InternationalINTERNATIONAL JOURNAL OF ELECTRONICS AND               Journal of Electronics and Communication Engineering &...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN  0976 – 6464(Print), ISSN 0...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
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Pilot induced cyclostationarity based method for dvb system identification

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Pilot induced cyclostationarity based method for dvb system identification

  1. 1. 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.aspJournal Impact Factor (2012): 3.5930 (Calculated by GISI) ©IAEMEwww.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
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEVarious algorithms for signal detection are based on different methods such as energydetection, matched filter and signal feature detection (prefix cyclic, subcarriers spacing…).The technique based on the cyclostationarity factor reflects better performance, in terms ofcorrect classification rates, than the other identification methods. A signal is said to becyclostationary 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 systemsclassification 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 adiscriminative parameter according to the concept of the opportunistic radio. Indeed, eachnetwork, to be connected, search of unoccupied frequency. So, we can not assign a welldefined frequency band to a standard. As explained by the OFDM technique, each subcarriershould 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 theenvironment. As a result, the modulation parameter can not be used for OFDM systemsdistinction.Concerning the cyclic prefix, different standards operate with almost similar cyclic prefix.This parameter then can not be efficient. Furthermore, the subcarriers spacing is impracticalbecause there are standards which their subcarriers spacing values are very close and thedifference can reach 0.11kHz such as DAB and DVB-T which its spacing values are1.116kHz and 1kHz respectively. That may avoid from reaching the accurate systemclassification. The last parameter is the pilot structure with a specific signature. Some papersdeal 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 thatinduced cyclostationarity. There are three pilot symbols distribution which are the block typeconfiguration, comb-type configuration and the circular configuration. This parameter lookspromising for OFDM signal identification. The pilot induced cyclostationarity algorithm(PIC) [9] presents a good performance in terms of correct classification rate. However, thisidentification rate decreases in the case of the DVB recognition as shown in figure 5. Thisdecrease is essentially due to the joint use of two types’ pilots. Figure 1. Opportunist receiver protocolIn our work, we focus on the PIC approach. We applied it to the DVB system identificationin 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 wediscuss the performance of this proposed DVB identification method.The paper is organized as following. Section II describes the mathematical model of theOFDM signal. The PIC algorithm is discussed in section III, followed by the proposedmethod in section IV. Section V outlines the numerical results and discussion according tothe probability of correct classification. Finally, the paper is concluded in section VI.2. SIGNAL MODEL FOR OFDMAn OFDM transmitted signal, consisting of N subcarriers, can be given by: 50
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME 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 thecyclic prefix length, g (t ) is the pulse shaping filter and a k ,n represent the transmitted data andpilot symbols at the n−th subcarrier and k − th OFDM block. The expression (1) can bewritten as: Esx(t ) = [ xd (t ) + x p (t )] (2) NWhere: 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=0And 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=0d 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 andmultipath propagation channel. It can be written as: Ly (t ) = ∑ hl x(t − l − τ ) + b(t ) (5) l =1Where h(l ) is the baseband equivalent discrete-time channel impulse response of length L , τis the timing offset and b(t ) is the white Gaussian noise.3. THE PIC APPROACHAs mentioned above, the PIC technique is based on the fact that the pilot symbols have a welldefined configuration on the time-frequency domain that introduces the periodicity [9]. Threepilot tone arrangements used for the OFDM systems are depicted in Figure 2. Thesearrangements are established to meet the needs of the channel estimation requirements. Thefirst configuration named the block type arrangement (A in figure 2) is used in the case ofslow fading channel. The pilot tones are sited on all the subcarriers of OFDM symbols with aspecific 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 onsome subcarriers of each OFDM symbol so I ( k ) = I where I is any subset of {0 ,..., N − 1} . TheWiFi system is an example for this structure type where the period is K = 1 . Concerning thethird structure, named circular configuration (C in figure 2), the set of pilot subcarriers varyin 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 theseconfigurations.The periodicity, due to the pilot tones, can be considered as the main factor for the PICtechnique and can be represented by a signature S defined as [9]: { S = ( z , w, d ( z ,w ) , K ) A( z ,w ) ≠ 0 } (7) 51
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME ( 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 twopilot 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 configurationThe 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 functiongiven 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 todetect 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 iπYk ( 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) 2Var[Y (n)] = ˆ (12) M k =0And * 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
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEIn the present manuscript, H 0 represents the WiFi, WiMAX, LTE or DVB signals and H 1 isthe DVB system.The principle is to compare the cost function against a positive threshold as: H1 > J PIC< Λ (13) H0Where 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 0probability 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 −1mk = ∑ 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)∈ζ4. THE PROPOSED ALGORITHMIn 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 50blocks of OFDM, the signal power E s is equal to 1, L = 4 and the Pfa considered is fixed to0.01 . In comparison with other probabilities of correct classification calculated whenapplying the PIC technique to identify other systems (WiMAX, WiFi) [9, 14], we concludethat the PIC can not outline good performance in the case of the DVB classification (Pccequal to 1 for an SNR equal to 8dB).Studying the DVB standard given in [10], we can easily deduce that decreasing Pcc value isessentially due to the fact that the DVB system is characterized by a joint use of the pilotsymbol distributions. The DVB system uses two types of pilots: scattered pilots andcontinuals pilots as given in figure 3 [10]. In order to improve the probability of correctclassification, we should first of all distinguish between these two types and then apply PICindependently 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
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME { [ (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 PICFigure 4 resumes the proposed method.It is important to note that timing synchronization τ , given in (5), should be achieved beforeapplying PIC. In the case of timing missynchronization, an interference signals occurred andthe cost function J PIC will be attenuated that can degrade the performance technique. Forthese reasons, τ should be estimated as:τˆ = arg max J PIC (20) τ Figure 3. The scattered pilot insertion pattern [10] 54
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME 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
  8. 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEFigure 6 presents the correct classification probability of the DVB signal versus the signal tonoise 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 ofclassification rates. In fact, Pcc reaches 1 for a SNR equal to -5dB for a Doppler frequencyequal to 0 Hz . Even for a high Doppler frequency ( f =100 Hz ), the approach shows dpromising results with Pcc=1 for SNR= - 4 dB.Figure 7 shows the effect of number of OFDM blocks on the performance of the proposedtechnique in term of probability of correct classification. It proves that the classification rateis reduced when the number of OFDM symbols decrease. In fact, the Pcc reaches 1 for M=25for 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 SNRequal 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 techniquespresent and discussed in literature [13]. The first one is the normalized kurtosis based on thekurtosis minimization. The second one is the Gaussian Maximum-Likelihood approach(GML) based on the maximum likelihood function. The third one based on the matched filterand the last one on the cyclic correlation of the received signal.Figure 8 illustrates the comparison between the five algorithms. We can easily deduce thatour proposed method outperforms others approaches. In fact, the probability of correctclassification 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 thebest 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
  9. 9. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEFigure 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
  10. 10. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME 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 06. CONCLUSIONSIn this paper we proposed a new OFDM DVB system identification approach. The proposedmethod is based on the pilot induced cyclostationarity technique. The principle is to separatethe two types’ pilot used by the DVB standard: continuals and scattered. For each type, wedefine a signature and then we apply the PIC algorithm.The evaluation of the performance was made basing on the probability of correctclassification. The simulation results prove that the proposed technique reflect excellentperformance even in the most difficult environment. On the other hand, we can say that thistechnique presents a simple structure without use of any system overhead.REFERENCES1. Mitola. J (2000), “Cognitive radio: an integrated agent architecture for software definedradio”. Ph.D. thesis, Royal Institute of Technology, Stockholm, Sweden.2. Liu. P, Li. B, Lu. Z.-Y and Gong. E-K (2005), “A blind time parameters estimation schemefor OFDM in multi-path channel”. International Conference on Wireless Communications,Networking and Mobile Computing, pp. 242-247.3. Wang. B and Ge. L (2005), “Blind identification of OFDM signal in Rayleigh channels”.International Conference on Wireless Communications, Networking and Mobile Computing,pp. 950-954.4. Bouzegzi. A, Jallon. P and Ciblat. P (2008), “A fourth-order based algorithm forcharacterization of OFDM signals”. IEEE Workshop on Signal Processing Advances inWireless Communications.5. Bouzegzi. A, Ciblat. P, and Jallon. P (2008), “Maximum Likelihood based methods forOFDM intercarrier spacing characterization”. IEEE Symp. on Personal, Indoor and MobileRadio Communications.6. Sutton. P.D, Nolan. K.E and Doyle. L.E (2008), “Cyclostationary Signatures in PracticalCognitive Radio Applications”. IEEE Jounal on selected areas in communications, vol. 26,no. 1, pp. 13–24. 58
  11. 11. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME7. Maeda. K, Benjebbour. A, Asai. T, Furuno. T and Ohya. T (2007), “Recognition AmongOFDM-Based Systems Utilizing Cyclostationarity-Inducing Transmission”. IEEE Symp. OnNew Frontiers in Dynamic Spectrum Access Networks, pp. 516–523.8. Socheleau. F.X, Houcke. S. , Aissa-El-Bey. A and Ciblat. P (2008), “OFDM systemidentification based on m-sequence signatures in cognitive radio context”. in IEEE Conf. onPersonal, Indoor and Mobile Radio Communications.9. Socheleau. F. X, Ciblat. P and Houcke. S (2009), “OFDM System Identification forCognitive Radio Based on Pilot-Induced Cyclostationarity”. IEEE Wireless Communicationsand Networking Conference (WCNC), Budapest (Hongrie).10. ETSI EN 300 744 V1.5.1, Digital VideoBroadcasting (DVB); Framing structure, channelcoding and modulation for digital terrestrial television, 2004.11. IEEE Std. 802.16, Part 16: Air Interface for Fixed and Mobile Broadband WirelessAccess Systems, Amendment 2: Physical and Medium Access Control Layers for CombinedFixed and Mobile Operations in License Bands and Corrigendum 1 (2006).12. Gardner. W. A, Napolitano. A and Paurac. L (2006), “Cyclostationarity: Half a century ofresearch”, Signal processing, vol. 86, no. 4, pp. 639–697.13. Bouzegzi. A, Ciblat. P and Jallon. P (2009), “New algorithms for blind recognition ofOFDM based systems”. Elsevier Journal.14. Alyaoui. N, Kachouri. A, and Samet. M (2011), “The pilot-induced cyclostationarityapproach for the OFDM Wifi identification”, IEEE International Multi-Conference onSystems, Signals and Devices (SSD), Sousse (Tunisia) 59

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