Pilot induced cyclostationarity based method for dvb system identification

InternationalINTERNATIONAL JOURNAL OF ELECTRONICS AND
Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 3, Issue 3, October- December (2012), pp. 49-59
IJECET
© IAEME: www.iaeme.com/ijecet.asp
Journal Impact Factor (2012): 3.5930 (Calculated by GISI) ©IAEME
www.jifactor.com

PILOT INDUCED CYCLOSTATIONARITY BASED METHOD FOR
DVB SYSTEM IDENTIFICATION
Nouha Alyaoui 1 , Abdennaceur Kachouri 2 and Mounir Samet 1
1
Electronic Laboratories of Technology’s Information (L.E.T.I), National Engineering School of
Sfax, BP W 3038 Sfax –Tunisia
2
ISSIG Higher Institute of Industrial Systems CP 6011 Gabes –Tunisia
Email: nouha.alyaoui@issatgf.rnu.tn, abdennaceur.kachouri@enis.rnu.tn, mounir.samet@enis.rnu.tn

ABSTRACT
This paper presents an approach that enables the receiver to identify the DVB standard
among different systems employing the OFDM technique (Orthogonal Frequency Division
Multiplexing) consisting of large number of mutually orthogonal sub carriers. This
characteristic provides high robustness against multi-path effects. The pilot induced
cyclostationarity (PIC) approach is one of the new algorithms introduced recently to discuss
the OFDM systems identification problem. Although this algorithm reflects good
performance in term of correct classification, it still suffers from some limitations when
identifying some standard such as DVB. In this paper, we are looking for the DVB
recognition based on the PIC. Simulation results show that system recognition based on the
proposed method exhibits excellent correct detection probability.
KEYWORDS: Cognitive Radio, DVB Identification, Pilot Induced Cyclostationarity,
Probability of correct classification
1. INTRODUCTION
In recent years, cognitive radio, introduced by Mitola [1], has attracted much attention as a
key solution for the spectrum scarcity problem that arises with increased number of users and
applications. The main important characteristic of the cognitive radio and the opportunist
radio is its ability to identify the available unused spectrum. This characteristic is related to
its capability to identify and classify different wireless networks. In fact, an opportunist
receiver, before making a communication, must follow the four steps given in figure 1. First
of all, opportunist receiver senses the environment in order to detect the present signal and so
the occupation of the frequency band. If this band is occupied, an identification algorithm
should be applied to recognize the present standard. If it is a secondary system, the
opportunist receiver will analyse the occupation level.
As the multi-carrier techniques such as OFDM are commonly used in the modern
communications (WiFi, DVB, WiMAX, LTE…), an OFDM identification approach becomes
a necessity to identify the corresponding wireless network.

49

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN

Various algorithms for signal detection are based on different methods such as energy
detection, matched filter and signal feature detection (prefix cyclic, subcarriers spacing…).
The technique based on the cyclostationarity factor reflects better performance, in terms of
correct classification rates, than the other identification methods. A signal is said to be
cyclostationary if its autocorrelation function is a periodic function of time with a period.
There are various parameters and features may be used to ensure the OFDM systems
classification such as frequency band, modulation, cyclic prefix [2, 3], subcarriers spacing [4,
5] and the pilot tone structure [9]. The first parameter, frequency band, can not be a
discriminative parameter according to the concept of the opportunistic radio. Indeed, each
network, to be connected, search of unoccupied frequency. So, we can not assign a well
defined frequency band to a standard. As explained by the OFDM technique, each subcarrier
should be modulated independently with the digital modulations QAM-4, QAM-16, QAM-
64… The M-QAM modulation may be changed in the same system according to the
environment. As a result, the modulation parameter can not be used for OFDM systems
distinction.
Concerning the cyclic prefix, different standards operate with almost similar cyclic prefix.
This parameter then can not be efficient. Furthermore, the subcarriers spacing is impractical
because there are standards which their subcarriers spacing values are very close and the
difference can reach 0.11kHz such as DAB and DVB-T which its spacing values are
1.116kHz and 1kHz respectively. That may avoid from reaching the accurate system
classification. The last parameter is the pilot structure with a specific signature. Some papers
deal with this parameter [6 - 8]. The pilot symbols are structurally well-defined in the time-
frequency domain with a particular distribution that forms a certain periodicity so that
induced cyclostationarity. There are three pilot symbols distribution which are the block type
configuration, comb-type configuration and the circular configuration. This parameter looks
promising for OFDM signal identification. The pilot induced cyclostationarity algorithm
(PIC) [9] presents a good performance in terms of correct classification rate. However, this
identification rate decreases in the case of the DVB recognition as shown in figure 5. This
decrease is essentially due to the joint use of two types’ pilots.

Figure 1. Opportunist receiver protocol

In our work, we focus on the PIC approach. We applied it to the DVB system identification
in order to measure its performance and so to outline its limitation to identify this standard.
We present a new algorithm which is a developed version of the PIC technique and we
discuss the performance of this proposed DVB identification method.
The paper is organized as following. Section II describes the mathematical model of the
OFDM signal. The PIC algorithm is discussed in section III, followed by the proposed
method in section IV. Section V outlines the numerical results and discussion according to
the probability of correct classification. Finally, the paper is concluded in section VI.

2. SIGNAL MODEL FOR OFDM
An OFDM transmitted signal, consisting of N subcarriers, can be given by:

50


n
Es K−1 N−1 2iπ (t −D−k ( N +D))
(1)
x(t) = ∑∑ak,ne N
N k =0 n=0
ga (t − k(N + D))

With E s is the signal power, K represent the number of OFDM transmitted block, D is the
cyclic prefix length, g (t ) is the pulse shaping filter and a k ,n represent the transmitted data and
pilot symbols at the n−th subcarrier and k − th OFDM block. The expression (1) can be
written as:
Es
x(t ) = [ xd (t ) + x p (t )] (2)
N
Where:
K−1 N−1 n
2iπ (t −D−k ( N+D))
xd (t) = ∑∑dk,ne N
.g(t − k(N + D)) (3)
k=0 n=0

And
K −1 N −1 n
2iπ (t −D−k ( N +D))
x p (t) = ∑∑ pk,n e N
.g(t − k(N + D)) (4)
k =0 n=0

d k , n and p k ,n are the transmitted data and pilot symbols, respectively.
At the reception, the received signal is disturbed by additive white Gaussian noise and
multipath propagation channel. It can be written as:
L
y (t ) = ∑ hl x(t − l − τ ) + b(t ) (5)
l =1
Where h(l ) is the baseband equivalent discrete-time channel impulse response of length L , τ
is the timing offset and b(t ) is the white Gaussian noise.
3. THE PIC APPROACH
As mentioned above, the PIC technique is based on the fact that the pilot symbols have a well
defined configuration on the time-frequency domain that introduces the periodicity [9]. Three
pilot tone arrangements used for the OFDM systems are depicted in Figure 2. These
arrangements are established to meet the needs of the channel estimation requirements. The
first configuration named the block type arrangement (A in figure 2) is used in the case of
slow fading channel. The pilot tones are sited on all the subcarriers of OFDM symbols with a
specific period K . Let I(k) be the set of pilot tones:
 {0 ,..., N − 1} if k = mK (m ∈ Z )
  (6)
I (k ) =  
φ otherwise 
 

The second arrangement is comb-type configuration (B in figure 2). Pilot tones are placed on
some subcarriers of each OFDM symbol so I ( k ) = I where I is any subset of {0 ,..., N − 1} . The
WiFi system is an example for this structure type where the period is K = 1 . Concerning the
third structure, named circular configuration (C in figure 2), the set of pilot subcarriers vary
in a periodic manner so that we can note I (k + K ) = I (k ) .
Note that some standards such as DVB and WiMAX [10, 11] present a joint use of these
configurations.
The periodicity, due to the pilot tones, can be considered as the main factor for the PIC
technique and can be represented by a signature S defined as [9]:
{
S = ( z , w, d ( z ,w ) , K ) A( z ,w ) ≠ 0 } (7)

51


( z ,w)
z and w are the pilot tones with pk ( z) = pk + d ( z , w) (w)e iϕ , d is the distance between the two
pilot subcarrier indexes z and w , ϕ ∈ [ −π , π [ and A( z ,w) =  r − [ K / 2] , r ∈ {0,1,..., K − 1} .
 
 K 
[.] stand for integer floor. A( z ,w) is the set of nonzero cyclic frequencies [12].

Figure 2 Examples of pilot tone configuration
The classification approach uses the energy evaluation of the cyclic cross correlation function
(CCCF) at cyclic frequencies α . This energy can be calculated according to the cost function
given by:
 2
J PIC = ∑ ∑

~ (
RY ( z ,w ) d ( z ,w ) 
ˆα

) (8)
z , w ∈  ∈A( z , w )
( )ξ α 
As noted before, α represent the cyclic frequencies, α ∈ A( z ,w) . The cyclic frequency is used to
detect the presence of cyclostationarity. The CCCF is periodic in α with period 1.
ζ = {( z , w) A( z ,w ) ≠ 0etd ( z ,w ) + K ≤ M } where M is the observation window length. RY ( z ,w ) , Yk (n )
ˆα
~

~
and Yk (n ) are defined as follow:
M−d( z ,w) −1
1 ~ ~*
ˆα ( )
RY (z,w) d (z,w) = *
( z,w) ∑Y (z)Y k +d ( z ,w)
(w)e−i2παk (9)
M −d k=0
N −1 nm
1 − 2 iπ
Yk ( n) =
N
∑ y[k ( N + D) + D + m]e
m= 0
N (10)
~
Yk ( n) represent the normalized Yk (n) given by:
~ Yk (n)
Yk (n) = (11)
ˆ
Var[Y (n)]
With Var[.] is the variance:
1 M −1
∑ Yk (n)
2
Var[Y (n)] =
ˆ (12)
M k =0
And * stands for the complex conjugation.
The identification problem is to distinguish between two hypotheses H 0 and H 1 :
H 0 : Whether a noise signal, signal not defined by a PIC structure or a signal written
by a signature S′ different from that of S .
H1 : Signal written by a signature S = {( z , w, d ( z , w) , K ) A( z ,w ) ≠ 0}.

52


In the present manuscript, H 0 represents the WiFi, WiMAX, LTE or DVB signals and H 1 is
the DVB system.
The principle is to compare the cost function against a positive threshold as:
H1
>
J PIC< Λ (13)
H0

Where FJ PIC H 0 (Λ ) = 1 − Pfa
(14)
And FJ H (Λ) is the cumulative distribution function of J PIC when the signal is H 0 , Pfa is the
PIC 0

probability of false alarm.
The function FJ PIC can be written as Laguerre series of the form [9]:
x
−
e 2ω xξ k!m (ξ +1) x (15)
FJPIC H0 (x) = ξ +1 ∑ k L(kξ ) ( 2ων )
(2ω) Γ(ξ + 1) k≥0 (ξ + 1) k
∀ν > 0 and ω > 0 with ξ = ∑ card ( A ( z ;w) ),
( z , w )∈ζ

x −ξ d k (16)
L(kξ ) ( x) = exp( x). [exp(− x).x k +ξ ]
k! dx k
1 k −1
mk = ∑ m j gk− j , k ≥ 1
k j =0
(17)

With
−card( A( z ,w) )
ωξ +1 ξ +1−ν (18)
m0 = 2(ξ +1)ξ +1 ∏ζ (ων + 2(M − d ( z, w)
)
ξ +1−ν ( z,w)∈ )
and
 −ν 
j
ν (2(M − d (z,w) )ω −1) j (19)
 ξ +1−ν  + ∑card( A( z,w) )( 2(M − d (z,w) )ων + ξ +1−ν )
gj = 
  (z,w)∈ζ

4. THE PROPOSED ALGORITHM
In order to approve the PIC method limitation, we apply it for the DVB system identification.
The simulation results are described in figure 5. For this evaluation, we consider 25 to 50
blocks of OFDM, the signal power E s is equal to 1, L = 4 and the Pfa considered is fixed to
0.01 . In comparison with other probabilities of correct classification calculated when
applying the PIC technique to identify other systems (WiMAX, WiFi) [9, 14], we conclude
that the PIC can not outline good performance in the case of the DVB classification (Pcc
equal to 1 for an SNR equal to 8dB).
Studying the DVB standard given in [10], we can easily deduce that decreasing Pcc value is
essentially due to the fact that the DVB system is characterized by a joint use of the pilot
symbol distributions. The DVB system uses two types of pilots: scattered pilots and
continuals pilots as given in figure 3 [10]. In order to improve the probability of correct
classification, we should first of all distinguish between these two types and then apply PIC
independently on each of the two types’ pilot.
Our proposed approach is based on three steps:
i. Separation and distinction between the two types’ pilot.
ii. For each types’ pilot (scattered and continuals), we specify the correspondent
signature as follow:
For the scattered pilots, {
S = ( z, w, d ( z ,w) , K ) } with:

53


{ [
(z, w) ∈ k = Kmin + 3*(l mod4) +12p p integer p ≥ 0, k ∈ Kmin;
, K
max
]} [10]
(20)
K =1 ; d ( z , w) = 0

For the continuals pilots, S = {( z, w, d ( z ,w)
} with:
,K)

( z , w) got from table 7 in [10]

K =1 ; d ( z, w) = 0
iii. Application of PIC
Figure 4 resumes the proposed method.
It is important to note that timing synchronization τ , given in (5), should be achieved before
applying PIC. In the case of timing missynchronization, an interference signals occurred and
the cost function J PIC will be attenuated that can degrade the performance technique. For
these reasons, τ should be estimated as:
τˆ = arg max J PIC (20)
τ

Figure 3. The scattered pilot insertion pattern [10]

54


Received signal

Determination of z and w

Yes
s Consider
{
z ∈ k = K min + 3 * (l mod 4) + 12 p p int eger, p ≥ 0, k ∈ [ K min ; K max
{ }
w ∈ k = K min + 3 * (l mod 4) + 12 p p int eger, p ≥ 0, k ∈ [ K min ; K max ]
No
N

{
z '∈ indices from table1 } S = {z , w , d , K } d = 0; K = 1

{
Consider w'∈ indices from table1 }

S ' = {z ' , w' , d , K } d = 0; K = 1

No
Application of PIC WiFi, WiMAX, LTE
J PIC > seuil systems

Yes

DVB system

Figure 4. Proposed Method

5. SIMULATIONS AND DISCUSSIONS
This section is devoted to simulations and numerical results realized using Matlab. We
considered the random-phase AWGN channel and time variant multipath channel.
To evaluate the performance of the PIC approach, we look for the probability of correct
classification Pcc of the DVB system versus the Signal to Noise Ratio (SNR). The technique
aims to identify the DVB signal among the DVB, WiFi, WiMAX and LTE systems. We here
simulate the DVB 2K mode system. The parameters of the considered DVB system are given
by Table 1. First of all, we apply the PIC approach without modification. The result is
represented by figure 5 for 100 samples. It’s clear that the rate of the DVB identification is
very poor even when the frequency Doppler is equal to 0Hz. As we explained before, that
weak performance is due to the use of the two pilots’ type.
We pass now to the evaluation of the proposed method. In our simulations, we consider 25 to
50 blocks of OFDM, the signal power E s is equal to 1 and L = 4 . Concerning the false alarm
probability considered Pfa is fixed to 0.01.

55


Figure 6 presents the correct classification probability of the DVB signal versus the signal to
noise ratio ranging from -10 to 15 dB. We considered 10 OFDM blocks for this simulation.
The results prove that the proposed algorithm provides excellent performance in terms of
classification rates. In fact, Pcc reaches 1 for a SNR equal to -5dB for a Doppler frequency
equal to 0 Hz . Even for a high Doppler frequency ( f =100 Hz ), the approach shows
d
promising results with Pcc=1 for SNR= - 4 dB.
Figure 7 shows the effect of number of OFDM blocks on the performance of the proposed
technique in term of probability of correct classification. It proves that the classification rate
is reduced when the number of OFDM symbols decrease. In fact, the Pcc reaches 1 for M=25
for SNR equal to -6dB. However, for M=50, the Pcc attaint the same value for SNR = -9dB.
The Doppler frequency considered in figure 7 is 0 Hz .
Table 2 and 3 present the confusion matrix of the proposed method for 100 samples for SNR
equal to -10dB and 0 dB respectively. The Doppler frequency considered is 0 Hz .
We concentrate now on the comparison between the proposed method and four techniques
present and discussed in literature [13]. The first one is the normalized kurtosis based on the
kurtosis minimization. The second one is the Gaussian Maximum-Likelihood approach
(GML) based on the maximum likelihood function. The third one based on the matched filter
and the last one on the cyclic correlation of the received signal.
Figure 8 illustrates the comparison between the five algorithms. We can easily deduce that
our proposed method outperforms others approaches. In fact, the probability of correct
classification of the DVB system for proposed method reaches 1 for SNR equal to -9dB.
However, this value is attained for SNR equal to -4dB for the GML algorithm which is the
best algorithm between GML, matched filter, cyclic correlation and kurtosis algorithms.

Figure 5. Correct classification rate vs. SNR for f =0 Hz and f =100 Hz (PIC method)
d d

56


Figure 6. Correct classification rate vs. SNR for f =0 Hz and f =100 Hz (proposed method)
d d

Figure 7. Correct classification rate vs. SNR for M=25 and M=50 (proposed method)

Figure 8. Classification rate comparison between proposed method and algorithms discussed
in literature

Table 1. Parameters of OFDM DVB
Parameters 2K mode

57


Number of Carriers 1705
K min 0
K max 1704
Tu symbol duration 22µs
Subcarriers spacing 4464 Hz

Table 2. Confusion matrix for SNR=-10dB

DVB WiFi WiMAX LTE
DVB 46
DVB 0
DVB 30
DVB 24

Table 3. Confusion matrix for SNR=0dB

DVB WiFi WiMAX LTE
DVB 100
DVB 0
DVB 0
DVB 0

6. CONCLUSIONS
In this paper we proposed a new OFDM DVB system identification approach. The proposed
method is based on the pilot induced cyclostationarity technique. The principle is to separate
the two types’ pilot used by the DVB standard: continuals and scattered. For each type, we
define a signature and then we apply the PIC algorithm.
The evaluation of the performance was made basing on the probability of correct
classification. The simulation results prove that the proposed technique reflect excellent
performance even in the most difficult environment. On the other hand, we can say that this
technique presents a simple structure without use of any system overhead.

REFERENCES
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4. Bouzegzi. A, Jallon. P and Ciblat. P (2008), “A fourth-order based algorithm for
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7. Maeda. K, Benjebbour. A, Asai. T, Furuno. T and Ohya. T (2007), “Recognition Among
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Systems, Signals and Devices (SSD), Sousse (Tunisia)

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Pilot induced cyclostationarity based method for dvb system identification

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