CognitiveRadio.ppt

8/10/2019 CognitiveRadio.ppt

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AI Technologies for the

Cognitive RadioDipti Deodhare

Centre for Artificial Intelligence andRobotics

[email protected]


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Cognitive Radio = Software Defined Radio

+

Cognition Engine


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Cognitive Capabilities

Awareness: should be aware of its

own abilities, the regulating policies that govern it, itsneighbours and their abilities etc.


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Perception: should be able to

sense its environment


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Learning: consequent to

perception it should be able tolearn about the generalcharacteristics of its environment

and trends


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Reasoning: relationships between

the various entities should beunderstood and sound decisionsinferred, obviating the

overwhelming, and perhapsimpossible, task of enumeratingever single alternative


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Memory: should demonstrate

improved performance afteroperating in the same environmentover an e!tended period of time


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Architecture

"oftware Radio(perception)

Cognition #ngine $ %earning #ngine (learning)

$ Reasoning #ngine (reasoning)

$ &nowledge 'ase(awareness, memory (

"oftwareRadio

Cognition #ngine

Knowledge Base

Reasoning Engine

Learning Engine


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)eters and &nobs Cognitive capabilities are

predicated on availabilitof suitable meters and knobs

in the "DR* )eters to continuousl

monitor the performanceof the radio*

&nobs to reconfigure the radio so that the performance of the radio ismaintained at an acceptable level satisf ing various conte!t drivenconstraints*

The mandate of the cognitive engine is to +read the meters andappropriatel +tune the knobs*

Cognition #ngine

Knowledge Base

Reasoning Engine

Learning Engine

Read meters

Set knobs

"DR

Application

Transport

-etwork Data %ink

. MAC+LLC (

/01

"ecurit


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%a er2wise distribution of )eters

and &nobs Contd*)#T#R"

Interference,

'#R, receivedsignal power,noise power,"-R, fadingstatistics,doppler spread,dela spread,angle of arrival,d namic range

&-3'" /ower, frequenc band

of operation, carriermodulation t pe,

baseband modulationt pe, pulse shaping,data rate, number ofchannels, bandwidth,equali4ation, antennatuning, antennasteering, antenna heightad5ustment, t pe ofantenna .if more thanone t pe of antennaavailable(

Physical Layer

Data Link Layer(MA ! LL )

"etwork

#ransport

$ec%rity

Application


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%a er2wise distribution of )eters

and &nobs Contd*)#T#R

6rame error rate&-3'"

6rame format,6rame si4e,multiple access,duple!ing, 6#C,AR7.enable8disable(

Physical Layer

Data Link Layer(MA ! LL )

"etwork

#ransport

$ec%rity

Application


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%earning #ngine

The %earning #ngine $ Collection of classifiers

developed using supervised and unsupervisedlearning techniques*

-umeric data " mbolic data

%earning.collection of

classifiers(

)odulation,Carrier 6req*,'#R, "-R,/wr, Coding

Classify intoqualitative classes such as good, bad high, average, low


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)T9 "i4e vs ThroughputRef: Anna Calveras Auge;, ??@

6or each '#R value,an optimal )T9e!ists

'#R: meter at the

ph sical la er* )T9: knob at the network la er*

-eural -etworkClassifier

'#R Throughput

)T9

6igure is a graph for /oint to /oint /rotocol .///( anddeterministic errors*Braphs would be different for other protocols like 6rameRela , As nchronous Transfer )ode .AT)(, #thernet etc*

The would also var depending on the error t pe, namel ,deterministic and burst*It is proposed that the graphs can be +learnt b a )ulti2%a er /erceptron .)%/( or some other suitable mechanism*Reinforcement %earning mechanisms are also potentiallrelevant*


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36D) Transmitter

Modulator (QAMQPSK BPSK)

Parallelto

serialincomingbits

C 1

C 2

C 3

C n

IFF

! 1

! 2

! 3

! n

36D) transmission offers opportunitfor adaptation


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36D) Receiver

Serial to"arallel

#e$modulator

incomingbits

C 2

C 3

C n

IFF

! 1

! 2

! 3

! n

C 1


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Capacit ma!imi4ation in a non2A=B- channelcan be modeled as a Reinforcement %earning

problem* M modulation t pes: M > , M C, , M M *

N coding t pes: C > , C C, , C N *

M i can transmit d i data bits per s mbol and has a probabilit of bit error ei.S ( for signal to noiseratio S *

Coding t pe C has rate r and can correct c bit

errors per block of si4e b bits* ! S : s mbol time, a constant* " : corrected bit error rate coming out of the

decoderE measured b the radio*


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6or modulation t pe M i and coding t pe C ,the resulting capacit is given b :

C i# F .. d ir (8# $ ( .>2 " (

C i# can act as a performance measure* 3b5ective function:

ma$ f%M i #C #"&' d ir .>2 " (


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&nowledge Representation

#ach node or relation is associated to anontolog that defines the concept* An

ontolog consists of slots representingvarious attributes of that concept* 6or e.(.here is the +Device ontolog created in

3=% .=eb 3ntolog %anguage(* %)e uset*e S) , editor to create and edit ontolo(iesand R- /ravity to visuali0e t*em.&


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Device Ontology


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Reasoning

Reasoning involves traversing the semanticgraphs to obtain relevant conclusions*

"ome ontologies for the cognitive radio: radio,channel, spectrum, power, coding, modulation,etc*

"ome inferences: frequency f c is sparsely used from time t 1 to time t 23 for c*annel c# capacity isma$imi0ed 4it* modulation type m i and codin(met*od c and so on.


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A "imple #!ample using

/redicate %ogic .papers include authors from 9" DoD( "oftware Radio ."R( e!ports predicates to the

knowledge2base regarding detected signals s> , sC,, s

N#of the form

si(nal req%s i #f i &G si(nal") . s i #) i &

Boal: To find some f c and ) that does not overlapan detected signal, while ma!imi4ing ) and

hence the radio;s capacit * Define, not verlap%f c #)#s i &

' . f i+) i 526f c7)52 ( H . f i7) i 528f c+)52 (


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A "imple #!ample using

/redicate %ogic Define predicate:action: moveBand & old ,' old , & new , ' new !

precond: i 6' N : not verlap . f ne4 #) ne4 #si( postcond: 2. center req . f old ( center req . ) old ((

. center req . f ne4 ( center req . ) ne4 (( Define the +Reasoning ob5ective function:

f R .center req . f c( band4idt* . ) (( F ) This will give a polic 2based cognitive radio that will

search out the largest continuous piece of bandwidth forcommunication*

H

GG

G

G


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)emor

Case2based Reasoning Tools are useful "warm Intelligence technologies

0))s Reinforcement %earningCan all pla a role in searching large knowledge

banks to enable quick responses*


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" of fre#$encies %ammed t&is is

'ess t&an t&et&res&o'd!

(op rate )EC Data Rate

Increases to reduce 5amming

J8@Reduces to meet

'#R requirements

K)ore robust totolerate errors

)a! L threshold

/reliminar Anal sis for6requenc 0opping "olutions

using D#A% Inputs


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#stablishing a Test2bed

"imulation in )atlab B-9 radio $ local vendors available


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Thank 1ou

CognitiveRadio.ppt

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