An Automatic Digital Modulation Classifier for ... - IEEE Xplore

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 56, NO. 5, OCTOBER 2007

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An Automatic Digital Modulation Classifier for Measurement on Telecommunication Networks Domenico Grimaldi, Member, IEEE, Sergio Rapuano, Member, IEEE, and Luca De Vito

Abstract—This paper presents a method for the automatic classification of digital modulations without a priori knowledge of the signal parameters. This method can recognize classical singlecarrier modulations such as M -ary phase-shift keying, M -ary frequency-shift keying, M -ary amplitude-shift keying, and M -ary quadrature amplitude modulation, as well as orthogonal frequency-division multiplexing modulations such as discrete multitone that is used for asymmetric digital subscriber line and very high speed digital subscriber line standards and for power-line carrier transmissions. After identification of the modulation type, the method automatically estimates some parameters characterizing the modulation. To evaluate the method performance, several simulations have been carried out in different operating conditions that should be particularly critical by varying the values of signalto-noise ratio and the main parameters of each identifiable modulation. To assess the advantages, comparison with other classification methods has been given. To validate the assumption that is made, experimental tests have been performed. Index Terms—Classification, digital modulations, orthogonal frequency-division multiplexing (OFDM), zero crossing.

I. I NTRODUCTION

A

N automatic modulation classifier is a system that automatically identifies the modulation type of the received signal without the preventive knowledge of some parameters. Such an instrument could play an important role in electronic surveillance systems, military communications, emitter interception, signal verification, and interference identification. In particular, great interest is devoted to the development of an automatic instrument that is able to characterize the signaling quality whichever digital modulation is used. In that direction, the first step is the setting up of a universal decoder that is able to recognize the modulation type with the minimum a priori knowledge [1]–[3]. A software modulation classifier would allow the automatic selection of the right demodulation and measurement procedures for each modulation type (Fig. 1). Many efforts have been made, and several classifiers have been proposed in literature, but the majority identifies only a few modulation schemes, such as M -ary phase-shift Manuscript received February 5, 2006; revised November 30, 2006. D. Grimaldi is with the Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabria, 87036 Rende, Italy (e-mail: grimaldi@ deis.unical.it). S. Rapuano is with the Facoltà di Ingegneria, Università degli Studi del Sannio, 82100 Benevento, Italy (e-mail: rapuano@unisannio.it). L. De Vito is with the Facoltà di Ingegneria, Università degli Studi del Sannio, 82100 Benevento, Italy, and also with Telsey Telecommunications, 82018 San Giorgio del Sannio, Italy (e-mail: devito@unisannio.it). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIM.2007.895675

Fig. 1. Automatic modulation and characterization procedure.

keying (M-PSK) or M -ary quadrature amplitude modulation (M-QAM) [4]–[7], or requires the knowledge of some parameters of the signal [8]–[11]. The methods that use a statistical approach [12], [13] are particularly interesting, but they require the knowledge of the symbol rate. Other methods use neural network [14], [15] or time–frequency representations [16], [17], achieving better results but requiring more complexity. Another classification strategy [18], [19] by means of a zero-crossing technique consists of computing the instantaneous frequency and its variations that are used to classify and explore the properties of the modulated signal. The zero-crossing analysis is an attractive method [20], [21], and it has been used also in a formerly presented paper to classify the digitally modulated signals. The method proposed in [22] is based on extracting and analyzing the properties of the zero-crossing sequence shape (ZCSS). According to [18] and [19], the zero-crossing sequence is obtained by processing the incoming signal and is used for distinguishing single-tone (M-PSK) from multitone [M -ary frequency-shift keying (MFSK)] modulation. Different from [18] and [19], the successive classification on the basis of the modulation parameters is carried out by computing the instantaneous frequency variance and the amplitude of the instantaneous frequency sequence. The results that are obtained by the classifier during the first simulations show that by assuming equally likely symbol in the signal, the correct classification is obtained at a carrier-to-noise ratio as low as 11 dB. All the proposed methods in literature do not allow an efficient classification because they work only on few modulation schemes, and they are not able to recognize orthogonal frequency-division multiplexing (OFDM) modulations. To overcome this aspect, a new hierarchical/modular architecture has been developed. Basing itself on the tree structure that is

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Fig. 2. Tree structure of the automatic classification method.

shown in Fig. 2, it effects a sequence of decisions to identify the modulation type. First, it selects the type of modulation between the single-carrier (SC) and multiple-carrier (MC) ones (step 1). Then, among the SC modulations, it discriminates between the angle-modulated signals and the amplitudemodulated ones (step 2). Finally, a series of classifier modules select the correct modulation type. 1) Among the SC amplitude-modulated signals, a module discriminates the M-QAM from the M -ary amplitudeshift keying (M-ASK) (step 3). 2) Among the SC angle-modulated signals, the previous classifier [22] has been used to select between M-PSK and M-FSK modulated signals (step 4). 3) Among the MC-modulated signals, a module has been developed to identify OFDM modulated signals with cyclic extension and to carry out compliance tests for the asymmetric digital subscriber line (ADSL) and very high speed digital subscriber line (VDSL) standards and power-line carrier (PLC) modulated signals (step 5). In steps 1 and 2, the classification method is based on procedures that are already known in the scientific literature [4], [6]–[9], [12]–[17], [24], [26]. Steps 3–5 are based, instead, on specific methods that are pointed out during the research. Moreover, remarkable effort is devoted to determine the consistent criteria to set up the threshold values that are used in the sequence of decisions. Due to its modular structure, the proposed classifier could be easily expanded to work on further modulation types. In the following sections, a description of all the method steps and modules is given. Then, the simulations that are carried out to evaluate the method performances will be presented and discussed. Finally, the validation on both simulated and actual signals is shown. II. C LASSIFICATION M ETHOD As previously stated, the proposed method works on the following digital modulations: ASK, QAM, PSK, FSK, and

OFDM with cyclic extension. The general model that has been used in this paper for the modulated signals is s(t) = x(t) + n(t)

(1)

where n(t) is the additive white Gaussian noise, and x(t) depends on the modulation type. In particular, the following models have been used for x(t) [23]: xASK (t) = ARe

j2πfc t

Ak e

g(t − kTs )

k

Ak = 2i − M − 1; i = 0, 1, . . . , M − 1 j2πfc t Ck e g(t − kTs ) xPSK (t) = ARe

(2)

k 2πi

Ck = ej M ; i = 0, 1, . . . , M − 1 j2π(fc +∆fk )t e g(t − kTs ) xFSK (t) = ARe k

M −1 ∆fk = i− 2 xQAM (t) = ARe

(3)

∆f ; i = 0, 1, . . . , M −1

(4)

Ck ej2πfc t g(t − kTs )

k

Ck = ak + jbk ; ak , bk = 2i − M − 1; i = 0, 1, . . . , M − 1   p −1 N xOFDM (t) = ARe  Cn,k ej2πn∆f t  k

(5)

n=0

Cn,k ∈ C; E{Cn,k } = 0

(6)

GRIMALDI et al.: AUTOMATIC DIGITAL MODULATION CLASSIFIER

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where A depends on the power of received signal; Ak , Ck , Cn,k , and ∆fk map the transmitted symbols; Ts is the symbol period; fc is the carrier frequency; ∆f is the frequency deviation; Np is the number of OFDM subcarriers; M is the modulation level; g(t) is a finite energy signal with a Ts duration; E{·} is the expected value operator; and C is the set of the complex numbers. The method executes the classification process in five stages. This approach assures to avoid parallel execution of useless tests such as an OFDM test over an SC signal. On the other hand, each time two modules can be executed in a parallel way, this solution has been preferred. This could reduce the overall processing time, such as in the case of the compliance tests for OFDM modulations that are shown in Fig. 2. In the following, each module is briefly introduced. A. SC/MC Selection An important step in the classification problem consists of the estimation of the number of carriers that are involved in the signaling technique. A main class division can be done between SC and MC modulations. The OFDM modulated signal can be considered as composed of a great number of independent identically distributed random variables. Therefore, due to the central limit theorem, the amplitude distribution of the sampled signal can be approximated with a Gaussian one. On the contrary, the amplitude distribution of an SC modulated signal cannot be approximated with a Gaussian distribution. Given this observation, the MC/SC test becomes a normality test. Unfortunately, most of the widely used normality tests such as the χ2 test or the Epps test are noise sensitive; thus, they are not well suited for the digital modulation analysis task [24]. A more useful test for such task, proposed in [24], is the Giannakis–Tsatsanis test [25]. It is based on the fourth-order cumulants, which are defined for a zero-mean stochastic process as c4r (α, β, γ) = E {s∗ (t)s(t + α)s(t + β)s∗ (t + γ)} − E {s∗ (t)s(t + α)} E {s(t + β)s∗ (t + γ)} − E {s∗ (t)s(t + β)} E {s(t + α)s∗ (t + γ)} − E {s∗ (t)s∗ (t + γ)} E {s(t + α)s(t + β)} (7) where α, β, and γ are time delays, and E{·} is the previously used expected value operator. It can be demonstrated that c4r (α, β, γ) can be reliably used to validate a normality hypothesis as c4r goes to zero, such as 1/Np [24]. Moreover, the added noise has no effect over c4r as x(t) and n(t) are uncorrelated, and n(t) is Gaussian [25]. Therefore, to verify if the incoming signal is an MC, c4r should be computed and compared with a threshold. To compute the fourth-order cumulants of sampled signals, the former formula is digitized by using three indexes, i.e., i1 , i2 , and i3 , instead of α, β, and γ, where {0 ≤ i1 ≤ i2 ≤≤ i3 ≤ N00.4 − 1}, and N0 is the number of acquired samples. In [24], it has been demonstrated that for signals that can be modeled as s(t) = k ck g(t − kT ), the c4r calculation can be

simplified by using i1 = 0, i2 = i3 = η, with η ∈ [0, 1.5Ns ], where Ns is the number of samples corresponding to a symbol period. Since for the tasks in this paper, no a priori knowledge on the signal should be required, the former boundaries stated in [25] have been used for η ranging from 0 to N00.4 − 1. Therefore, a c vector estimate is obtained, whose elements cˆ4s (η) are defined as follows: 1 cˆ4s (η) = NO

−1−η NO

s (i)s (i + η) − 2

i=0

−2

2

1 NO

−1−η NO

2 NO −1 1 2 s (i) NO i=0

2 s(i)s(i + η)

i=0

η = 0, . . . , NO0.4 − 1.

(8)

This solution overcomes the need for a priori knowledge of Ts . Finally, if the following inequality is verified: dG,4 = cT c < τc

(9)

where τc is an opportunely set threshold, the normality test is passed. As a consequence, the analyzed signal is an MC. Remarkable effort is devoted to set up the reliable value of τc . This value is obtained using three analyses. 1) In the case of SC and signal-to-noise ratio (SNR) > 1, Fig. 3(a) shows that dG,4 > −3 dB. 2) In the case of MC, the trend of dG,4 depends on the value of the SNR, as shown in Fig. 3(b), and the interpolation factor, as shown in Fig. 3(c). 3) On the basis of the trend that is shown in Fig. 3(b) and (c), by setting the value of the interpolation factor of the OFDM signals to 16, dG,4 < −3 dB. As a consequence, the threshold value dG,4 = −3 dB permits classifying between SC (dG,4 > −3 dB) and MC (dG,4 < −3 dB). B. Amplitude Modulation Test If the incoming signal is classified as SC, it is necessary to decide if it is amplitude modulated or angle modulated. In obtaining this, the instantaneous amplitude of the signal is evaluated as follows: a(t) = |z(t)| = s2 (t) + s˜2 (t) (10) where s(t) is the analytic representation of s(t) [15]. From the sampled signal s[k], an instantaneous amplitude sequence a[k] is obtained. It is then centered and normalized to make it independent of the channel gain, thus building a new sequence, i.e., acn [k] =

a[k] −1 ma

where ma is the mean value of acn [k].

(11)

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is uniform and centered around 0.5. On the contrary, the pdf of the QAM signal is not symmetric and has a lower mean value. Therefore, a simple figure-of-merit can be used to carry out the test as follows: 1 |acn | (13) maa = E {|acn |} ≈ N0 k

and the signal can be classified as ASK if maa ≥ τa , where the threshold τa is less than 0.5. D. PSK/FSK Test The classification between FSK and PSK signals can be made by using the ZCSS-based technique that is presented in [22]. This method computes the time intervals between two subsequent zero-crossing instants of the signal. Indicating the sequence of this instants as ξ[i], a new sequence can be defined as y[i] = ξ[i + 1] − ξ[i].

Fig. 3. Trend of dG,4 (a) versus SNR for SC signals, (b) versus SNR for MC signals, and (c) versus the interpolation factor for ADSL-32 and SNR = 20 dB.

Finally, the classification is carried out by evaluating [26] γm = max |DF T (acn (k))|2 /N0 .

(12)

This feature is an approximation of the power spectral density (PSD) of the signal. Then, as can be seen in Fig. 4, the signals containing information that is encoded by angle variations have a quite constant amplitude-versus-time plot; thus, their γm < 1. Therefore, remarkable effort is necessary in setting up the reliable threshold τm to discriminate angle-modulated signals from amplitude-modulated ones. By following the experimental approach, the value of τm can be achieved by analyzing the trend of γm versus the SNR in the case of different modulation schemes. The trend of γm that is shown in Fig. 4(c) permits setting the value of τm to 6 to discriminate between anglemodulated signals and amplitude-modulated signals. C. ASK/QAM Test To exploit the differences between the ASK and QAM modulation schemes, the acn [k] sequence is also used. As can be seen in Fig. 5, given the symbol equiprobability, the probability density function (pdf) of |acn [k]| for an ASK- modulated signal

(14)

This sequence is named as ZCSS and has different trends for the two modulations. It can be observed that the ZCSS is an instantaneous measure of the signal period. As shown in [22], for a PSK signal, the ZCSS sequence is constant at 1/2fc inside each symbol and presents a peak corresponding to each phase change in the carrier signal. The peak amplitude depends on the value of the phase jump [Fig. 6(a)]. On the other hand, for an FSK signal, the instantaneous frequency, and therefore the ZCSS, is constant inside each symbol, but it varies from one symbol to another [Fig. 6(b)]. The classification between FSK and PSK signals can be made by analyzing the shape of the ZCSS. E. OFDM Signal Identiﬁcation The OFDM transmission over dispersive channels presents two main drawbacks. The channel introduces intercarrier interference, which results from the loss of orthogonality of the carriers, and intersymbol interference. To fix these problems, in many OFDM modulation schemes, a cyclic extension is added by copying the symbol heading to its end and/or the symbol tail to its front [27]. The cyclic extension duration Tce depends on the modulation type and can be used as a characterizing parameter. If the subcarrier number is large enough, the normality hypothesis can be done on the receiving signal due to the central limit theorem. However, the related stochastic process is not white, as the cyclic extension has an autocorrelation function rss (m) = 0 when the ending samples of each symbol overlap with the first ones of the subsequent symbol (Fig. 7). More formally, if I is the cyclic extension interval and Ns is the symbol duration of the samples [28], it follows that:   σs2 + σn2 , m=0 rss (m) = E {s(k)s(k + m)} = σs2 P {k ∈ I}, m = Ns  0, elsewhere (15) where σs2 is the signal variance, and σn2 is the noise variance.


Fig. 4.

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Feature γ for (a) 2PSK, (b) 4ASK modulated signal, and (c) trend of γm versus the SNR in the case of different modulated signals.

Fig. 5. (a) QAM constellation diagram. (b) QAM |acn [k]|. (c) ASK diagram. (d) ASK |acn [k]|. As can be seen in (a) and (c), the relative frequencies are constant in ASK modulated signals, whereas they vary with amplitude in QAM modulated signals.

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Fig. 6. ZCSS for (a) PSK and (b) FSK modulated signals.

Fig. 7. Autocorrelation function for an OFDM signal.

By observing such a function, it is simple to estimate the symbol duration Ns as Ns = arg max [rss (m)] (16) m>m

where m is a time threshold, which is given in terms of samples that are set up to exclude the region near zero from the maximum search. Finally, the cyclic extension percentage of the total symbol duration can be estimated as Tˆce SNR rss (Ns ) = P {k ∈ I} 2 = ˆ ˆ rss (0) σs + σn2 SNR +1 Ts + Tce σs2

(17)

where Tˆs is the estimated overall symbol duration, and Tˆce is the estimated cyclic extension duration. Starting from these observations, a generic digital modulation compliance test can be carried out: 1) by comparing the estimated Tˆs and Tˆce with the values required by the standard and 2) by comparing the s(t) PSD with the power mask that is provided by the standard, if any exist. F. Level Estimate of SC Modulations Observing the number of different amplitudes of the ZCSS, an estimate of the modulation level M can be obtained. In particular, for PSK modulations, this estimate can be made

by comparing the histogram of the measured phase deviations with the theoretical one. For FSK signals, the histogram of frequencies is computed, and the number of different levels is counted. The number of levels of ASK and QAM modulations is estimated with the same method that is used for PSK signals. The histogram, however, is computed from the centered and normalized amplitude sequence acn [k]. III. S IMULATION R ESULTS A. Method Validation The method has been characterized in simulation with several values of the SNR. For SC signals, the carrier frequency fc , the sample rate fs , and the symbol rate fd were fixed at 1 kHz, 10 kHz, and 100 Hz, respectively. As in [22], the frequency spacing of FSK signals is 0.5fc for 2FSK, 0.25fc for 4FSK, and 0.15fc for 8FSK. The OFDM signals that are used for simulations are the following: ADSL with 32 and 256 subcarriers (in compliance with ITU G.992.1 standard for upstream and downstream transmissions, respectively [29]); VDSL with 256 and 1024 subcarriers (in compliance with ETSI standard TS 101270 [30]); and PLC with 128 subcarriers. All OFDM signals were realized with 4 bits for each subcarrier and different values of the interpolation factor. Observation time corresponds to 30 000 samples for SC modulation and 100 000 and 200 000 samples for MC modulations. An early characterization of the method was made by testing each step


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TABLE I SUCCESS PERCENTAGES FOR SC/MC CLASSIFICATION TESTS

TABLE IV SUCCESS PERCENTAGES OF THE LEVEL ESTIMATE IN (a) M-PSK, (b) M-FSK, (c) M-ASK, AND (d) M-QAM MODULATIONS

TABLE II SUCCESS PERCENTAGES FOR THE CLASSIFICATION BETWEEN AMPLITUDE AND ANGLE MODULATION

TABLE V SUCCESS PERCENTAGES FOR OFDM SIGNAL CLASSIFICATION

TABLE III SUCCESS PERCENTAGES OF (a) ASK/QAM AND (b) PSK/FSK TESTS

with 100 signals for each modulation scheme. The simulation results of this characterization are shown in terms of correct classification percentages for each modulation type and for different SNR values. In Table I, the results of the SC/MC classification test are shown. For this test, 1500 signals were used (100 for each modulation scheme). As can be seen in Table I, a high percentage of correct classifications can be achieved with the SNR equal to 2 dB. For OFDM signals, these values were obtained with an interpolation factor of 4. With higher values of this parameter, the performances slightly decrease. In Table II, the results for the classification test between amplitude and angle signals are shown. Table II shows that the method achieves very good results also with very low values of SNR. Table III shows the results for ASK/QAM and PSK/FSK classifications that are made on 400 and 600 signals, respectively. The ASK/QAM module has shown better performances than the PSK/FSK module; however, both ensure correct classification for SNR values higher than 10 dB. The results for the estimates of SC modulation levels are presented in Table IV.

The minimum SNR that is needed for a correct classification is 10 dB for PSK and ASK modulations and 5 dB for FSK. For QAM signals, the minimum SNR to ensure correct classification is 20 dB. In this case, a higher value of SNR is required as compared with other modulation schemes since the number of transmitted bits is higher, and therefore, the symbols on the constellation diagram are much closer. To obtain a bit error rate of 10−3 , for example, the SNR equal to 21.5 dB is needed [23]. In Table V, the classification results for OFDM signals are shown. These results refer to an observation window of 200 000 samples. Using a shorter window, lower success percentages have been achieved. Also in this case, the method assures the satisfactory percentage of correct classification between very similar modulation schemes such as ADSL and VDSL. Moreover, it is capable of identifying PLC signals with high success percentages also with the SNR equal to 10 dB. Moreover, the characterization of the entire method was carried out. In Table VI, the overall result matrix is shown with an SNR of 20 dB. The (i, j) element of the matrix is the success percentage corresponding to the classification of the ith signal as the jth modulation type. As can be seen, the method achieves the success percentage of 100% in most part of the modulation schemes. The worst results have been obtained for ADSL and VDSL with 256 subcarriers. This can be explained by observing that these signals differ only in terms of cyclic extension length. For completeness, other numerical tests highlighted the method robustness in the case where the signal to be classified is affected by colored noise and multipath distortion. In particular, the percentage of correct classification is almost unchanged. The correct classification percentage is slightly reduced only in the case of M-PSK signals. To evaluate the method sensitivity to the parameter variation, a new simulation phase has been carried out for different values of the ratio between the sampling frequency and the carrier frequency. The results are summarized in Table VII, where the percentages of correct classification are reported for all

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TABLE VI OVERALL METHOD MATRIX FOR SNR = 20 dB

TABLE VII SIMULATION RESULTS FOR VALUES OF THE RATIO SAMPLING FREQUENCY VERSUS CARRIER FREQUENCY RANGING BETWEEN 8 AND 16

considered SC modulations and for values of the ratio between the sampling frequency and the carrier frequency ranging from 8 to 16. Also in this case, 100 signals for each modulation scheme have been used, and 30 000 samples have been acquired for the classification. The carrier frequency has been fixed to 1 kHz, whereas the sampling frequency has been varied. In all cases, high classification percentages have been observed. The incorrect classifications occur because not all the symbol levels can be detected. As a consequence, the modulation types are correctly classified, and the number of levels that are detected is reduced with respect to its real value.

TABLE VIII PERCENTAGES OF THE CORRECT CLASSIFICATION OF THE (a) PROPOSED METHOD AND (b) METHOD IN [13], WITH SNR EQUAL TO 20 dB

TABLE IX PERCENTAGES OF THE CORRECT CLASSIFICATION OF THE (a) PROPOSED METHOD AND (b) METHOD IN [17], WITH SNR EQUAL TO 10 dB

B. Comparison With Other Methods To assess the advantageous results, the proposed method has been compared with classification methods operating in similar conditions. The assumed operating conditions regard separately the method and the test signals. In particular, the methods that are taken into account are characterized by 1) capability of classifying almost the same set of modulation schemes and 2) absence of any a priori assumption on the signal properties. Moreover, the test signals are characterized by 1) equal value of the SNR; 2) same signal model; 3) 100 signals for each modulation scheme; and 4) equal value of the observation time that is settled to 30 000 samples. In Table VIII, the comparison of the results obtained with the proposed method [Table VIII(a)] and the method in [13] [Table VIII(b)] has been shown. It refers to a classification of 2PSK, 4PSK, 8PSK, and 16QAM, with an SNR equal to 20 dB. A further comparison is reported in Table IX between the proposed method [Table IX(a)] and the method in [17] [Table IX(b)] when the considered modulations are 2FSK, 4FSK, 2PSK, and 4PSK and the SNR value is 10 dB.

As can be seen, although the proposed method works on a wider range of modulation schemes, it achieves results that are similar to other methods that are proposed in literature.

IV. E XPERIMENTAL R ESULTS The proposed method has been implemented in C language on the Texas Instruments TMS320C6711 digital signal processing (DSP) starter kit (DSK) board. It has been tested on signals that are generated by the National Instruments NI 6110 data acquisition board.


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TABLE X PERCENTAGES OF THE CORRECT CLASSIFICATION IN THE EXPERIMENTAL TESTS, WITH NUMBER OF BITS EQUAL TO 16, 10, AND 6

TABLE XI PERCENTAGES OF THE CORRECT CLASSIFICATION IN THE EXPERIMENTAL TESTS BY USING RECTANGULAR PULSE SHAPE FILTER

Table X shows the results of these experimental tests by modifying the resolution of the analog-to-digital converter that equips the input section of the DSK board. In particular, 6, 10, and 16 bits have been selected from the acquired samples. It can be noted that the correct classification is achieved with a minimum resolution of 6 bits for SC modulations and 10 bits for MC modulations. Other experimental tests were performed by using the Agilent E3348C vector signal generator and the LeCroy SDA6000 digital oscilloscope. In this case, the values of the test parameters are as follows: 1) carrier frequency equal to 1 MHz; 2) symbol rate equal to 100 kHz; and 3) sampling frequency equal to 10 MHz. All signals that are considered in the experimental tests have been generated by using the rectangular pulse shape as the band-limiting filter. In Table XI, the results of the experimental tests are shown. These tests confirmed the results that are obtained in the simulation environment, where a quite correct classification has been reached for each modulation scheme. In the case of different pulse shapes, such as the raised cosine or the Gaussian pulses, the method should be used in combination with an additional zero-crossing analysis. Indeed, such analysis allows the recovery of signal information that is otherwise altered by the pulse modulation effects. The proposed method operates as a basic processing kernel that is to be enhanced with processing modules on the basis of the expected signal characteristics. V. C ONCLUSION This paper presents a new method for automatic digital modulation classification. It is capable of identifying both SC and MC signals without a priori information about them. The method has been characterized in simulation by considering different operating conditions that should be particularly critical as 1) a signal that is corrupted by additive white Gaussian noise; 2) a signal that is corrupted by colored noise; and 3) a signal that is affected by multipath fading. In

particular, in the case of Gaussian noise, the method shows very interesting results with low SNR values. Moreover, the sensitivity of the method is investigated by varying the ratio between the sampling frequency and the carrier frequency of each identifiable modulation. The advantages of the method have been highlighted by the comparison with other classification methods operating in the same conditions. The assumptions that were made point out that the method have been experimentally tested by investigation on actual modulated signal. Extensive experiments have been performed by implementing the method on DSP board and by using the vector signal generator. Both the simulation and the experimental tests confirm the interesting characteristics of the classification method. Furthermore, new modules for the classification of other modulation schemes such as minimum-shift keying (MSK) and standards such as digital audio broadcasting, digital video broadcasting, and MSK will be developed. The final target of the research work is the implementation of a method on efficient hardware to set up the measurement instrument that is able to automatically characterize and demodulate the unknown received signal without a priori knowledge of the modulated signal parameters. ACKNOWLEDGMENT The authors would like to thank Prof. P. Daponte for his helpful suggestions during the research and Dr. G. Truglia for his active collaboration during all the phases of this work. R EFERENCES [1] P. Daponte, D. Grimaldi, and L. Michaeli, “Neural network and DSP based decoder for DTMF signals,” Proc. Inst. Electr. Eng.—Science Meas. Technol., vol. 147, no. 1, pp. 34–40, Jan. 2000. [2] L. Angrisani, P. Daponte, and M. D’Apuzzo, “A measurement method based on time–frequency representations for the qualification of GSM equipment,” IEEE Trans. Instrum. Meas., vol. 49, no. 5, pp. 1050–1056, Oct. 2000.

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Domenico Grimaldi (M’95) received the Dr. Ing. degree (cum laude) in electrical engineering from the University of Naples, Naples, Italy, in 1979. After working as an independent consultant, he joined the Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabria, Arcavacata di Rende, Italy, in the 1990s, where he is currently an Associate Professor of electronic measurement. He has remained there in a variety of research and management positions. From 1999 to 2006, he was responsible for the research unit in the frame of National Project PRIN, which was supported by the Italian Ministry for University and Research. From 1997 to 2001, he was responsible for the Tempus Project and the Leonardo da Vinci Project, which were supported by the European Union. From 1998 to 2004, he was a delegate of the Rector of the University of Calabria. From 1998 to 2000, he was the Vice President of the Italian Institute of Electrical Engineers (AEI) for the Calabria Region. He has authored and coauthored over 160 papers published in international journals and conference proceedings. His current research interests include the characterization of measurement transducers, neural modeling for ADC and measuring systems, digital signal processing for monitoring and testing, virtual instrumentation and distributed measurements, and telecommunication system measurement. Prof. Grimaldi is a member of AEI. He is the Coordinator of the Working Group "e-tools for I&M Education" of the IEEE Instrumentation and Measurement Society Technical Committee on Education in Instrumentation and Measurement (TC-23).

Sergio Rapuano (M’00) received the M.S. degree (cum laude) in electronic engineering and the Ph.D. degree in computer science, telecommunications and applied electromagnetism from the University of Salerno, Salerno, Italy, in 1999 and 2003, respectively. In 2002, he joined the Facoltà di Ingegneria, Università degli Studi del Sannio, Benevento, Italy, as an Assistant Professor in electric and electronic measurement. He is currently developing his research work in digital signal processing for measurement in telecommunications, data converters, and distributed measurement systems at the University of Sannio. He is a member of the Waveform Generation, Measurement and Analysis Technical Committee (TC-10) and the Secretary of the working group on etools for education in instrumentation and measurement formed within the Technical Committee on Education for Instrumentation and Measurement (TC-23) of the IEEE Instrumentation and Measurement Society.

Luca De Vito received the master’s degree (cum laude) in software engineering, presenting a thesis work that deals with automatic classification and characterization of digitally modulated signals, and the Ph.D. degree in information engineering from the University of Sannio, Benevento, Italy, in 2001 and 2005, respectively. Then, he joined the research activities carried out at the Laboratory of Signal Processing and Measurement Information, University of Sannio. He is currently developing his research work in digital processing for measurement in telecommunications and data converters.