Abstract:
A modulation recognition method and device for digitally modulated signals with multi-level magnitudes are provided. The modulation recognition method includes selecting plural quantization sizes used to construct plural statistic histograms related to the magnitude of a sequence of data, setting up an off-line processing to extract plural useful feature patterns for each modulation type of interest, receiving a sequence of samples of a modulated object signal and constructing plural statistic histograms related to the magnitude of these samples, and adopting a hierarchical classification method for modulation recognition. It can be applied to the adaptive-modulation communication system, software defined radio, digital broadcasting systems and military communication systems. It can also be integrated with modulation recognition techniques for other types of modulated signals to function in a universal demodulator. It recognizes digitally modulated signals of multi-level magnitudes with low computational complexity in advancing the efficiency of communication systems.

Description:
FIELD OF THE INVENTION 
   The present invention generally relates to modulation recognition in digital communications, and more specifically to a method and device for modulation recognition of digitally modulated signals with multi-level magnitudes. 
   BACKGROUND OF THE INVENTION 
   For efficient management of bandwidth and maintenance of service quality, a communication or broadcasting transmitter should be able to adaptively adjust transmission data rate according to channel&#39;s conditions. Specifically, different channel conditions may lead to the transmitted signal modulated with different types of modulation techniques or a type of modulation technique with different levels, such as quadrature amplitude modulation (QAM) with different levels. Correspondingly, the receiver must be able to automatically recognize the type of modulation technique or the level of the modulation technique used in the transmitting side so that the demodulation process can demodulate the signal correctly. This associated technique required by the receiver is known as modulation recognition. 
   There have been numerous research reports and patents on modulation recognition since 1984. Among them, the developed algorithms can be categorized, based on their techniques or theories used, into five basic types: (1) pattern recognition, (2) decision theory, (3) second- and higher-order statistics, (4) neural networks, and (5) ad hoc. On the other hand, the aforementioned techniques can also be categorized, based on the type of the modulated signal that can be recognized, into only on digital modulation, and simultaneously on digital and analog modulations. Note that the types of digitally modulated signals include amplitude shift keying (ASK), phase shift keying (PSK), amplitude-phase shift keying (APSK), frequency shift keying (FSK), and QAM. 
   Specifically, Dominguez et al. (“A General Approach to the Automatic Classification of Radio-communication Signals,” Signal Processing, vol. 22, no. 3, Mar. 1991) disclosed a universal modulation recognition method by utilizing the histograms of the magnitude, phase and frequency of the received signals followed by a linear pattern recognition procedure, as shown in  FIG. 1 . This method could recognize almost all types of analog and/or digitally modulated signals. However, its performance is sensitive to phase error for the case of recognition of QAM signals. 
   QAM is a modulation technique that simultaneously places magnitude and phase of digital data onto a carrier. Because of its high spectrum efficiency, QAM is widely adopted by the standards of high-speed wired and wireless digital communication or broadcasting systems, such as V.29, V.34, DVB-C, DVB-T and ISDB-T, etc. Thereby, the QAM-related technologies, including modulation recognition technique, become the core technologies of designing receivers for communication and broadcasting systems with high-speed transmission and flexible bandwidth management allowed. However, recognition of the QAM signals with different levels (such as 64-QAM and 256-QAM) is far more difficult than that of the digitally modulated signals with different types of modulation techniques, such as those with ASK and PSK, or with PSK and QAM. The difficulty lies in that the modulated signals using different types of modulation techniques have significantly different features, whereas the differences between the features of the QAM signals for different levels are too small to discern easily. 
   For example, the method developed by Swami and Sadler (“Maximum-likelihood Modulation Classification for PSK/QAM,” IEEE Trans. Communications, vol. 48, no. 3, March 2000) uses the higher-order statistics of signals as the features. It can recognize the ASK, PSK, and QAM signals successfully, but fails to recognize the QAM signals of different levels due to the close similarity of the higher-order statistics of the QAM signals for different levels. In addition, with simultaneous utilization of the degrees of freedom of both the magnitude and phase, the QAM signals may appear more complicated compared to the ASK (simply utilizing the degree of freedom of magnitude) and PSK (simply utilizing the degree of freedom of phase) signals. For example, the constellations of higher-level QAMs (256-QAM or 1024-QAM) may have constellations overlapping those for lower-level QAM. This causes the confusion among the QAM signals of different levels and increases the difficulty of modulation recognition. 
   Another problem often in designing a receiver for a wired or wireless environment is the imperfect frequency (including phase) synchronization problem. The imperfect frequency synchronization comes from either the residual frequency offset or the phase offset for any practical frequency synchronization procedure, or the unpredictable phase noise created by some components, such as crystals and oscillators. Some frequency synchronization algorithms, such as the phase-lock loop (PLL) based carrier recovery algorithm, require the information about the modulation type of the received signals. This means that the modulation recognition must be done before performing the frequency synchronization. On the other hand, the receiver always suffers from some sorts of noise and interference. At the moment, the power ratio of desired signal v.s. noise is defined as signal-to-noise ratio (SNR). The lower the SNR, the more serious effect the noise and interference produce on received signals. Both the effect of imperfect frequency synchronization and the effect of noise and interference on the receiver may cause the failure of modulation recognition. In particular, the imperfect frequency synchronization will make some conventional modulation recognition algorithms assuming perfect frequency synchronization fail to recognize the type of received signals. 
   To eliminate the impact of the imperfect frequency synchronization, techniques similar to the maximum likelihood methods have been developed either by averaging the phases of received signals or by considering only the magnitude information of received signals. However, these methods are based on the assumption that the noise must be white Gaussian distributed with known variance, i.e., the SNR must be known. Although these algorithms have optimal performance when the assumption is satisfied, these algorithms still have two serious disadvantages. First, when actual signals are different from the assumed signal model, the performance of the algorithm will be significantly degraded. Second, the large amount of computational load becomes a burden on the hardware implementation. In addition, without cooperation between transmitting side and receiving side, the variance of the noise can only be estimated from the received signals at the receiver. When the employed modulation technique is unknown, additional efforts, such as multiple receivers or oversampling techniques, must be paid for the estimation of the noise variance. This essentially limits the design of receivers. Besides, there always exists an error between the estimated variance and the actual variance of the noise. 
   On the other hand, under the pre-requirements that (i) no additional information about the noise variance is needed, (ii) the performance is free from the imperfect frequency synchronization effect, and (iii) the computational complexity is low, the aforementioned modulation recognition method shown in  FIG. 1  can be modified by using only the histograms of the magnitude of the received signals followed by a linear pattern recognition procedure. However, its performance will not meet the requirement of a reliable receiver, especially for the case of low SNR. Because of the wide applications of the QAM in communication and broadcasting systems and because of the aforementioned problems in existing modulation recognition techniques, a modulation recognition method that can recognize digitally modulated signals of multi-level magnitudes with low computational complexity is imperative in advancing the efficiency of communication systems. 
   SUMMARY OF THE INVENTION 
   The present invention has been made to overcome the aforementioned drawbacks of the conventional modulation recognition techniques for the signals with multi-level magnitudes, including QAM, APSK and ASK signals. The primary object of the present invention is to provide a method and device for modulation recognition of digital signals with multi-level magnitudes. 
   The method uses the distribution of the quantized constellation magnitudes as a feature pattern in a hierarchical classification procedure to undergo the pattern recognition and achieve the modulation recognition of received object signals. It can be applied to the adaptive-modulation communication systems, software defined radio (SDR), digital broadcasting systems and military communication systems. It can also be integrated with modulation recognition techniques for other types of modulated signals to function in a universal demodulator. 
   Thereby, the method for modulation recognition of digitally modulated signals with multi-level magnitudes mainly comprises the following steps: (a) selecting a plurality of quantization sizes used to construct a plurality of statistic histograms related to the magnitude of a sequence of data, (b) setting up an off-line processing to extract a plurality of useful feature patterns as functions of the magnitude related statistic histograms for each modulation type of interest via simulations, (c) receiving a sequence of samples of the modulated object signal whose modulation type is to be recognized and constructing a plurality of statistic histograms related to the magnitude of this sequence of samples, and (d) adopting a classification method based on pattern recognition to recognize the modulation type of the received signal. 
   According to the invention, the classification method in step (d) uses a hierarchical classification to recognize the modulated signal. The hierarchical classification may determine the optimal number of layers in the hierarchy and the corresponding feature patterns used for each layer according to the considerations on computational complexity and the modulation types to be recognized. In the first embodiment of the invention, the classification is proceeded without checking the confidence in reliability for the modulation recognition. In the second embodiment of the invention, the classification is proceeded together with the use of a threshold decision mechanism to enhance the reliability for the modulation recognition. 
   The foregoing and other objects, features, aspects and advantages of the present invention will become better understood from a careful reading of a detailed description provided herein below with appropriate reference to the accompanying drawings. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  shows a conventional modulation recognition technique via pattern recognition. 
       FIG. 2  shows a flowchart of the modulation recognition method for digitally modulated signals with multi-level magnitudes according to the first embodiment of the present invention. 
       FIG. 3  further illustrates the steps for the off-line processing in  FIG. 2 . 
       FIG. 4  further illustrates the steps for the hierarchical classification in  FIG. 2 . 
       FIG. 5  shows a flowchart of the second embodiment of the present invention, in which the classification is proceeded together with the use of a threshold decision mechanism to check the confidence in reliability for the modulation recognition. 
       FIG. 6  shows a block diagram of a communication system having a transmitter and a receiver, wherein the modulation recognition of the present invention is applied to the communication system. 
       FIG. 7  shows a schematic representation of a modulation recognition device according to the present invention. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   At a digital communication receiving side, the modulated object signal that is obtained by frequency down-conversion, discrete-time sampling and blind channel equalization to the received radio frequency (RF) signal can be modeled as:
 
 r[n]=u[n]·e   j(Δωn+Δθ+φ[n])   +w[n]   (I)
 
where u[n] represents the modulated real or complex symbol sequence, w[n] represents the real or complex noise that is uncorrelated with u[n] and has a mean E{w[n]}=0 and variance E{|w[n]| 2 }=σ w   2 , and Δω, Δθ and φ[n] represent the unknown frequency shift, phase shift and phase noise, respectively. The phase noise has a variance E{|φ[n]| 2 }=σ φ   2 . The SNR of the received object signal r[n] is defined as:
 
   
     
       
         
           
             
               
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   Because the modulated signal of each modulation type corresponds to a different constellation with a unique magnitude distribution, the magnitude distribution of constellation can be used as a feature pattern to recognize the modulation type of the object signal. Because this feature pattern only uses the information about signal magnitude, theoretically, the recognition result based on the feature pattern will not be affected by the effects of the frequency shift Δω, the phase shift Δθ and the phase noise φ[n]. Definitely, a feature pattern for each modulation type of interest can be represented as a vector of finite-length L whose components are composed of the elements of the histogram obtained by quantizing the magnitude of the modulated signals with the modulation types of interest. 
   Let the magnitude |r[n]| of the received object signal r[n], n=0, 1, . . . , N−1, be uniformly quantized by using the same quantization size d to establish the statistic histogram of |r[n]|&#39;s distribution (probability versus quantized magnitudes). The statistic histogram can also be expressed with an object vector having the length L. As the conventional linear pattern recognition technique, comparing the object vector of the statistic histogram with the feature patterns of various modulation types that have been established in advance by off-line processing, the received object signal can be classified as belonging to the modulation type whose feature pattern is the most similar to the object vector. However, when the noise and interference severely distort the received signal, the recognition technique depending on a single feature for each modulation type may cause erroneous recognition and classification. This is especially true for the modulation recognition among certain levels of QAM signals. 
   To reduce the probability of erroneous recognition and classification, the present invention uses at least two feature patterns based on the distribution of quantized constellation power levels, and collaborates with a classification method to achieve the modulation recognition. Accordingly, at least one quantization size is selected and at least one statistic histogram for the power level&#39;s distribution of the object signal is built.  FIG. 2  shows a flowchart for the modulation recognition method according to the first embodiment of the present invention. For easy explanation and without loss of generality, the modulation types of the object signal to be recognized include 16-QAM, 32-QAM, 64-QAM, 128-QAM and 256-QAM as shown in  FIG. 2 . 
   In the embodiment of  FIG. 2 , two quantization sizes, d 1  and d 2 , are used. At the first step  201  in  FIG. 2 , an off-line processing is set up in order to establish the database of the modulated signals of interest and extract some useful features for each modulation type based on the statistic histograms for the power level&#39;s distribution of the modulated signals in the database. This off-line processing  201  is further divided into two steps  201   a  and  201   b , as shown in the  FIG. 2 . At the step  201   a , a database is set up via simulation for the sequences of modulated signals of interest. From the database, some useful feature patterns, for example f 1 , f 2 , and f 3 , are extracted according to the power information recorded on the statistic histograms, as shown in the step  201   b . These useful feature patterns are to be adopted by a classification method. 
   Next, a sequence of samples r[n], n=0, 1, . . . , N−1, is received as shown in the step  202 . By utilizing the power distribution (the distribution of |r[n]| 2 ) of each sample as the feature of the object signal, two statistic histograms, h 1  and h 2 , are constructed as shown in the step  203 . The quantity |r[n]| 2  is simply obtained by multiplying r[n] with r*[n], and is quantized by the quantization size d i , i=1, 2. The occurrences of different magnitudes of |r[n]| 2  are counted, and then each count is divided by the total sample number N to obtain the probability distribution of |r[n]| 2  (i.e. the statistic histogram), which is represented by a vector h i , i=1, 2, to be adopted by a classification method. 
   For the classification method, there are numerous algorithms that may be employed, such as linear discriminant classification, maximum-likelihood classification, and minimum relative-entropy classification. In our embodiments, linear discriminant classification is employed. According to the invention, the classification method may be used in a hierarchy for the modulation recognition. In the embodiment of  FIG. 2 , a hierarchical classification of two-layers is illustrated without loss of generality. 
   In the first-layer classification (also referred to as coarse classification) of the hierarchical classification, the vector h 1  is compared to the feature patterns f 1 , f 2  and f 3  (also referred to as the layer- 1  features) as shown in the step  204 . A layer- 1  feature represents the characteristic of a group of modulated signals corresponding to at least one type of modulations. The details of extracting f 1 , f 2  and f 3  will be defined and described later. The first-layer classification by h 1  classifies the received object signal as one of the three possible classes: (i) the “16-QAM” class, (ii) the group of “32-QAM and 128-QAM”, and (iii) the group of “64-QAM and 256-QAM”. If the classification result is in either the group of “32-QAM and 128-QAM” or the group of “64-QAM and 256-QAM”, a further second-layer classification (also referred to as the fine classification) is needed. In the second-layer classification, the vector h 2  is compared to the feature patterns f 2,1  and f 2,2 , (also referred to as the layer- 2  features) or to the feature patterns f 3,1  and f 3,2  (also referred to as the layer- 2  features). A layer- 2  feature represents the characteristic of a class of modulated signals corresponding to a modulation type. The details in obtaining f 2,1 , f 2,2 , f 3,1 and f 3,2  will be defined and described later. 
   The reason of executing the second-layer classification is that it is easy to distinguish 16-QAM from other levels of QAM signals when using d 1  as the quantization size, while it is prone to incorrectly classify 32-QAM as 128-QAM, and vice versa. The same mistakes may occur between 64-QAM and 256-QAM. Therefore, it is necessary to use a second vector h 2  (i.e., the statistic histogram corresponding to the quantization size d 2 ) to further distinguish 32-QAM from 128-QAM, or 64-QAM from 256-QAM. As shown in step  205   a  of  FIG. 2 , the vector h 2  is compared to the layer- 2  features f 2,1  and f 2,2  for distinguishing 32-QAM from 128-QAM. While in step  205   b , the vector h 2  is compared to the layer- 2  features f 3,1  and f 3,2  for distinguishing 64-QAM from 256-QAM. Based on this design scheme, a system designer may determine the optimal number of layers in the hierarchy and the corresponding feature patterns used for each layer according to the considerations on computational complexity and the modulation types to be recognized. Thereby, the system design can meet the expected needs on system functionalities. 
     FIG. 3  further illustrates the off-line processing  201  assuming that the modulation types to be recognized include 16-QAM, 32-QAM, 64QAM, 128-QAM and 256-QAM. The function of the off-line processing can be implemented with a computer. At the first step  301  in  FIG. 3 , noisy 16-QAM, 32-QAM, 64-QAM, 128-QAM and 256-QAM signals are obtained under various SNR from simulations. First, the SNR range of the signals to be recognized is determined according to the application of interest. Different modulation types may have different SNR ranges. Then, the simulated signals having the length NT are generated in the determined SNR range for different modulated signals interfered by different noises. 
   At the second step in  FIG. 3 , an individual database for each type of signal is constructed, as marked by  302   a ,  302   b ,  302   c ,  302   d , and  302   e  in  FIG. 3 . The power of each simulated signal is further quantized using the quantization size d 1  (d 2 ), and represented by a vector v i   (k)  (g i   (k) ) where k denotes the modulation level. If a set of simulated signals are generated every 1 dB spacing of SNR, the databases shown in the following table, Table 1, can be constructed. 
   
     
       
             
             
             
             
           
             
             
             
             
           
         
             
                 
               TABLE 1 
             
             
                 
                 
             
             
                 
               SNR Range 
               Database (quantization size d 1 ) 
               Database (quantization size d 2 ) 
             
             
                 
                 
             
           
           
             
                 
             
           
        
         
             
               16-QAM 
               12~40 dB 
               v 1   (16) , v 2   (16) , . . . , v 29   (16)   
                 
             
             
               32-QAM 
               15~40 dB 
               v 1   (32) , v 2   (32) , . . . , v 26   (32)   
               g 1   (32) , g 2   (32) , . . . , g 26   (32)   
             
             
               64-QAM 
               18~40 dB 
               v 1   (64) , v 2   (64) , . . . , v 23   (64)   
               g 1   (64) , g 2   (64) , . . . , g 23   (64)   
             
             
               128-QAM 
               21~40 dB 
               v 1   (128) , v 2   (128) , . . . , v 20   (128)   
               g 1   (128) , g 2   (128) , . . . , g 20   (128)   
             
             
               256-QAM 
               24~40 dB 
               v 1   (256) , v 2   (256) , . . . , v 17   (256)   
               g 1   (256) , g 2   (256) , . . . , g 17   (256)   
             
             
                 
             
           
        
       
     
   
   At the third steps in  FIG. 3 , useful feature patterns are respectively extracted from databases  302   a ,  302   b ,  302   c ,  302   d , and  302   e , as marked by  304   a ,  303 , and  304   b  in  FIG. 3 . The useful feature patterns include the layer- 2  features f 2,1 , f 2,2 , layer- 1  features f 1 , f 2 , f 3  and layer- 2  features f 3,1 , f 3,2 , and are respectively calculated by the following equations (3)˜(9). 
   
     
       
         
           
             
               
                 
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   Returning to  FIG. 2 , step  204  is the first-layer classification and step  205   a  and step  205   b  are the second-layer classifications. The classification function of steps  204 ,  205   a , and  205   b  can be implemented with inner-product calculator, magnitude comparator, and flow-path selector. The flow for performing the classification function is as shown in  FIG. 4 . Referring to  FIG. 4 , the inner products c 1 (=f 1   T ·h 1 ), c 2 (=f 2   T ·h 1 ), c 3 (=f 3   T ·h 1 ) are computed and compared at step  404 . If c 1  is the maximum among the three, the received signal is recognized as belonging to 16-QAM class. If c 2  is the maximum among the three, the received signal is recognized as belonging to the group of “32-QAM and 128-QAM”, and a further computation and comparison is required, as in step  405   a . Similarly, if c 3  is the maximum among the three, the received signal is recognized as belonging to the group of “64-QAM and 256-QAM”, and a further computation and comparison is required, as in step  405   b.    
   For further distinguishing 32-QAM from 128-QAM in step  405   a , c 2,1  (=f 2,1   T ·h 2 ) and c 2,2 (=f 2,2   T ·h 2 ) are computed and compared. If c 2,1 ≧c 2,2 , the received signal is recognized as belonging to 32-QAM. Otherwise, the received signal is recognized as belonging to 128-QAM. For further distinguishing 64-QAM from 256 QAM in step  405   b , c 3,1 (=f 3,1   T ·h 2 ) and c 3,2 (=f 3,2   T ·h 2 ) are computed and compared. If c 3,1 ≧c 3,2 , the received signal is recognized as belonging to 64-QAM. Otherwise, the received signal is recognized as belonging to 256-QAM. 
     FIG. 5  shows a flowchart for another embodiment of the present invention, in which the upper-layer classification uses a threshold decision to check the confidence in the reliability for the modulation recognition. In the second embodiment of  FIG. 5 , the second-layer classification further includes a step of confidence check  505  to enhance the reliability for the modulation recognition before deciding the modulation type of the object signal. The second-layer classification uses a predetermined threshold λ and checks the confidence via threshold decision. If the difference between two inner product outputs (for example, c 2,1  and c 2,2 ) is less than the predetermined threshold λ, then more data from the object signal are requested for enhancing the reliability of the modulation recognition, as illustrated in  FIG. 5 . The predetermined threshold λ is dependent on the modulation type to be recognized. 
   In the invention, the parameters of quantization sizes and the number of required layers in the hierarchy are the main factors that may affect the accuracy of final modulation recognition. Because the modulated signals to be recognized are different in different application environments, this invention provides a feasible rule to help in selecting these parameters as stated below. 
   In the invention, at least two quantization sizes are first selected. Then, plural feature patterns for each modulation type are generated corresponding to the quantization sizes. For each quantization size, this invention observes the differences among the feature patterns. If the difference between two different feature patterns is too small, then the quantization size is not suitable for recognizing the modulated signals corresponding to these two different feature patterns, which are therefore categorized to a group. Otherwise, the quantization size is used to recognize the modulated signals to which the two different feature patterns correspond. According to the rule, this invention continues to observe the differences among other quantization sizes. Based on the observed results, the preferred quantization sizes and the number of required layers in the hierarchy can be finally determined. 
     FIG. 6  shows a block diagram for a communication system having a transmitter  610  and a receiver  620 , wherein the modulation recognition method of the present invention is applied to the communication system. Referring to the transmitter  610  in  FIG.6 , an analog information signal  601 , if presents, is first converted into a binary data  603  by an analog/digital converter (ADC)  602  for transmission. Transmitter  610  then uses a QAM mapper  604  of certain level, for example, 16-QAM, 32-QAM, 64-QAM, 128-QAM, or 256-QAM to modulate the binary data  603 , and uses a pulse-shaping filter  605   a  and a digital/analog converter (DAC)  605   b  to convert the digitally modulated signal into a baseband analog signal. It further uses an up-converter  606   a  to translate the baseband signal to an RF signal, and uses an analog RF transmitting circuit  606   b , such as antenna or cable plug, to emit the RF signal into an EM-wave transmitting medium, such as air or cable. 
   Receiver  620  includes an analog RF receiving circuit  611   a , a frequency down-converter  611   b , an analog matched filter  612   a , an ADC  612   b , a sampling clock  613 , a constant modulus algorithm (CMA)-based blind channel equalizer  614 , a QAM de-mapper  616   a , a frequency and phase synchronizer  616   b , a decision-directed equalizer  616   c  and a modulation recognition device  615 . The analog matched filter  612   a  may be implemented with a digital filter for processing digitized signals. The sampling clock  613  can adjust the sampling rate to be greater than the transmission symbol rate. The CMA-based blind channel equalizer  614  can be used in a baud-spaced or a fractionally-spaced structure depending on the sampling rate. The frequency and phase synchronizer  616   b  may implement a carrier recovery algorithm. 
   The received analog signal is often interfered by the non-ideal channel, such as multipath reflection effects, and the down-converter often has frequency and phase shift caused by mismatch between the local oscillator and the transmitter&#39;s oscillator. If there is no sampling clock drift and the CMA-based blind channel equalizer  614  can perfectly compensate for the distortion caused by the non-ideal channel, the output signal of the CMA-based blind channel equalizer  614  can be modeled as equation (1). Based on the CMA, it can be obtained that E{|r[n]| 4 /|r[n]| 2 }=γ, where γ is a parameter in the CMA. 
   The modulation recognition device  615  according to the present invention can recognize the output signal r[n] of the CMA-based blind channel equalizer  614 . One of the preferred implementation method is to choose N T =10000, d 1 =0.2γ and d 2 =0.488γ to construct a table as Table 1, where v i   (k)  is a vector of length  13  and g i   (k)  is a vector of length  6 . Based on equations (3)-(9), the feature patterns f 1 , f 2 , f 3 , f 2,1 , f 2,2 , f 3,1 , and f 3,2  are obtained. After the output signal r[n] of the CMA-based blind channel equalizer  614  successfully recognized the modulation type according to the process flow in  FIG. 2 , the signal r[n] can be demodulated by the corresponding QAM demodulator to obtain the original data. 
     FIG. 7  shows a schematic representation of the modulation recognition device  615  according to the present invention. Referring to  FIG. 7 , the modulation recognition device  615  comprises a statistic histogram generator  701 , an off-line processing unit  703 , and a classification unit  705 . The modulation recognition device  615  receives an input sequence of the samples r[n], n=0, 1, . . . , N−1. The statistic histogram generator  701  quantizes the power of the input sequence and constructs a plurality of statistic histograms of vectors h 1 , h 2 , . . . , h v , by utilizing the distribution of |r[n]| 2 . Note that any form of |r[n]| can also be used to construct the statistic histograms. The off-line processing unit  703  sets up a database via simulation for the modulated signals of interest under various SNR as mentioned earlier, and extracts a plurality of useful feature patterns, such as f 1 , f 2 , f 3 , . . . f 2,1 , f 2,2 , . . . , f 3,1 , f 3,2 , . . . , etc. The classification unit  705  recognizes the modulation type of the received object signal r[n] based on the useful feature patterns and the plurality of statistic histograms h 1 , h 2 , . . . , h v . 
   As mentioned earlier, the function of the off-line processing unit  703  may be implemented with a computer, thereby, the computer may include a data processing unit. The function of the classification unit  705  may also be implemented with an inner product calculator  705   a , a magnitude comparator and a flow path selector  705   b  for the first embodiment of  FIG. 2 . For the second embodiment of  FIG. 5 , the classification unit  705  further includes a confidence checker  705   b , which can be implemented with a magnitude comparator. 
   Although the present invention has been described with reference to the preferred embodiments, it will be understood that the invention is not limited to the details described thereof. Various substitutions and modifications have been suggested in the foregoing description, and others will occur to those of ordinary skill in the art. Therefore, all such substitutions and modifications are intended to be embraced within the scope of the invention as defined in the appended claims.