DE102014101793B4 - Method, apparatus and computer program for determining a modulation method with which a plurality of received symbols has been modulated - Google Patents

Abstract

A method of determining a modulation method that modulates a plurality of received symbols, comprising: determining (1120) a probability value for each modulation method from a group of possible modulation methods using a subset of the plurality of received symbols, wherein the probability value is a probability that all symbols used to determine the probability value have been modulated with the respective modulation method; Determining a candidate modulation scheme candidate (1140) as the modulation scheme from the group whose probability score quantifies the highest probability of all the modulation schemes of the group; using all symbols of the plurality, determining (1160) a final probability value for the candidate; for at least one of the further modulation methods from the group, determining (1180) another probability value using an additional received symbol; and removing (1210) a modulation method from the group of possible modulation methods if the further probability value of the modulation method is less probable than the final probability value of the candidate.

Description

Technical area

Embodiments are concerned with a method and apparatus for determining a used modulation method from a group of possible modulation methods with which a plurality of received symbols have been modulated.

background

Applications in which the modulation methods for generating transmitted symbols are varied depending on the instantaneous quality of a transmission channel between a transmitter and a receiver are diverse. Adaptive modulation is a method to efficiently utilize the channel capacity of a time-varying multipath transmission channel, for example, in an Orthogonal Frequency Domain Multiplexing (OFDM) transmission system. For this purpose, the modulation methods are adapted to the current state of the transmission channel. The aim is to use more spectrally efficient modulation techniques on those subcarriers of a multiple subcarrier OFDM signal having higher channel quality, while simpler, more robust modulation schemes are used on lower quality subcarriers. This can lead to an improvement in the performance or the efficiency of the overall system, which, for example, with a bit error probability of 10 ^-3 and constant data rate, can allow a reduction of the transmission power between 5 and 15 dB compared to a system with fixed modulation. In order to enable the receiver to correctly demodulate the signal, the information about the modulation methods used is conventionally transmitted from the transmitter to the receiver, ie the receiver is informed on which subcarrier or for which symbol which modulation method was used in order to demodulate it Able to correctly demodulate the contents of the symbol. Usually, this information is explicitly signaled by control bits or control information, which can lead to a partial considerable reduction in the bandwidth efficiency, that is, the useful information that can be transmitted per bandwidth. One approach to prevent this is to enable the receiver to independently identify the modulation method itself by means of an automatic modulation classification.

One way to conclude the modulation used in a subcarrier for the currently received symbol, for example, is to maximize the likelihood function that a specific modulation was originally used for an actually received point in the constellation diagram. This probability is based on a sum of exponential functions, the sum having a number of summands corresponding to the number of constellation points of the modulation method. The exponents each have a term that corresponds to the distance of the received symbol from one of the possible constellation points. Therefore, in this approach, for each modulation type, depending on its order or possible points in the constellation diagram, a plurality of partially complex calculations are performed. This can cause the method to be inapplicable in systems that communicate approximately in real time. Reasons may be that the necessary calculations take too long or that on the receiving device, such as a mobile phone or any wireless communication device necessary for pursuing this approach computing capacity is not available.

Another way to deduce the modulation used is feature-based methods or approaches. In these feature-based methods, a set of features is extracted from the signal that may be characteristic of the modulation used to generate the received signal. Based on the extracted features, the assignment then takes place to the estimated modulation used. While this method may possibly be implemented more computationally than the probabilistic approach, the reliability with which the modulation actually used is determined is typically less than that of the probabilistic approach due to the fact that feature-based methods use only a portion of the available information , Furthermore, feature-based methods have the disadvantage that they function only to a limited extent in modulation methods of the same class because of the then similar features. Both of the methods use only the received signal of a subcarrier for the classification in order to estimate the modulation on the relevant subcarrier. This requires that the signal possibly has to be monitored for a long time or for several consecutive frames of an OFDM signal in order to obtain a comparatively reliable statement about the modulation method used, despite considerable computing capacity. This, in turn, reduces applicability in real-time applications where there are low latencies required are. In OFDM systems, for example, the length of an OFDM frame is additionally limited by possible synchronization errors and channel estimation errors due to the time-varying channels, so that a short frame length or observation time can result. Examples of such applications are WLAN networks, in particular a WLAN client, a laptop, a mobile telephone, a measured value recorder or the like, as well as all possible unidirectional or bidirectional communication systems, which can assign contents to be transmitted to symbols of different modulation levels, the efficiency of the transmission to increase.

Specific implementations of the likelihood-based modulation classification are, for example, in the Chinese patent application CN202918327U , as well as in U.S. patent applications US2006115013A, US8358723B.

Examples of implementations of the feature-based modulation classification can be found inter alia in the Korean patent application KR20070000274A in US patent application US2006072679A and in the French patent application FR2713799A ,

Simplifications have already been proposed for the likelihood-based methods in order to reduce the computational burden and to perform the classification faster and / or to reduce the energy consumption so that it can also be performed on mobile devices. For example, in the so-called four-point approximation (Y. Chen, L. Häring, and A. Czylwik, MAP-based automatic modulation classification with reduced complexity for TDD-based adaptive OFDM systems in Proceedings of the 16-th International OFDM Workshop (InOWo ), Hamburg, Germany, 2011), which takes into account the four nearest possible symbols of a received symbol to estimate a probability of whether the received symbol was modulated by the corresponding modulation method. However, this and similar methods may in part lead to a significant reduction in the reliability of the classification.

For a classification attempt, a plurality of symbols are taken into account for reasons of reliability and to reduce the probability of misclassification, so that the computational effort increases with the number of symbols considered. For each of the plurality of symbols, a number of probability values corresponding to the number of possible modulation methods are determined, which each contain information about the probability that the received symbol was modulated with the respective modulation method.

The International Patent Publication WO 2010/080920 A1 describes a method for classifying and decoding wirelessly transmitted signals that determines a usage probability for classification for different possible modulation schemes.

There is thus a need to reduce the computation time or the complexity of the calculations for a classification.

Summary

Embodiments for determining a modulation method using a plurality of received symbols first determine a likelihood value for each modulation method from a group of possible modulation methods using a subset of the plurality of received symbols. The complete number or the majority of received symbols that are to be included in the classification for statistical reasons is initially not calculated. For the subset of the plurality of received symbols, a probability value is determined for each modulation method from the group of possible modulation methods which quantifies a probability that all symbols used to determine the probability value were modulated with the relevant or the respective modulation method. Then a preliminary candidate for the modulation method used is determined. The candidate is that modulation method from the group whose probability value, which was first determined using only a subset of the plurality of received symbols, quantifies the highest probability of all the modulation methods of the group. For the candidate, a final probability value is determined using all the symbols of the plurality of symbols. The final probability score for the candidate takes into account all received symbols of the plurality.

For at least one or each of the further modulation methods from the group, a further probability value is determined using an additionally received symbol. If the further probability value of a modulation method already numbers a lesser probability than the final probability value of the candidate, the relevant modulation method is removed from the group of possible modulation methods, so that further iterations for the remote modulation method no longer determine any further probability values, whereby significant computing capacity can be saved without affecting the result of the classification.

Compared with methods in which a probability value is first calculated for each of the modulation methods taking into account all of the plurality of received symbols before the decision for the classification is made, embodiments of the invention can significantly reduce the computational load without compromising the accuracy of the classification. According to some embodiments, the group of possible modulation methods includes, for example, Binary Phase Shift Keying (BPSK), 4QAM (Quadrature Amplitude Modulation), 16QAM and 64QAM. Furthermore, according to some embodiments, reception situations are considered in which time-limited or unlimited transmission is not possible. That is, according to some embodiments, the set of possible modulation methods also includes a method that does not involve the sending of a symbol such that only noise is received by the receiver, or the transmitter merely generates noise.

The greater the number of possible modulation methods, the more complex the task and the greater the potential savings in computational load according to the embodiments.

According to some embodiments, in an iterative process, in each case one of the received symbols is taken into account in order to determine an updated probability value for all elements or modulation methods still remaining in the group of possible modulation methods, and to check whether the candidate is aware of the updated probability value is still considered as the most likely modulation method for the modulation method. As soon as the group of possible modulation methods no longer contains a modulation method, the current candidate for the modulation method used is the modulation method with the highest probability and is determined or classified as the modulation method used.

Insofar as at the end of an iterative process during which several of the plurality of received symbols have been successively considered, several modulation methods remain in the group of possible modulation methods, that modulation method can be classified or designated as used, whose probability value is the highest probability.

In accordance with some embodiments, a similar method for determining the source of a signal from a group of possible sources is determined, the method being based on a plurality of received signals. At first, a probability value for each of the possible sources is determined using a subset of the plurality of received signals. Then, a candidate is determined for the actual source corresponding to that of the possible sources of the group whose probability value is the highest probability of all sources of the group. Using all symbols of the plurality, a final probability value for the candidate is determined, whereupon for each of the further possible sources or for at least one of the sources from the group, a further probability value is determined using an additional received signal. If the further probability value of the source is less probable than the final probability value of the candidate, the corresponding source is removed from the group of possible sources.

According to these embodiments, classifications other than those of the modulation can be speeded up while maintaining the same classification accuracy, or their computational load can be reduced. For example, different signal patterns, for example optical or acoustic signals, can be assigned to a series of actually received signals with comparatively low computational load. This can be used, for example, to identify which of a plurality of different objects is being observed, for example, if their acoustic or optical emission is known.

Brief Description

Embodiments are explained below with reference to the accompanying figures. Show it:

1 a flowchart of a method for determining a modulation method with which a plurality of received symbols has been modulated;

2 an illustration of an embodiment in tabular form;

3 a block diagram of a device for determining a modulation method with which a plurality of received symbols has been modulated;

4 Fig. 12 schematically shows a block diagram of one embodiment of a method for determining information about a probability that a received symbol has been modulated with a modulation scheme under test;

5 FIG. 12 is an illustration of a constellation diagram of 16-QAM illustrating the determination of a first approximation and a second approximation; FIG.

6 shows as a comparative example a four-point approximation for estimating a probability that a received symbol has been modulated with a modulation scheme under test;

7 shows an overview of the required process steps to implement an in 5 illustrated embodiment;

8th Figure 2 illustrates two partial constellation diagrams of 16-QAM and another embodiment for determining first and second approximations;

9 illustrates an overview of the process steps for implementing the in 8th illustrated embodiment;

10 illustrates the approximation of a function dependent on a distance between the received symbol and a position associated with the four symbols of the first group which may be used to evaluate the information about the probability;

11 illustrates a comparison of the probabilities of a faulty classification, between the embodiments and an evaluation of the complete likelihood function;

12 schematically illustrates an embodiment of an apparatus for determining information about a probability that a received symbol has been modulated with a modulation scheme under test;

13 illustrates another application scenario for embodiments; and

14 shows a plurality of possible modulation methods.

description

Various embodiments will now be described in more detail with reference to the accompanying drawings, in which some embodiments are illustrated. In the figures, the thickness dimensions of lines, layers and / or regions may be exaggerated for the sake of clarity.

In the following description of the attached figures, which show only some exemplary embodiments, like reference characters may designate the same or similar components. Further, summary reference numerals may be used for components and objects that occur multiple times in one embodiment or in a drawing but are described together in terms of one or more features. Components or objects described by like or summarizing reference numerals may be used with respect to single, multiple, or all Features, for example, their dimensions, the same, but may also be different, unless the description explicitly or implicitly results.

Although embodiments may be modified and changed in various ways, exemplary embodiments are illustrated in the figures as examples and will be described in detail herein. It should be understood, however, that it is not intended to limit embodiments to the particular forms disclosed, but that embodiments are intended to cover all functional and / or structural modifications, equivalents and alternatives that are within the scope of the invention. Like reference numerals designate like or similar elements throughout the description of the figures.

Note that an element referred to as being "connected" or "coupled" to another element may be directly connected or coupled to the other element, or intervening elements may be present. Conversely, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements. Other terms used to describe the relationship between elements should be interpreted in a similar manner (eg, "between" versus "directly in between," "adjacent" versus "directly adjacent," etc.).

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the embodiments. As used herein, the singular forms "a," "a," "an," and "the" are also meant to include the plural forms unless the context clearly indicates otherwise. It should also be made clear that the terms such. "Including," "including," "and / or having," as used herein, indicates the presence of said features, integers, steps, operations, elements, and / or components, but the presence or addition of one or more features, integers, steps, operations, elements, components and / or groups thereof.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly assigned to one of ordinary skill in the art to which the embodiments pertain. Furthermore, it should be clarified that expressions, e.g. For example, those defined in commonly used dictionaries are to be interpreted as having the meaning consistent with their meaning in the context of the relevant art and not to be interpreted in an idealized or overly formal sense, as long as this is so not expressly defined herein.

1 1 illustrates an embodiment of a method for determining a modulation method with which a plurality of received symbols has been modulated, with reference to a flow chart.

First, in an initialization step 1120 for each of the i modulation methods, a probability value W _s (i) is determined from a group of possible modulation methods, a subset of s symbols of the plurality of n received symbols α _n (the n th symbol or symbol vector) being taken into account for its determination. In a step of preselection 1140 a candidate k is determined for the modulation method used. The candidate used is the modulation method whose probability value after the initialization step quantifies the highest probability of all modulation methods within the group.

For the candidate k, the final probability value W _n (k) is calculated considering all n symbols in a step of reference value formation 1160 certainly. This is followed by an iteration step 1180 for each, or at least for a further modulation method i, another probability value W _{s + 1} (i) is determined from the group. For the determination of the further probability value W _{s + 1} (i) an additional further received symbol is considered. In a comparison step 1200 it is checked whether the further probability value W _{s + 1} (i) for each element i of the group is a lesser probability than the final probability value W _n (k) of the candidate. If this is the case, the associated modulation method in step 1210 removed from the group before another iteration step 1180 is made. If this is not the case, the further iteration step begins 1180 without removing the modulation method from the group.

In other words, according to some embodiments, a classification effort can be reduced by optimizing the order of evaluation or the determination of the probability values. This may be particularly advantageous if the probability values are monotone, such as This is the case, for example, in the example described in the following paragraphs, in which the logarithmic likelihood function is analytically used exactly to classify a modulation method used in order to calculate for each symbol information about a probability that the received symbol was modulated with the respective modulation method used as a working hypothesis. Generally speaking, any mapping of the likelihood function to a set of values may be the basis of a further embodiment, provided that the value of the image either becomes larger or smaller with the additional consideration of another symbol. In this case, the function values assigned to the individual symbols can also be combined in a different way than by addition, for example by multiplication.

In these examples described, the function values of the logarithmic likelihood function or the approximations used for all modulations and all receive symbols are always less than or equal to 0, so that the function values decrease monotonically with additive combination or with each addition of another received symbol considered. This can be exploited to the effect that, unlike in conventional solutions, the function values do not have to be evaluated completely for all possible modulations up to the last received symbol n in order to be able to compare the functional values with one another at the end. Instead, according to the exemplary embodiments, initially only one probability value is calculated or determined taking into account the first s symbols.

This is for illustrative purposes in the table of 2 shown. The table shows in the first column the number of symbols considered (1 to n) and in columns 2 to 5 the associated probability values for the possible modulation methods indicated in the first line and considered in the method. In the concrete case, these are the probability values for the case where no modulated transmission has taken place (NoTx) in column 1. Column 2 gives the probability for BPSK, column 3 for 4-QAM, column 4 for 16-QAM and column 5 for 64-QAM on. As can be seen from the table, according to the exemplary embodiments, only the function values or approximate probability values are initially evaluated for each of the possible modulation methods, taking into account the symbols up to the s-th symbol. Depending on the specific implementation, s may be considerably smaller than the majority of received symbols n, which should be taken into account for statistical reasons.

At the in 2 In the exemplary embodiment shown, s = 10 and n = 120. After the first estimate for a candidate, only that modulation method is completely evaluated or calculated up to the last received symbol n, which has the largest accumulated function value after s = 10 symbols. In the in 2 As shown, this is the modulation method 4-QAM. This modulation method is then evaluated until the end of the receive vector, ie for all n received symbols of the plurality. The number of receive symbols (the length of the receive vector of the plurality of received symbols) is n. The probability value or the accumulated function value of the first evaluated modulation after n symbols is the reference value W _n (k) of the candidate and at the same time the current estimate. Next, the second modulation method can be evaluated, for example the 16-QAM method, which after s symbols has the second largest probability value or has the second largest probability numbered. In this case, after each evaluated symbol, ie after the consideration of a respective further received symbol, it is checked whether the further probability value, which was determined for the next modulation method or for the further modulation method from the group of possible modulation methods, is smaller than the reference value W _n ( _FIG. k). If, for each individual receive symbol, the function value ≤ 0, the function value or probability value of the total vector, which represents the sum of each individual function value for the received symbols, decreases monotonically with the number of received receive symbols evaluated. If the probability value or the accumulated function value is already smaller than the reference value W _n (k) after c (c <n) evaluated symbols, it follows from the monotony that the probability value must be smaller than the reference value even after n symbols. If the modulation method having the largest accumulated function value or the greatest probability value is used or determined as the classified or finally determined modulation or as a specific modulation method, the modulation with the smaller accumulated function value, ie the currently considered modulation method, can be selected from the group of others possible modulation method, not the modulation or the most likely modulation method. Therefore, the evaluation of the remaining (n-c) received symbols can be dispensed with and the otherwise required computing capacity can be saved without impairing the accuracy of the classification.

If the accumulated function value or the probability value for the second modulation method 1 after n evaluated symbols is greater than the reference value, this modulation becomes the current estimation or the corresponding modulation method of the current candidate and the associated accumulated function value or the associated probability value W _n (1) also the new reference value. In a similar manner, the same iterative method can be applied to the remaining possible modulations. Alternatively, according to some embodiments, in each iteration, another symbol may be considered for each of the possible modulation methods, which may lead to the same result with the same saving of computational capacity. At the in 2 In the example shown, only for the 4-QAM a probability value up to the last symbol n has to be calculated. In the event that no transmission has taken place or that no modulation has been made, only the symbol b had to be calculated, for BPSK up to symbol a, for 16-QAM up to symbol c and for 64-QAM up to symbol b , As a result, as compared with conventional methods, the evaluation of an information about a probability for a total of n-a + (n-b) + (n-c) + (n-b) = 4n - (a + b + c + b) symbols could be saved. In summary, the number of necessary mathematical operations can be reduced by the embodiments outlined above, while at the same time ensuring that the accuracy of the classification is not reduced. The probability of performing a faulty classification is not increased.

The following table illustrates, based on a specific application scenario, the savings in computing capacity achieved by applying an embodiment. The results of the table are based on the evaluation of 128 symbols and a classification, which considers possible modulation methods NoTx, BPSK, 4-QAM, 16-QAM and 64-QAM. A SNR (Signal to Noise Ratio) range from 7 dB to 15 dB is taken into account. The table indicates the average computation required per received symbol when using an embodiment of a method for determining a modulation method. For the approximate methods of the 16-point approximation and the reference point approximation described in more detail below, the average number of calculation steps per symbol shown in the table and the number of memory calls represented there result. Without applying the exemplary embodiments, the conventional application of the 16-point approximation would yield a value of approximately 110 multiplications and for the reference point approximation a value of approximately 80 multiplications. Compared with a conventional approach in which the probability values are first calculated for all received symbols and all modulation methods and the comparison is done at the end, by means of the embodiments a reduction of the multiplication operations of about (110-80) / 110 ~ 27% for the 16 Point approximation can be achieved. Equivalent to this is a reduction of the multiplications of about (70-50) / 70 ~ 29% for the reference point approximation. exponential function multiplication memory Recall 16 point APPROX 0 ≈ 80 7.5 Ref. Point APPROX 0 ≈ 50 4.5

3 schematically illustrates an embodiment of a device for determining a modulation method with which a plurality of received symbols has been modulated.

The device 3000 has an input interface 3020 , an estimator 3040 , a selector 3060 as well as a selector 3080 on. The input interface is configured to receive a plurality of received symbols α ₁ , ... α _n . The estimator is configured to obtain a probability value W _s (i) for each of the possible modulation methods i using a subset α ₁ ,... Α _{n of} the plurality of received symbols 3100 to determine. The probability value W quantifies a probability that all symbols α ₁ ,..., _S used for determining the probability value W _s (i) have been modulated with the respective modulation method i.

The selector 3060 is designed to determine a candidate k for the modulation method used. The candidate is the modulation method from the group whose probability value W _s (k) quantifies the highest probability of all modulation methods of the group.

The estimator 3040 is further formed using all n symbols 3100 determine a final probability value for the candidate W _n (k). Furthermore, the estimator is designed to determine at least one further probability value for each or at least one possible modulation method i from the group (without the candidate) using an additional received symbol (W _{s + 1} (i)). The selector 3080 is designed to remove a modulation method from the group of possible modulation methods if the further probability value W _{s + 1} (i) of the Modulation method numbered less probability than the final probability value of the candidate W _n (k).

The figures described below show further exemplary embodiments which can be combined with the preceding exemplary embodiments. 4 FIG. 11 is a flow chart illustrating one embodiment of a method for determining information about a probability that a received symbol has been modulated with a modulation scheme under test.

First, in a sub-step 102 determining a first approximation for the information about the probability, wherein in the determination of the first approximation a distance between the received symbol and the symbols of a first group of possible symbols of the modulation method is taken into account, wherein the first group comprises four symbols.

In a second step 104 a second approximation for the probability information is determined, taking into account a distance between a position corresponding to the received symbol and the symbols of a second group of possible symbols of the modulation method.

In a combination step 106 the first and second approximations are combined to obtain information about the likelihood that a received symbol has been modulated with a modulation scheme under test.

According to some embodiments, the first approximation by means of 6 determined in more detail below four-point approximation and / or with a similar to the four-point approximation method.

5 schematically shows an embodiment of the 16-QAM modulation. This in 5 The constellation diagram shown shows the positions of the sixteen possible symbols 302a - 302p the 16-QAM in the constellation diagram. In the constellation diagram, the real part is plotted on the x-axis and the imaginary part of the complex-valued symbols on the y-axis, which are themselves illustrated as filled circles. In the case of 5 In the illustrated embodiment, both the first approximation and the second approximation are determined for every four points based on one of four-point approximation of at least similar methods and four-point approximation identical methods, respectively. That is, the first approximation is made using a distance d _i between the received symbol 202 and the symbols 204a - 204d a first group 204 possible symbols of the modulation method. Equivalently, the second approximation for the probability information is made using the distance between the received symbol 202 and the symbols 206a - 206d a second group 206 of four possible symbols in the constellation diagram. The second group of symbols 206 is in the first group 204 opposite quadrants of the constellation diagram. This is based, as will become clear in the following from the discussion of the four-point approximation, in that due to the symmetry properties of the 16-QAM or higher modulation levels, such as 64-QAM and 256-QAM, the explicit execution of individual computation steps is dispensed with can, as these can be derived from the calculations performed for other possible symbols.

A slight modification of the four-point approximation, taking advantage of the symmetry properties of the coordinates of the possible symbols in the constellation diagram, may result in the likelihood function by the embodiments taking into account all of the sixteen in FIG 5 approximated constellation points shown. This is due to the fact that the X or Y coordinates of the distances between the received symbol 202 and the symbols of the first group used to calculate the approximations 204 and the second group 206 each identical to the coordinates of the distances between the received symbol 202 and the positions of constellation points in the arithmetically unrecognized quadrant are. As an example may serve here that, for example, the X component 208a the distance between the received symbol 202 and the symbol 210a the X component of the distance between the received symbol 202 and the calculated symbol 206b or 206c the second group 206 equivalent. The same applies to the Y component 208b the distance between the received symbol 202 and the possible symbol 210a , the Y component of the distance between the received symbol 202 and the symbol 204c or 204d of the modulation method.

By exploiting the pairwise symmetries can, in the, of the concrete received or decoded symbol 202 dependent determination of information about probability every sixteen possible symbols of in 5 are fully considered, where mathematically only a four-point approximation for the constellation points in the first group of four 204 and in the second group of four 206 must be performed.

Before further discussion of in 5 illustrated embodiment is first based on the representation in 6 a way of determining an approximation to the information about the probability that a received symbol has been modulated with a modulation scheme under test. To determine the approximation, the likelihood function or its mapped version is evaluated using a distance between the received symbol and the four closest possible symbols within the constellation diagram. For this reason, the approximation performed is also referred to as a four-point approximation. Although shown below for the sake of clarity for the 16-QAM modulation, ie for a modulation in which sixteen possible symbols are arranged within the constellation diagram, the four-point approximation can be readily applied to higher-order modulations, such as Apply 64-QAM and 256-QAM modulation.

The likelihood function for the modulation method Mi can be specified by

Here ρ ^{2 is} the signal to noise ratio (SNR) that can be measured at the beginning of each reception process. L _i is the number of symbols, ie the possible constellation points of the modulation method M _i ; and | r _k - a _{i, l} | ² is the distance between the reception symbol r _k and the l-th constellation point a _{i, l of} the modulation method M _i .

exponentiellen Funktionen erforderlich, wobei b_i die Modulationsstufe oder Anzahl von bits pro Symbol ist. Für 16-QAM wären somit 2⁴ = 16 exponentielle Funktionen auszuwerten, bei 64-QAM sogar 64. Um die Berechnung von exponentiellen Funktionen zu vermeiden, wird eine erste Approximation eingeführt. Das Prinzip wird nachfolgend und in 6 anhand des Beispiels von 16-QAM erläutert. Dabei werden die folgenden Schritte durchgeführt:
Bilden des Logarithmus der Likelihood-Funktion. Dies ist möglich, weil die logarithmische Funktion eine monoton steigende Funktion ist. Ferner werden nur die vier Punkte berücksichtigt, die das Empfangssymbol als nächste Nachbarn einschließen. In 6 sind die vier nächsten möglichen Symbole 302k, 3021, 3020 und 302p, in der mit 1 bezeichnete Region angeordnet, die das empfangene Symbol 320 umgeben. Als Näherung für die logarithmierte Likelihood Funktion für 4 mögliche Symbole gilt:

To calculate the likelihood function, for each possible modulation method M _{i would be} an evaluation of

exponential functions, where b _{i is} the modulation level or number of bits per symbol. For 16-QAM 2 ⁴ = 16 exponential functions would have to be evaluated, 64 even for 64-QAM. In order to avoid the calculation of exponential functions, a first approximation is introduced. The principle is below and in 6 using the example of 16-QAM. The following steps are performed:
Form the logarithm of the likelihood function. This is possible because the logarithmic function is a monotone increasing function. Furthermore, only the four points that include the receive symbol as nearest neighbors are considered. In 6 are the four closest possible symbols 302k . 3021 . 3020 and 302p , located in the region denoted by 1, representing the received symbol 320 surround. As an approximation for the logarithmic likelihood function for 4 possible symbols:

The four symbols a _{i, l} with l = 1, 2, 3, 4 are in the in 6 illustrated embodiment, the above-described four next possible symbols 302k . 3021 . 3020 and 302p , Considering the complex representation of the positions of the possible symbols and taking advantage of the symmetry property of the quadratic constellation points of the 16-QAM, after a coordinate transformation of the origin to the position 330 : x ^(4,1) = x ^(4,2) = -x ^(4,3) = -x ^(4,4) y ^(4,1) = -y = -y ^(4,3) = y ^{(4 , 4)} , as in 6 is shown. Here i = 4 because 16-QAM is the fourth modulation scheme.

Using these symmetries, the logarithmic likelihood function can be approximated as follows: ln (p (r _k | M _i = 16QAM)) ≈ ln (4) - ρ ² (r 2 / k + (x ₀ ^{(4.1)) 2} + (y ₀ ^{(4,1)) 2} - 2D ² - 2x _k x ₀ ^(4,1) - 2y _k y ₀ ^(4,1) ) + ln (cosh (2Dρ | x _k - x ₀ ^(4,1) |)) + ln (cosh (2Dρ | y _k - y ₀ ^(4,1) |))

The complex calculation of ln (cosh (x)) can be circumvented by the following approximation:

The four-point approximation may be performed according to the in 5 sketched embodiment can be used to both for the first group 204 with four possible symbols a first approximation as well as for the second group 206 with four possible symbols to determine a second approximation for the information about the probability that a received symbol has been modulated with a modulation method under test. Because of the above and based on 5 Illustrated symmetry properties of the possible symbols in the quadrants 1 and 4 or 2 and 3, only two diagonally opposite squares need to be evaluated or calculated by means of the four-point approximation and the corresponding function values or thermals for the remaining quadrants can be determined from such Approximations are obtained.

wobei 1/σ² die gleiche Bedeutung hat wie ρ² und das SNR darstellt, und für die zweite Näherung Λ₄(x, y) der zweiten Gruppe 206 im Quadranten 4:

In particular, the approximations of the four-point approximation Λ (x, y) can be calculated separately for the x-components Λ _x (x) of the distances and the y-components Λ _y (y) of the distances, since Λ (x, y) = Λ _x (x) + Λ _y (y). For example, for the approximation Λ ₁ (x, y) of the first group 204 in the quadrant applies 1 :

where 1 / σ ^{2 has} the same meaning as ρ ² and represents the SNR, and for the second approximation Λ ₄ (x, y) of the second group 206 in quadrant 4:

Further, due to the symmetry of the symbols of the third approximation modulation method, Λ ₂ (x, y) of the third group 210 of symbols ( 210a - 210d ) in quadrant 2: Λ _{2, x} (x) = Λ _{4, x} (x) Λ _{2, y} (y) = Λ _{1, y} (x) and for the fourth approximation Λ ₃ (x, y) of the fourth group 212 of symbols ( 212a - 212d ) in quadrant 3: Λ _{3, x} (x) = Λ _{1, x} (x) Λ _{3, y} (y) = Λ _{4, y} (x)

Thus, according to some embodiments, a third approximation for a third group ( 210 ) with 4 possible symbols ( 210a - 210c ) can be determined by combining part of the first approximation and part of the second approximation. Similarly, a fourth approximation for a fourth group ( 212 ) with 4 possible symbols ( 212a - 212d ) can be obtained by combining another part of the first approximation and another part of the second approximation. For calculating an approximation taking into account all 16 symbols, therefore, only the coordinates of 8 symbols are to be considered or two four-point approximations to be carried out.

In other words, in 5 1 shows how, with one exemplary embodiment, the evaluation area can be extended in the approximate determination of the probability without the complexity or the required computing capacity increasing to an unreasonable degree. 5 shows the idea of a 16-point approximation, in which a first approximation for the information about the probability taking into account or use of the four symbols of the first group 204 and a second approximation using the symbols of the second group 206 is determined. In this case, an approximation of the likelihood function is formed to determine the approximations, which takes into account all sixteen points of the constellation diagram, which is why the exemplary embodiment can also be referred to as a sixteen-point approximation.

5 thus shows the idea of a 16-point approximation. In contrast to the original 4-point approximation not only four but 16 points are evaluated or considered. The points are each divided into four groups and each group is then evaluated using the method described above. Due to the symmetry properties of the QAM methods, the function values of the squares and quadrants one and four can be used to construct the function values of the squares two and three. Consequently, to obtain the function values of all four groups, only squares 1 and 4 need to be calculated. Only a 4-point approximation is performed to account for four times the number of possible symbols. These function values or approximations correspond to the logarithmic likelihood functions of the separate four groups. The searched likelihood function for all 16 points is the sum of the function values of the individual four groups in their original form, but not in logarithmic form, since the logarithm of the sum of the arguments is generally not equal to the sum of individual logarithms. ln (a + b) ≠ ln (a) + ln (b)

Therefore, the function values of the 4-point approximation are first exposed before they can be added together.

By providing a respective approximate value for an exponential function of the first, second, third and fourth approximations and combining the approximation values for the exponential function of the first, second, third and fourth approximations, a summation value containing the information about the probability can be obtained. In the present case, this would be the case in 5 illustrated constellation, in which the 16-QAM is used as a working hypothesis, the information about the probability that the received symbol 202 was modulated by means of 16-QAM.

By logarithmizing the sum value thus formed, the information about the probability can be provided so that it can be compared with other modulation methods tested, if their probabilities are also estimated in logarithmic form.

The evaluation of an exponential function (e-function) is much more complex than a multiplication or addition operation. To avoid this expense, a further approximation may be used in some embodiments.

For this, the range of values in which function values are taken into account can be restricted. A natural limit is given up. The probability that the transmitted symbol (transmission symbol) corresponds to one of the four constellation points or possible symbols examined is a maximum of 1. Thus, the largest meaningful value for the logarithm of a probability is 0 (ln (1) = 0). Down there is no clear limit. Simulations show that for some embodiments, a minimum value of -20 may be sufficient for the logarithm of probability. All smaller elements can be rounded up to this value. The function value of -20 corresponds to a probability of 10 ^-9 . This value is also the maximum error caused by the restriction of the value range. The possible value range is too large to approximate the e-function with a Taylor polynomial.

According to some embodiments, this problem can be solved by means of a table: x e ^x 0 e ⁰ = 1 -0.1 e ^-0.1 = 0.905 ... ... -19.9 e ^-19.9 = 2.27 × 10 ^-9 -20 e ^-20 = 2.06 × 10 ^-9

The table contains in the interval [-20, 0] at a distance of 0.1 arguments and function values of the e-function. The values are used as a development point for a Taylor polynomial. Since the e-function and all its derivatives are small in the interval under investigation, in some embodiments a linear polynomial suffices. After the transformation from logarithmic form to exponential form, the four function values can be added. Subsequently, the sum could be logarithmized to compare with the results of the likelihood function of BPSK, 4-QAM, 16-QAM, 64-QAM, and "No Transmission" and other modulation schemes, should these be in logarithmic form only , The same table can be used as when exposed.

In other words, in some embodiments, the exponential function of the approximations is first formed and then summed to a sum value. If advantageous for comparison with other modulation methods, the sum value is subsequently logarithmized in accordance with some embodiments.

7 schematically illustrates which computational steps to perform to perform the sixteen point approximation. In order to determine information about a probability that a received symbol has been modulated with a modulation method tested, according to the method described in 5 illustrated embodiment, first a two-fold calculation 402 the four-point approximation required. To subsequently form the exponential function 404 for the approximations from the four-point approximation, four memory calls are required, as well as the subsequent interpolation 406 between the exponential function approximations obtained from the table or memory. In a subsequent summation 408 For example, the approximated exponential functions can be added to a sum function that corresponds to the summands in the likelihood function. By means of another memory call 410 and subsequent third-order Taylor polynomial approximation, the logarithm can be formed, so that, as a result, a quantity or information directly comparable with the result of the four-approximation is formed via a probability. That is, the information about the likelihood formed by the embodiment of the method can be directly compared with the information formed for other modulation methods on the same received symbol, so that it can be assumed that the largest specific value corresponds to the modulation with the highest likelihood was used for the received symbol on the transmitter side.

By using the second group of symbols, which according to the in 5 As shown in the embodiment shown leads to consideration of all sixteen possible symbols of the 16-QAM, an approximation error, which is caused in the conventional four-point approximation by disregarding some symbols, can be reduced.

The increase in the required computing capacity is, in particular on the basis of 7 As previously stated, extremely low.

8th illustrates another embodiment in which, as illustrated in the left view, a first approximation for the information about the probability using a distance between the received symbol 502 and the symbols of a first group 504 with four possible symbols 504a - 504d of the modulation method is determined. Because of better comparability is also in 8th the 16-QAM modulation method is shown.

At the in 8th A second approximation for the probability information is shown using a distance between one to the received symbol 502 corresponding position 508 and the symbols 506a to 506L a second group 506 determined by symbols. That is, in the 8th the second approximation for the likelihood function is for a distance between the remaining possible symbols and a position 508 determines not necessarily the position of the received symbol 502 in the constellation diagram corresponds. At the in 8th concretely illustrated embodiment is the to the received symbol 502 corresponding position 508 given by the position of the center of the square through the symbols of the first group 504 is formed.

This can be advantageously exploited to the effect that the distances between the to the received symbol 502 corresponding position 508 and the positions of the other possible symbols 506a - 506L the modulation method for a whole class of received symbols 502 do not change. Rather, there are a small number of possible corresponding positions 508 to the respective received symbol 502 and deduced therefrom a small number of possible second approximations. According to some embodiments, all possible second approximations are calculated in advance and stored, for example in the form of a table in a memory, so that during the actual classification the second approximations need not be calculated. This can contribute to a significant increase in the accuracy of the determination of the probability and thus of the entire classification, without the computational burden being significantly increased.

In other words shows 8th another embodiment. This is partly based on the in 6 illustrated four-point approximation or a four-point approximation ajar method. In the example of 8th become the four inner constellation points 504a - 504d evaluated by the 4-point approximation. The remaining 12 constellation points 506a - 506L in the right figure or their contributions to the likelihood function are used for a reference point 508 or for a corresponding to the position of the received symbol position 502 approximated. As a reference point 508 becomes the center of the square of the four symbols evaluated in the 4-point approximation 504a - 504d selected. In the example of 8th the reference point is the origin of the coordinate system. In order to add the function value of the original 4-point approximation with the approximated contributions of the remaining constellation points, the function value can be exposed again. The same procedure is used as for the 16-point approximation. For example, for the 16-QAM there are 9 possible reference points. In the 64-QAM there are 49 different possible four-point combinations or reference points. Due to the symmetrical structure of the modulations, however, the contribution to the likelihood function is identical for many reference points. Therefore, to obtain the contributions for all 58 (9 + 49) comparing 16-QAM and 64-QAM possible reference points, only 10 reference points need to be evaluated. Again, the contributions of these 10 points need only be calculated once per classification after the SNR has been determined by the receiving system.

9 illustrates the process steps performed to practice this embodiment.

In the approximation step 602 First, a 4-point approximation according to 6 for the received symbol to obtain the first approximation. Two memory calls 604 and a subsequent interpolation 606 serve to form the exponential function of the first approximation. This is done with the predetermined second approximation for the receive symbol in 608 to obtain the sum value and an optional second memory call 610 may be used for logarithmizing the sum value if the information about the probabilities for the other modulation forms is also in logarithmic form.

10 illustrates an optional further embodiment in which for one of the above-performed approximations, the accuracy further improved, so the probability of misclassification can be further reduced. This embodiment is also separate from the Ausführungsbespielen the 5 and 8th usable to increase the accuracy of a single 4-point approximation.

In order to obtain a good approximation of the logarithm of the hyperbolic cosine ln (coshx) for small arguments, a Taylor series development around 0 can in principle be used. This has the following form:

The Taylor series development around zero has the advantage that it is straight. Therefore, only one multiplication is necessary for their calculation than for a polynomial of the second degree, since the following applies: x ⁴ = (x ² ) ² .

Nevertheless, the error has the order O (x ⁵ ) and is therefore very small for smaller x. Consequently, one achieves high accuracy with little effort.

mit der Gerade x – ln(2) im positiven Definitionsbereich ist bei einem Funktionswert von 0,82. Für alle Funktionswerte, die größer als 0.82 sind, kann die lineare Funktion verwendet werden. Gemäß einigen Ausführungsbeispielen wird also unterhalb eines Schwellwertes x für das Argument des ln(cosh(x)) eine erste Approximationsfunktion verwendet

und oberhalb eine zweite (x – ln(2)), wobei der Schwellwert im Intervall [1,4; 1,6] liegen kann. Both the Taylor polynomial and the linear function (| x | - ln (2)) used in the 4-point approximation for larger arguments are smaller than the objective function for all real numbers. Therefore, the magnitude larger function is always the better approximation of ln (cosh (x)). The intersection of the Taylor polynomial

with the straight line x - ln (2) in the positive domain is at a function value of 0.82. For all function values greater than 0.82, the linear function can be used. Thus, according to some embodiments, below a threshold x for the argument of ln (cosh (x)), a first approximation function is used

and above a second one (x-ln (2)), wherein the threshold value in the interval [1,4; 1.6] can lie.

With the combination of Taylor polynomial and original approximation, the approximation error can be greatly reduced.

10 illustrates in the left illustration the original approximation 702 max (0; (| x | - ln (2))) to the ln (cosh (x)) 704 , On the right is the approximation used in some embodiments 706 shown. How out 7 As can be seen, this approximation is significantly more accurate, although no significant increase in computational load occurs.

11 illustrates a comparison of some embodiments with the exact determination of the likelihood function and the conventional 4-point approximation. The SNR is plotted on the x-axis and the probability of making an incorrect classification on the y-axis. Per SNR value, 50,000 packages have been classified. A packet has a length of 128 transmission symbols. On the y-axis the misclassification rate is plotted on a logarithmic scale. graph 804 illustrates the result for the determination of the complete likelihood function and graph 802 that of the conventional 4-point approximation. graph 808 illustrates the result for the in 5 illustrated embodiment and graph 808 that of the 8th , The markers 810 correspond to the combination of the best results of each of the two embodiments of the graphs 806 and 808 ,

The results are summarized in tabular form in the following overview. 10 ^-1 10 ^-2 10 ^-3 Maximum likelihood 8.25 dB 11.25 dB 12.3 dB 4 point APPROX 13.25 dB 14.0 dB 14.5 dB 16 point APPROX 8.3 dB 11.75 dB 12.8 dB Ref. Point APPROX 10.5 dB 11.5 dB 12.6 dB

In the low SNR range at a misclassification rate of about 10 ^-1 , the approximation loss is by the 16-point approximation 808 very low. This area is interesting for practical applications, as in the many current applications, a packet error rate of about 10% is accepted. In the middle SNR range are both methods 806 and 808 very close to the performance of the optimal maximum likelihood method.

The reference point-based approximation 806 seems more efficient in the mid and high SNRs because it achieves at least comparable or better results with less complexity. A better solution can be achieved according to some embodiments with a combination of both embodiments. This would provide a very good performance through the 16-point approximation in the low SNR range and the efficiency of the reference point method could be used in the middle SNR range. It could be decided after determining the SNR value on the basis of an SNR threshold, which of the two proposed approximations according to the 5 or 8th is used for classification.

The following table gives the effort of calculating the classification metric per receive symbol taking into account No-Tx, BPSK, 4-QAM, 16-QAM and 64-QAM as possible modulation methods. The calculation of any look-up tables is not included as they can be done offline and then saved. exponential function multiplication memory Recall Maximum likelihood 87 ≈ 300 0 4 point APPROX 0 ≈ 40 0 16 point APPROX 0 ≈ 110 10 Ref. Point APPROX 0 ≈ 70 6

12 schematically shows an embodiment of a device 900 for determining information about a probability that a received symbol has been modulated with a modulation method under test. The device 900 includes an input interface 902 , a first circuit 904 , a second circuit 906 and a combiner 908 ,

The input interface is for receiving a received symbol, for example by means of a wireless transmission or a symbol transmitted over a wire-bound transmission, for example a DSL or similar modem. The first circuit 904 is designed to determine a first approximation of the probability information. The determination is made using a distance between the received symbol and the symbols of a first group with four possible symbols of the modulation method.

The second circuit 906 is adapted to determine a second approximation for the information about the probability using a distance between a position corresponding to the received symbol and the symbols of a second group of possible symbols of the modulation method.

The combiner is configured to receive the first approximation and the second approximation and to combine the at least two approximations to obtain the information about the probability that the symbol received via the input interface has been modulated with a tested modulation method used as a working hypothesis.

A received symbol here is a symbol received after a demodulation of possible further signal processing steps, ie a point in the constellation diagram that was received by means of a receiver via a wireless or wired signal transmission. It is then the task of an intelligent receiver (cognitive radio) to determine, without additional signaling, how a single symbol or a series of symbols has been modulated on the transmitter side if a plurality of possible modulation methods are available. This can reduce the overhead caused by the signaling of the modulation method used.

Although in the preceding paragraphs predominantly motivates for an OFDM transmission system, further embodiments can also be used in other application scenarios. The 13 and 14 illustrate schematically a system with an intelligent receiver 1100 , which is able to classify signals as a typical application scenario for modification classification. The four transmitters (TX 1-4) 1102a - 1102d send all on the same channel. The different transmitters differ by different modulation methods (modulation levels). Uses the receiver 1100 An Automatic Modulation Classification allows the latter to detect the different transmitters based on the modulation they are using, and to decide which of the four transmitters is currently being communicated. There is no further signaling necessary for identification. The intelligent receiver 1100 In addition, it should also be able to identify a free channel, ie none of the four transmitters is currently broadcasting.

Assuming that all five cases occur with the same probability, in this scenario a maximum likelihood method would be an approach for modulation classification and advantageous in terms of misclassification rate. The following five cases are characterized by the modulation methods: {NoTx 1110a , BPSK 1110b , 4QAM 1110c , 16QAM 1110d , 64QAM 1110e }, whose constellation diagrams in are shown.

Although the previous sections are predominantly motivated by the digital modulation that is common in some mobile radio methods, other embodiments may be used in other technical fields or with other wireless transmission standards using modulation techniques.

Other possible applications include Long-Term Evolution (LTE), LTE-Advanced (LTE-A), High Speed Packet Access (HSPA), Universal Mobile Telecommunication System (UMTS) or UMTS Terrestrial Radio Access Network (UTRAN), evolved-UTRAN (UMTS). e-UTRAN), Global System for Mobile Communication (GSM) or Enhanced Data rates for GSM Evolution (EDGE) networks, GSM / EDGE Radio Access Network (GERAN), or other standard communications networks, such as Worldwide Inter-operability for Microwave Access (WIMAX) IEEE 802.16 or Wireless Local Area Network (WLAN) IEEE 802.11, Generally Spoken Orthogonal Frequency Division Multiple Access (OFDMA) Networks, Time Division Multiple Access (TDMA) Networks, Code Division Multiple Access (CDMA) Networks, Wideband CDMA ( WCDMA) networks, Frequency Division Multiple Access (FDMA) networks, Spatial Division Multiple Access (SDMA) networks, etc.

Further fields of application are single systems with adaptive modulation, but also applications such as intelligent systems, where multiple transmitters and receivers exist and each transmitter first uses signal classification and / or modulation detection to detect vacant frequency bands and to transmit their information to specific receivers. All receivers, in turn, scan the frequency bands and, if necessary, perform signal classification and modulation detection to demodulate the transmitted content intended for them. Blind modulation detection can also play a major role in military applications. z. For example, in electronic warfare and surveillance systems to suppress or selectively detect foreign signals for espionage purposes or to take measures to protect their own signal better against foreign signals or interference. Also in the context of Cognitive Radio (CR), where a licensed so-called primary user shares his spectrum resources with one or more secondary users. The secondary users use so-called spectrum holes (vacant timeslots or frequency bands) for signal transmission. The secondary users can also use modulation classification to identify vacant timeslots or frequency bands. On the receiver side, you can then use blind modulation detection again to receive the signals intended for it.

The features disclosed in the foregoing description, the appended claims and the appended figures may be taken to be and effect both individually and in any combination for the realization of an embodiment in its various forms.

Although some aspects have been described in the context of a device, it will be understood that these aspects also constitute a description of the corresponding method, so that a block or a component of a device is also to be understood as a corresponding method step or as a feature of a method step. Similarly, aspects described in connection with or as a method step also represent a description of a corresponding block or detail or feature of a corresponding device.

Depending on particular implementation requirements, embodiments of the invention may be implemented in hardware or in software. The implementation may be performed using a digital storage medium, such as a floppy disk, a DVD, a Blu-Ray Disc, a CD, a ROM, a PROM, an EPROM, an EEPROM or FLASH memory, a hard disk, or other magnetic disk or optical memory are stored on the electronically readable control signals, which can cooperate with a programmable hardware component or cooperate such that the respective method is performed.

A programmable hardware component may be integrated by a processor, a central processing unit (CPU), a graphics processing unit (GPU), a computer, a computer system, an application-specific integrated circuit (ASIC) Circuit (IC), a system on chip (SOC), a programmable logic element or a field programmable gate array with a microprocessor (FPGA = Field Programmable Gate Array) may be formed.

The digital storage medium may therefore be machine or computer readable. Thus, some embodiments include a data carrier having electronically readable control signals capable of interacting with a programmable computer system or programmable hardware component such that one of the methods described herein is performed. One embodiment is thus a data carrier (or a digital storage medium or a computer readable medium) on which the program is recorded for performing any of the methods described herein.

In general, embodiments of the present invention may be implemented as a program, firmware, computer program, or computer program product having program code or data, the program code or data operative to perform one of the methods when the program resides on a processor or a computer programmable hardware component expires. The program code or the data can also be stored, for example, on a machine-readable carrier or data carrier. The program code or the data may be present, inter alia, as source code, machine code or bytecode as well as other intermediate code.

Another embodiment is further a data stream, a signal sequence, or a sequence of signals that represents the program for performing any of the methods described herein. The data stream, the signal sequence or the sequence of signals can be configured, for example, to be transferred via a data communication connection, for example via the Internet or another network. Embodiments are also data representing signal sequences that are suitable for transmission over a network or a data communication connection, the data representing the program.

For example, a program according to one embodiment may implement one of the methods during its execution by, for example, reading or writing one or more data into memory locations, optionally switching operations or other operations in transistor structures, amplifier structures, or other electrical, optical, magnetic or caused by another operating principle working components. Accordingly, by reading a memory location, data, values, sensor values or other information can be detected, determined or measured by a program. A program can therefore acquire, determine or measure quantities, values, measured variables and other information by reading from one or more storage locations, as well as effect, initiate or execute an action by writing to one or more storage locations and control other devices, machines and components ,

The embodiments described above are merely illustrative of the principles of the present invention. It will be understood that modifications and variations of the arrangements and details described herein will be apparent to others of ordinary skill in the art. Therefore, it is intended that the invention be limited only by the scope of the appended claims and not by the specific details presented in the description and explanation of the embodiments herein.

Claims (11)

A method of determining a modulation method with which a plurality of received symbols has been modulated, comprising: determining ( 1120 ) a probability value for each modulation method from a group of possible modulation methods using a subset of the plurality of received symbols, the probability value quantifying a probability that all symbols used to determine the probability value have been modulated by the respective modulation method; Determining a candidate ( 1140 ) for the modulation method used as the modulation method from the group whose probability value quantifies the highest probability of all the modulation methods of the group; using all symbols of the plural, determining ( 1160 ) a final probability value for the candidate; for at least one of the further modulation methods from the group, determining ( 1180 ) another probability value using an additional received symbol; and remove ( 1210 ) of a modulation method from the group of possible modulation methods if the further probability value of the modulation method is less probable than the final probability value of the candidate. The method of claim 1, wherein the group of possible modulation schemes comprises BPSK, 4-QAM, 16-QAM and 64-QAM. The method of claim 1 or 2, wherein the set of possible modulation methods further comprises a method without generating a symbol to be transmitted. The method of claim 1, wherein determining the probability value for each symbol comprises determining information about a probability that the received symbol has been modulated with the respective modulation method. The method of claim 4, wherein as information about the probability a parameter is determined, which corresponds to an approximation or mapping of the likelihood function. The method of claim 5, wherein the parameters determined for each of the symbols used are summed to determine the probability value. Method according to one of the preceding claims, wherein the candidate for the modulation method used is determined as the modulation method used if the group of possible modulation methods no longer contains a modulation method. Method according to one of the preceding claims, wherein, as the modulation method or as the new candidate, the modulation method is determined within the group of possible modulation methods whose probability value, using all the symbols of the plurality, is the highest probability. A method of determining the source of a signal based on a plurality of received signals, comprising: Determining a likelihood value for each source from a group of possible sources using a subset of the plurality of received signals, the likelihood value quantifying a probability that all signals used to determine the likelihood value originate from the respective source; Determining a candidate for the actual source as that of the possible source of the group whose probability score is the highest probability of all sources of the group; using all symbols of the plurality, determine a final probability value for the candidate; for at least one of the further possible sources from the group, determining a further probability value using an additional received signal; and Removing a source from the group of possible sources, if the further probability value of the source is less probable than the final probability value of the candidate. Apparatus for determining a modulation method with which a plurality of received symbols has been modulated, comprising: an input interface ( 3020 ) for receiving the plurality of received symbols ( 3100 ); an estimator ( 3040 ) configured to determine a likelihood value for each modulation method from a group of possible modulation methods using a subset of the plurality of received symbols, the likelihood value quantifying a probability that all symbols used to determine the likelihood value have been modulated with the respective modulation method; a selector ( 3060 ) which is adapted to determine a candidate for the modulation method used as the modulation method from the group whose probability value quantifies the highest probability of all the modulation methods of the group; the estimator ( 3040 ) is further configured, using all of the symbols of the plurality, to determine a final probability value for the candidate and for each of the further possible modulation methods from the group at least one further probability value using an additional received symbol; and a selector ( 3080 ) adapted to remove a modulation method from the group of possible modulation methods if the further probability value of the modulation method is less probable than the final probability value of the candidate. Computer program having a program code which, when executing the program code on a processor, effects a performance of one of the methods according to claims 1 to 9.

Images