Signal Clasification OFDM Based

8/10/2019 Signal Clasification OFDM Based

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Signal Classification in Heterogeneous

OFDM-based Cognitive Radio Systems

Wael Guibe`ne !RCOM-Cam"us So"#ia$ec# Mobile

Communication D"t% mail&

Wael%Guibene'eurecom%fr

Dir( Sloc( !RCOM-Cam"usSo"#ia$ec# Mobile

Communication D"t% mail&

Dir(%Sloc('eurecom%fr

)bstract*+n t#is "a"er , e "ro"ose to study t#e s"ectrumaareness also called classification of various signals enablingt#e OFDM-based cognitive radio systems .CRS/% +n order todo so, some (ey "ro"erties relevant to t#e detection of t#eOFDM-based t#ird-Generation 0artners#i" 0ro1ect 2ong $ermvolution .3G00 2$/ and digital video broadcast for terrestrial$4 .D45-$/ signals as ell as "rogramme-ma(ing and s"ecialevents .0MS/ signals are derived and a robust classificationsc#eme based on "arallel standards discrimination is derived%Simulations results for t#e "ro"osed tec#ni6ue s#o its effective-ness and robustness to additive #ite Gaussian noise c#annels asell as Rayleig# multi"at# fading "lus s#adoing c#annels%

+nde7 $erms*Signal classification, S"ectrum )areness,OFDM signals, 2$, D45-$, 0MS%

+% +8$ROD!C$+O8

Cognitive radio .CR/ as "resented by Mitola 9:; as one

t#e "romising tec#nologies enabling t#e dynamic s"ectrum

access and s#aring t#e s"ectral resources beteen different

users% )not#er interesting definition as given by t#e +!$-R,

describing t#e cognitive radio as& time/% )nd t#is monitoring of 0!

"resence>abscence is called s"ectrum sensing feature of t#e

CR% +n overlay s"ectrum s#aring "olicies, t#is (noledge oft#e o"erational environment come from t#e s"ectrum sensing

feature of t#e cognitive radio%

$#e ot#er main functions of Cognitive Radios, a"art from

s"ectrum sensing are&

S"ectrum management& #ic# ca"tures t#e most satisfy-

ing s"ectrum o""ortunities in order to meet bot# 0! and

S! 6uality of service .?oS/%

S"ectrum mobility& #ic# involves t#e mec#anisms and

"rotocols alloing fre6uency #o"es and dynamic s"ec-

trum use%

S"ectrum s#aring& #ic# aims at "roviding a fair s"ec-

trum s#aring strategy in order to serve t#e ma7imum

number of S!s%

$#e "resented or( fits in t#e conte7t of s"ectrum sens-

ing>s"ectrum s#aring frameor( for CR netor(s and more

"recisely single node detection> standard identification% Re-lated to t#is or(, many statistical a""roac#es for t#e s"ectrum

sensing "art #ave been develo"ed% One of t#e most"erforming

sensing tec#ni6ues is t#e cyclostationary features detection 9@;,

93;% $#e main advantage of t#e cyclostationarity detection is

t#at it can distinguis# beteen noise signal and 0! transmitted

data% +ndeed, noise #as no s"ectral correlation #ereas t#e

modulated signals are usually cyclostationary it# non null

s"ectral correlation due to t#e embedded redundancy in t#e

transmitted signal% $#e reference sensing tec#ni6ue is t#e

energy detector 9@;, as it is t#e easiest to im"lement and

t#e less com"le7 detection tec#ni6ue% On t#e ot#er #and,

some "a"ers #ave been dedicated to t#e signal identification

"art 9A;B9;% +n t#is "a"er, e "resent a robust classification

tec#ni6ue based on "arallel s"ectrum sensing tec#ni6ues in

order to combine t#e sensing > classification feature of t#e

CR% +n section ++, e "resent t#e targeted scenario, #ere

t#e netor( to be considered is a #eterogeneous netor(

it# at least t#ree ty"es of coe7isting signals .2$, D45-$

and 0MS signals/% +n section +++, e go t#roug# t#e details

of t#e "ro"osed classification sc#eme based on "arallel and

simultaneous s"ectrum sensing tec#ni6ues% +n section +4, e

"ro"ose to evaluate t#e "ro"osed sc#eme it# to scenarios

and finally section 4 concludes about t#e "resent or(%

++% $)RG$D SC8)R+OS

$#e goal of t#is "a"er is to derive a classification sc#eme

for different systems it# s"ecific "arameters and signal c#ar-

acteristics, o"erating in t#e $4 W#ite S"aces .$4WS/% $#e

transmitters considered in S)CR)>S0C$R) are identified

and c#aracteried belo&

:/ ) D45-$ 0rimary !ser .0!/ #ic# uses an OFDM

modulation% )s s#on later, t#ere are several D45-$

configurations, de"ending on

a/ t#e bandidt# .E MH, MH, MH, MH/

of t#e c#annel being used,

b/ t#e modulation .?0S>?)M>:-?)M>A-?)M/

used by t#e subcarriers from t#e OFDM symbol,
mailto:[email protected]:[email protected]:[email protected]:[email protected]


2/5

nIl n

c/ t#e useful symbol and guard "eriods& system c#ar-

acteristics #ave been "redefined by standards, and

t#ey are fi7ed (non values for t#e useful $! and

guard $G "eriod .t#e latter is also called cyclic

"refi7"eriod/%

@/ )n 2$ Secondary !ser .S!/ #ic# uses OFDM Mod-

ulation .in donlin( D2/ and SC-FDM) .in u"lin( !2/

combined it# 50S>?)M>:-?)M>A-?)M% System

c#aracteristics it# fi7ed symbol and guard "eriods .$!and $G/ #ave been "redefined by 3G00 standardiation

activities%

3/ ) 0MS 0! #ic# uses ?0S Modulation .AJJ H

5andidt#/ or FM Modulation .@JJ H/% 7ce"ting

t#e bandidt#, t#e system c#aracteristics are not very

ell defined for 0MS% $#ese devices ill furt#er be

discussed in latter sections%

+n Figure .:/, terminal !E is connected to a base station

o"erating t#roug# t#e licensed band .@%GH/, e853, and may

be aut#oried to use resources in anot#er band .DD>$4WS/

to communicate it# a second base station, e85:% $#is use

case is based on t#e s"ectrum aggregation conce"t, introduced

in 2$-)dvanced standard% $#e terminal is t#us o"erating in

a #eterogeneous netor(, it# OFDM 2$-), OFDM D45-

$ and 0MS signals co#abitating in t#e netor(% From t#is

coe7istence came t#e need to classify eac# standard in order

to enable t#e o""ortunistic use of t#e $4WS bands%

+++% $H S+G8)2 C2)SS+F+R SCHM

A. Conventional Spectrum Sensing for CRS

+n order to model t#e s"ectrum sensing "roblem, letKs

su""ose t#at t#e detector receives signal yn L )nsn I en, #ere )n models t#e c#annel, sn is t#e transmit signal sent

from "rimary user and en is t#e additive noise% $#e goal

of s"ectrum sensing is to decide beteen to conventional

#y"ot#eses modeling t#e s"ectrum occu"ancy HJ and H:modeling res"ectively, t#e decision by t#e detector of 0!

signal absence and "resence% +n order to ma(e suc# a decision,

t#e detector im"lements a scalar test statistic function of

t#e in"ut signal yn% $#is test statistic is to be com"ared to a

t#res#old level N function of t#e S8R and t#e "robability of

false alarm 0F ) and e t#us obtain&

if L F.yn/ O N decide H:if L F.yn/ P N decide HJ

.:/

+n t#e "ro"osed classification sc#eme, e "ro"osed to mount

as many "arallel detectors as t#e number of standards e

ould li(e to discriminate% +n t#is or( for e7am"le, e ould

li(e to focus on to OFDM-based standards .2$, D45-$/

and 0MS signals .for ireless micro"#ones/, t#erefore t#e

classifier ould #ave t#ree "arallel stages%

B. Multistandard Classification Technique for CRS

+n t#is section, e briefly "resent eac# signal to be classified

and t#e corres"onding test statistic and t#res#old to be a""lied

for t#e eac# detection stage% Since e are considering t#ree

standards, t#e "ro"osed classifier #as to im"lement t#ree stages

as "resented and e7"lained afterards%1) DB!T signals detection" For t#e detection of D45-

$ signals, a robust algorit#m to be a""lied could be t#e

autocorrelation based detector .)D/% $#is tec#ni6ue is based

on t#e fact t#at many communication signals contain redun-

dancy, introduced for e7am"le to facilitate sync#roniation,

by c#annel coding or to circumvent inter-symbol interference%

$#is redundancy occurs as non-ero average autocorrelation

at some time lag l% $#e autocorrelation function at some lag

l can be estimated from&

rQl.y/ L :

" l

"l

:S

nLJ

y y

l O J.@/

Fig% :% $argeted ide-band cognitive radio netor( scenario

#ere " is t#e lengt# of t#e 0! signal in sam"les% )ny

signal e7ce"t for t#e #ite noise case ill #ave values of t#e

autocorrelation function different from ero at some lags largert#an ero, alt#oug# some mig#t be e7actly ero de"ending

on t#e ero crossings% +n 9;, aut#ors #ave "ro"osed an

autocorrelation-based detector for D45-$ OFDM signals% $#is

detector is limited to t#e case #en t#e 0! is using D45-$% $o

detect t#e e7istence>non e7istence of signal e use functions

of t#e autocorrelation lags, #ere t#e autocorrelation is based

on .@/% $#erefore, t#e autocorrelation-based decision statistic

is given by 9;

2Re TrQU

MD45$ )D.y/ LS

ll

.3/

lL:rQJ


3/5

RV

yb /

y

: V t

#ere t#e number of lags, 2, is selected to be an odd number%

$#e eig#ting coefficients l could be com"uted to ac#ieve

t#e o"timal "erformance, and is given by&

2 I : I WlW

of $eager-aiser energy o"erator a""lied to y.(/, e7"ressed

as&

M0MS$ HD L .X9y.(/;/ .Y/@

l L .A/

2 I :

L .X97.(/;/I Zn .:J/

#) $T% signals detection" )s far as 2$ is concerned,

e a""ly a second order cyclostationary features detector

.CFD/ in order to fully cover 2$ standards classification%

$#e algorit#m e are ado"ting is fully described in 9Y;% $o

sum-u", t#e algorit#m is based on t#e fact t#at 2$-OFDM

signals e7#ibit reference signals-introduced second-order cy-

clostationarity it# t#e cyclic autocorrelation function .C)F/,

y.[ / L J at cyclic fre6uency V L J and delay [ L DF .DF

is t#e frame duration/ for all transmission modes% $#is "ro"-

erty e7#ibited by FDD donlin( 2$-OFDM transmissions

can t#us be used to detect "resence of 2$ signals regardless

of t#e mode% $#e C)F of t#e received signal, yn, is estimated

from8s sam"les at t#e delay [ and t#e CF V and e form

t#e folloing vector& RbV L 9Re.RV .[ //+m.RV .[ //; inorder

For t#is detector, e ill use a Monte-Carlo simulation to

derive t#e desired t#res#old function of t#e 0F )%

&) Com'ining rule for Classification" So far, t#e c#oice

made for eac# detectors as based on t#e criterion t#at eac#

sensing tec#ni6ue s#ould be suitable for only one standard%

$#at is #y t#e c#oice for D45-$ as t#e autocorrelation

detector .D45$-)D/ t#at #ig#lig#ts t#e D45-$ c#aracteristics

among t#e ot#er standards= and for 2$ e o"ted for t#e

second order cyclostationary feature detector .2$-CFD/= and

finally for 0MS signal e used t#e $eager-aiser energy

o"erator .0MS-$D/ t#at is convenient for narroband

signals% +n order to combine t#e out"uts of t#ese standard-

dedicated detectors, e ill fuse t#e data from different stagesof OFDM-based tec#ni6ues as in 6uation .::/%

y y y

to com"ute t#e test statistic given by&

2$ CF D L 8s

RbV\

.Rby .E/

+n 6uation .::/, for t#e to first decisions, e onKt focus

on $D out"ut, as if it is an 2$ or D45-$ signal it #as an

out"ut energy greater t#an t#e t#res#old, so its out"ut is H/:%

#ere \b is t#e estimate of t#e RbV

covariance

matri7%

We ill focus rat#er on t#e out"uts of t#e CFD and t#e )D in

order to discriminate beteen 2$ and D45-$ res"ectively%

$#e test statistic2$ CFD

#as no to be com"ared to someWe ill focus on $D only #en t#e CFD and )D give

t#res#old value ] to ma(e t#e decision% )s "reviously stated,

t#is t#res#old is function of t#e "robability of false alarm 0F )%

+n our case, and given t#e test statistic, a "ossible definition

of 0F ) could be& t#e "robability of deciding t#at t#e testedfre6uency V is a CF at delay [ #en t#is is actually not%fre6uency is a CF at delay, or & 0F ) L 0r.M2$ CF DO ]WHJ/% (ee"ing in mind t#at 2$ CF D is folloing a

c#i- s6uared distribution 9:J;, t#e t#res#old ] is obtained from

t#e tables of t#e c#i-s6uared distribution for a given value of0F )"robability%

() MS% signals detection" For t#e 0MS signal, e

o"t for a ireless-micro"#ones oriented detector& t#e $eager-

aiser energy detector for narroband ireless micro"#one as

"resented in 9::;% $#e 0MS signal as transmitted from t#e

0MS e6ui"ment can be modeled by&

^f_

bot# null #y"ot#esis testing results HJ %

+4% S+M!2)$+O8S )8D RS!

2$S

A. Simulation Settings

We define to scenarios to evaluate t#e "ro"osed solution&

Scenario :& +n t#is scenario, e use D45-$ and 2$

OFDM signals "lus a ?0S ireless micro"#one as

0MS signal over an )WG8 c#annel% +t is assumed t#at

t#e detection "erformance in )WG8 ill "rovide a good

im"ression of t#e "erformance, but it is necessary to

e7tend t#e simulations to include signal distortion due

to multi"at# and s#ado fading%

Scenario @& +n t#is case, e use t#e same signals as

Scenario :, but to ma(e t#e simulations more realistic,

t#e signal is sub1ected to Rayleig# multi"at# fading and

7.t/ L ) cos.@`fJt Ism s.[ /d[ / ./[ s#adoing folloing a log normal distribution in additionto t#e )WG8% $#e ma7imum Do""ler s#ift of t#e c#annel#ere #ere fJ is t#e carrier fre6uency, ^f t#e fre6uency

deviation of t#e FM modulation, and s.t/ t#e modulating

signal #aving an am"litude of sm% $#e signal 7.t/ #as a"oer

is :JJH and t#e standard deviation for t#e log normal

s#adoing is :Jd5%

$#e simulation "arameters used in t#is "a"er for t#e D45-Z@

@7 L ) >@% )nd t#e received signal over an )WG8 is &

y.t/ L 7.t/ I n.t/ ./

+n order to derive t#e test statistic of t#is detector, $#e $eager-

aiser energy o"erator X is used to e7tract directly t#e energy

from t#e instantaneous signal and is e7"ressed by&

X9y.(/; L X97.(/; I X9n.(/; I @X97.(/, n.(/; ./

and since t#e noise and t#e signal are uncorrelated,

X97.(/, n.(/; L J% and t#e test statistic is t#e average value

$ signals are are given in 9:@;, 9:3;, #ile 2$ signals are

of bandidt# :J MH and using s#ort cyclic "refi7 .C0/% For

more details on 2$ "arameters used in t#is "a"er see ref 9:A;,

9:E; and 9:; for 2$ s"ecifications and simulations% )nd as

far as 0MS signals are concerned a ?0S narroband signal

as considered for t#e simulation of ireless micro"#ones%

B. Simulation Results

Figures .@/ and .3/, re"ort t#e results of t#e to simulated

scenarios% ) general remar( t#at could be made is t#at t#e


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O : and P : decide H

NCF DO : and P : decide H

N$ HD O : P : and 2$ RCF

Probabilityofcorrectclassification(P

)(%)

C

Probabilityofcorrectclassification(P

)(%)

C

if

D 4 5 $ R)D

N)D

2$RCF DNCF D

D4 5$

if2$ RCF D

D 4 5 $ R)D

N)D2$ .::/

if

0 MSR$ HD D4 5$R)D

N)DNCF D P : decide H0 MS

100100

90 90

80 80

70 70

60 60

50 50

40 40

30 30

20 20

10DVB!

10"!#

P$%#

DVB!

"!#

P$%#

025 20 15 10 5 0

%i&naltonoiseration(%')(B)

025 20 15 10 5 0

%i&naltonoiseration(%')(B)

Fig% @% 0robability of correct classification .0C / 4s% Signal to 8oise Ratio.S8R/ for a 0robability of False )larm 0F ) L J%JE and classification

"eriod of @E ms& Scenario :

D45-$ classification out"erforms 2$ and 0MS% $#at is

fully com"re#ensible as for D45-$ t#e detection is made

using t#e autocorrelation function of t#e #ole signal, but

for 2$ e only made it for t#e RS .reference signals/

#ic# ma(es t#e correlation lengt# loer t#an t#e D45-$

one= and t#is gets orst for 0MS as t#e signal itself is a

narroband one .5and-idt# AJJ H /% +n Figure .@/,

t#e classification is done over an )WG8 c#annel for @E

ms ac6uisition #ic# is meant to give a first overvie of

t#e classifier "erformance and in Figure .3/, for t#e same

"eriod t#e classification sc#eme is tested under a more realistic

c#annel model, a Rayleig# multi"at# fading and s#adoing

folloing a log normal distribution in addition to t#e )WG8%

$#e ma7imum Do""ler s#ift of t#e c#annel is :JJH and t#e

standard deviation for t#e log normal s#adoing is :Jd5%

4% CO8C2!S+O8

+n t#is "a"er e "resented a novel robust classifica-

tion sc#eme% $#e use-case considered in t#is "a"er is t#e

S)CR)>S0C$R) "ro1ects case #ic#, it#out any loss of

generalities can be e7tended to any ot#er cognitive netor(

Fig% 3% 0robability of correct classification .0C / 4s% Signal to 8oise Ratio.S8R/ for a 0robability of False )larm 0F ) L J%JE and classification

"eriod of @E ms& Scenario @

scenario% $#e robustness of t#e "ro"osed classifier resides in

t#e c#oice of t#e sensing algorit#m for eac# standard% Here t#e

)D as c#osen for D45-$ because it as assumed to be t#e

best detector e7"loiting t#e OFDM D45-$ "ro"erties and so

is t#e c#oice for CFD for OFDM 2$ standard, but since t#e

0MS signals are 6uite #ard to model in terms of statistics,

e o"ted for t#e e7"loitation of t#e narroband "ro"erty of

t#ose signals%

)C8OW2DGM8$

$#e researc# or( leading to t#ese results #as received

funding from t#e uro"ean CommunityKs Sevent# Frameor(

0rogramme .F0>@JJ-@J:3/ under grant agreement S)CR)

0ro1ect number @AYJJ, WHR@ 0ro1ect +C$-@AYA and

F0 C2$+C S0C$R) 0ro1ect%

RFR8CS

9:; Mitola, Cognitive radio" An integrated agent architecture for

soft*are defined radio, Doctor of $ec#nology, Royal +nst% $ec#nol%.$H/, @JJJ%

9@; $%uce(, H%)rslan, A Surve+ of Spectrum Sensing Algorithms forCog! nitive Radio Applications, + Communications Surveys $utorials

@JJY, "ages ::-:3J%


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93; S% u, _% _#ao, % S#ang, Spectrum Sensing Based on C+clostationarit+,Wor(s#o" on& 0oer lectronics and +ntelligent $rans"ortation System,

@JJ% 0+$S KJ, )ug% @JJ%

9A; W% Guibene, D% Sloc(, Signal Separation and Classification Algorithm forCognitive Radio ,et*or-s, +SWCS-:@, $#e Yt# +nternational Sym"osium

on Wireless Communication Systems, )ugust @-3:, @J:@, 0aris, France

9E; D% 0anaito"ol, )% 5agayo(o, C% Mouton, 0% Dela#aye and G% )bril,

rimar+ ser /dentification *hen Secondar+ ser is Transmitting *ithoutusing 0uiet eriod, :@t# +nternational Sym"osium on Communications

and +nformation $ec#nologies .+SC+$ @J:@/, Gold Coast, )ustralia,

October @J:@

9; S)CR) F0 "ro1ect, Deliverable @%@,

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Signal Clasification OFDM Based