Modulation pattern identification method and device for adaptive modulation based orthogonal frequency division multiplexing signal

Abstract

This disclosure provides a method and apparatus for modulation pattern recognition of orthogonal frequency division multiplexing (OFDM) signals based on adaptive modulation, which can be applied to the field of wireless communication technology. The method includes: acquiring an OFDM signal, which comprises N symbols and K subcarriers; performing a fast Fourier transform on the OFDM signal to obtain an NxK-dimensional frequency domain matrix; based on the subcarrier dimension and according to the identifiers of the K subcarriers, extracting multiple frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix to obtain K target data sequences; performing at least one operation, such as clustering or accumulation, on any of the K target data sequences to obtain multiple modulation pattern recognition features associated with the target data sequences; and determining the target modulation pattern based on the multiple modulation pattern recognition features and a preset modulation pattern mapping relationship.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and more specifically to a method and apparatus for identifying the modulation pattern of orthogonal frequency division multiplexing signals based on adaptive modulation. Background Technology

[0002] The widespread application of software-defined radio (SDR) technology has spurred the development of signal processing, leading to the emergence of various new protocols and signal patterns. Based on SDR technology, different modulation schemes can be achieved by applying various modulation, spread spectrum, coding, and encryption techniques to baseband signals. Signal modulation pattern identification is a crucial issue in communication signal detection, especially in non-cooperative communication. Effective modulation pattern identification is essential for the receiver to correctly demodulate data and receive information. However, existing modulation pattern identification methods struggle to achieve high-precision identification of the modulation patterns of adaptively modulated orthogonal frequency division multiplexing (OFDM) signals. Summary of the Invention

[0003] In view of the above problems, this disclosure provides a method, apparatus, device, medium and program product for modulation pattern recognition of orthogonal frequency division multiplexing signals based on adaptive modulation.

[0004] According to a first aspect of this disclosure, a modulation pattern recognition method for an orthogonal frequency division multiplexing (OFDM) signal based on adaptive modulation is provided, comprising: acquiring an OFDM signal, the OFDM signal including N symbols and K subcarriers; performing a fast Fourier transform on the OFDM signal to obtain an NxK-dimensional frequency domain matrix; based on the subcarrier dimension, extracting multiple frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix according to the identifiers of the K subcarriers to obtain K target data sequences; performing at least one operation, such as clustering or accumulation, on any one of the K target data sequences to obtain multiple modulation pattern recognition features associated with the target data sequences, wherein the modulation pattern recognition features are used to determine the modulation order; and determining a target modulation pattern according to the multiple modulation pattern recognition features and a preset modulation pattern mapping relationship, wherein the target modulation pattern characterizes the modulation mode of the adaptively modulated OFDM signal, and the preset modulation pattern mapping relationship characterizes the mapping relationship between the modulation pattern recognition features and the modulation pattern.

[0005] According to embodiments of this disclosure, based on 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, including: for any subcarrier among the K 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; and a data sequence of a preset length is extracted from the intermediate data sequences as a target data sequence.

[0006] According to embodiments of this disclosure, the modulation pattern identification feature includes a first modulation pattern identification feature and a second modulation pattern identification feature. The method involves performing at least one operation—clustering or accumulation—on any one of K target data sequences to determine multiple modulation pattern identification features associated with the target data sequence. This includes: clustering the target data sequence for any one of the K target data sequences to determine cluster centers and the number of cluster centers; accumulating the target data sequences to obtain a cumulative amount; determining a first modulation pattern identification feature associated with the target data sequence based on at least one of the number of cluster centers and the cumulative amount; averaging the phase difference between each cluster center and a preset reference point to obtain an average phase offset of the cluster centers; obtaining an offset angle based on the average phase offset of the cluster centers and a preset average phase offset; and determining a second modulation pattern identification feature associated with the target data sequence based on the offset angle and a preset offset angle range.

[0007] According to embodiments of this disclosure, determining a target modulation pattern based on multiple modulation pattern identification features and a preset modulation pattern mapping relationship includes: determining a gradient sequence of each modulation pattern identification feature based on the similarity between adjacent modulation pattern identification features, and fusing the gradient sequences of each modulation pattern identification feature to obtain a target gradient sequence; determining at least one gradient extremum from the target gradient sequence as a candidate modulation boundary point; determining at least one target modulation boundary point from the candidate modulation boundary points based on a preset threshold; determining at least two target modulation regions with different modulation patterns based on at least one target modulation boundary point, and obtaining at least two feature vector averages based on the modulation pattern identification features of the at least two target modulation regions; and determining the target modulation pattern based on the at least two feature vector averages and the preset modulation pattern mapping relationship.

[0008] According to embodiments of this disclosure, 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 a target gradient sequence includes: filtering each modulation pattern recognition feature to obtain intermediate modulation pattern recognition features associated with each modulation pattern recognition feature; determining the gradient sequence of each intermediate modulation pattern recognition feature based on the similarity between each adjacent intermediate modulation pattern recognition feature; and fusing the gradient sequences of each intermediate modulation pattern recognition feature according to a preset weight to obtain the target gradient sequence.

[0009] According to embodiments of this disclosure, at least two target modulation regions with different modulation patterns are determined based on at least one target modulation boundary point, and at least two feature vector averages are obtained based on the modulation pattern identification features of the at least two target modulation regions, including: determining at least two target modulation regions based on at least one modulation boundary point; and averaging the modulation pattern identification features of the at least two target modulation regions to obtain at least two feature vector averages.

[0010] According to embodiments of this disclosure, the average feature vector includes a first average feature vector associated with a first modulation pattern identification feature and a second average feature vector associated with a second modulation pattern identification feature. Determining a target modulation pattern based on at least two average feature vectors and a preset modulation pattern mapping relationship includes: for any of the at least two average feature vectors, determining a modulation order associated with the first average feature vector from the preset modulation pattern mapping relationship based on the first average feature vector, and determining at least one candidate target modulation pattern based on the modulation order; and determining a target modulation pattern from at least one candidate target modulation pattern based on the second average feature vector and the modulation order.

[0011] According to embodiments of the present disclosure, determining a target modulation pattern from at least one candidate target modulation pattern based on the average value of a second feature vector and the modulation order includes: when the modulation order is determined, determining the target modulation pattern from at least one candidate target modulation pattern based on the average value of the second feature vector.

[0012] According to embodiments of this disclosure, the above-mentioned method for identifying the modulation pattern of an orthogonal frequency division multiplexing signal based on adaptive modulation further includes: generating a frequency domain distribution map corresponding to the modulation pattern based on the modulation pattern.

[0013] The second aspect of this disclosure provides a modulation pattern recognition device for an orthogonal frequency division multiplexing (OFDM) signal based on adaptive modulation, comprising: an acquisition module for acquiring an OFDM signal, the OFDM signal including N symbols and K subcarriers; a first obtaining module for performing a fast Fourier transform on the OFDM signal to obtain an NxK-dimensional frequency domain matrix; a second obtaining module for extracting 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; a third obtaining module for performing at least one operation, such as clustering or accumulation, on any one of the K target data sequences to determine multiple modulation pattern recognition features associated with the target data sequences, wherein the modulation pattern recognition features are used to determine the modulation order; and a determination module for determining a target modulation pattern based on the multiple modulation pattern recognition features and a preset modulation pattern mapping relationship, wherein the target modulation pattern represents the modulation method of the adaptively modulated OFDM signal, and the preset modulation pattern mapping relationship represents the mapping relationship between the modulation pattern recognition features and the modulation pattern.

[0014] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and 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 described above.

[0015] A fourth aspect of this disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0016] The fifth aspect of this disclosure also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0017] According to the modulation pattern recognition method for orthogonal frequency division multiplexing (OFDM) signals based on adaptive modulation provided in this disclosure, an NxK-dimensional frequency domain matrix can be obtained by performing a fast Fourier transform on the acquired OFDM signal. Based on the subcarrier dimension, multiple frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix according to the subcarrier identifiers, thereby obtaining K target data sequences. At least one operation, such as clustering or accumulation, is performed on the target data sequences to obtain multiple modulation pattern recognition features associated with the target data sequences. Based on these multiple modulation pattern recognition features, a preset modulation pattern mapping is performed. The target modulation pattern is determined in the relationship. Based on the subcarrier dimension, the frequency domain symbol vector associated with the subcarrier is extracted from the frequency domain matrix, which lays the data foundation for detecting different modulation patterns at the subcarrier level with fine granularity. Then, by performing at least one operation such as clustering or superposition on the target data sequence, multiple 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. This realizes the identification of the target modulation pattern of orthogonal frequency division multiplexing signals with adaptive modulation including multiple modulation methods at the subcarrier level with fine granularity, which improves the identification accuracy and granularity of the modulation pattern. Attached Figure Description

[0018] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0019] Figure 1 This diagram illustrates an application scenario of the modulation pattern recognition method for orthogonal frequency division multiplexed signals based on adaptive modulation, according to an embodiment of the present disclosure.

[0020] Figure 2 A flowchart illustrating a modulation pattern recognition method for orthogonal frequency division multiplexed signals based on adaptive modulation according to an embodiment of the present disclosure is shown.

[0021] Figure 3 The diagram schematically illustrates a symbol constellation diagram associated with a second modulation pattern identification feature according to an embodiment of the present disclosure;

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

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

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

[0025] Figure 7 A schematic block diagram of a modulation pattern recognition device for orthogonal frequency division multiplexed signals based on adaptive modulation according to an embodiment of the present disclosure is shown; and

[0026] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a modulation pattern recognition method for orthogonal frequency division multiplexed signals based on adaptive modulation, according to embodiments of the present disclosure. Detailed Implementation

[0027] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0028] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude 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 meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0030] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0031] Orthogonal Frequency Division Multiplexing (OFDM) technology, as a core physical layer solution for modern wireless communication such as 5G New Radio (5G-NR) and sixth / seventh generation wireless technologies (Wi-Fi 6 / 7), boasts high spectral efficiency and resistance to multipath fading. To approach the Shannon capacity limit in complex time-varying frequency-selective channels, Adaptive Modulation and Coding (AMC) technology is widely adopted. The development of AMC technology has evolved from traditional coarse-grained adjustment at the resource block level to fine-grained adaptive modulation (i.e., non-uniform modulation) at the subcarrier or subcarrier group level, aiming to achieve better global spectral efficiency. Furthermore, in practical communication systems, OFDM signal frames typically embed pilot signals, synchronization signals, and control signaling. These functional signals often employ fixed and robust modulation schemes different from the main traffic channel and occupy only a very small number of subcarriers in the frequency domain. This results in inherent heterogeneity and non-uniformity of the modulation scheme in the frequency domain within a single OFDM symbol.

[0032] Effective processing of non-uniformly modulated signals relies heavily on the receiver's accurate understanding of its modulation structure. However, in key application scenarios such as non-cooperative communication, spectrum monitoring, and intelligent operation and maintenance, the receiver usually cannot know the transmitter's modulation strategy in advance, thus requiring blind modulation identification technology. Modulation pattern identification technology in related technologies mainly includes (1) methods based on classical feature extraction. These methods rely on extracting high-order statistics, constellation diagram features, etc. from the received signal as classification criteria. However, these features usually originate from the entire time-domain signal or frequency-domain symbols that are not distinguished, mixing the entire or most frequency band signals. When the signal is non-uniform in the frequency domain, the extracted global features are actually the superposition and averaging of all subcarrier features, resulting in fuzziness of the feature space. This makes it difficult for the classifier to effectively distinguish and locate modulation changes within the frequency domain. For fine modulation adjustments at the subcarrier level and small abnormal modulation structures, such as special pilot failures, it is even more difficult to locate the boundary of modulation pattern changes in the frequency domain. (2) Likelihood theory-based methods: Although the likelihood ratio test-based methods have good theoretical performance, their computational complexity increases exponentially with the number of unknown parameters. For a problem that requires joint hypothesis testing of modulation patterns on tens to thousands of subcarriers, the hypothesis space is huge, making the method lack engineering feasibility. (3) Deep learning-based methods: Deep learning models, especially Convolutional Neural Networks (CNNs), have shown great potential in modulation recognition. However, the decision-making mechanism of such end-to-end "black box" models is not transparent, making it difficult to apply to scenarios with high reliability requirements. Moreover, deep learning models have insufficient generalization ability when facing new signal patterns not covered by training data. Given the explosive number of possible combinations of non-uniform modulation, collecting a complete training dataset is difficult to achieve in practice, resulting in a significant reduction in the reliability of deep learning models in such tasks. Therefore, modulation pattern recognition technology in related technologies is mainly based on the ideal assumption of "uniform modulation pattern", which makes it difficult to effectively deal with modulation pattern recognition of OFDM non-uniform modulation signals while maintaining fine granularity and interpretability.

[0033] In view of this, embodiments of the present disclosure provide a modulation pattern recognition method for orthogonal frequency division multiplexing (OFDM) signals based on adaptive modulation, comprising: acquiring an OFDM signal, the OFDM signal including N symbols and K subcarriers; performing a fast Fourier transform on the OFDM signal to obtain an NxK-dimensional frequency domain matrix; based on the subcarrier dimension, extracting multiple frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix according to the identifiers of the K subcarriers to obtain K target data sequences; performing at least one operation, such as clustering or accumulation, on any of the K target data sequences to obtain multiple modulation pattern recognition features associated with the target data sequences, wherein the modulation pattern recognition features are used to determine the modulation order; and determining a target modulation pattern according to the multiple modulation pattern recognition features and a preset modulation pattern mapping relationship, wherein the target modulation pattern represents the modulation method of the adaptively modulated OFDM signal, and the preset modulation pattern mapping relationship represents the mapping relationship between the modulation pattern recognition features and the modulation pattern.

[0034] Figure 1 The diagram illustrates an application scenario of the modulation pattern recognition method for orthogonal frequency division multiplexed signals based on adaptive modulation according to an embodiment of the present disclosure.

[0035] like Figure 1 As shown, application scenario 100 according to this embodiment may 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 serves as 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 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0036] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via 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 media platform software, etc. (for example only).

[0037] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0038] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0039] It should be noted that the modulation pattern recognition method for orthogonal frequency division multiplexing (OFDM) signals based on adaptive modulation provided in this embodiment can generally be executed by server 105. Correspondingly, the modulation pattern recognition device for OFDM signals based on adaptive modulation provided in this embodiment can generally be located in server 105. The modulation pattern recognition method for OFDM signals based on adaptive modulation provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the modulation pattern recognition device for OFDM signals based on adaptive modulation provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0040] It should be understood that Figure 1 The number of first terminal devices, second terminal devices, third terminal devices, networks, and servers shown in the diagram is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0041] Figure 2 A flowchart illustrating a modulation pattern recognition method for orthogonal frequency division multiplexed signals based on adaptive modulation according to an embodiment of the present disclosure is shown.

[0042] like Figure 2 As shown, the modulation pattern recognition method 200 for orthogonal frequency division multiplexing signals based on adaptive modulation in this embodiment includes operations S210 to S250.

[0043] In operation S210, an orthogonal frequency division multiplexing (OFDM) signal is acquired. The OFDM signal includes N symbols and K subcarriers.

[0044] In operation S220, a fast Fourier transform is performed on the orthogonal frequency division multiplexed signal to obtain an NxK dimension frequency domain matrix.

[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 one implementation, each subcarrier is numbered according to its frequency, resulting in K subcarrier identifiers. The frequency domain matrix is ​​an NxK matrix comprising subcarrier dimensions and frequency domain symbol vector dimensions. Based on the subcarrier dimension and the identifiers of the K subcarriers, multiple frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix. That is, the multiple frequency domain symbol vectors associated with each subcarrier can constitute a target data sequence, thus obtaining K target data sequences. The frequency domain symbol vectors can represent the complex numerical representation of the signal's amplitude and phase.

[0054] In one implementation, the identifier k of the subcarrier represents the k-th subcarrier. For the k-th subcarrier, the frequency domain symbol vectors from the N1-th to the N2-th associated subcarrier are extracted from the frequency domain matrix to obtain the k-th target data sequence. As shown in formula (2).

[0055] (2)

[0056] in, This represents the N1-th frequency domain symbol vector of the k-th subcarrier. This represents the second frequency domain symbol vector of the k-th subcarrier. This represents the N2nd frequency domain symbol vector of the kth subcarrier.

[0057] Given any one of 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 identification features associated with the target data sequence. These modulation pattern identification features are used to determine the modulation order. In one implementation, clustering the target data sequence yields the modulation pattern identification features associated with it. In another implementation, accumulating the target data sequence yields the modulation pattern identification features associated with it. In yet another implementation, clustering and accumulating the target data sequence yields the modulation pattern identification features associated with it. These modulation pattern identification features are used to determine the modulation order, which characterizes the information carrying capacity of the frequency domain symbol vector and the number of frequency domain symbol points in the constellation diagram.

[0058] The target modulation pattern can characterize the modulation method of the adaptively modulated orthogonal frequency division multiplexing (OFDM) signal. The preset modulation pattern mapping relationship can characterize the mapping relationship between modulation pattern identification features and modulation patterns. Based on multiple modulation pattern identification features and the preset modulation pattern mapping relationship, the target modulation pattern can be determined. Since the modulation of the OFDM signal is adaptive, the target modulation pattern can include multiple modulation methods. For example, the target modulation pattern can include Quadrature Phase Shift Keying (QPSK) and 16-Quadrature Amplitude Modulation (16QAM).

[0059] By performing a Fast Fourier Transform on the acquired orthogonal frequency division multiplexing (OFDM) signal, an NxK-dimensional frequency domain matrix can be obtained. Based on the subcarrier dimension and according to the subcarrier identifier, multiple frequency domain symbol vectors associated with each subcarrier are extracted from the frequency domain matrix, thus obtaining K target data sequences. At least one operation, such as clustering or summing, is performed on the target data sequences to obtain multiple modulation pattern recognition features associated with the target data sequences. Based on these multiple modulation pattern recognition features, the target modulation pattern is determined from a preset modulation pattern mapping relationship. Extracting frequency domain symbol vectors associated with subcarriers from the frequency domain matrix based on the subcarrier dimension lays the data foundation for detecting different modulation patterns at a fine-grained subcarrier level. Further operations, such as clustering or superposition, are performed on the target data sequences to obtain multiple modulation pattern recognition features associated with the target data sequences. Finally, the target modulation pattern is determined based on the preset modulation pattern mapping relationship. This achieves fine-grained identification of the target modulation pattern of OFDM signals with adaptive modulation including multiple modulation methods at the subcarrier level, improving the recognition accuracy and granularity of the modulation pattern.

[0060] 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, including: for any subcarrier among the K subcarriers, according to 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; and a data sequence of a preset length is extracted from the intermediate data sequences as the target data sequence.

[0061] In one implementation, for any one of the K subcarriers, all frequency domain symbol vectors associated with the subcarrier can be determined from the frequency domain matrix based on the subcarrier's identifier. All frequency domain symbol vectors can include multiple frequency domain symbol vectors, and the sequence of multiple frequency domain symbol vectors is used as an intermediate data sequence.

[0062] The preset length can be determined based on the range of frequency domain symbol vectors. 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 used as the target data sequence. In another implementation, when the preset length is a portion of the range of all frequency domain symbol vectors, a portion of the frequency domain symbol vectors, i.e., a portion of the intermediate data sequence, can be extracted from all frequency domain symbol vectors and used as the target data sequence.

[0063] For any subcarrier among K subcarriers, based on its identifier, multiple frequency domain symbol vectors associated with the subcarrier can be determined from the frequency domain matrix. Based on this, and based on a preset length, the target data sequence can be extracted from the multiple frequency domain symbol vectors, thus obtaining the target data sequence at the subcarrier granularity. This breaks the limitation of related technologies that treat the signal as a whole, and lays a finer-grained data foundation for detecting different modulation methods.

[0064] Based on any one of K target data sequences, perform at least one operation, either clustering or accumulation, on the target data sequence to determine multiple modulation pattern identification features associated with the target data sequence, including: clustering the target data sequence for any one of the K target data sequences to determine the cluster centers and the number of cluster centers; accumulating the target data sequences to obtain a cumulative amount; determining a first modulation pattern identification feature associated with the target data sequence based on at least one of the number of cluster centers and the cumulative amount; averaging the phase difference between each cluster center and a preset reference point to obtain the average phase offset of the cluster centers; obtaining an offset angle based on the average phase offset of the cluster centers and the preset average phase offset; and determining a second modulation pattern identification feature associated with the target data sequence based on the offset angle and a preset offset angle range.

[0065] Modulation pattern recognition features may include a first modulation pattern recognition feature and a second modulation pattern recognition feature.

[0066] For any of the K target data sequences, cluster analysis is performed on the frequency domain symbol vectors in the target data sequence to determine the cluster centers and their number. The cumulative value is obtained by superimposing the frequency domain symbol vectors in the target data sequence. This feature is insensitive to Gaussian noise and has strong robustness. In one implementation, the fourth-order cumulative value of the frequency domain symbol vector is calculated using the following formula (3). .

[0067] (3)

[0068] A first modulation pattern identification feature associated with the target data sequence can be determined based on at least one of the number of cluster centers and the cumulative amount. 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 cumulative amount 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 cumulative amount can be jointly determined as the first modulation pattern identification feature associated with the target data sequence; this first modulation pattern identification feature can also be called a macro-modulation pattern identification feature. In one implementation, when 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 this first modulation pattern identification feature is 4. In one implementation, when 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 this first modulation pattern identification feature is 16.

[0069] The preset reference point can represent the origin of the complex plane coordinate system, corresponding to the zero-amplitude and zero-phase reference point of the signal. Each cluster center corresponds to a phase. The phase difference between each cluster center and the preset reference point is determined, and the average of the summations of the phase differences between each cluster center and the preset reference point is calculated to obtain the average phase offset of the cluster centers. Preset average phase offset The average phase of the standard QPSK can be represented by the following formula (4): Subtract the preset average phase shift from the average phase shift of the cluster centers to obtain the shift angle. .

[0070] (4)

[0071] The preset offset angle range can include a range significantly greater than 0° and not equal to multiples of 90°. Based on the relationship between the offset angle and the preset offset angle range, a second modulation pattern identification feature associated with the target data sequence can be determined. In one implementation, when the offset angle falls within 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, the existence of an overall phase rotation can be determined through the second modulation pattern identification feature, which can also be called a micro-modulation variant identification feature.

[0072] Cluster analysis of the target data sequence yields cluster centers and their number. Accumulation of the target data sequence results in a cumulative value. Based on at least one of the number of cluster centers and the cumulative value, a first modulation pattern identification feature associated with the target data sequence can be determined. The average phase offset of the cluster centers obtained by averaging the phase difference between each cluster center and a preset reference point, minus the preset average phase offset, yields the offset angle. Based on the offset angle and a preset offset angle range, a second modulation pattern identification feature associated with the target data sequence can be determined. The target modulation pattern can be determined from a preset modulation pattern mapping relationship based on the first modulation pattern identification feature (macro-modulation pattern identification) and the second modulation pattern identification feature (micro-modulation variant identification), improving the accuracy and granularity of target modulation pattern identification and achieving high-precision quantization of standard modulation and its subtle variants.

[0073] The target modulation pattern is determined based on multiple modulation pattern recognition features and a preset modulation pattern mapping relationship, including: 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 a target gradient sequence; determining at least one gradient extremum from the target gradient sequence as a candidate modulation boundary point; determining at least one target modulation boundary point from the candidate modulation boundary points based on a preset threshold; determining at least two target modulation regions with different modulation patterns based on at least one target modulation boundary point, and obtaining the average value of at least two feature vectors based on the modulation pattern recognition features of at least two target modulation regions; and determining the target modulation pattern based on the average value of at least two feature vectors and the preset modulation pattern mapping relationship.

[0074] Modulation pattern recognition features record the frequency domain characteristics of signal modulation properties, and the modulation pattern recognition features associated with the k-th subcarrier. In one implementation, the modulation pattern recognition feature can include three features, which together form a feature matrix F, as shown in formula (5) below.

[0075] (5)

[0076] (6)

[0077] in, This represents the first modulation pattern identification feature associated with the k-th subcarrier. This represents the second first modulation pattern identification feature associated with the k-th subcarrier. This represents the second modulation pattern identification feature associated with the k-th subcarrier. Modulation pattern identification features associated with the first subcarrier transpose, Modulation pattern identification features associated with the second subcarrier transpose, Modulation pattern identification features associated with the third subcarrier The transpose of .

[0078] The gradient sequence of each modulation pattern recognition feature can be determined based on the similarity between adjacent modulation pattern recognition features, and the gradient sequences of each modulation pattern recognition feature can be fused to obtain the target gradient sequence. This includes: filtering each modulation pattern recognition feature to obtain intermediate modulation pattern recognition features associated with each modulation pattern recognition feature; determining the gradient sequence of each intermediate modulation pattern recognition feature based on the similarity between each adjacent intermediate modulation pattern recognition feature; and fusing the gradient sequences of each intermediate modulation pattern recognition feature according to preset weights to obtain the target gradient sequence.

[0079] The gradient sequence of each intermediate modulation pattern identification feature can be determined based on the similarity between each adjacent intermediate modulation pattern identification feature. In one implementation, the gradient sequence of the d-th intermediate modulation pattern identification feature associated with the k-th subcarrier can be calculated using the following formula (7). The target gradient sequence can be obtained by fusing the gradient sequences of each intermediate modulation pattern recognition feature according to preset weights. In one implementation, the target gradient sequence can be obtained by fusing the gradient sequences of D intermediate modulation pattern recognition features according to preset weights. As shown in formula (8).

[0080] (7)

[0081] (8)

[0082] Where D represents the number of intermediate modulation pattern recognition features. This represents the intermediate modulation pattern recognition features obtained by filtering the modulation pattern recognition features. This represents the preset weight of the d-th intermediate modulation pattern recognition feature.

[0083] In one implementation, the sum of the preset weights of the D intermediate modulation pattern recognition features is 1, which means that... .

[0084] In one implementation, when adjacent subcarriers have the same modulation scheme, the feature changes are small, and the gradient sequence... Approaching 0, in another implementation where adjacent subcarriers have different modulation schemes, the features will abruptly change, resulting in a gradient sequence. It will increase significantly.

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

[0086] The preset threshold can be a dynamic threshold. 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 point can be used to divide the entire frequency band into multiple target modulation regions. The modulation method of the same target modulation region is the same, and the modulation methods of different target modulation regions can be the same or different. Specifically, the modulation methods of two adjacent target modulation regions based on the same target modulation boundary point are different, while the modulation methods of two adjacent target modulation regions based on different target modulation boundary points can be the same.

[0087] For each target modulation region, the average value of all modulation pattern recognition features within it is calculated to obtain the average value of the feature vector. Then, based on the average value of the feature vector, the target modulation pattern is determined from the preset modulation pattern mapping relationship.

[0088] The gradient sequence of each modulation pattern identification feature can be determined based on the similarity between adjacent modulation pattern identification features. Then, the gradient sequences of all modulation pattern identification features are fused based on weights to obtain the target gradient sequence. The target modulation boundary point, i.e., the point in the orthogonal frequency division multiplexing signal where the modulation pattern changes, is then determined based on the target gradient sequence. The average value of the modulation pattern identification features of the target modulation region is calculated to obtain the average value of the feature vector. Combined with the preset modulation pattern mapping relationship, the target modulation pattern is determined. By analyzing gradient changes, the modulation boundary is automatically and accurately located by detecting the target modulation boundary point, avoiding manual interpretation or assumptions based on fixed resource blocks in related technologies, thus improving the recognition accuracy of modulation patterns.

[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 based on the modulation pattern identification features of the at least two target modulation regions. This includes: determining at least two target modulation regions based on at least one modulation boundary point; and calculating the average of the modulation pattern identification features of the at least two target modulation regions to obtain at least two feature vector averages.

[0090] Based on at least one modulation boundary point, the regions on both sides of the modulation boundary point can be determined as target modulation regions, that is, at least two target modulation regions can be obtained. Then, the average value of the modulation pattern recognition features in each target modulation region can be obtained, that is, the average value of at least two feature vectors can be obtained.

[0091] Based on the modulation boundary point, the regions on both sides of the modulation boundary point can be used to determine the target modulation region. Then, the average value of the modulation pattern recognition features in the target modulation region is calculated to reduce noise interference, thereby more stably representing the modulation pattern recognition of the target modulation region.

[0092] Determining a target modulation pattern based on at least two eigenvector averages and a preset modulation pattern mapping relationship includes: for any eigenvector average among the at least two eigenvector averages, determining the modulation order associated with the eigenvector average from the preset modulation pattern mapping sub-relation based on the first eigenvector average, and determining at least one candidate target modulation pattern based on the modulation order; and determining the target modulation pattern from at least one candidate target modulation pattern based on the second eigenvector average and the modulation order.

[0093] The average feature vector may include the average first feature vector associated with the first modulation pattern recognition feature and the average second feature vector associated with the second modulation pattern recognition feature.

[0094] For any of the average values ​​of at least two eigenvectors, based on the first eigenvector average value, the modulation order associated with the first eigenvector average value can be determined from a preset modulation pattern mapping relationship, and at least one candidate target modulation pattern can be determined based on the modulation order. In one implementation, when the modulation order is 4, the candidate target modulation pattern may include a Quadrature Phase Shift Keying (QPSK) type A implementation (QPSK-A) and a Quadrature Phase Shift Keying (QPSK-B) type B implementation, where QPSK-A is also known as standard Quadrature Phase Shift Keying. In another implementation, when the modulation order is 16, the candidate target modulation pattern may include 16QAM.

[0095] A target modulation pattern can be determined from at least one candidate target modulation pattern based on the average value of the second feature vector and the modulation order, including: when the modulation order is determined, determining the target modulation pattern from at least one candidate target modulation pattern based on the average value of the second feature vector.

[0096] In one implementation, when the modulation order is 4, the target modulation pattern QPSK-B can be determined from at least one candidate target modulation pattern (QPSK-A, QPSK-B) based on the average value of the second feature vector.

[0097] The process described above, which determines the modulation order associated with the average value of the first feature vector from the preset modulation pattern mapping relationship and determines at least one candidate target modulation pattern based on the modulation order, can be called macro-modulation pattern recognition. The process described above, which determines the target modulation pattern from at least one candidate target modulation pattern based on the average value of the second feature vector when the modulation order is determined, can be called micro-modulation pattern recognition. Through two-level modulation pattern recognition, the application scope can be expanded from simple adaptive modulation pattern recognition to the discovery and location of small and special modulation regions inside the signal, thereby improving the accuracy of modulation pattern recognition.

[0098] By extracting target data sequences at the subcarrier level, fine-grained modulation pattern recognition at the subcarrier level is achieved. This enables clear localization of modulation boundaries and the discovery of microstructures that are difficult to detect in related technologies, such as special pilots. The complete process from extracting target data sequences at the subcarrier level and extracting modulation pattern recognition features to determining the target gradient sequence makes the modulation pattern recognition path fully traceable, improving the reliability of modulation pattern recognition methods for orthogonal frequency division multiplexing (OFDM) signals based on adaptive modulation. Since it does not rely on training for specific modulation combinations, it possesses naturally strong generalization capabilities for unknown non-uniform modulation modes. Furthermore, through parallel processing and efficient target gradient sequence scanning, higher-precision modulation pattern recognition is achieved with linear / polynomial complexity, demonstrating significant engineering application value.

[0099] The above-mentioned method for identifying the modulation pattern of orthogonal frequency division multiplexing signals based on adaptive modulation also includes generating a frequency domain distribution map corresponding to the modulation pattern.

[0100] In one implementation, the frequency domain distribution map is output precisely as a data tuple, for example: [(1, 15, 'QPSK-A'), (16, 24, 'QPSK-B'), (25, 500, '16QAM'), (501, 1024, 'QPSK-A')]. This frequency domain distribution map not only reveals the macroscopic modulation boundaries from QPSK to 16QAM, but also accurately locates the microscopic pilot region occupying only 9 subcarriers and employing a special modulation, QPSK-B, achieving a deep analysis of the internal structure of the orthogonal frequency division multiplexing signal.

[0101] By displaying the modulation patterns of different subcarriers within the frequency band of the orthogonal frequency division multiplexing (OFDM) signal in the form of frequency domain distribution maps, the modulation patterns are transformed into intuitive graphics, facilitating the analysis and optimization of communication systems using this OFDM signal.

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

[0103] like Figure 3 As shown, this includes 25 symbol constellation diagrams (symbol1~symbol25) associated with the second modulation pattern recognition features. In the symbol dimension, it is difficult to discern changes in the modulation pattern from these 25 symbol constellation diagrams. For example... Figure 4 As shown, the diagram includes a constellation of 25 frequency domain symbol vectors (carrier1 to carrier25) associated with the second modulation pattern identification features. It can be seen that the modulation pattern changes from QPSK-A to QPSK-B at carrier17, and from QPSK-B to QPSK-A at carrier25. Therefore, in comparison... Figure 3 and Figure 4 It is evident that finer-grained modulation pattern recognition is difficult to achieve at the symbol dimension.

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

[0105] like Figure 5 As shown, this includes 16 symbol constellation diagrams (symbol1~symbol16) associated with the first modulation pattern recognition features. In the symbol dimension, it is difficult to discern changes in the modulation pattern from these 16 symbol constellation diagrams. For example... Figure 6 As shown, this includes a constellation diagram of frequency domain symbol vectors (carrier518~carrier533) associated with the first modulation style identification features, with a total of 1024 subcarriers. It can be seen that the modulation style of carriers 522~carrier529 changes from 64-Quadrature Amplitude Modulation (64QAM) to QPSK. Therefore, compared to... Figure 5 and Figure 6 It can be seen that finer-grained modulation pattern recognition, which is difficult to achieve in the symbol dimension, can be realized in the frequency domain symbol vector dimension.

[0106] Based on the above-described modulation pattern recognition method for orthogonal frequency division multiplexing (OFDM) signals based on adaptive modulation, this disclosure also provides a modulation pattern recognition device for OFDM signals based on adaptive modulation. The following will be combined with... Figure 7 The device is described in detail.

[0107] Figure 7A schematic block diagram of a modulation pattern recognition device for an orthogonal frequency division multiplexed signal based on adaptive modulation according to an embodiment of the present disclosure is shown.

[0108] like Figure 7 As shown, the modulation pattern recognition device 700 for orthogonal frequency division multiplexing signals based on adaptive modulation in this embodiment includes an acquisition module 710, a first acquisition module 720, a second acquisition module 730, a third acquisition module 740, and a determination module 750.

[0109] The acquisition module 710 is used to acquire an orthogonal frequency division multiplexing (OFDM) signal, which includes N symbols and K subcarriers. In one embodiment, the acquisition module 710 can be used to perform the operation S210 described above, which will not be repeated here.

[0110] The first obtaining module 720 is used to perform a fast Fourier transform on the orthogonal frequency division multiplexed signal to obtain an NxK-dimensional frequency domain matrix. In one embodiment, the first obtaining module 720 can be used to perform the operation S220 described above, which will not be repeated here.

[0111] The second obtaining module 730 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, thereby obtaining K target data sequences. In one embodiment, the second obtaining module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0112] The third obtaining module 740 is configured 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. In one embodiment, the third obtaining module 740 may be used to perform the operation S240 described above, which will not be repeated here.

[0113] The determining module 750 is used to determine a target modulation pattern based on multiple modulation pattern identification features and a preset modulation pattern mapping relationship. The target modulation pattern represents the modulation method 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 features and the modulation pattern. In one embodiment, the determining module 750 can be used to perform the operation S250 described above, which will not be repeated here.

[0114] According to an embodiment of this disclosure, the second obtaining module 730 includes: a first obtaining submodule, configured to, for any one of the K subcarriers, determine, based on the identifier of the subcarrier, multiple 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 a target data sequence.

[0115] According to embodiments of this disclosure, the modulation pattern recognition feature includes a first modulation pattern recognition feature and a second modulation pattern recognition feature. The third obtaining module 740 includes: a third obtaining submodule, used to cluster any one of the K target data sequences to determine cluster centers and the number of cluster centers; a fourth obtaining submodule, used to accumulate the target data sequences to obtain a cumulative amount; a fifth obtaining submodule, used to determine the first modulation pattern recognition feature associated with the target data sequence based on at least one of the number of cluster centers and the cumulative amount; a sixth obtaining submodule, used to average the phase difference between each cluster center and a preset reference point to obtain an average phase offset of the cluster centers; a seventh obtaining submodule, used to obtain an offset angle based on the average phase offset of the cluster centers and a preset average phase offset; and an eighth obtaining submodule, used to determine the second modulation pattern recognition feature associated with the target data sequence based on the offset angle and a preset offset angle range.

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

[0117] According to an embodiment of this disclosure, the first determining submodule includes: a first determining unit, configured to filter each modulation pattern recognition feature to obtain intermediate modulation pattern recognition features associated with each modulation pattern recognition feature; a second determining unit, configured to determine the gradient sequence of each intermediate modulation pattern recognition feature based on the similarity between each adjacent intermediate modulation pattern recognition feature; and a third determining unit, configured to fuse the gradient sequences of each intermediate modulation pattern recognition feature according to a preset weight to obtain a target gradient sequence.

[0118] According to an embodiment of this disclosure, the fourth determining submodule includes: a fourth determining unit, configured to determine at least two target modulation regions based on at least one modulation boundary point; and a fifth determining unit, configured to calculate the average value of the modulation pattern recognition features of the at least two target modulation regions to obtain the average value of at least two feature vectors.

[0119] According to embodiments of this disclosure, the average feature vector includes a first average feature vector associated with a first modulation pattern identification feature and a second average feature vector associated with a second modulation pattern identification feature. The fifth determining submodule includes: a sixth determining unit, configured to, for any one of the at least two average feature vectors, determine a modulation order associated with the first average feature vector from a preset modulation pattern mapping relationship based on the first average feature vector, and determine at least one candidate target modulation pattern based on the modulation order; and a seventh determining unit, configured to determine a target modulation pattern from at least one candidate target modulation pattern based on the second average feature vector and the modulation order.

[0120] According to an embodiment of the present disclosure, the seventh determining unit includes: a first determining subunit, configured to determine a target modulation pattern from at least one candidate target modulation pattern based on the average value of a second feature vector, provided that the modulation order is determined.

[0121] According to an embodiment of this disclosure, the modulation pattern recognition device 700 for orthogonal frequency division multiplexing signals based on adaptive modulation further includes: a generation module, used to generate a frequency domain distribution map corresponding to the modulation pattern based on the modulation pattern.

[0122] According to embodiments of this disclosure, any plurality of modules among the acquisition module 710, the first obtaining module 720, the second obtaining module 730, the third obtaining module 740, and the determining module 750 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the acquisition module 710, the first obtaining module 720, the second obtaining module 730, the third obtaining module 740, and the determining module 750 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of software, hardware, and firmware methods, or in a suitable combination of any of these methods. Alternatively, at least one of the acquisition module 710, the first acquisition module 720, the second acquisition module 730, the third acquisition module 740, and the determination module 750 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0123] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a modulation pattern recognition method for orthogonal frequency division multiplexed signals based on adaptive modulation, according to embodiments of the present disclosure.

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

[0125] RAK 803 stores various programs and data required for the operation of electronic device 800. Processor 801, RAK 802, and RAK 803 are interconnected via bus 804. Processor 801 performs various operations of the method flow according to embodiments of this disclosure by executing programs in RAK 802 and / or RAK 803. It should be noted that the programs may also be stored in one or more memories other than RAK 802 and RAK 803. Processor 801 may also perform various operations of the method flow according to embodiments of this disclosure by executing programs stored in said one or more memories.

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

[0127] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0128] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, RAK (Random Access Memory), ROK (Read Only Memory), erasable programmable read-only memory (EPROK or flash memory), portable compact disk read-only memory (CD-ROK), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include one or more memories other than ROK 802 and / or RAK 803 and / or ROK 802 and RAK 803 described above.

[0129] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the modulation pattern recognition method for orthogonal frequency division multiplexing signals based on adaptive modulation provided in embodiments of this disclosure.

[0130] When the computer program is executed by the processor 801, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0131] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 809, and / or installed from a removable medium 811. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0132] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by processor 801, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0133] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0135] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0136] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this 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 multiplexing 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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