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C t l i i iti t kControl issues in cognitive networks

Marko Höyhtyä and Tao ChenCWC-VTT-Gigaseminar

4th December 2008

VTT TECHNICAL RESEARCH CENTRE OF FINLAND

OutlineOutline

• Cognitive wireless networksC iti h• Cognitive mesh

• Topology control• Frequency selectionFrequency selection• Power control

22


Cognitive radio (CR)g ( )

• Cognition cycle• Research topics in cognitive Orient

Infer from Context Infer from Radio Model

Research topics in cognitive radio

• Spectrum sensing• Dynamic spectrum access NormalUrgent

Orient

Parse Stimuli

Pre-process

Establish Priority

PlanNormal

Immediate

Generate AlternateGoals

• Dynamic spectrum access• Coexistence

• Technical challenges• Spectrum sensing

NormalUrgentParse Stimuli Immediate

LearnNObserve• Spectrum sensing

Reliability, Sensitivity, and Response time.• Coexistence of heterogeneous

systems, especially primary users

NewStates

Observe

DecideUser Driven

and secondary users.• Multi-dimension resource

allocationSi li t t CR

Act

(Buttons) Autonomous Determine “Best”

Plan

States

Determine “Best” Known WaveformGenerate “Best” WaveformOutside• Signaling to support CR Allocate Resources

Initiate Processes

NegotiateNegotiate Protocols

o a e oWaveformOutside World

33

Adapted from J. Mitola, “Cognitive Radio for Flexible Mobile MultimediaCommunications ”, Mobile Networks and Applications, vol. 5, No. 4, pp 435-441, 2001 [5]


Cognitive wireless networks (CWN)Cognitive wireless networks (CWN)

• Cognitive radio: learn from the environment and adapt certain radio operating parameters to incoming RF stimuli (by Simon Haykin [6])parameters to incoming RF stimuli. (by Simon Haykin [6])

• Cognitive wireless networks: learn from network-wide environment and adapt network configuration to incoming RF and network stimuli.

• Similarity of CR and CWN• Use cognitive process, which is goal driven and relies on observations

and learning to reach decisionand learning to reach decision.• Use software tunable platform.

• Difference of CR and CWN• Scope of controlling goals.• Degree of heterogeneity.

D f f d• Degree of freedom.

44

Example of CWN architecture . Proposed by Thomas et al. from Virginia Tech.


Future Wireless NetworksFuture Wireless NetworksUbiquitous Communication Among People and Devices

Wireless Internet accessNth generation CellularWireless Ad Hoc

q g p

NetworksSensor Networks Wireless EntertainmentSmart Homes/SpacesAutomated HighwaysAll this and more…

Future wireless networks will be CWNs!

55


A cognitive wireless mesh networks (CogMesh)g ( g )

Primary UserPrimary UserCR User

ndnd

Licensed Band I CR Networkwith Infrastructurem

ban

m b

an

with InfrastructureCR Ad-Hoc Networkwithout Infrastructure

pect

rupe

ctru

CR User

P i UUnlicensed Band

Primary User

SpSp Primary User

Multi-channel

CR User

66

Licensed Band II Coexistence with CR


Topology control in cognitive h t kmesh network

7


Topology control in CogMeshTopology control in CogMesh• Scenario

• Secondary users (SU) coexist with primary users (PU).Secondary users (SU) coexist with primary users (PU).• SUs form a CR ad hoc network.

• Distributed control • Self-organization• Self-healing

SU uses spectrum holes {1 2 3}

{1,2,3}

{1 2 3}{1 3}• SU uses spectrum holesfor communications, nocommon control available. 2

{1,2,3} {1,2,3}

{2 3}

{1,3}

{1,3}{2}

{2}• Solution

• Cluster based networkformation

1 1 3

{2,3}

{1 2 3}

{ }

{1,3}

formation.• Goal: reduce cluster numbers in network• Minimal dominating set (MDS)

{1,2,3}

{1,2,3} Channel listSecondary user

1Primary user on channel 1

8

algorithm to control the connectiontopology and adapt to radio environment changes.

1


Cluster formation at initial cluster construction (ICC) hphase

{1,2,3}

{1,2,3}

{1,2,3}

{1,2,3}{1,3}

{1 3}

1

2

1 3

{2,3}

{1,3}{2}

{2}

{1,3}

{1,2,3}

{1,2,3} Channel listCluster head

Ordinary nodeCluster head

1Primary user on channel 1Cluster head

99

Cluster member


MDS algorithm to reduce cluster numberMDS algorithm to reduce cluster number

• Reduce cluster number

{1 2 3}

{2,3}

{1,2}{2,3}

{1 2}{1,2,3}{ }

{1,2,3}{1,2}

{1,2} {1,2}{2 3}{1} {2 3}{2,3}

{1,2,3}{1}

{2,3}{2,3}

{1,2,3}{1}

{2,3}

{1,2} {1,2} {1,2} {1,2} {1,2} {1 2}

10

10

{ } {1,2}


Simulation ResultSimulation Result

Before

Number of clusters before and after proposed algorithm when spectrum holes change

Number of clusters after different of algorithms

After

1111

After


Control clo d conceptControl cloud concept• Assumption: no common channel available.• A control cloud is form by a group of connected nodes who share a• A control cloud is form by a group of connected nodes who share a

common control channel. • The objective is to make control clouds as large as possible in order to

reduce control overheadreduce control overhead.• Control channel clouds may grow or shrink according to the available

common channels.

1212


Use swarm intelligence for control cloud formationUse swarm intelligence for control cloud formation

• A population of simple agents interacting locally with one another and with their environment to performone another and with their environment to perform complex tasks.

• Use the principle of division of laborUse the principle of division of labor• Parallel optimization method• Examples: ant colonies, bird flocking, animal herding, bacterial

growth, and fish schooling

• SI in communicationsR ti• Routing

• AntNet• AntHocNet

• Spectrum hole detection• Particle Swarm Optimization

1313


Swarm intelligence algorithm for control cloudSwarm intelligence algorithm for control cloud• A node chooses its control channel according to quality of available

channels and choices of neighbors. g• Each node broadcasts HELLO messages to its neighbors on

control channel. The channel lists and statistic states are included in HELLO messagesin HELLO messages.

• The receiving of HELLO act as pheromone in SI to affect the decision of the node on its control channel. The objective is to let neighbor nodes select a common channel with good quality as their common control channel.

{1,2,3}

{1,2,3}

{1,2,3}{1,3}

{1 3}{1,2,3}

{1,2,3}

{1,2,3}{1,3}

{1,2,3}

{1,2,3}

{1,2,3}{1,3}

{1 3}

1

2

1 3

{2,3}

{1,2,3}

{1,3} {2}{2}

{1,3}

1

2

1 3

{2,3}

{1,3} {2}{2}

{1,3} 1

2

1 3

{2,3}

{1,2,3}

{1,3} {2}{2}

{1,3}

1414

{ }{1,2,3}


Performance comparison

1515


Frequency selectionFrequency selection

16


Control challenge

• The biggest challenge in cognitive networks is designing clever algorithms that will take all needed information that are available

i l di l ti f CR d i i f ti t ffi– including location of CR nodes, sensing information, traffic patterns of different users, database information of nations and regulations etc. – and make decisions about where in the spectrum to operate at any given moment and how much power to use in that band.

DSM module

RF stimuli Spectrum sensing Power control

Ch l l ti

RF stimuli

PU t t

17

DatabaseChannel selectionPU system parameters


Learning in frequency selectionLearning in frequency selection

• Cognitive radio should be more than only an opportunistic radio, i di t ki i di t d t f t t itii.e., radio taking immediate advantage of spectrum opportunities

• Ability to learn from experiences makes the operation more efficient compared to the case where only information available p yonly at the design time is possible

L i d di ti h l iti di t fi d t• Learning and prediction helps cognitive radio to find out frequency channels offering longest idle times for secondary use

18


System model

A CR t i i f ti t th d t b• A CR stores sensing information to the database• It classifies the traffic patterns of different channels and selects

the prediction method for each channel based on classification• When a CR has to switch channel, it selects an available one

offering the longest idle time into use

Channel history

1) Spectrum sensing

) ff

6) Data transmission

Channel state flag

2) Traffic pattern

classification

Switchchannel

yes

19

3) Prediction method decision

4) Idle time prediction

5) Switching decision

no


Intelligent channel selection

• Sensing of primary channels is a periodic sampling process to determine the state (ON or OFF) of the channels at every sampling instantp g

• Traffic patterns are basically divided into stochastic and deterministic onesCl ifi ti f tt i d b d th i di it• Classification of patterns is made based on the periodicity information

• Rules for prediction based on measurement studies, analysis, p yverification with simulations

20


Results

With ti l t ffi i t lli t l ti d th• With exponential traffic, intelligent selection can reduce the amount of switches with 40 %

• Weibull and Pareto distributed traffic give same kind of resultsg• With deterministic traffic the gain is really high, amount of

switches can be one third compared to random selection.

21


Power controlPower control

22


P lPower control

• CR uses sensing to obtain information about local spectrum use• CR uses sensing to obtain information about local spectrum use, sensitivity of sensor together with primary transmission power defines the sensing range rs

T i i f h CR d fi b h h i i• Transmission power of the CR defines both the communication range rc and the interference range ri of it.

• Maximum power limit for secondary transmission can be p yestimated based on PU parameters and sensitivity of the sensor

PU txrs

di Psu ≤ LF(rs–dc) + N + NF – 6 dB.

PU rx

SU t

SU rx

dc

ri

rc

23

SU tx


Ad i i i lAdaptive transmission power control

• Adaptive inverse power control algorithms• Adaptive inverse power control algorithms• Maintaining required QoS with minimum transmission power

(not exceeding the limit) to minimize interference • Applicable to centralized architecture, also possible in

clustered network• We have developed adaptive filtered-x LMS (FxLMS) powerWe have developed adaptive filtered-x LMS (FxLMS) power

control method that is close to optimal• Truncation can be used in a system/application that is not

d l iti t f th i th fdelay-sensitive to further improve the performance

][ˆ kx

][ˆ kh

][kn

][kx

][kh

24

][kh


Results

• Secondary power limit increases with increasing primary transmission power • Truncated method offers more energy efficient transmission and decreases

the created interference allowing the less sensitive sensing.

Transmission power limit for secondary user

20

25

Bm

] Transmitted SNR values for different power control methods

Transmission power limit for secondary user

5

10

15

nsm

issi

on p

ower

[dB

Method Average transmittedSNR

Maximumtransmitted SNR

full inversion 27± 2 - dB 41–48 dB

-5

0

5

Sec

onda

ry tr

an

truncated inversion 20.1 dB 25.7 dB

25

20 25 30 35 40 45 50-10

Primary transmission power [dBm]


Conclusions

• Control in cognitive networks is challenging and different from traditional networks due to dynamic environment

• We studied three different topics• Topology control

• Control clouds for common control channel problem• Control clouds for common control channel problem• Clustering for network formation

• Power control• Power limits• Algorithms

• Frequency selection based on classification and prediction

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Thank you!

A i ?Any questions?

• Contact information:

[email protected]@vtt.fi

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