A N

etw

orke

d A

ppro

ach

to S

pect

rum

Se

nsin

g in

Cog

nitiv

e R

adio

Sys

tem

s

Cla

udio

da

Silv

a

Join

t wor

k w

ith C

hris

Hea

dley

, Jes

se R

eed,

Bria

n C

hoi,

and

Am

y M

alad

y.

Invi

ted

pres

enta

tion

at:

Cla

udio

da

Silv

a

Intro

duct

ion

•The

app

licat

ion

of c

ogni

tion

to c

omm

unic

atio

ns ra

dios

and

net

wor

ks

open

s ne

w a

nd e

xciti

ng o

ppor

tuni

ties

for i

mpr

ovin

g th

e tra

nsm

issi

on o

f in

form

atio

n

•Firs

t app

licat

ion

(?):

Opp

ortu

nist

ic s

pect

rum

re-u

se –

e.g.

, 802

.22

•In

an o

ppor

tuni

stic

spe

ctru

m a

cces

s sc

enar

io, h

arm

ful i

nter

fere

nce

and

perfo

rman

ce d

egra

datio

n m

ay re

sult

if sp

ectru

m s

ensi

ng is

not

relia

ble

•Fac

t: Sk

eptic

ism

•His

tory

: Ove

rlay

anal

og c

ellu

lar n

etw

ork,

UW

B (D

AA

–e.

g., E

urop

e)•M

ore

rese

arch

is n

eede

d

Cla

udio

da

Silv

a

Spe

ctru

m S

ensi

ng

•Doe

s it

fall

into

the

“bea

ten

to d

eath

” cat

egor

y?

•Com

mer

cial

sys

tem

s →

firs

t tim

e M

ilita

ry s

yste

ms →

hard

to k

now

, diff

eren

t req

uire

men

ts a

nd a

pplic

atio

ns

•Opp

ortu

nist

ic s

pect

rum

acc

ess

requ

ires

guar

ante

es

•Do

thes

e gu

aran

tees

lead

to re

ason

able

requ

irem

ents

?

Prob

lem

: The

oret

ical

bou

nds

on th

e sp

ectru

m s

ensi

ng p

erfo

rman

ce

Cla

udio

da

Silv

a

Spe

ctru

m S

ensi

ng: U

nkno

wn

Cha

nnel

•C

ompl

ex ta

sk:

•No

know

ledg

e of

the

trans

mitt

ed d

ata

and

man

y un

know

n pa

ram

eter

s (s

igna

l pow

er, c

arrie

r fre

quen

cy a

nd p

hase

offs

ets,

tim

ing

info

)•N

o ch

anne

l kno

wle

dge

•Exa

mpl

e: T

he d

evel

opm

ent o

f sig

nal d

etec

tion

and

clas

sific

atio

nal

gorit

hms

for m

ultip

ath

fadi

ng c

hann

els

is s

urpr

isin

gly

scar

ce

-20

-15

-10

-50

510

1520

0.5

0.550.6

0.650.7

0.750.8

0.850.9

0.951

SN

R (

dB)

Average Probability of Correct Classification

BP

SK

/QP

SK

Cla

ssifi

er (

1000

0 Tr

ials

, N

clas

s = 1

0)

P

erfe

ct E

stim

ates

MoM

Est

imat

es o

f α,

θM

oM E

stim

ates

of α

, θ,

ε (

Nes

t = 1

0)

MoM

Est

imat

es o

f α, θ

, ε

(Nes

t = 1

00)

-25

-20

-15

-10

-50

510

1520

250.

1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.91

SN

R (

dB)

Average Probability of Correct ClassificationBP

SK

/QP

SK

/8-P

SK

/16-

PS

K/1

6-Q

AM

/64-

QA

M C

lass

ifier

(10

00 T

rials

, N

clas

s = 1

00)

P

erfe

ct E

stim

ates

MoM

Est

imat

es o

f α,

θM

oM E

stim

ates

of α

, θ,

ε (

Nes

t = 1

00)

Prob

lem

: Per

form

ance

lim

its o

f sen

sing

alg

orith

ms

for r

ealis

tic

impl

emen

tatio

ns a

nd p

ract

ical

sce

nario

s?

Cla

udio

da

Silv

a

Dis

tribu

ted

Sen

sing

Why

? It

onl

y m

akes

sen

se•R

educ

e de

tect

ion

requ

irem

ents

of i

ndiv

idua

l sen

sors

•T

ake

adva

ntag

e of

the

radi

o si

gnal

var

iabi

lity

•Spe

ctru

m u

tiliz

atio

n is

a s

patia

l phe

nom

enon•U

ser 2

mig

ht n

ot d

etec

t the

pr

imar

y us

er

•Tog

ethe

r, us

ers

1, 2

, and

3 h

ave

a hi

gher

pro

babi

lity

of d

etec

ting

and

clas

sify

ing

othe

r sys

tem

s

→di

vers

ity

→hi

dden

nod

es

•Nod

e-pr

oces

sing

→

Det

ectio

n/cl

assi

ficat

ion

algo

rithm

s→

Dec

isio

n m

akin

g (“

data

redu

ctio

n”)

→ D

ata

asso

ciat

ion

(spa

ce, t

ime,

freq

uenc

y)

•Net

wor

k-pr

oces

sing

→ D

ata

fusi

on→

Dat

a tra

nsm

issi

on(w

ho?

whe

n? w

hat?

)→

Dat

a as

soci

atio

n (s

pace

, tim

e, fr

eque

ncy)

Cla

udio

da

Silv

a

Dis

tribu

ted

Sen

sing

Alg

orith

m E

xam

ple

P(H

0) =

0.4

1 se

nsor

→

thr=

0.6

72

sens

ors →

thr=

1.2

5

Fusi

on ru

le: O

R

P(H

0) =

0.6

1 se

nsor

→

thr=

1.5

2 se

nsor

s →

thr=

0.8

Fusi

on ru

le: A

ND

Exa

mpl

e: T

wo

inde

pend

ent s

enso

rs, H

0N

(0,1

), an

d H

1N

(1,1

).

Fusi

on c

ente

r:

Rad

ios:

Cla

udio

da

Silv

a

Dis

tribu

ted

Sen

sing

: Exa

mpl

es

Dis

trib

uted

det

ectio

n of

a k

now

n si

gnal

in

AW

GN

, with

opt

imal

th

resh

olds

obt

aine

d by

a G

auss

-Sei

del

itera

tive

algo

rithm

.

Dis

trib

uted

mod

ulat

ion

clas

sific

atio

n (A

WG

N),

usin

g a

cycl

ic fe

atur

e-ba

sed

clas

sifie

r and

op

timal

thre

shol

ds

obta

ined

by

a G

auss

-Se

idel

iter

ativ

e al

gorit

hm.

Prob

lem

: The

oret

ical

bou

nds

on th

e di

strib

uted

spe

ctru

m s

ensi

ng

perfo

rman

ce

Cla

udio

da

Silv

a

Dis

tribu

ted

Sen

sing

: Com

plex

ity•P

robl

em: “

On

the

com

plex

ity o

f dec

entra

lized

dec

isio

n m

akin

g an

d de

tect

ion

prob

lem

s, ..

. our

resu

lts p

oint

to th

e in

here

nt d

iffic

ulty

of d

ecen

traliz

ed d

ecis

ion

mak

ing

and

sugg

est t

hat o

ptim

ality

may

be

an e

lusi

ve g

oal”

[Tsi

tsik

lis’8

5]

•Pos

sibl

e ap

proa

ches

:•I

tera

tive,

mes

sage

-pas

sing

alg

orith

ms

•Gra

phic

al m

etho

ds•D

istri

bute

d de

tect

ion

prob

lem

s ar

e ve

ry w

ell r

epre

sent

ed b

y gr

aphi

cal

mod

els.

Suc

h m

odel

s, in

clud

ing

Bay

esia

n ne

twor

ks, c

aptu

re th

e di

vers

e in

terd

epen

denc

ies

amon

g a

set o

f var

iabl

es (n

ode

obse

rvat

ions

and

dec

isio

ns) a

nd th

e no

des’

con

nect

ions

•The

inte

grat

ion

of th

is s

et o

f var

iabl

es is

then

cap

ture

d in

a

prob

abili

stic

infe

renc

e pr

oble

m (e

.g.,

belie

f pro

paga

tion)

•Oth

er im

porta

nt is

sues

: net

wor

k co

nfig

urat

ion,

pow

er/b

andw

idth

re

quire

men

ts, d

ata

asso

ciat

ion…

Prob

lem

: Des

ign

of e

ffici

ent,

low

-com

plex

ityal

gorit

hms

for d

istri

bute

d sp

ectru

m s

ensi

ng

Cla

udio

da

Silv

a

Spa

tial A

spec

ts o

f Dis

tribu

ted

Sen

sing

Mea

sure

of r

elia

bilit

yC

orre

latio

n

Pos

ition

ing

(you

and

you

r pee

rs):

Ass

ume

that

exp

erim

enta

l res

earc

h sa

ys th

at o

n av

erag

e th

e ob

serv

atio

ns o

f ra

dios

sep

arat

ed b

y on

e ya

rd h

ave

corr

elat

ion

coef

ficie

nt 0

.6. H

ow c

an tw

o co

gniti

ve ra

dios

that

are

sep

arat

ed b

y th

is d

ista

nce

(and

kno

w o

f thi

s fa

ct) t

ake

adva

ntag

e of

this

ave

rage

cor

rela

tion

whe

n pe

rform

ing

sens

ing?

Exa

mpl

e:C

orre

latio

n co

effic

ient

of S

NR

est

imat

es

0.35

0.41

0.47

0.49

ρ10

050

101

d

(R =

100

0, S

NR

t=

70 d

B)

Cla

udio

da

Silv

a

Spa

tial S

pect

rum

: Clu

ster

ing

Prob

lem

: How

sho

uld

a ne

twor

k of

cog

nitiv

e ra

dios

con

figur

e its

elf i

n or

der t

o pe

rform

spe

ctru

m s

ensi

ng?

→ D

o w

e re

ally

wan

t a g

loba

l dec

isio

n?

→ P

ut th

ings

in p

ersp

ectiv

e: n

o ne

ed fo

r CR

sin

Sea

ttle

to k

now

wha

t’s h

appe

ning

in

Bla

cksb

urg

→ G

ranu

larit

y vs

. pow

er/b

andw

idth

sav

ings

(w

arni

ng: m

inim

um n

umbe

r of r

adio

s is

re

quire

d to

ach

ieve

a g

iven

relia

bilit

y)

This

wor

k w

as s

uppo

rted

in p

art b

y a

gift

from

Tex

as In

stru

men

ts.

10-4

10-3

10-2

10-1

100

10-4

10-3

10-2

10-1

100

Pro

b. In

terfe

renc

e (G

rey

Are

a)

Prob. False Spectrum Access Denial

All

10 r

adio

s

2 cl

uste

rs4

clus

ters

Non

-col

labo

rativ

e

10-4

10-3

10-2

10-1

100

10-3

10-2

10-1

100

Pro

b. F

alse

Ala

rm

Prob. Detection

All

10 r

adio

s

2 cl

uste

rs4

clus

ters

Non

-col

labo

rativ

e

Cla

udio

da

Silv

a

The

10 1

00 1

000

1P

roje

ct

Que

stio

n: W

hat i

s th

e be

st re

liabi

lity

we

can

achi

eve

in

su

ch a

sce

nario

?G

oal:

Prob

abilit

y of

inte

rfere

nce

to p

rimar

y sy

stem

equ

al to

0.0

1%an

d pr

obab

ility

of “f

alse

den

ial”

equa

l to

1% fo

r99.

9%of

the

time.

A C

R a

d-ho

c ne

twor

k……

of 1

0co

gniti

ve ra

dios

that

…

by tr

ansm

ittin

g a

tota

l of 1

00bi

ts

each

…by

per

form

ing

1000

DSP

oper

atio

ns…

and

in u

nder

1se

cond

colla

bora

tivel

y pe

rform

spe

ctru

m s

ensi

ng

Cla

udio

da

Silv

a

VT

CR

Net

wor

k Te

stbe

d (V

T-C

OR

NE

T)

Prob

lem

: How

can

we

incr

ease

con

fiden

ce in

the

tech

nolo

gy –

deve

lope

rs,

regu

lato

rs, u

sers

?

•Foc

us a

nd c

ontr

ibut

ion:

PH

Y, c

ogni

tive

engi

ne, a

nd c

ogni

tion

at n

etw

ork

leve

l•I

nstit

ute

for C

ritic

al T

echn

olog

y an

d A

pplie

d Sc

ienc

e (IC

TAS)

Arc

hite

ctur

e•N

ew IC

TAS

bui

ldin

g, c

ontro

lled

rem

otel

y, c

eilin

g•4

8 no

des

to b

e in

stal

led:

12/

floor

x 4

floo

rs•U

SR

P +

RF

Fron

tend

: New

Mot

orol

a ch

ip (1

00M

Hz

to 4

GH

z)•S

oftw

are:

Any

plat

form

. Fi

rst s

tage

: OS

SIE

, an

impl

emen

tatio

n of

the

Join

t Tac

tical

Rad

io S

yste

m’s

S

oftw

are

Com

mun

icat

ion

Arc

hite

ctur

e (S

CA

)

Som

e as

pect

s of

CR

N th

at n

eed

expe

rimen

tal v

erifi

catio

n &

test

ing

•Mod

el a

ccur

acy:

Alg

orith

ms,

pro

toco

ls, a

pplic

atio

ns•C

olle

ct p

erfo

rman

ce a

nd Q

oSm

easu

rem

ents

for f

urth

er a

naly

sis

•Rea

listic

con

ditio

ns•V

erify

legi

timat

e op

erat

ion

of c

ogni

tive

engi

nes

Cla

udio

da

Silv

a

Con

clus

ions

We

have

a lo

t of w

ork

to d

o:•T

heor

etic

al b

ound

s on

the

spec

trum

sen

sing

per

form

ance

•Per

form

ance

lim

its o

f sen

sing

alg

orith

ms

for r

ealis

tic im

plem

enta

tions

an

d pr

actic

al s

cena

rios

•The

oret

ical

bou

nds

on th

e di

strib

uted

spe

ctru

m s

ensi

ng p

erfo

rman

ce•D

esig

n of

effi

cien

t, lo

w-c

ompl

exity

alg

orith

ms

for d

istri

bute

d sp

ectru

m

sens

ing

•How

can

(par

tial a

nd c

ompl

ete)

pos

ition

info

rmat

ion

be u

sed

to im

prov

e sp

ectru

m s

ensi

ng?

•How

sho

uld

a ne

twor

k of

cog

nitiv

e ra

dios

con

figur

e its

elf i

n or

der t

o pe

rform

spe

ctru

m s

ensi

ng?

•How

can

we

incr

ease

con

fiden

ce in

the

tech

nolo

gy –

deve

lope

rs,

regu

lato

rs, u

sers

?