Method and device for identifying modulation pattern of orthogonal frequency division multiplexing signal based on adaptive modulation

Abstract

The invention provides a modulation pattern identification method and device for an orthogonal frequency division multiplexing signal based on adaptive modulation, which can be applied to the technical field of wireless communication. The method comprises the following steps: acquiring an orthogonal frequency division multiplexing signal, wherein the orthogonal frequency division multiplexing signal comprises N symbols and K subcarriers; performing fast Fourier transform on the orthogonal frequency division multiplexing signal to obtain a frequency domain matrix of an NxK dimension; extracting a plurality of frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix according to the identifiers of the K subcarriers based on the subcarrier dimensions to obtain K target data sequences; according to any target data sequence in the K target data sequences, performing at least one operation of clustering or accumulation on the target data sequences to obtain a plurality of modulation pattern recognition features associated with the target data sequences; and determining a target modulation pattern according to the plurality of modulation pattern recognition features and a preset modulation pattern mapping relationship.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of wireless communication, and more particularly to a modulation pattern identification method and device for an adaptive modulation orthogonal frequency division multiplexing signal. BACKGROUND

[0002] With the wide application of software radio technology, the development of signal processing field is promoted, so that various new protocols and new pattern signals emerge in an endless stream. Based on software radio technology, different modulation, spreading, coding and encryption of baseband signals can complete the modulation of signals of different systems. The identification of signal modulation pattern is an important problem in communication signal detection, especially the automatic identification of signal modulation pattern in non-cooperative communication. The receiving party of communication information can correctly demodulate data and complete the reception of information by effectively identifying the modulation pattern of the signal. The modulation pattern identification method in the related art is difficult to accurately identify the modulation pattern of the adaptive modulation orthogonal frequency division multiplexing signal. SUMMARY

[0003] In view of the above problems, the present disclosure provides a modulation pattern identification method and device for an adaptive modulation orthogonal frequency division multiplexing signal.

[0004] According to a first aspect of the present disclosure, a modulation pattern identification method for an adaptive modulation orthogonal frequency division multiplexing signal is provided, comprising: obtaining an orthogonal frequency division multiplexing signal, the orthogonal frequency division multiplexing signal comprising N symbols and K subcarriers; performing fast Fourier transform on the orthogonal frequency division multiplexing signal to obtain a frequency domain matrix of NxK dimensions; based on the subcarrier dimension, extracting a plurality of frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix according to the identification of the K subcarriers to obtain K target data sequences; performing at least one of clustering or accumulation on the target data sequence according to any target data sequence in the K target data sequences to obtain a plurality of modulation pattern identification features associated with the target data sequence, wherein the modulation pattern identification feature is used to determine the modulation order; determining a target modulation pattern according to the plurality of modulation pattern identification features and a preset modulation pattern mapping relationship, wherein the target modulation pattern represents the modulation mode of the adaptive modulation orthogonal frequency division multiplexing signal, and the preset modulation pattern mapping relationship represents the mapping relationship between the modulation pattern identification feature and the modulation pattern.

[0005] According to an embodiment of the present disclosure, the extracting, from the frequency domain matrix, a plurality of frequency domain symbol vectors associated with each of the K subcarriers according to the identification of the K subcarriers to obtain K target data sequences comprises: for any one of the K subcarriers, determining, from the frequency domain matrix, a plurality of frequency domain symbol vectors associated with the subcarrier as an intermediate data sequence according to the identification of the subcarrier; and extracting a data sequence of a preset length from the intermediate data sequence as a target data sequence.

[0006] According to an embodiment of the present disclosure, the modulation pattern identification features include first modulation pattern identification features and second modulation pattern identification features, and wherein the determining, according to any one of the K target data sequences, at least one of clustering or accumulation of the target data sequence to determine a plurality of modulation pattern identification features associated with the target data sequence comprises: clustering the target data sequence to determine a cluster center and a number of cluster centers for any one of the K target data sequences; accumulating the target data sequence to obtain an accumulation quantity; determining the first modulation pattern identification features associated with the target data sequence according to at least one of the number of cluster centers and the accumulation quantity; averaging a phase difference between each cluster center and a preset reference point to obtain a cluster center average phase offset; obtaining an offset angle according to the cluster center average phase offset and a preset average phase offset; and determining the second modulation pattern identification features associated with the target data sequence according to the offset angle and a preset offset angle range.

[0007] According to an embodiment of the present disclosure, the determining, according to the plurality of modulation pattern identification features and a preset modulation pattern mapping relationship, a target modulation pattern comprises: determining a gradient sequence of each modulation pattern identification feature according to a similarity between adjacent modulation pattern identification features, and fusing the gradient sequence of each modulation pattern identification feature to obtain a target gradient sequence; determining at least one gradient extreme value from the target gradient sequence as a candidate modulation boundary point; determining at least one target modulation boundary point from the candidate modulation boundary point based on a preset threshold; determining at least two target modulation regions with different modulation patterns based on the at least one target modulation boundary point, and obtaining at least two feature vector averages according to modulation pattern identification features of the at least two target modulation regions; and determining the target modulation pattern according to the at least two feature vector averages and the preset modulation pattern mapping relationship.

[0008] According to an embodiment of the present disclosure, the gradient sequence of each modulation pattern identification feature is determined according to the similarity between adjacent modulation pattern identification features, and the gradient sequence of each modulation pattern identification feature is fused to obtain a target gradient sequence, including: filtering each modulation pattern identification feature to obtain an intermediate modulation pattern identification feature associated with each modulation pattern identification feature; determining the gradient sequence of each intermediate modulation pattern identification feature according to the similarity between each adjacent intermediate modulation pattern identification feature; and fusing the gradient sequence of each intermediate modulation pattern identification feature according to a preset weight to obtain the target gradient sequence.

[0009] According to an embodiment of the present disclosure, based on at least one target modulation boundary point, at least two target modulation regions with different modulation patterns are determined, and at least two feature vector averages are obtained according to modulation pattern identification features of the at least two target modulation regions, including: determining the at least two target modulation regions according to the at least one modulation boundary point; and obtaining at least two feature vector averages by averaging the modulation pattern identification features of the at least two target modulation regions, respectively.

[0010] According to an embodiment of the present disclosure, the feature vector average includes a first feature vector average associated with a first modulation pattern identification feature and a second feature vector average associated with a second modulation pattern identification feature, and the target modulation pattern is determined according to the at least two feature vector averages and a preset modulation pattern mapping relationship, including: for any feature vector average in the at least two feature vector averages, determining a modulation order associated with the first feature vector average from a preset modulation pattern mapping sub-relation based on the first feature vector average, and determining at least one candidate target modulation pattern based on the modulation order; and determining the target modulation pattern from the at least one candidate target modulation pattern according to the second feature vector average and the modulation order.

[0011] According to an embodiment of the present disclosure, the target modulation pattern is determined from the at least one candidate target modulation pattern according to the second feature vector average and the modulation order, including: in the case where the modulation order is determined, the target modulation pattern is determined from the at least one candidate target modulation pattern based on the second feature vector average.

[0012] According to an embodiment of the present disclosure, the modulation pattern identification method of the adaptive modulation-based OFDM signal further includes: generating a frequency domain distribution atlas corresponding to the modulation pattern based on the modulation pattern.

[0013] The second aspect of the present disclosure provides a modulation pattern identification device for an adaptive modulation based orthogonal frequency division multiplexing signal, comprising: an acquisition module configured to acquire an orthogonal frequency division multiplexing signal, the orthogonal frequency division multiplexing signal comprising N symbols and K subcarriers; a first obtaining module configured to perform fast Fourier transform on the orthogonal frequency division multiplexing signal to obtain a frequency domain matrix of NxK dimensions; a second obtaining module configured to extract, based on the subcarrier dimension, a plurality of frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix according to the identification of the K subcarriers to obtain K target data sequences; a third obtaining module configured to perform at least one of clustering or accumulation on any target data sequence in the K target data sequences to determine a plurality of modulation pattern identification features associated with the target data sequence, wherein the modulation pattern identification features are used to determine a modulation order; and a determination module configured to determine a target modulation pattern according to the plurality of modulation pattern identification features and a preset modulation pattern mapping relationship, wherein the target modulation pattern represents a modulation mode of the adaptive modulation based orthogonal frequency division multiplexing signal, and the preset modulation pattern mapping relationship represents a mapping relationship between the modulation pattern identification features and the modulation pattern.

[0014] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having stored thereon a computer program or instructions, wherein the computer program or instructions are executed by a processor to implement the steps of the method.

[0016] The fifth aspect of the present disclosure further provides a computer program product comprising a computer program or instructions, wherein the computer program or instructions are executed by a processor to implement the steps of the method.

[0017] According to the modulation pattern recognition method of the adaptive modulation based OFDM signal provided in the present disclosure, by performing fast Fourier transform on the obtained OFDM signal, an NxK dimensional frequency domain matrix can be obtained, based on the subcarrier dimension, according to the identification of the subcarriers, a plurality of frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix, so that K target data sequences can be obtained, and at least one of clustering or accumulation is performed on the target data sequences, a plurality of modulation pattern recognition features associated with the target data sequences can be obtained, and based on the plurality of modulation pattern recognition features, a target modulation pattern is determined from a preset modulation pattern mapping relationship, the frequency domain symbol vectors associated with the subcarriers are extracted from the frequency domain matrix based on the subcarrier dimension, which lays a data foundation for detecting different modulation patterns at a subcarrier level fine granularity, and at least one of clustering or accumulation is performed on the target data sequences, a plurality of modulation pattern recognition features associated with the target data sequences can be obtained, and then the target modulation pattern is determined according to the preset modulation pattern mapping relationship, so that the target modulation pattern of the adaptive modulation based OFDM signal including a plurality of modulation modes is recognized at a subcarrier level fine granularity, and the recognition accuracy and granularity of the modulation pattern are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure, taken in conjunction with the accompanying drawings, in which:

[0019] Figure 1 An application scenario diagram of the modulation pattern recognition method of the adaptive modulation based OFDM signal according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 2 A flowchart of the modulation pattern recognition method of the adaptive modulation based OFDM signal according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 3 A symbol constellation diagram associated with the second modulation pattern recognition feature according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 4 A frequency domain symbol vector constellation diagram associated with the second modulation pattern recognition feature according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 5 A symbol constellation diagram associated with the first modulation pattern recognition feature according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 6 A frequency domain symbol vector constellation diagram associated with the first modulation pattern recognition feature according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 7 A structure block diagram of a modulation pattern recognition apparatus for an adaptive modulation based OFDM signal according to an embodiment of the present disclosure is shown schematically; and

[0026] Figure 8 A block diagram of an electronic device adapted to implement a modulation pattern recognition method for an adaptive modulation based OFDM signal according to an embodiment of the present disclosure is shown schematically. DETAILED DESCRIPTION

[0027] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood, however, that the description which follows is merely illustrative and is not intended to limit the scope of the present disclosure. In the following detailed description of embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it would be apparent to one skilled in the art that one or more embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known structures and functions have not been described in detail in order to avoid obscuring aspects of the present disclosure.

[0028] The terms used herein are merely used to describe specific embodiments and are not intended to limit the present disclosure. The terms "include" and "have" and the like used herein indicate the presence of the described features, steps, operations, and / or components but do not preclude the presence or addition of one or more other features, steps, operations, or components.

[0029] All terms used herein, including technical and scientific terms, have the same meanings as those generally understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having meanings consistent with the context of the present description, and should not be interpreted in an idealized or overly formal way.

[0030] In the case of using expressions similar to "at least one of A, B, and C, etc.", it should generally be interpreted to include at least one of each item enumerated, but not necessarily one of each item enumerated only. In other words, it should be interpreted to mean that it is equivalent to "at least one of A or B; A or C; B or C; etc." or "A, B, and / or C".

[0031] Orthogonal Frequency Division Multiplexing (OFDM) technology, as the core physical layer scheme of modern wireless communication such as 5G New Radio (5G-NR) and Wi-Fi 6 / 7, has the ability of high spectral efficiency and anti-multipath fading. In order to approach the Shannon capacity limit in complex time-varying frequency-selective channels, adaptive modulation and coding (AMC) technology is widely used. The development of AMC technology has evolved from the traditional coarse-grained adjustment based on resource blocks to fine-grained adaptation based on subcarriers or subcarrier groups (i.e., non-uniform modulation), in order to achieve better global spectral efficiency. In addition, in actual communication systems, pilot, synchronization signal and control signaling are usually embedded in the OFDM signal frame. These functional signals often use different, fixed and robust modulation methods from the main service channel, and only occupy a small number of subcarriers in the frequency domain. This leads to inherent heterogeneity and non-uniformity of modulation methods in the frequency domain within a single OFDM symbol.

[0032] Effective processing of non-uniform modulation signals highly depends on the accurate cognition of the modulation structure by the receiver. However, in key application scenarios such as non-cooperative communication, spectrum monitoring and intelligent operation and maintenance, the modulation strategy of the sending end cannot be known in advance by the receiving end, and therefore blind modulation identification technology needs to be relied on. The modulation pattern identification technology in the related art mainly includes (1) a method based on classical feature extraction, which relies on extracting high-order statistics, constellation features and the like from the received signal as the classification basis. However, these features are usually derived from the entire time domain signal or frequency domain symbol without distinction, and the entire or most of the frequency band signal is mixed and processed. When the signal is non-uniform in the frequency domain, the global features extracted are actually the aliasing and averaging of all subcarrier features, resulting in feature space fuzzification, making it difficult for the classifier to effectively distinguish and locate the modulation changes in the frequency domain. For fine modulation adjustment at the subcarrier level and slight abnormal modulation structure, such as special pilot failure, it is more difficult to locate the change boundary of the modulation pattern in the frequency domain. (2) A method based on likelihood theory. Although the likelihood ratio test-based method has good performance in theory, the computational complexity thereof increases exponentially with the number of unknown parameters. For a problem of jointly hypothesis testing of modulation modes on tens to thousands of subcarriers, the hypothesis space scale is huge, resulting in a lack of engineering feasibility of the method. (3) A method based on deep learning. Deep learning models, especially convolutional neural networks (CNN), have shown great potential in modulation identification. However, such an end-to-end "black box" model has a non-transparent decision mechanism, which is difficult to apply to scenarios with high reliability requirements. Moreover, deep learning models have insufficient generalization ability when facing new signal patterns that have not been covered by training data. Given the explosive number of possible combinations of non-uniform modulation, it is difficult to collect a complete training data set in practice, resulting in a significant reduction in the reliability of deep learning models in related art in such tasks. Therefore, the modulation pattern identification technology in the related art is mainly based on the ideal assumption of "uniform modulation pattern", and it is difficult to effectively cope with the modulation pattern identification of OFDM non-uniform modulation signals while maintaining fine granularity and explainability.

[0033] In view of this, the embodiment of the present disclosure provides a modulation pattern identification method of an adaptive modulation based orthogonal frequency division multiplexing signal, comprising: acquiring an orthogonal frequency division multiplexing signal, the orthogonal frequency division multiplexing signal comprising N symbols and K subcarriers, performing fast Fourier transform on the orthogonal frequency division multiplexing signal to obtain a frequency domain matrix of NxK dimensions; based on the subcarrier dimension, extracting a plurality of frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix according to the identification of the K subcarriers to obtain K target data sequences; performing at least one of clustering or accumulation on any target data sequence in the K target data sequences to obtain a plurality of modulation pattern identification features associated with the target data sequence, wherein the modulation pattern identification feature is used to determine the modulation order; and determining a target modulation pattern according to the plurality of modulation pattern identification features and a preset modulation pattern mapping relationship, wherein the target modulation pattern represents the modulation mode of the adaptive modulation based orthogonal frequency division multiplexing signal, and the preset modulation pattern mapping relationship represents the mapping relationship between the modulation pattern identification feature and the modulation pattern.

[0034] Figure 1 An application scenario diagram of the modulation pattern identification method of the adaptive modulation based orthogonal frequency division multiplexing signal according to the embodiment of the present disclosure is schematically shown.

[0035] As shown in Figure 1 , the application scenario 100 according to the embodiment can include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 can include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0036] A user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0037] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers, etc.

[0038] The server 105 can be a server that provides various services, such as a background management server that provides support for a website browsed by a user using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (as an example only). The background management server can perform analysis and the like on received user requests and the like, and feed back a processing result (such as a web page, information, or data obtained or generated according to a user request) to the terminal device.

[0039] It should be noted that the modulation pattern identification method of the OFDM signal based on adaptive modulation provided in the embodiments of the present disclosure can generally be executed by the server 105. Accordingly, the modulation pattern identification apparatus of the OFDM signal based on adaptive modulation provided in the embodiments of the present disclosure can generally be arranged in the server 105. The modulation pattern identification method of the OFDM signal based on adaptive modulation provided in the embodiments of the present disclosure can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Accordingly, the modulation pattern identification apparatus of the OFDM signal based on adaptive modulation provided in the embodiments of the present disclosure can also be arranged in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105.

[0040] It should be understood that Figure 1 The number of the first terminal device, the second terminal device, the third terminal device, the network, and the server in the embodiments of the present disclosure is only illustrative. Any number of terminal devices, networks, and servers can be provided according to implementation needs.

[0041] Figure 2 A flowchart of the modulation pattern identification method of the OFDM signal based on adaptive modulation according to the embodiments of the present disclosure is schematically shown.

[0042] As shown in Figure 2 The modulation pattern identification method of the OFDM signal based on adaptive modulation 200 of this embodiment includes operations S210-S250.

[0043] In operation S210, an OFDM signal is acquired, and the OFDM signal includes N symbols and K subcarriers.

[0044] In operation S220, a fast Fourier transform is performed on the OFDM signal to obtain a frequency domain matrix of NxK dimensions.

[0045] In operation S230, based on the subcarrier dimension, according to the identifiers of K subcarriers, multiple frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix to obtain K target data sequences.

[0046] In operation S240, based on any one of the K target data sequences, at least one operation, such as clustering or accumulation, is performed on the target data sequence to obtain multiple modulation pattern recognition features associated with the target data sequence.

[0047] In operation S250, the target modulation style is determined based on multiple modulation style identification features and preset modulation style mapping relationships.

[0048] Orthogonal Frequency Division Multiplexing (OFDM) signals can characterize signals modulated using OFDM technology. By applying classic synchronization algorithms to the OFDM signal, such as correlation algorithms based on preambles or cyclic prefixes, symbol timing synchronization and carrier frequency synchronization can be achieved. Subsequently, the cyclic prefix (CP) of each symbol is removed to obtain the valid symbols. .

[0049] An orthogonal frequency division multiplexing (OFDM) signal can include N symbols and K subcarriers. An OFDM signal can be a segment of signal. The modulation method in an OFDM signal is adaptive. That is, an OFDM signal can be obtained by adaptively modulating the signal using adaptive modulation and coding techniques. Here, N and K are positive integers.

[0050] By performing a Fast Fourier Transform (FFT) on the orthogonal frequency division multiplexed signal, we can obtain an NxK dimension frequency domain matrix S, as shown in the following formula (1).

[0051] (1)

[0052] Where N represents the number of symbols and K represents the number of subcarriers. This represents the frequency domain symbol vector of the first symbol on the first subcarrier. This represents the frequency domain symbol vector of the first symbol on the second subcarrier, ... Let represent the frequency domain symbol vector of the first symbol on the Kth subcarrier, ..., Let N be the frequency domain symbol vector of the Nth symbol on the 1st subcarrier, ... Indicates the first The frequency domain symbol vector of a symbol on the Kth subcarrier.

[0053] In an implementation, each subcarrier can be numbered according to the size of the frequency of the subcarrier, and the identification of the K subcarriers is obtained. The frequency domain matrix is an NxK matrix including the subcarrier dimension and the frequency domain symbol vector dimension. Based on the subcarrier dimension, according to the identification of the K subcarriers, a plurality of frequency domain symbol vectors associated with each subcarrier is extracted from the frequency domain matrix, that is, a plurality of frequency domain symbol vectors associated with each subcarrier can constitute a target data sequence, thereby obtaining K target data sequences. Wherein, the frequency domain symbol vector can represent the complex value representation form of the amplitude and phase of the signal.

[0054] In an implementation, the identification k of the subcarrier represents the kth subcarrier, and for the kth subcarrier, the subcarrier is associated with the extraction of the N1th frequency domain symbol vector to the N2th frequency domain symbol vector from the frequency domain matrix, thereby obtaining the kth target data sequence As shown in the following formula (2).

[0055] (2)

[0056] Wherein, represents the N1th frequency domain symbol vector of the kth subcarrier, represents the 2th frequency domain symbol vector of the kth subcarrier, represents the N2th frequency domain symbol vector of the kth subcarrier.

[0057] According to any target data sequence in the K target data sequences, at least one of clustering or accumulation is performed on the target data sequence, and a plurality of modulation pattern recognition features associated with the target data sequence is obtained, wherein the modulation pattern recognition feature is used to determine the modulation order. In an implementation, clustering is performed on the target data sequence, and the modulation pattern recognition feature associated with the target data sequence can be obtained. In an implementation, accumulation is performed on the target data sequence, and the modulation pattern recognition feature associated with the target data sequence can be obtained. In an implementation, clustering is performed on the target data sequence and accumulation is performed on the target data sequence, and the modulation pattern recognition feature associated with the target data sequence can be obtained. The modulation pattern recognition feature is used to determine the modulation order, and the modulation order can represent the information carrying capacity of the frequency domain symbol vector, and the modulation order represents the number of frequency domain symbol points of the constellation diagram.

[0058] The target modulation pattern can represent a modulation mode of the adaptive modulation OFDM signal. The preset modulation pattern mapping relationship can represent a mapping relationship between the modulation pattern identification feature and the modulation pattern. According to the plurality of modulation pattern identification features and the preset modulation pattern mapping relationship, the target modulation pattern can be determined. The modulation of the OFDM signal is adaptive, so the target modulation pattern can include multiple modulation modes, for example, the target modulation pattern can include QPSK and 16QAM.

[0059] By performing fast Fourier transform on the obtained OFDM signal, an NxK-dimensional frequency domain matrix can be obtained. Based on the subcarrier dimension, a plurality of frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix according to the identification of the subcarriers, so that K target data sequences can be obtained. At least one of clustering or accumulation is performed on the target data sequence, and a plurality of modulation pattern identification features associated with the target data sequence can be obtained. Based on the plurality of modulation pattern identification features, the target modulation pattern is determined from the preset modulation pattern mapping relationship. Based on the subcarrier dimension, the frequency domain symbol vectors associated with the subcarriers are extracted from the frequency domain matrix, which lays a data foundation for detecting different modulation patterns at a subcarrier level. At least one of clustering or accumulation is performed on the target data sequence, and a plurality of modulation pattern identification features associated with the target data sequence can be obtained. Then, the target modulation pattern is determined according to the preset modulation pattern mapping relationship, which realizes the identification of the target modulation pattern of the adaptive modulation OFDM signal including multiple modulation modes at a subcarrier level, and improves the identification accuracy and granularity of the modulation pattern.

[0060] Based on the subcarrier dimension, a plurality of frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix according to the identification of the K subcarriers, and K target data sequences are obtained. This includes: for any subcarrier in the K subcarriers, a plurality of frequency domain symbol vectors associated with the subcarrier determined from the frequency domain matrix according to the identification of the subcarrier are used as intermediate data sequences; and a data sequence of a preset length is extracted from the intermediate data sequence as a target data sequence.

[0061] In an implementation manner, for any subcarrier in the K subcarriers, all frequency domain symbol vectors associated with the subcarrier can be determined from the frequency domain matrix according to the identification of the subcarrier. The all frequency domain symbol vectors can include a plurality of frequency domain symbol vectors, and a sequence formed by the plurality of frequency domain symbol vectors is used as an intermediate data sequence.

[0062] The preset length can be determined according to the range of the frequency domain symbol vector. In one implementation, when the preset length is the range of all frequency domain symbol vectors, all frequency domain symbol vectors, i.e., the intermediate data sequence, can be taken as the target data sequence. In another implementation, when the preset length is part of the range of all frequency domain symbol vectors, part of the frequency domain symbol vectors, i.e., part of the intermediate data sequence, can be extracted from all frequency domain symbol vectors as the target data sequence.

[0063] For any subcarrier in the K subcarriers, based on the identification thereof, a plurality of frequency domain symbol vectors associated with the subcarrier can be determined from the frequency domain matrix, and on this basis, based on the preset length, a target data sequence can be extracted from the plurality of frequency domain symbol vectors, so that a subcarrier-granularity target data sequence can be obtained, breaking the limitation of the related art that the signal is regarded as a whole, and laying a finer-granularity data foundation for detecting different modulation modes.

[0064] According to any target data sequence in the K target data sequences, at least one of clustering or accumulation is performed on the target data sequence to determine a plurality of modulation pattern recognition features associated with the target data sequence, including: clustering the target data sequence to determine a cluster center and a number of cluster centers; accumulating the target data sequence to obtain an accumulation quantity; determining a first modulation pattern recognition feature associated with the target data sequence according to at least one of the number of cluster centers and the accumulation quantity; averaging the phase difference between each cluster center and a preset reference point to obtain a cluster center average phase offset; obtaining an offset angle according to the cluster center average phase offset and a preset average phase offset; and determining a second modulation pattern recognition feature associated with the target data sequence according to the offset angle and a preset offset angle range.

[0065] The modulation pattern recognition features can include the first modulation pattern recognition feature and the second modulation pattern recognition feature.

[0066] For any target data sequence in the K target data sequences, clustering analysis can be performed on the frequency domain symbol vectors in the target data sequence to determine a cluster center and a number of cluster centers, and superposition can be performed on the frequency domain symbol vectors in the target data sequence to obtain an accumulation quantity. This feature is not sensitive to Gaussian noise and has strong robustness. In one implementation, the 4th-order accumulation quantity of the frequency domain symbol vector is calculated by the following formula (3) .

[0067] (3)

[0068] According to at least one of the number of cluster centers and the accumulation, the first modulation pattern identification feature associated with the target data sequence can be determined. For example, the number of cluster centers can be determined as the first modulation pattern identification feature associated with the target data sequence. For example, the accumulation can be determined as the first modulation pattern identification feature associated with the target data sequence. For example, the number of cluster centers and the accumulation can be determined together as the first modulation pattern identification feature associated with the target data sequence, which can also be referred to as a macro modulation pattern identification feature. In an implementation, in the case where the number of cluster centers is 4, the number of cluster centers 4 can be determined as the first modulation pattern identification feature, and the modulation order associated with the first modulation pattern identification feature is 4. In an implementation, in the case where the number of cluster centers is 16, the number of cluster centers 16 can be determined as the first modulation pattern identification feature, and the modulation order associated with the first modulation pattern identification feature is 16.

[0069] The preset reference point can represent the complex plane coordinate origin, corresponding to the zero amplitude, zero phase reference point of the signal. Each cluster center corresponds to a phase, and the phase difference between each cluster center and the preset reference point is determined, and the phase difference between each cluster center and the preset reference point is summed and then averaged to obtain the cluster center average phase shift . The preset average phase shift may represent the average value of the phase of the standard QPSK, and the cluster center average phase shift minus the preset average phase shift can be obtained by using the following formula (4) to obtain the offset angle .

[0070] (4)

[0071] The preset offset angle range can include a range that is significantly greater than 0° and not equal to a multiple of 90°. According to the relationship between the offset angle and the preset offset angle range, the second modulation pattern identification feature associated with the target data sequence can be determined. In an implementation, in the case where the offset angle belongs to the preset offset angle range, the offset angle can be determined as the second modulation pattern identification feature associated with the target data sequence, that is, whether there is an overall phase rotation can be determined through the second modulation pattern identification feature, and the second modulation pattern identification feature can also be referred to as a micro modulation variant identification feature.

[0072] The clustering center and the number of clustering centers can be obtained by clustering analysis on the target data sequence. The cumulative quantity is obtained by accumulating the target data sequence. According to at least one of the number of clustering centers and the cumulative quantity, the first modulation pattern recognition feature associated with the target data sequence can be determined. The average phase offset of each clustering center is obtained by averaging the phase difference between each clustering center and a preset reference point. The offset angle is obtained by subtracting the preset average phase offset from the average phase offset of each clustering center. According to the offset angle and the preset offset angle range, the second modulation pattern recognition feature associated with the target data sequence can be determined. According to the first modulation pattern recognition feature (macro modulation pattern recognition) and the second modulation pattern recognition feature (micro modulation variant recognition), the target modulation pattern can be determined from the preset modulation pattern mapping relationship, which improves the recognition accuracy and granularity of the target modulation pattern, and realizes high-precision quantization of the standard modulation and its subtle variants.

[0073] According to the plurality of modulation pattern recognition features and the preset modulation pattern mapping relationship, the target modulation pattern is determined, including: determining the gradient sequence of each modulation pattern recognition feature according to the similarity between adjacent modulation pattern recognition features, and fusing the gradient sequence of each modulation pattern recognition feature to obtain a target gradient sequence; determining at least one gradient extreme value as a candidate modulation boundary point from the target gradient sequence; determining at least one target modulation boundary point from the candidate modulation boundary point based on a preset threshold; determining at least two target modulation regions with different modulation patterns based on the at least one target modulation boundary point, and obtaining at least two feature vector averages according to the modulation pattern recognition features of the at least two target modulation regions; determining the target modulation pattern according to the at least two feature vector averages and the preset modulation pattern mapping relationship.

[0074] The modulation pattern recognition feature records the signal modulation characteristic along the frequency domain feature, and the modulation pattern recognition feature associated with the kth subcarrier As shown in the following formula (5), in an implementation manner, the modulation pattern recognition feature can include 3, and the 3 modulation pattern recognition features constitute a feature matrix F, as shown in the following formula (6).

[0075] (5)

[0076] (6)

[0077] Wherein, represents the first first modulation pattern recognition feature associated with the kth subcarrier, represents the second first modulation pattern recognition feature associated with the kth subcarrier, represents the second modulation pattern recognition feature associated with the kth subcarrier. represents the modulation pattern recognition feature associated with the 1st subcarriertranspose of transpose of denotes a modulation pattern identification feature associated with the 2nd subcarrier transpose of denotes a modulation pattern identification feature associated with the 3rd subcarrier transpose of.

[0078] The gradient sequence of each modulation pattern identification feature can be determined according to the similarity between adjacent modulation pattern identification features, and the gradient sequence of each modulation pattern identification feature is fused to obtain a target gradient sequence, including: filtering each modulation pattern identification feature to obtain an intermediate modulation pattern identification feature associated with each modulation pattern identification feature; determining the gradient sequence of each intermediate modulation pattern identification feature according to the similarity between each adjacent intermediate modulation pattern identification feature; and fusing the gradient sequence of each intermediate modulation pattern identification feature according to a preset weight to obtain the target gradient sequence.

[0079] The gradient sequence of each intermediate modulation pattern identification feature can be determined according to the similarity between each adjacent intermediate modulation pattern identification feature. In an implementation manner, the gradient sequence of the dth intermediate modulation pattern identification feature associated with the kth subcarrier can be calculated by using the following formula (7) The gradient sequence of each intermediate modulation pattern identification feature is fused according to a preset weight to obtain a target gradient sequence. In an implementation manner, the gradient sequence of D intermediate modulation pattern identification features is fused according to a preset weight to obtain a target gradient sequence , as shown in the following formula (8).

[0080] (7)

[0081] (8)

[0082] wherein D represents the number of intermediate modulation pattern identification features, denotes an intermediate modulation pattern identification feature obtained by filtering a modulation pattern identification feature, denotes a preset weight of the dth intermediate modulation pattern identification feature.

[0083] In an implementation manner, the sum of the preset weights of the D intermediate modulation pattern identification features is 1, that is, the following formula is satisfied .

[0084] In an implementation manner, in the case that the modulation modes of adjacent subcarriers are the same, the feature changes little, and the gradient sequence is close to 0, and in another implementation manner, in the case that the modulation modes of adjacent subcarriers are different, the feature will be mutated, and the gradient sequence Will significantly increase.

[0085] At least one gradient extremum can be determined from the target gradient sequence as a candidate modulation boundary point, and the subcarrier position corresponding to the extremum is the modulation boundary, that is, the modulation pattern of adjacent subcarriers changes, for example, from QPSK to 16QAM.

[0086] The preset threshold can be a dynamic threshold, and at least one target modulation boundary point can be determined from the candidate modulation boundary points based on the preset threshold. The target modulation boundary points can be used to divide the entire frequency band into multiple target modulation regions, wherein the modulation modes of the same target modulation region are the same, and the modulation modes of different target modulation regions can be the same or different. Specifically, the modulation modes of adjacent two target modulation regions based on the same target modulation boundary point are different, and the modulation modes of adjacent two target modulation regions based on different target modulation boundary points can be the same.

[0087] For each target modulation region, the average of all modulation pattern recognition features inside it can be obtained to obtain a feature vector average, and then the target modulation pattern is determined from the preset modulation pattern mapping relationship based on the feature vector average.

[0088] The gradient sequence of each modulation pattern recognition feature can be determined according to the similarity between adjacent modulation pattern recognition features, and then the gradient sequences of all modulation pattern recognition features are fused based on weights to obtain a target gradient sequence. Then, the target modulation boundary points are determined according to the target gradient sequence, that is, the points where the modulation pattern changes in the orthogonal frequency division multiplexing signal. The average of the modulation pattern recognition features of the target modulation region is obtained to obtain a feature vector average, and the target modulation pattern is determined in combination with the preset modulation pattern mapping relationship. Through the analysis of the gradient change, the modulation boundary is automatically and accurately positioned in the target modulation boundary point detection mode, avoiding the manual interpretation or the assumption based on the fixed resource block in the related art, and the identification accuracy of the modulation pattern is improved.

[0089] Based on at least one target modulation boundary point, at least two target modulation regions with different modulation patterns are determined, and at least two feature vector averages are obtained according to the modulation pattern recognition features of the at least two target modulation regions, including: determining at least two target modulation regions according to at least one modulation boundary point; and obtaining at least two feature vector averages by averaging the modulation pattern recognition features of the at least two target modulation regions, respectively.

[0090] At least two target modulation regions can be determined according to at least one modulation boundary point, that is, at least two target modulation regions can be obtained, and the average of the modulation pattern recognition features in each target modulation region can be obtained to obtain a feature vector average, that is, at least two feature vector averages can be obtained.

[0091] The modulation boundary point can be used to determine the target modulation region on both sides of the modulation boundary point, and then the average of the modulation pattern recognition features in the target modulation region is calculated to reduce noise interference, so that the modulation pattern recognition of the target modulation region can be more stable.

[0092] According to the average of the at least two feature vectors and the preset modulation pattern mapping relationship, the target modulation pattern is determined, including: for any one of the average of the at least two feature vectors, based on the first feature vector average, determining the modulation order associated with the feature vector average from the preset modulation pattern mapping sub-relationship, and based on the modulation order, determining at least one candidate target modulation pattern; according to the second feature vector average and the modulation order, determining the target modulation pattern from the at least one candidate target modulation pattern.

[0093] The average of the feature vectors can include a first feature vector average associated with the first modulation pattern recognition feature and a second feature vector average associated with the second modulation pattern recognition feature.

[0094] For any one of the average of the at least two feature vectors, based on the first feature vector average, the modulation order associated with the first feature vector average can be determined from the preset modulation pattern mapping sub-relationship, and at least one candidate target modulation pattern can be determined based on the modulation order. In an implementation, in the case of a modulation order of 4, the candidate target modulation pattern can include a quadrature phase shift keying A type implementation (QPSK-A for short) and a quadrature phase shift keying B type implementation (QPSK-B for short), wherein QPSK-A is also called standard quadrature phase shift keying. In another implementation, in the case of a modulation order of 16, the candidate target modulation pattern can include 16QAM.

[0095] The target modulation pattern can be determined from the at least one candidate target modulation pattern according to the second feature vector average and the modulation order, including: in the case of a modulation order determination, the target modulation pattern is determined from the at least one candidate target modulation pattern based on the second feature vector average.

[0096] In an implementation, in the case of a modulation order of 4, the target modulation pattern QPSK-B can be determined from the at least one candidate target modulation pattern (QPSK-A, QPSK-B) according to the second feature vector average.

[0097] The process of determining the modulation order associated with the first feature vector average from the preset modulation pattern mapping sub-relationship and determining the at least one candidate target modulation pattern based on the modulation order can be referred to as macro modulation pattern recognition. In the case of determining the modulation order, the process of determining the target modulation pattern from the at least one candidate target modulation pattern according to the second feature vector average can be referred to as micro modulation pattern recognition. Through two-stage modulation pattern recognition, the application range is expanded from pure adaptive modulation pattern recognition to discovery and positioning of small and special modulation regions inside the signal, improving the accuracy of modulation pattern recognition.

[0098] Through extraction of the target data sequence in the subcarrier dimension, subcarrier-level fine modulation pattern recognition is achieved, which can clearly position the modulation boundary and discover the microstructure that is difficult to detect in related technologies, for example, a special pilot. Through the complete process of extraction of the target data sequence in the subcarrier dimension, modulation pattern recognition feature extraction, and determination of the target gradient sequence, the path of modulation pattern recognition is completely traceable, improving the credibility of the modulation pattern recognition method of the OFDM signal based on adaptive modulation. Since it does not rely on training of specific modulation combinations, it has strong generalization ability for unknown non-uniform modulation modes. In addition, through parallel processing and efficient target gradient sequence scanning, higher-precision modulation pattern recognition is achieved with linear / polynomial complexity, which has important engineering application value.

[0099] The modulation pattern recognition method of the OFDM signal based on adaptive modulation further includes generating a frequency domain distribution atlas corresponding to the modulation pattern based on the modulation pattern.

[0100] In an implementation manner, the frequency domain distribution atlas is accurately output in the form of a data tuple, for example: [(1, 15, 'QPSK-A'), (16, 24, 'QPSK-B'), (25, 500, '16QAM'), (501, 1024, 'QPSK-A')]. The frequency domain distribution atlas not only reveals the macro modulation boundary from QPSK to 16QAM, but also accurately positions the micro pilot region of only 9 subcarriers using special modulation QPSK-B, realizing deep analysis of the internal structure of the OFDM signal.

[0101] By displaying the modulation patterns of different subcarriers in the frequency band of the OFDM signal in the form of a frequency domain distribution atlas, the modulation pattern is converted into an intuitive graph, facilitating analysis and optimization of the communication system using the OFDM signal.

[0102] Figure 3 A symbol constellation diagram associated with the second modulation pattern recognition feature according to an embodiment of the present disclosure is schematically shown.Figure 4 A frequency domain symbol vector constellation diagram associated with the second modulation pattern identification feature is schematically shown according to an embodiment of the present disclosure.

[0103] As shown in Figure 3 , 25 symbol constellation diagrams (symbol1~symbol25) associated with the second modulation pattern identification feature are included, and in the symbol dimension, the 25 symbol constellation diagrams are difficult to show that the modulation pattern changes. Figure 4 As shown in Figure 3 , 25 frequency domain symbol vector constellation diagrams (carrier1~ carrier25) associated with the second modulation pattern identification feature are included, and it can be seen that the modulation pattern changes from QPSK-A to QPSK-B at carrier17, and the modulation pattern changes from QPSK-B to QPSK-A at carrier25. Figure 4 Therefore, it can be known that in the symbol dimension, it is difficult to achieve finer granularity modulation pattern identification.

[0104] Figure 5 A symbol constellation diagram associated with the first modulation pattern identification feature is schematically shown according to an embodiment of the present disclosure. Figure 6 A frequency domain symbol vector constellation diagram associated with the first modulation pattern identification feature is schematically shown according to an embodiment of the present disclosure.

[0105] As shown in Figure 5 , 16 symbol constellation diagrams (symbol1~symbol16) associated with the first modulation pattern identification feature are included, and in the symbol dimension, the 16 symbol constellation diagrams are difficult to show that the modulation pattern changes. Figure 6 As shown in Figure 5 , 16 frequency domain symbol vector constellation diagrams (carrier518~ carrier533) associated with the first modulation pattern identification feature are included, and the total number of subcarriers is 1024, and it can be seen that the modulation pattern changes from 64-Quadrature Amplitude Modulation (64QAM) to QPSK at carrier522~ carrier529. Figure 6 Therefore, it can be known that in the frequency domain symbol vector dimension, finer granularity modulation pattern identification that is difficult to achieve in the symbol dimension can be achieved.

[0106] Based on the above modulation pattern identification method of the adaptive modulation based orthogonal frequency division multiplexing signal, the present disclosure further provides a modulation pattern identification device of the adaptive modulation based orthogonal frequency division multiplexing signal. The device will be described in detail below. Figure 7

[0107] Figure 7 ​A structural block diagram of a modulation pattern recognition apparatus for adaptive modulation based OFDM signals is shown according to an embodiment of the present disclosure.

[0108] As shown in Figure 7 The modulation pattern recognition apparatus 700 for adaptive modulation based OFDM signals according to the embodiment comprises an obtaining module 710, a first obtaining module 720, a second obtaining module 730, a third obtaining module 740 and a determining module 750.

[0109] The obtaining module 710 is configured to obtain an OFDM signal, wherein the OFDM signal comprises N symbols and K subcarriers. In an embodiment, the obtaining module 710 can be configured to perform the operation S210 described above, and details are not repeated here.

[0110] The first obtaining module 720 is configured to perform fast Fourier transform on the OFDM signal to obtain a frequency domain matrix of NxK dimensions. In an embodiment, the first obtaining module 720 can be configured to perform the operation S220 described above, and details are not repeated here.

[0111] The second obtaining module 730 is configured to extract, based on the subcarrier dimension, a plurality of frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix according to the identification of the K subcarriers to obtain K target data sequences. In an embodiment, the second obtaining module 730 can be configured to perform the operation S230 described above, and details are not repeated here.

[0112] The third obtaining module 740 is configured to perform at least one of clustering or accumulation on any one of the K target data sequences to determine a plurality of modulation pattern recognition features associated with the target data sequence, wherein the modulation pattern recognition features are used to determine the modulation order. In an embodiment, the third obtaining module 740 can be configured to perform the operation S240 described above, and details are not repeated here.

[0113] The determining module 750 is configured to determine a target modulation pattern according to the plurality of modulation pattern recognition features and a preset modulation pattern mapping relationship, wherein the target modulation pattern represents the modulation mode of the adaptive modulation based OFDM signal, and the preset modulation pattern mapping relationship represents the mapping relationship between the modulation pattern recognition features and the modulation pattern. In an embodiment, the determining module 750 can be configured to perform the operation S250 described above, and details are not repeated here.

[0114] According to an embodiment of the present disclosure, the second obtaining module 730 comprises: a first obtaining submodule, configured to, for any subcarrier in the K subcarriers, determine, according to the identifier of the subcarrier, a plurality of frequency-domain symbol vectors associated with the subcarrier from the frequency-domain matrix as an intermediate data sequence; and a second obtaining submodule, configured to extract a data sequence of a preset length from the intermediate data sequence as the target data sequence.

[0115] According to an embodiment of the present disclosure, the modulation pattern identification feature comprises a first modulation pattern identification feature and a second modulation pattern identification feature, and the third obtaining module 740 comprises: a third obtaining submodule, configured to, for any target data sequence in the K target data sequences, cluster the target data sequence, determine a cluster center and a number of cluster centers; a fourth obtaining submodule, configured to accumulate the target data sequence to obtain an accumulated quantity; a fifth obtaining submodule, configured to determine the first modulation pattern identification feature associated with the target data sequence according to at least one of the number of cluster centers and the accumulated quantity; a sixth obtaining submodule, configured to average a phase difference between each cluster center and a preset reference point to obtain a cluster center average phase offset; a seventh obtaining submodule, configured to obtain an offset angle according to the cluster center average phase offset and a preset average phase offset; and an eighth obtaining submodule, configured to determine the second modulation pattern identification feature associated with the target data sequence according to the offset angle and a preset offset angle range.

[0116] According to an embodiment of the present disclosure, the determining module 750 comprises: a first determining submodule, configured to determine a gradient sequence of each modulation pattern identification feature according to a similarity between adjacent modulation pattern identification features, and fuse the gradient sequence of each modulation pattern identification feature to obtain a target gradient sequence; a second determining submodule, configured to determine at least one gradient extreme value from the target gradient sequence as a candidate modulation boundary point; a third determining submodule, configured to determine at least one target modulation boundary point from the candidate modulation boundary point based on a preset threshold; a fourth determining submodule, configured to determine at least two target modulation regions with different modulation patterns based on the at least one target modulation boundary point, and obtain at least two feature vector averages according to modulation pattern identification features of the at least two target modulation regions; and a fifth determining submodule, configured to determine a target modulation pattern according to the at least two feature vector averages and a preset modulation pattern mapping relationship.

[0117] According to an embodiment of the present disclosure, the first determining sub-module comprises: a first determining unit configured to filter each modulation pattern identification feature to obtain an intermediate modulation pattern identification feature associated with each modulation pattern identification feature; a second determining unit configured to determine a gradient sequence of each intermediate modulation pattern identification feature according to a similarity between each adjacent intermediate modulation pattern identification feature; and a third determining unit configured to fuse the gradient sequence of each intermediate modulation pattern identification feature according to a preset weight to obtain a target gradient sequence.

[0118] According to an embodiment of the present disclosure, the fourth determining sub-module comprises: a fourth determining unit configured to determine at least two target modulation regions according to at least one modulation boundary point; and a fifth determining unit configured to respectively average the modulation pattern identification features of the at least two target modulation regions to obtain at least two feature vector averages.

[0119] According to an embodiment of the present disclosure, the feature vector average comprises a first feature vector average associated with the first modulation pattern identification feature and a second feature vector average associated with the second modulation pattern identification feature, wherein the fifth determining sub-module comprises: a sixth determining unit configured to, for any one of the at least two feature vector averages, determine a modulation order associated with the first feature vector average from a preset modulation pattern mapping sub-relation based on the first feature vector average, and determine at least one candidate target modulation pattern based on the modulation order; and a seventh determining unit configured to determine the target modulation pattern from the at least one candidate target modulation pattern according to the second feature vector average and the modulation order.

[0120] According to an embodiment of the present disclosure, the seventh determining unit comprises: a first determining sub-unit configured to, in the case where the modulation order is determined, determine the target modulation pattern from the at least one candidate target modulation pattern based on the second feature vector average.

[0121] According to an embodiment of the present disclosure, the modulation pattern identification device 700 of the adaptive modulation-based OFDM signal further comprises: a generation module configured to generate a frequency domain distribution atlas corresponding to the modulation pattern based on the modulation pattern.

[0122] According to an embodiment of the present disclosure, any of the modules 710, 720, 730, 740 and 750 can be combined in one module, or any of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of the modules can be combined with at least part of the functions of the other modules, and implemented in one module. According to an embodiment of the present disclosure, at least one of the modules 710, 720, 730, 740 and 750 can be implemented at least in part as a hardware circuit, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system in package, an application-specific integrated circuit (ASIC), or any other reasonable manner of integrating or packaging a circuit, etc., or implemented in hardware or firmware, or implemented in any one of software, hardware and firmware or in a proper combination of any of them. Alternatively, at least one of the modules 710, 720, 730, 740 and 750 can be implemented at least in part as a computer program module, which can perform the corresponding functions when the computer program module is run.

[0123] Figure 8 A block diagram of an electronic device suitable for implementing the modulation pattern recognition method of the adaptive modulation based OFDM signal is schematically shown according to an embodiment of the present disclosure.

[0124] As shown in Figure 8 The electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROK) 802 or loaded into a RAK (RandoK Access KeKory, Random Access Memory) from a storage portion 808. The processor 801 can include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset, and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 can also include an on-board memory for cache use. The processor 801 can include a single processing unit or a plurality of processing units for performing different actions of the method processes according to an embodiment of the present disclosure.

[0125] In the RAK 803, various programs and data required by the electronic device 800 to operate are stored. The processor 801, the ROK 802, and the RAK 803 are connected to each other via the bus 804. The processor 801 performs various operations of the method processes according to the embodiments of the disclosure by executing the programs in the ROK 802 and / or the RAK 803. It is noted that the programs can also be stored in one or more memories other than the ROK 802 and the RAK 803. The processor 801 can also perform various operations of the method processes according to the embodiments of the disclosure by executing the programs stored in the one or more memories.

[0126] According to an embodiment of the disclosure, the electronic device 800 can further include an I / O interface 805, which is also connected to the bus 804. The electronic device 800 can further include one or more of the following components connected to the I / O interface 805: an input part 806 including a keyboard, a mouse, and the like; an output part 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker, and the like; a storage part 808 including a hard disk, and the like; and a communication part 809 including a network interface card such as a LAN card, a modem, and the like. The communication part 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 810 as necessary, so that a computer program read therefrom is installed in the storage part 808 as necessary.

[0127] The disclosure also provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments; or can exist separately without being assembled into the device / apparatus / system. The above computer readable storage medium carries one or more programs, when the one or more programs are executed, the method according to the embodiments of the disclosure is implemented.

[0128] According to an embodiment of the present disclosure, the computer readable storage medium can be a nonvolatile computer readable storage medium, for example, can include, but is not limited to, a portable computer diskette, a hard disk, a RAK (RandoK Access KeKory, Random Access Memory), a ROK (Read Only KeKory, Read Only Memory), an erasable programmable read only memory (EPROK or flash memory), a portable compact disk read only memory (CD-ROK), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, the computer readable storage medium can include the ROK 802 and / or the RAK 803 described above and / or one or more memories other than the ROK 802 and the RAK 803.

[0129] Embodiments of the present disclosure also include a computer program product, which includes a computer program containing program codes for executing the method shown in the flow chart. When the computer program product is run in a computer system, the program codes are used to make the computer system implement the modulation pattern identification method of the adaptive modulation based OFDM signal provided by the embodiments of the present disclosure.

[0130] The above functions defined in the system / apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 801. According to an embodiment of the present disclosure, the system, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0131] In one embodiment, the computer program can rely on tangible storage media such as optical storage media, magnetic storage media, etc. In another embodiment, the computer program can also be transmitted, distributed, and downloaded in the form of a signal on a network medium, and installed and downloaded through the communication part 809, and / or installed from the detachable medium 811. The program codes contained in the computer program can be transmitted by any suitable network medium, including but not limited to wireless, wired, etc., or any suitable combination of the above.

[0132] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the detachable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiments of the present disclosure are performed. According to an embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0133] According to embodiments of the present disclosure, program code of the computer programs provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages, and specifically, these computer programs can be implemented using a high-level procedural and / or object-oriented programming language, and / or an assembly / machine language. The programming language includes, but is not limited to, a programming language such as Java, C++, Python, "C" language, or a similar programming language. The program code can be executed entirely on a user computing device, partially on a user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected to the Internet through an Internet service provider).

[0134] The flow diagrams and the block diagrams in the drawings are illustrations of possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0135] Those skilled in the art will understand that features recited in the various embodiments of the present disclosure can be combined and / or integrated in various combinations and / or permutations, even if such combinations and / or permutations are not expressly noted in the present disclosure. In particular, features recited in the various embodiments of the present disclosure can be combined and / or integrated in various combinations and / or permutations without departing from the spirit and teachings of the present disclosure. All such combinations and / or integrations are within the scope of the present disclosure.

[0136] The embodiments of the present disclosure are described above. However, these embodiments are merely for illustrative purposes, and are not intended to limit the scope of the present disclosure. Although each embodiment is described above separately, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Those skilled in the art can make various substitutions and modifications without departing from the scope of the present disclosure, and these substitutions and modifications should all fall within the scope of the present disclosure.

Claims

1. A modulation pattern recognition method for orthogonal frequency division multiplexing signals based on adaptive modulation, characterized in that, The method includes: Obtain an orthogonal frequency division multiplexed signal, which includes N symbols and K subcarriers; perform a fast Fourier transform on the orthogonal frequency division multiplexed signal to obtain an NxK dimension frequency domain matrix; Based on the subcarrier dimension, according to the identifiers of the K subcarriers, multiple frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix to obtain K target data sequences; Based on any one of the K target data sequences, perform at least one operation, such as clustering or accumulation, on the target data sequence to obtain multiple modulation pattern identification features associated with the target data sequence, wherein the modulation pattern identification features are used to determine the modulation order; Based on the multiple modulation style identification features and the preset modulation style mapping relationship, a target modulation style is determined, wherein the target modulation style represents the modulation method of the orthogonal frequency division multiplexing signal of adaptive modulation, and the preset modulation style mapping relationship represents the mapping relationship between the modulation style identification features and the modulation style.

2. The method according to claim 1, characterized in that, Based on the subcarrier dimension, according to the identifiers of the K subcarriers, multiple frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix to obtain K target data sequences, including: For any one of the K said subcarriers, Based on the identifier of the subcarrier, multiple frequency domain symbol vectors associated with the subcarrier, determined from the frequency domain matrix, are used as intermediate data sequences; Extract a data sequence of a preset length from the intermediate data sequence as the target data sequence.

3. The method according to claim 2, characterized in that, The modulation pattern recognition features include a first modulation pattern recognition feature and a second modulation pattern recognition feature. Specifically, based on any one of the K target data sequences, at least one operation, such as clustering or accumulation, is performed on the target data sequence to determine multiple modulation pattern recognition features associated with the target data sequence, including: For any one of the K target data sequences, Cluster the target data sequence to determine the cluster centers and the number of cluster centers; The target data sequence is accumulated to obtain the cumulative amount; Based on at least one of the number of cluster centers and the cumulative amount, a first modulation pattern identification feature associated with the target data sequence is determined; The average phase shift of the cluster centers is obtained by averaging the phase difference between each cluster center and a preset reference point. The offset angle is obtained based on the average phase offset of the cluster centers and the preset average phase offset; Based on the offset angle and the preset offset angle range, a second modulation pattern identification feature associated with the target data sequence is determined.

4. The method according to claim 3, characterized in that, The step of determining the target modulation style based on the multiple modulation style identification features and the preset modulation style mapping relationship includes: Based on the similarity between adjacent modulation pattern recognition features, the gradient sequence of each modulation pattern recognition feature is determined, and the gradient sequences of each modulation pattern recognition feature are fused to obtain the target gradient sequence. At least one gradient extremum is determined from the target gradient sequence as a candidate modulation boundary point; Based on the preset threshold, at least one target modulation boundary point is determined from the candidate modulation boundary points; Based on the at least one target modulation boundary point, at least two target modulation regions with different modulation patterns are determined, and features are identified according to the modulation patterns of the at least two target modulation regions to obtain the average value of at least two feature vectors; The target modulation pattern is determined based on the average value of the at least two feature vectors and the mapping relationship of the preset modulation pattern.

5. The method according to claim 4, characterized in that, The step of determining the gradient sequence of each modulation pattern recognition feature based on the similarity between adjacent modulation pattern recognition features, and fusing the gradient sequences of each modulation pattern recognition feature to obtain the target gradient sequence, includes: Each of the modulation pattern recognition features is filtered to obtain an intermediate modulation pattern recognition feature associated with each of the modulation pattern recognition features; The gradient sequence of each intermediate modulation pattern identification feature is determined based on the similarity between each adjacent intermediate modulation pattern identification feature; The gradient sequences of each intermediate modulation pattern recognition feature are fused according to preset weights to obtain the target gradient sequence.

6. The method according to claim 5, characterized in that, The step of determining at least two target modulation regions with different modulation patterns based on the at least one target modulation boundary point, and obtaining the average value of at least two feature vectors based on the modulation pattern identification features of the at least two target modulation regions, includes: Based on the at least one modulation boundary point, at least two target modulation regions are determined; The average value of the modulation pattern recognition features of at least two target modulation regions is calculated to obtain the average value of at least two feature vectors.

7. The method according to claim 4, characterized in that, The average feature vector includes a first average feature vector associated with the first modulation pattern recognition feature and a second average feature vector associated with the second modulation pattern recognition feature, wherein determining the target modulation pattern based on the at least two average feature vectors and the preset modulation pattern mapping relationship includes: For the average value of any one of the at least two eigenvectors, Based on the average value of the first feature vector, the modulation order associated with the average value of the first feature vector is determined from the preset modulation pattern mapping sub-relation, and at least one candidate target modulation pattern is determined based on the modulation order. The target modulation pattern is determined from the at least one candidate target modulation pattern based on the average value of the second feature vector and the modulation order.

8. The method according to claim 7, characterized in that, Determining the target modulation pattern from the at least one candidate target modulation pattern based on the average value of the second feature vector and the modulation order includes: Given that the modulation order is determined, a target modulation pattern is determined from the at least one candidate target modulation pattern based on the average value of the second feature vector.

9. The method according to any one of claims 1 to 8, further comprising: Based on the modulation pattern, a frequency domain distribution map corresponding to the modulation pattern is generated.

10. A modulation pattern recognition device for orthogonal frequency division multiplexed signals based on adaptive modulation, characterized in that, The device includes: The acquisition module is used to acquire an orthogonal frequency division multiplexing (OFDM) signal, which includes N symbols and K subcarriers. The first obtaining module is used to perform a fast Fourier transform on the orthogonal frequency division multiplexing signal to obtain an NxK dimension frequency domain matrix; The second obtaining module is used to extract multiple frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix based on the subcarrier dimension and according to the identifiers of the K subcarriers, to obtain K target data sequences; The third obtaining module is used to perform at least one operation, such as clustering or accumulation, on any one of the K target data sequences to determine multiple modulation pattern identification features associated with the target data sequence, wherein the modulation pattern identification features are used to determine the modulation order; The determining module is used to determine a target modulation style based on the multiple modulation style identification features and the preset modulation style mapping relationship, wherein the target modulation style represents the modulation method of the orthogonal frequency division multiplexing signal of adaptive modulation, and the preset modulation style mapping relationship represents the mapping relationship between the modulation style identification features and the modulation style.

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