b dFrequency band Between 20MHz and 3GHz

D. Chen, S. Yin, Q. Zhang, M. Liu, and S. Li, “Mining Spectrum Usage Data: a Large scale“Mining Spectrum Usage Data: a Large-scale Spectrum Measurement Study”, in ACM Mobicom, 2009.

M E iMeasurement EquipmentSpectrum analyzer type: R&S EM550

Scanning frequency range: 20MHz~3.6GHz

Measurement resolution: one per 0.2MHz

14,900 frequency readings per time slot (roughly 75 seconds)14,900 frequency readings per time slot (roughly 75 seconds)

8,058 (7 days/ 75s) time slots

14 900×8 058 data points in the data set (roughly 2GB in size) per 14,900×8,058 data points in the data set (roughly 2GB in size) per location

Spectrum Allocation of PopularSpectrum Allocation of Popular ServicesServices

D t St ti ti A l iData Statistic AnalysisData OverviewData Overview

Energy level overview for location 3 over 7 days

Data Statistic AnalysisConvert power level to 0/1 state

Data Statistic Analysis

Channel state information (CSI)CSI(t, c) = 0 denotes that Channel c is idle at time slot t

( )CSI(t, c) = 1 otherwise

Convert energy level to CSI dataThreshold is set to be 3dB higherThreshold is set to be 3dB higher

than the minimum value seen in

this channel over the entire duration of the trace collected

D t St ti ti A l iData Statistic AnalysisChannel Vacancy Duration (CVD)Channel Vacancy Duration (CVD)

Period in which a channel remains idle

Data Statistic AnalysisData Statistic AnalysisCVD distribution

CVD follows an exponential‐like distribution in all channels we studied, but are not independently distributed

cxb −cxbeay +=

Data Statistic AnalysisData Statistic AnalysisTwo messages delivered by CVD distribution:Two messages delivered by CVD distribution:1. CVD distribution can be nearly exactly estimated

2 Ch l t t t b d l d 1 t d M k Ch i (1 t2. Channel state cannot be modeled as 1st‐order Markov Chain (1st‐MC) model

If the channel state can be modeled as 1st‐MC, it should be:1. b=c. However, b=1.67, c=1.07 )( cxbeay −+=

2.

However

,...],,|Pr[]|Pr[ 2111 −−++ = tttttt xxxxxx

9189530]0|0P [However 918953.0]0|0Pr[ 1 ===+ tt xx55454.0]1,0|0Pr[ 11 ==== −+ ttt xxx

24

Data Statistic AnalysisData Statistic AnalysisTemporal / Spectral / Spatial CorrelationTemporal / Spectral / Spatial CorrelationIntuitively speaking, correlation coefficient (from ‐1 to 1) indicates the likeness of two series (curves)e ess o o se es (cu es)

25

Data Statistic AnalysisData Statistic Analysis• Channel state CS(t, c) is a microscopic level measure of

the channel utilization• Service congestion rate (SCR)

• Examine it across channels within the same service for a given • Examine it across channels within the same service for a given time slot

Data Statistic AnalysisIntra service spectral correlation

Data Statistic AnalysisIntra‐service spectral correlation

CSI: Channel State Infomation. The value is 0 or 1.

High spectral correlation is found between the CSI series of the channels within the same service.

27

Diverse Ideas, Confusing TermsDynamic spectrum accessDynamic spectrum allocationSpectrum property rightsSpectrum commonspOpportunistic spectrum accessSpectrum poolingSpectrum poolingSpectrum underlayS t lSpectrum overlayCognitive radio……

A Taxonomy of DSAA Taxonomy of DSA

Three DSA ModelsThree DSA Models

Exclusive Use ModelExclusive Use Model

Maintains the basic structure: license for exclusive use

Introduces flexibility in allocation and spectrum usage

Exclusive Use ModelExclusive Use Model

Maintains the basic structure: license for exclusive use

Introduces flexibility in allocation and spectrum usage

Spectr m Propert RightsSpectrum Property Rights

Allows selling and trading spectrum and freely choosing technology

Let economy & market determine the most profitable use of spectrum

Dynamic Spectrum AllocationDynamic Spectrum Allocation

Dynamic spectrum assignment to different services

Exploiting spatial and temporal traffic statistics

Open Sharing ModelOpen Sharing Model

Open sharing among peer users (spectrum commons)

Draws support from the success of unlicensed ISM bands

Hierarchical Access ModelHierarchical Access Model

Hierarchical access with primary and secondary usersHierarchical access with primary and secondary users

sharing with limited interference to primary users (licensees)

Hierarchical Access ModelHierarchical Access Model

Spectrum underlay: constraint on transmission power

Spectrum overlay: constraint on when and where to transmit

Underla s O erlaUnderlay vs. Overlay

Dynamic Spectrum Access (DSA)Dynamic Spectrum Access (DSA)Concept: secondary (SEC) users with cognitive radio (CR) opportunistically use spectrum of primary (PRI) usersoppo tu st ca y use spect u o p a y ( R ) use s

Public Network TV Broadcast (PRI)

InfrastructureInfrastructure(SEC)

Ad hocVideo

Ad hoc (SEC)

File sharingg

39Public Safety (PRI)

Research Challenges (1)Research Challenges (1)Spectrum rights: PRI’s incentive to share spectrum for SECPublic Network TV Broadcast (PRI)


Ad hocAd hoc (SEC)


Research Challenges (2)Research Challenges (2)Spectrum sensing: discovery of spectrum opportunity



Ad hocAd hoc (SEC)


Research Challenges (3)Research Challenges (3)Spectrum sharing: opt. SEC’s performance w/o harmful interference for PRIR



Ad hocAd hoc (SEC)


i ddQuestions to Address

Economics (Policy and Marketing) Aspect

T h l P t t dTechnology Aspect

Prototype and Experiments

E isting Primar Spectr m MarketExisting Primary Spectrum MarketFirst come first served Lottery Beauty contestFirst come first served, Lottery, Beauty contestAuction

Cases:$100 billion from European auctions in 2000$19.592 billion from US 700 MHz auction in 2008

Pros:Pros:Maximize social welfareTransparent

Cons:long‐term: years or decades

i l/ i lnational/regionalhuge up‐front investments

Secondary Spectrum MarketSecondary Spectrum Market Incentive of sellers Incentive of sellers (primary users)

Non governmentNon governmentRevenue generation

Fine spectrum usageFine spectrum usageSmall regionShort term

To propose an auction to h lmeet the goals?

J. Jia, Q. Zhang, Q. Zhang, and M. Liu, "Revenue Generation for Truthful Spectrum Auction in Dynamic Spectrum Access", in ACM MobiHoc, 2009.

Secondary Spectrum AuctionSecondary Spectrum Auction Spectrum resourcep

Space: interferenceFrequency: channels CCA

C

1 2 3

Time: slotted

Bidding language

AACC

BBA

C

Bg g g

Total Channels in All CellsSpecific cells and

di h lcorresponding channels, etc.

Desired propertiesRevenue: primary’s incentiveTruthfulness: prevent market manipulation & ease biddersC i l ffi i l i iComputational efficiency: real‐time operation

Wireless Service Providers (WSPs) in ( )Dynamic Spectrum Market

Dynamic spectrum marketShort‐term, real‐time

WSPs play an important role in wireless industryAcquire radio spectrumD l i f d id iDeploy infrastructure and provide service

Tight relation between spectrum acquisition and service pricingpricing

Short term spectrum contracts due to time‐varying spectrum availabilityConsider both spectrum acquisition and service provision

Severe competition among heterogeneous WSPsEnd users with intelligent devices have more selectionsShort‐term or traffic based contracts

Market StructureMarket StructureThree‐layer modelThree layer model

Spectrum Holder (SH)Primary users

Wi l S i P id (WSP)Wireless Service Providers (WSP)Competing Duopoly

End Users (EU) Freely switch

Relations between layersSpectrum trading (#of spectrum to purchase)to purchase)Service subscription (service price)

48J. Jia and Q. Zhang, “Competitions and Dynamics of Duopoly Wireless Service Providers in Dynamic Spectrum Market", in ACM MobiHoc, 2008.

Cooperation among WSPsCooperation among WSPs

An interesting observationSpectrum occupancy measurement (for CDMA, GSM900 and p p y 9GSM1800, which are occupied by China Mobile and China Unicom)

shown that the collaboration coefficient is high

Unbalanced spectrum usage of these two WSPs

If the two WSPs can cooperate by spectrum exchangeIf the two WSPs can cooperate by spectrum exchangeThey may not need to purchase more spectrum while

idi hi h li f iproviding higher quality of services

Both of them will be benefited by the cooperation

Sharing among WSPsSharing among WSPsCooperation among WSP is carried out by their base Cooperation among WSP is carried out by their base stationsThe particular WSP’s BSs are still fully controlled by p y ythemselves and serving their own end users

The spectrum resource can be shared by other WSP’s BSs, in terms f h i iof the time portion

The BSs serving large user demands and short of spectrum resource can reuse the underresource can reuse the under‐utilized spectrum of other WSPs

P. Lin, J. Jia, Q. Zhang, and M. Hamdi, "Cooperation l damong Wireless Service Providers: Opportunity,

Challenge and Solution", IEEE Wireless Communications Magazine, 2010.





S t M t F kSpectrum Management Framework

Spectrum S

Channel Capacity

DecisionSpectrum Sharing

Spectrum Decision

Spectrum S

HoleDecisionRequest

pSensingSpectrum

Mobility Primary User Detection

Spectrum (Channel)

Characterization

Radio Environment

TransmittedSignal

Radio Environment

Cogniti e MACCognitive MACC i i l l f f f i i l Critical layer for performance of cognitive protocol stack

f dNew situations for cognitive MAC designProtection of primary user

Sense spectrum to detect

Advanced hardware ffeature

Spectrum aggregation

H d iHardware constraints…

Hardware Constraints of CognitiveHardware Constraints of Cognitive Radio

Few off‐the‐shelf product“XG1TM” by adapt4 incXG1TM by adapt4, inc

Sensing constraintFine sensing within a small portion of spectrumFine sensing within a small portion of spectrum

Transmission constraintMax bandwidth aggregation limitMax bandwidth aggregation limitMax fragmentation number limit

Assumptions in this workAssumptions in this workSingle cognitive radioCan not sense and transmit simultaneouslyy

S t S i d A i T d ffSpectrum Sensing and Accessing Tradeoffsecondary sense t: sensing timeprimary occupied

secondary transmit

gT: maximum transmission timeB: unit bandwidth

BT/(T+2t) Chn3 Avail. Chn3 N.A.

2BT/(T+3t) BT/(T+3t)

55

2BT/(T+3t) BT/(T+3t)

Spectrum Sensing and Accessing Tradeoff

More channels explored

Instant data rate increased (in no primary users)Instant data rate increased (in no primary users)Shannon theory

Ti h d i dTime overhead increasedSensing time to check primary

Negotiation between sender and receiver

How to sense and access in MACBalance spectrum opportunity and sensing overhead

Different from the physical layer sensingDifferent from the physical layer sensing

T f hi W kTargets of this Work

SEC’s dilemma: whether to sense the next channel, or just stop and using current available channels?

More channels sensed, possible more available channels, but more overheadOptimal stopping: Classical Secretary ProblemOptimal stopping: Classical Secretary Problem

Formulate the tradeoff of sensing and accessing and solve the optimal sensing problemthe optimal sensing problemDesign a Cognitive MAC to facilitate optimal sensing solutionsolutionUse simulation to verify its performance

Preliminar of Optimal StoppingPreliminary of Optimal StoppingThe theory of optimal stopping:The theory of optimal stopping:

Choose a time to take an action based on sequentially observed random variables to maximize an expected payoff or to minimize an expected cost

Two objects:A sequence of random variables, X1; X2; : : :, whose joint distribution is assumed to be known, and

A sequence of real‐valued reward functions, y0; y1(x1); y2(x1; x2); : : :

For n = 1; 2; : : :, after observing X1 = x1; : : : ; Xn = xn, the decision may be to stop and receive reward yn(x1; : : : ; xn), or continue and observe Xn+1

Goal: choose a time to stop such that the Goal: choose a time to stop such that the

expected reward is maximized

58

O ti l St i f S t S iOptimal Stopping of Spectrum SensingTh t i d i i bl b f l t d The spectrum sensing decision problem can be formulated as an optimal stopping problem

We consider a finite horizon problemMax number of channels a user can probe is limited

Solvable by backward induction principle (has exponential complexity)

Reduce the computational complexity k stage look ahead rules to approximate the optimal stopping rulek‐stage look‐ahead rules to approximate the optimal stopping rule

1‐stage or 2‐stage look‐ahead is almost optimal

HC MACHC‐MACHC MAC H d t i d M lti Ch l C iti HC‐MAC: Hardware‐constrained Multi‐Channel Cognitive MAC

B i tiBasic assumptionsA common control channel is usedHardware constraints of secondary usersHardware constraints of secondary usersPrimary protection time (T)

3 types of control packets on common channel3 types of control packets on common channelC‐RTS/C‐CTSS‐RTS/S‐CTSS RTS/S CTST‐RTS/T‐CTS

HC MACHC‐MAC

ContentionUse C‐RTS/C‐CTS for contention and spectrum contention and spectrum reservation DCF model scheme

SensingUse physical layer sensingExchange spectrum information with S‐RTS/S‐

TransmissionSpectrum aggregationN tif i hb it information with S RTS/S

CTSUse optimal stopping rule

Notify neighbors its completion with T‐RTS/T‐CTS

61

Sim lation Res ltsSimulation ResultsDiff t l ti Different evaluation cases

Fully‐connected, spectrum‐homogeneous topologyhomogeneous topologyFully‐connected, spectrum‐heterogonous topologyheterogonous topologyRandom topology

62

Simulation Results (Cont )Simulation Results (Cont.)Case 1: The throughput with Case 1: The throughput with different total numbers of channel

Case 2: Individual throughput for 2 secondary pairs

• Case 3: A random topology with 15 secondary flowsy

63

Brief Summary for HC MACBrief Summary for HC‐MACEffi i t iti MAC t l th t tili lti l t Efficient cognitive MAC protocol that utilizes multiple spectrum opportunities (channels) to improve spectrum utilization

Consider hardware constraints of cognitive radio: sensing constraints and transmission constraints

Identify the problem for secondary user on how to maximize throughput by optimizing the sensing decision in a sequence of

i sensing processesCan be mapped to as a well‐defined optimal stopping problemB th ti l l ti d i ti l i bt i dBoth optimal solution and approximation rule is obtained

C ti R l i CRNCooperative Relay in CRNResource unbalance is severe in cognitive radio network Resource unbalance is severe in cognitive radio network (CRN)

Spectrum heterogeneityp g y

How to fulfill the heterogeneous traffic demand from secondary users? y

By fully leveraging the unbalanced spectrum usage and availability within the secondary CRN

J. Jia, J. Zhang, and Q. Zhang, "Cooperative g gRelay for Cognitive Radio Networks", in IEEE Infocom 2009.

I S Di iImprove Spectrum DiversityAssumptions Assumptions

Single radio (both AP and nodes)OFDMA100 Kbps per channel

[1] [3]

AP

[1] [3]

AP

rate demand

Traditional:

S1: 100 KbpsS2: 50 Kbps

S2S1

[ ] [ ]

S2S1

[ ] [ ]rate demand S2: 50 Kbps

AP [1,3,4]AP [1,3,4]

APAP APAPRelay:

S1: 150 Kbps

150Kbps 50Kbps150Kbps 50Kbps150Kbps150Kbps

S2S1

[1] [3,4]

S2S1

[1] [3,4]

[2] S2S1

[1]

[2] S2S1

[1]

il bl h l

5 pS2: 50 KbpsS2S1 [1,2] [2,3,4]S2S1 [1,2] [2,3,4]

[ ][ ]available channelsSlot 1 Slot 2

Key Issues to be DiscussedHow to enable three‐node multiple‐channel (probably discontinuous) transmission?

The receiver receives one flow of data from the source directlyRelay node decodes another flow of data on another channel and shift it to a third channel to forward it to the receivershift it to a third channel to forward it to the receiver

Cooperative relay brings new issues of resource allocationAmong secondary users, how to select proper node as relay node Among secondary users, how to select proper node as relay node and also how to allocate proper spectrum for secondary users

New routing metric that can exploit the performance gain bring by cooperative relay

Relay‐Assisted D‐OFDMSenderSender

Simultaneous TX from a single radio

R l d iRelay and receiverDecode packet correctly using part of the whole using part of the whole subcarriers

Alleviate the interference S:

CH1 CH2 CH3

fP2 (T1)

S:

CH1 CH2 CH3

fP2 (T1)Alleviate the interference

from other simultaneous transmitting channels to h h h h

R: f

f

P1(T1) P3(T2)P2 (T1)

P2(T2)

R: f

f

P1(T1) P3(T2)P2 (T1)

P2(T2)

achieve a higher SNR on the specific channel

D: f

fu1 fv1 fu2 fv2 fu3 fv3

D: f

fu1 fv1 fu2 fv2 fu3 fv3

A hit t f I f t t b dArchitecture for Infrastructure‐based CRNCRN

Upper Layer Upper Layer

AP End User

Upper Layer Upper Layer

AP End User

MACCoordination

Spectrum Allocation

Reconfigurable Radio

End User MAC


MACCoordination

Spectrum Allocation


End User MAC


Programmable Hardware (USRP)

Relay-assisted DOFDMReconfigurable Radio Reconfigurable Radio

Programmable Hardware(USRP)

Relay-assisted DOFDM

Programmable Hardware (USRP)

Relay-assisted DOFDMReconfigurable Radio Reconfigurable Radio

Programmable Hardware(USRP)

Relay-assisted DOFDM

(USRP)

Spectrum Channel

(USRP)(USRP)

Spectrum Channel

(USRP)

Joint Relay Selection and Channel AllocationJointly design Jointly design

relay selection spectrum allocation

Two types of transmissionsypTraditional transmission between AP and node viAdvanced transmission among AP, node vi and relay vjj

Constraints for relay relationshipone node can have only one relay; one relay can serve only one node; when one node is served by a relay, this node can not be the relay node of another noderelay node of another node.

⑴

rij = 1 means that vj performs as relay node for vi

J i t R l S l ti d Ch l All tiJoint Relay Selection and Channel Allocation (Cont.)( )

Constraint related to interference avoidanceA channel can be used by only one active transmission A channel can be used by only one active transmission link in one frameActive link can be either from AP or from relay

⑵

xik = 1 if channel k is assigned to node vi to transmit or

⑵

ik g ireceive data

Channel availability constraint yChannel allocation should also satisfy the channel availability constraint in each user

⑶

Joint Relay Selection and Channel AllocationJoint Relay Selection and Channel Allocation (Cont.)

Calculate throughput of node vi according to whether it is a relay or is helped by a relay

Normal receiver without any relay’s help

Receives data from the AP with the help of a relay vj

Acts as a relay for node vj

Single unified format for throughput calculation

J i t R l S l ti d Ch l All tiJoint Relay Selection and Channel Allocation (Cont.)( )

System optimizationNonlinear integer programming problemg p g g p

Propose a heuristic centralized solutionFirst partition nodes into two parts according to their traffic and spectrum availability Greedily select the best pair of destination and relay from Greedily select the best pair of destination and relay from the two parts to increase the system throughput

T b d I l iTestbed ImplementationGNU Radio + USRPGNU Radio + USRP

GNU RadioSignal processing libraryCommunicating with the USRPCommunicating with the USRP

Universal Software Radio Peripheral (USRP)Front‐end functionality A/D and D/A conversion

T tb d F kTestbed FrameworkUSRP + GNU Radio

Underlying MAC L

Upper Layer

MAC LayerOwn designed protocol

Network Layer Network Layer ent

men

t

Network LayerClick

Cross Layer Management

Ne wo aye

MAC Layer

er M

anag

eme

rum

Man

agem

Cross Layer ManagementControl path

Radio Spectrum Management Cro

ss-L

aye

Rad

io S

pect

r

GNURadioRadio Spectrum Management

Spectrum policy and market rules USRP

E i C fi iExperiment ConfigurationW i l i USRP d GNU We implement a system using USRP and GNU Radio

A d d l kAP mode, downlinkDAC rate (128 samples/ms), interpolation rate (256, 2 samples/symbol 1bit/symbol)samples/symbol, 1bit/symbol)FFT length (512 subcarriers), channel bandwidth (100 KHz = 100 contiguous subcarriers supporting about 60 KHz = 100 contiguous subcarriers, supporting about 60 Kbps data rate)Packet size (200 bytes)( y )Duration of each run (1000 packets)

Experiment ResultsRelay selection for multiple transmissionsRelay selection for multiple transmissions

AP 1on

2 11 20.40.60.8

1

ativ

e Fr

actio

Total Gain over TA

D2R1D1 1 R2 1

21.3 1.32 1.34 1.36 1.38 1.40

0.2

Throughput GainC

umul

a

2

AP0 8

1

actio

n

Node D1 Gain over TA

1 11 1 1

0.20.40.60.8

mul

ativ

e Fr

a

Node D2 Gain over TA

D2R1D1 R21 1 1.3 1.35 1.4 1.45 1.50

Throughput Gain

Cum

P bl St t tProblem StatementPropose a cooperative cognitive radio framework

Primary users, aware of the existence of secondary users, may select f th t b th ti lsome of them to be the cooperative relay

In return lease portion of the channel access time to them for their own data transmissiono data t a s ss o

Both primary and secondary users target at maximizing their utilities in terms of their transmission rate and their utilities in terms of their transmission rate and revenue/payment

J. Zhang and Q. Zhang, "Stackelberg Game for Utility-Based Cooperative Cognitive Radio Networks", in ACM MobiHoc, 2009.

Win‐Win CaseBenefit: win‐win

PU: Higher transmission rate , lower delaySU: More time left for secondary accessSU: Increased access opportunity and satisfied trafficP d PU: Increased revenue

Primary User Secondary User

Transmission Rate Access Opportunity

Primary User Secondary User

Transmitted TrafficRevenue

Ch llChallengesI i h iIncentive mechanism

Two independent networks trying to maximize their own utilityFor SUFor SU

What’s the direct benefit of serving as a relay? Access time What’s the overall utility it wants to maximize? Traffic ‐ Revenue

F PUFor PUWhat’s the direct benefit of selecting a relay? RevenueWhat’s the overall utility it wants to maximize? Traffic + Revenue

Optimal decisionHow to select relay nodes?How much money should SU pay for PU?How much money should PU charge the successful relays?Stackelberg game Nash EquilibriumStackelberg game Nash Equilibrium

CCRN M d lCCRN ModelOne primary transmission pair PT PR with required rate ROne primary transmission pair PT PR, with required rate R0

Several secondary transmission pairs STi SRi, totalSi∈

In a unit slotIn a unit slot,PT STi In first fractionPT PR, STi PR in next fraction

αβ)1( βα −

STi SRi in the last fraction in TDMA mode in proportional to the payment

α−1

ST2ST4

SR2

Fixed transmission powerPrimary user selects i i i b fi

),,( Sβα

ST4

SRPR

to maximize its benefitSecondary users select the payment to maximize its utility

PTSR1

ST1 SR4

payment to maximize its utilityST3

SR3

S k lb G A l iStackelberg Game AnalysislGame player:

Leader: primary userll dFollower: secondary user

Utility function:Primary user: weighted sum of rate and revenue

Secondary user: transmission rate penalized by the paymentSecondary user: transmission rate penalized by the payment

Stackelberg Game AnalysisUse backward induction to analyze the Stackelberg game

Secondary users form non‐cooperative payment selection game (NPG)

select payment under given parameters Primary user maximize its utility function by selecting Primary user maximize its utility function by selecting , assuming that secondary users act according to the NE point in NPG

Secondary User Payment Selection Game

The NPG is proved to have a unique NE point.pThe NE point is given by

Under the constraints:U de t e co st a ts:

M i i i P i U ’ UtilitAssume the secondary users select payment according to

Maximizing Primary User’s Utilityy p y g

the NE point, primary user’s utility is

Primary user maximize its utility at

WhereWhere

Under the condition:

Simulation ResultsSecondary nodes are placed at normalized distance d from y pPT and 1‐d from PRFading channelS consists k nodesValue of utility vs. d and k: y

Simulation ResultsParameters vs. d and k

ConclusionsA novel cooperative cognitive radio framework has been A novel cooperative cognitive radio framework has been proposed, which enable the primary user to actively involve secondary users as the cooperative relaysy p y

A payment mechanism, where secondary users pay charges to primary user in order to achieve the access opportunity, is to primary user in order to achieve the access opportunity, is designed

By formulating the model as a Stackelberg game, we prove By formulating the model as a Stackelberg game, we prove that there exists a unique Nash Equilibrium for the game and derive the analytical result

Numerical result shows that both primary and secondary systems achieve better performancey





Importance of TestbedImportance of TestbedSimulation based evaluation (e.g., NS2, Opnet)Simulation based evaluation (e.g., NS2, Opnet)

With simplification of realityEasy for implementationy pNot convincing enough for real performance

Cognitive radio has sophisticated PHY and MACMore concern about simulation (no widely used simulator)

With real testbedM f l i lMore useful testing resultHarder work, and long term implementation

90

WARP Pl f (Ri U i i )WARP Platform (Rice University)

WiNC2R Platform (WinLAB)

GNU R diGNU RadioGNU R diGNU Radio

Software “blocks” that run on the GPPUniversal Software Radio Peripheral (USRP)

Channelizer, DUCs/DDCs, Interpolator/DecimatorRF Daughter boards

RF Interface

Enter the USRPEnter the USRPUniversal Software Radio Peripheral (USRP)p ( )

ADC 64 Msps (4)DAC 128 Msps (4)DAC 128 Msps (4)USB 2.0 Interface*Small FPGA†Small FPGA

* Bottleneck: 8 MHz, 16-bit complex samples† Loads bitfile from the GPP

USRP2USRP2

Uses same RF Daughter boards as USRPGigabit Ethernetg100 Msps ADC (2)400 Msps DAC (2)400 Msps DAC (2)Larger FPGASD card readerSD card reader

RF Da ghter boardsRF Daughter boardsBasic RXReceiver for use with external RF hardware

Basic TXTransmitter for use with external RF hardware

RFX24002.3‐2.9 GHz Transceiver, 50+mW output

RFX900800‐1000MHz Transceiver, 200+mW output

RFX400 MH MH T i W t t 400 MHz ‐ 500 MHz Transceiver, 100+mW output

XCVR24502 4 2 5 GHz and 4 9 to 5 85 GHz Dual band Transceiver 2.4‐2.5 GHz and 4.9 to 5.85 GHz Dual‐band Transceiver, 100+mW output on 2.4 GHz, 50+mW output on 5GHz

Sora ApproachEnabling high system throughput

Design new PCIe‐based Interface cardDevelop new software architecture for efficient PHY/MAC processing

New optimizations to implement PHY algorithmsStreamline over multiple CPU cores

Support real‐timeCore dedication

S A hiSora Architecture

Radio Control BoardRadio Control Board

RCB Details

We need to Solve Real ProblemWe need to Solve Real Problem

There is a big gap between

Having a flexible cognitive radio, effectively a building block, g g , y g ,and

The large‐scale deployment of cognitive radio networks g p y gthat dynamically optimize spectrum use

Building and deploying a network of cognitive radios is a complex while essential taskis a complex while essential task

ConclusionConclusionC iti di d d i t i th “N t Cognitive radio and dynamic spectrum access is the “Next Big Thing” in future wireless communications Both economics and technology aspects of cognitive radio Both economics and technology aspects of cognitive radio networking for dynamic spectrum management need to be well studiedReal system deployment is essential to have better understanding about the impact of CR g pto the future wireless networks

Documents

Cognitive Radio Networks.ppt - UNSW School of Computer ...qianzh/comp562-2011/notes/CRN.pdf · • NTT DoCoMO, Motorola, Nokia ... yA radio can change transmit parameter based on