A control method and system for microwave millimeter wave communication signals

Abstract

The present application relates to a control method and system for microwave millimeter wave communication signals, and to the field of microwave millimeter wave communication technology. The method includes: generating a radio frequency band signal through a radio frequency divider and multiplier; performing internal detection on the radio frequency band signal to obtain a radio frequency detection signal; inputting the radio frequency detection signal to an ALC driver board for signal processing to obtain a radio frequency ALC drive signal. Then, an artificial intelligence technology based on deep learning is used to perform fine-grained time-frequency feature analysis on the radio frequency detection signal to capture the local time-frequency domain feature expressions of the signal, and optimize its feature distribution to dig out the deep-level time-frequency characteristics of the radio frequency detection signal, so as to generate a radio frequency ALC drive signal, realize power control of the radio frequency band signal, and realize precise power regulation of the communication signal, thereby ensuring signal transmission quality, reducing interference and improving system capacity.

Description

Technical Field

[0001] The present application relates to the field of microwave and millimeter wave communication technology, and in particular, to a method and system for controlling microwave and millimeter wave communication signals. Background Art

[0002] In current wireless communication technologies, microwave millimeter wave communication is widely used in 5G, 6G and future wireless communication systems due to its advantages such as large bandwidth and high speed. The control of microwave millimeter wave communication signals is crucial to ensure reliable, efficient and interference-free wireless communication. However, due to the high frequency characteristics of millimeter wave signals, their power control faces greater challenges, such as severe signal attenuation, large path loss and multipath propagation effects.

[0003] Since the power accuracy and dynamic range of the signal generator output signal are restricted by the frequency range, traditional power control methods are usually based on fixed frequency points or narrow frequency bands. This makes traditional power control methods limited in processing ultra-wideband signals, dynamic spectrum environments, and multi-band compatibility. It is easy to produce power discontinuity due to multi-band splicing, resulting in signal quality degradation and increased interference. In addition, in current microwave and millimeter wave communication systems, automatic level control (ALC) is a key technology for maintaining signal power stability, especially during signal detection and frequency conversion. However, traditional ALC methods mainly rely on fixed reference points or average power estimation, and have weak adaptability to time-varying channel conditions and complex RF environments. It is difficult to capture and suppress changes in the time-frequency characteristics of the signal in real time, thus affecting the performance of communication.

[0004] Therefore, an optimized control method and system for microwave and millimeter wave communication signals are needed to solve the above technical problems. Summary of the invention

[0005] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] In a first aspect, the present application provides a method for controlling a microwave and millimeter wave communication signal, the method comprising:

[0007] Generate a radio frequency band signal by using a radio frequency divider and multiplier;

[0008] Performing internal detection on the radio frequency band signal to obtain a radio frequency detection signal;

[0009] Inputting the radio frequency detection signal to the ALC driving board for signal processing to obtain a radio frequency ALC driving signal;

[0010] The modulator of the RF frequency divider and multiplier is controlled by the RF ALC driving signal to realize power control of the 250KHz-3GHz RF band, wherein the RF detection signal is input to the ALC driving board for signal processing to obtain the RF ALC driving signal, including:

[0011] Acquiring the radio frequency detection signal;

[0012] Performing a local scale-based time-frequency analysis on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal;

[0013] Performing feature distribution enhancement on the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal;

[0014] The radio frequency ALC driving signal is generated based on the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

[0015] Optionally, the RF detection signal is subjected to a local scale-based time-frequency analysis to obtain a sequence of local time-frequency semantic feature vectors of the RF detection signal, including: performing a wavelet analysis on the RF detection signal to obtain a time-frequency diagram of the RF detection signal; and extracting local area time-frequency features of the time-frequency diagram of the RF detection signal to obtain a sequence of local time-frequency semantic feature vectors of the RF detection signal.

[0016] Optionally, extracting the local area time-frequency features of the RF detection signal time-frequency diagram to obtain a sequence of local time-frequency semantic feature vectors of the RF detection signal, including: performing time-frequency diagram segmentation on the RF detection signal time-frequency diagram to obtain a sequence of local time-frequency diagrams of the RF detection signal; and inputting each local time-frequency diagram of the RF detection signal in the sequence of local time-frequency diagrams of the RF detection signal into a detection signal local time-frequency feature extractor based on a void convolutional neural network model to obtain a sequence of local time-frequency semantic feature vectors of the RF detection signal.

[0017] Optionally, the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal is subjected to feature distribution enhancement to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal, including: inputting the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal into a reverse screening and enhancement network based on the feature distribution of the anchor center to obtain the sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

[0018] Optionally, the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal is input into a reverse screening and strengthening network based on the feature distribution of the anchor center to obtain the sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal, including: calculating the anchor center of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain the anchor center feature vector; calculating the semantic implicit correlation coefficient between each local time-frequency semantic feature vector of the radio frequency detection signal in the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal and the anchor center feature vector to obtain a sequence of semantic implicit correlation coefficients; calculating the reciprocal of each semantic implicit correlation coefficient in the sequence of semantic implicit correlation coefficients to obtain a sequence of semantic anti-correlation coefficients; The sequence of semantic anti-correlation coefficients is normalized using a Softmax function to obtain a sequence of semantic anti-correlation energy coefficients; each semantic anti-correlation energy coefficient in the sequence of semantic anti-correlation energy coefficients is used as a weight to weight each local time-frequency semantic feature vector of the RF detection signal in the sequence of local time-frequency semantic feature vectors to obtain a sequence of local time-frequency semantic feature vectors of inversely suppressed RF detection signals; and the positional difference between the sequence of local time-frequency semantic feature vectors of the RF detection signal and the sequence of local time-frequency semantic feature vectors of inversely suppressed RF detection signals is calculated to obtain a sequence of optimized local time-frequency semantic feature vectors of the RF detection signal.

[0019] Optionally, calculating the anchor center of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain the anchor center feature vector includes: calculating the positional mean vector of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain the anchor center feature vector.

[0020] Optionally, the semantic implicit correlation coefficients between each local time-frequency semantic feature vector of the RF detection signal and the anchor center feature vector in the sequence of local time-frequency semantic feature vectors of the RF detection signal are calculated to obtain a sequence of semantic implicit correlation coefficients, including: calculating the inner product between the local time-frequency semantic feature vector of the RF detection signal and the anchor center feature vector to obtain a semantic correlation factor; calculating the addition result of the sum of squares of each eigenvalue in the local time-frequency semantic feature vector of the RF detection signal and the sum of squares of each eigenvalue in the anchor center feature vector, and calculating the square root of the addition result to obtain a feature distance factor; calculating the semantic correlation factor divided by the feature distance factor to obtain the semantic implicit correlation coefficient.

[0021] Optionally, based on the sequence of local time-frequency semantic feature vectors of the optimized RF detection signal, the RF ALC drive signal is generated, including: inputting the sequence of local time-frequency semantic feature vectors of the optimized RF detection signal into a decoder-based drive signal generator to obtain the RF ALC drive signal.

[0022] In a second aspect, the present application provides a control system for microwave and millimeter wave communication signals, the system comprising:

[0023] A radio frequency detection signal acquisition module is used to acquire a radio frequency detection signal;

[0024] A time-frequency analysis module, used for performing a local scale-based time-frequency analysis on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal;

[0025] A feature distribution enhancement module, used for enhancing the feature distribution of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal;

[0026] The driving signal generating module is used to generate a radio frequency ALC driving signal based on the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

[0027] Optionally, the time-frequency analysis module includes: a wavelet analysis unit, used to perform wavelet analysis on the RF detection signal to obtain a time-frequency diagram of the RF detection signal; and a time-frequency feature extraction unit, used to extract local area time-frequency features of the RF detection signal time-frequency diagram to obtain a sequence of local time-frequency semantic feature vectors of the RF detection signal.

[0028] The present application has at least the following technical effects: adopting the above-mentioned technical scheme, an RF frequency band signal is generated by an RF frequency divider and multiplier; the RF frequency band signal is internally detected to obtain an RF detection signal; the RF detection signal is input into the ALC driver board for signal processing to obtain an RF ALC drive signal, and then an artificial intelligence technology based on deep learning is used to perform fine-grained time-frequency feature analysis on the RF detection signal to capture the local time-frequency domain feature expressions of the signal, and optimize its feature distribution to explore the deep time-frequency characteristics of the RF detection signal, so as to generate an RF ALC drive signal, realize power control of the RF frequency band signal, and realize precise power regulation of the communication signal, thereby ensuring signal transmission quality, reducing interference and improving system capacity.

[0029] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings:

[0031] Figure 1 The present invention is a flowchart of a method for controlling a microwave and millimeter wave communication signal according to an exemplary embodiment.

[0032] Figure 2 It is a block diagram of a control system of a microwave and millimeter wave communication signal according to an exemplary embodiment.

[0033] Figure 3 It is a block diagram of an electronic device according to an exemplary embodiment.

[0034] Figure 4 It is an application scenario diagram of a method for controlling microwave and millimeter wave communication signals according to an exemplary embodiment.

[0035] Figure numerals: 200. control system; 201. RF detection signal acquisition module; 202. time-frequency analysis module; 203. characteristic distribution enhancement module; 204. drive signal generation module; 600. electronic device; 601. processing device; 602. read-only memory; 603. random access memory; 604. bus; 605. input / output interface; 606. input device; 607. output device; 608. storage device; 609. communication device. DETAILED DESCRIPTION

[0036] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0037] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0038] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0039] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0040] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0041] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0042] In order to solve the above problems, the present application provides a control method and system for microwave millimeter wave communication signals, which generates a radio frequency band signal through a radio frequency divider and multiplier; performs internal detection on the radio frequency band signal to obtain a radio frequency detection signal; and inputs the radio frequency detection signal into the ALC driver board for signal processing to obtain a radio frequency ALC drive signal. Then, an artificial intelligence technology based on deep learning is used to perform fine-grained time-frequency feature analysis on the radio frequency detection signal to capture the local time-frequency domain feature expressions of the signal, and optimizes its feature distribution to dig out the deep-level time-frequency characteristics of the radio frequency detection signal, thereby generating a radio frequency ALC drive signal and realizing power control of the radio frequency band signal. In this way, accurate power regulation of the communication signal can be achieved, thereby ensuring signal transmission quality, reducing interference, and improving system capacity.

[0043] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0044] Figure 1 is a flow chart of a method for controlling a microwave and millimeter wave communication signal according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0045] Step S101, generating a radio frequency band signal through a radio frequency divider and multiplier;

[0046] Step S102, performing internal detection on the radio frequency band signal to obtain a radio frequency detection signal;

[0047] Step S103, inputting the RF detection signal to the ALC driving board for signal processing to obtain a RF ALC driving signal;

[0048] The modulator of the RF frequency divider and multiplier is controlled by the RF ALC driving signal to realize power control of the 250KHz-3GHz RF band, wherein step S103, inputting the RF detection signal to the ALC driving board for signal processing to obtain the RF ALC driving signal, comprises:

[0049] Step S1031, obtaining the radio frequency detection signal;

[0050] Step S1032: performing a local scale-based time-frequency analysis on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal;

[0051] Step S1033, performing feature distribution enhancement on the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal;

[0052] Step S1034: Generate the radio frequency ALC driving signal based on the sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

[0053] The present application proposes a control method for microwave millimeter wave communication signals, which includes: generating a radio frequency band signal through a radio frequency divider and frequency multiplier; performing internal detection on the radio frequency band signal to obtain a radio frequency detection signal; outputting the radio frequency detection signal to an ALC driver board for signal processing to obtain a radio frequency ALC drive signal; controlling the modulator of the radio frequency divider and frequency multiplier through the radio frequency ALC drive signal to achieve 250KHz-3GHz radio frequency band power control. That is, a microwave millimeter wave signal covering a wide frequency band is generated through a radio frequency divider and frequency multiplier, and microwave millimeter wave band power control within a large dynamic range is achieved through an ALC driver board, thereby avoiding power discontinuity caused by multi-band splicing. Among them, since the traditional ALC driver board mainly relies on a fixed reference point or average power estimation, it has weak adaptability to time-varying channel conditions and complex radio frequency environments, and it is difficult to capture and suppress changes in the time-frequency characteristics of the signal in real time, thereby affecting the performance of communication.

[0054] In this regard, the technical concept of the present application is to use artificial intelligence technology based on deep learning to perform fine-grained time-frequency feature analysis on the RF detection signal, capture the local time-frequency domain feature expressions of the signal, and optimize its feature distribution to explore the deep-level time-frequency characteristics of the RF detection signal, so as to generate an RF ALC drive signal and realize power control of the RF frequency band signal. It can realize precise power regulation of the communication signal, thereby ensuring the quality of signal transmission, reducing interference and improving system capacity.

[0055] Based on this, in the technical solution of the present application, first, a radio frequency detection signal is obtained. The radio frequency detection signal originates from the signal of the radio frequency band, and after detection processing, the power strength of the radio frequency signal is intuitively revealed. By conducting an in-depth analysis of the radio frequency detection signal, the power of the radio frequency band signal can be precisely controlled based on its signal characteristics.

[0056] Next, considering that the RF detection signal is a non-stationary complex time-varying signal, that is, its statistical characteristics vary with time. Therefore, in order to accurately describe the time-varying characteristics of the RF detection signal, in the technical solution of the present application, the RF detection signal is further subjected to wavelet analysis to reveal the distribution of the RF detection signal at different times and frequencies, thereby obtaining a time-frequency diagram of the RF detection signal. Compared with traditional methods such as Fourier transform, wavelet analysis has higher accuracy and resolution when processing non-stationary signals, and can effectively describe the time-frequency characteristics of the RF detection signal.

[0057] In one embodiment of the present application, a local scale-based time-frequency analysis is performed on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal, including: performing a wavelet analysis on the radio frequency detection signal to obtain a time-frequency diagram of the radio frequency detection signal; and extracting local area time-frequency features of the time-frequency diagram of the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal.

[0058] Then, considering that the RF detection signal may contain multiple time-frequency characteristic components, directly processing the RF detection signal time-frequency diagram as a whole may not be able to fully capture the local dynamic change characteristics of the signal, and may increase the processing complexity due to the large amount of data. Therefore, in the technical solution of the present application, the RF detection signal time-frequency diagram is further segmented and decomposed into a series of local time-frequency diagrams, thereby obtaining a sequence of local time-frequency diagrams of the RF detection signal. Among them, each local time-frequency diagram of the RF detection signal corresponds to the time-frequency characteristic representation of the RF detection signal in a specific time-frequency window, which helps to reduce the complexity of subsequent processing and can effectively retain the time-frequency detail information of the RF detection signal.

[0059] In one embodiment of the present application, extracting the local area time-frequency features of the RF detection signal time-frequency diagram to obtain a sequence of local time-frequency semantic feature vectors of the RF detection signal includes: performing time-frequency diagram segmentation on the RF detection signal time-frequency diagram to obtain a sequence of local time-frequency diagrams of the RF detection signal; and inputting each local time-frequency diagram of the RF detection signal in the sequence of local time-frequency diagrams of the RF detection signal into a detection signal local time-frequency feature extractor based on a void convolutional neural network model to obtain a sequence of local time-frequency semantic feature vectors of the RF detection signal.

[0060] Secondly, a dilated convolutional neural network model with excellent performance in image feature extraction is used to extract time-frequency features of each local time-frequency map of the RF detection signal in the sequence of local time-frequency maps of the RF detection signal. The special network structure and convolution method of the dilated convolutional neural network are used to sensitively capture and effectively extract the time-frequency features of each local time-frequency map of the RF detection signal, so as to capture the key time-frequency features such as signal strength and frequency distribution of the RF detection signal, thereby obtaining a sequence of local time-frequency semantic feature vectors of the RF detection signal, providing a more refined feature description for the RF detection signal.

[0061] Next, considering that in the RF detection signal time-frequency diagram, the time-frequency feature representations of different position areas have different importance, and the traditional feature extraction method may not be able to effectively distinguish the differences between the features, resulting in feature redundancy or loss. To this end, in the technical solution of the present application, a feature distribution reverse screening and reinforcement network based on the anchor center is further used to perform feature screening and reinforcement on the sequence of the local time-frequency semantic feature vector of the RF detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the RF detection signal. Among them, the feature distribution reverse screening and reinforcement network introduces the anchor center mechanism, can identify key time-frequency features and irrelevant features according to the feature distribution in the sequence of the local time-frequency semantic feature vector of the RF detection signal, and strengthen the irrelevant features through the reverse screening and reinforcement process, and then calculate the difference between the sequence of the local time-frequency semantic feature vector of the RF detection signal and the irrelevant features to remove redundant features, thereby achieving feature purification and optimization, and further improving the feature discrimination and expression ability.

[0062] In one embodiment of the present application, the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal is subjected to feature distribution enhancement to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal, including: inputting the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal into a reverse screening and enhancement network based on the feature distribution of the anchor center to obtain the sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

[0063] Furthermore, in one embodiment of the present application, the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal is input into a reverse screening reinforcement network based on the feature distribution of the anchor center to obtain the sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal, including: calculating the anchor center of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain the anchor center feature vector; calculating the semantic implicit correlation coefficient between each local time-frequency semantic feature vector of the radio frequency detection signal in the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal and the anchor center feature vector to obtain a sequence of semantic implicit correlation coefficients; calculating the reciprocal of each semantic implicit correlation coefficient in the sequence of semantic implicit correlation coefficients to obtain a semantic inverse correlation coefficient. A sequence of connection numbers; using the Softmax function to normalize the sequence of semantic anti-correlation coefficients to obtain a sequence of semantic anti-correlation energy coefficients; using each semantic anti-correlation energy coefficient in the sequence of semantic anti-correlation energy coefficients as a weight, weighting each local time-frequency semantic feature vector of the RF detection signal in the sequence of local time-frequency semantic feature vectors to obtain a sequence of local time-frequency semantic feature vectors of inversely suppressed RF detection signals; calculating the positional difference between the sequence of local time-frequency semantic feature vectors of the RF detection signal and the sequence of local time-frequency semantic feature vectors of the inversely suppressed RF detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the RF detection signal.

[0064] Furthermore, in one embodiment of the present application, the anchor center of the sequence of local time-frequency semantic feature vectors of the RF detection signal is calculated to obtain the anchor center feature vector, including: calculating the positional mean vector of the sequence of local time-frequency semantic feature vectors of the RF detection signal to obtain the anchor center feature vector.

[0065] Furthermore, in one embodiment of the present application, the semantic implicit correlation coefficients between each local time-frequency semantic feature vector of the RF detection signal and the anchor center feature vector in the sequence of local time-frequency semantic feature vectors of the RF detection signal are calculated to obtain a sequence of semantic implicit correlation coefficients, including: calculating the inner product between the local time-frequency semantic feature vector of the RF detection signal and the anchor center feature vector to obtain a semantic correlation factor; calculating the addition result of the sum of the squares of each eigenvalue in the local time-frequency semantic feature vector of the RF detection signal and the sum of the squares of each eigenvalue in the anchor center feature vector, and calculating the square root of the addition result to obtain a feature distance factor; calculating the semantic correlation factor divided by the feature distance factor to obtain the semantic implicit correlation coefficient.

[0066] Specifically, the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal is processed by the following feature distribution reverse screening formula to obtain the sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal, wherein the feature distribution reverse screening formula is:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] in, is a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal, , , and They are respectively the first local time-frequency semantic feature vector of the radio frequency detection signal, the second local time-frequency semantic feature vector of the radio frequency detection signal, the The local time-frequency semantic feature vector of the RF detection signal and the The local time-frequency semantic feature vector of the RF detection signal, The value of is the number of local time-frequency semantic feature vectors of the radio frequency detection signal in the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal, Indicates the The local time-frequency semantic feature vector of the RF detection signal The eigenvalues ​​at the positions, is the anchor center feature vector, is the j-th eigenvalue of the anchor center eigenvector, represents the length of the local time-frequency semantic feature vector of the radio frequency detection signal, Indicates the The semantic implicit correlation coefficient between the local time-frequency semantic feature vector of the RF detection signal and the anchor center feature vector, For the The semantic anti-correlation coefficient, For the The semantic anti-correlation energy coefficient, represents an exponential function with a natural constant as base, is a sequence of local time-frequency semantic feature vectors of the reversely suppressed RF detection signal, , , and They are respectively the first local time-frequency semantic feature vector of the reverse suppression radio frequency detection signal, the second local time-frequency semantic feature vector of the reverse suppression radio frequency detection signal, the The local time-frequency semantic feature vector of the reverse-suppressed RF detection signal and the The local time-frequency semantic feature vector of the reverse-suppressed RF detection signal, is a sequence of local time-frequency semantic feature vectors that optimizes the RF detection signal. , , and They are the first optimization of the local time-frequency semantic feature vector of the RF detection signal, the second optimization of the local time-frequency semantic feature vector of the RF detection signal, and the The local time-frequency semantic feature vector of the optimized RF detection signal and the An optimized local time-frequency semantic feature vector of the RF detection signal.

[0074] Then, the sequence of the local time-frequency semantic feature vectors of the optimized RF detection signal is input into the decoder-based driving signal generator to obtain the RF ALC driving signal. That is, the decoding regression capability of the decoder is used to perform feature learning and nonlinear mapping on the sequence of the local time-frequency semantic feature vectors of the optimized RF detection signal, and a RF ALC driving signal that is highly matched with the time-frequency characteristics of the RF detection signal is reconstructed through the decoding process, accurately reflecting the real-time power demand of the RF signal. Then, according to the strength and characteristics of the RF ALC driving signal, the working state of the microwave multiplier is adjusted in real time to achieve dynamic and refined adjustment of the RF signal power, effectively suppressing the power fluctuation caused by changes in channel conditions and the complexity of the RF environment, thereby improving the stability and capacity of the communication system.

[0075] In one embodiment of the present application, the RF ALC drive signal is generated based on the sequence of local time-frequency semantic feature vectors of the optimized RF detection signal, including: inputting the sequence of local time-frequency semantic feature vectors of the optimized RF detection signal into a decoder-based drive signal generator to obtain the RF ALC drive signal.

[0076] In particular, in the technical solution of the present application, each local time-frequency semantic feature vector of the radio frequency detection signal in the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal respectively represents the semantic features of the local time-frequency image of the radio frequency detection. In the process of inputting the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal into the feature distribution reverse screening and reinforcement network based on the anchor center, the feature distribution reverse screening and reinforcement network based on the anchor center uses the global mean vector of the local time-frequency semantic feature vector of the radio frequency detection signal as the anchor center to perform feature distribution reverse screening and reinforcement on the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal, so that the effective components in the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal can be strengthened and the invalid components therein can be suppressed. However, this will also cause the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal as a feature distribution as a whole to have a local complementary signal missing due to the mismatch of attention weights, so it is expected to improve the fine-grained semantic perception aggregation expression effect of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal as a feature distribution as a whole.

[0077] Inputting the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal into a decoder-based driving signal generator to obtain the radio frequency ALC driving signal, comprising:

[0078] Cascading the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a local time-frequency semantic joint coding vector of the radio frequency detection signal;

[0079] Calculate the sum of the absolute values ​​of each eigenvalue of the local time-frequency semantic joint coding vector of the radio frequency detection signal to obtain the first radio frequency detection signal local time-frequency semantic joint coding and modulation value, and calculate the square root of the sum of the squares of each eigenvalue of the local time-frequency semantic joint coding vector of the radio frequency detection signal to obtain the second radio frequency detection signal local time-frequency semantic joint coding and modulation value;

[0080] After performing point-wise subtraction on the local time-frequency semantic joint coding vector of the RF detection signal and the local time-frequency semantic joint coding and modulation value of the second RF detection signal, perform point-wise multiplication with the number of eigenvalues ​​of the local time-frequency semantic joint coding vector of the RF detection signal and the inverse of the local time-frequency semantic joint coding and modulation value of the first RF detection signal, and take the inverse of each eigenvalue to obtain the local time-frequency semantic joint coding phase conversion vector of the first RF detection signal;

[0081] After performing point-wise subtraction on the local time-frequency semantic joint coding vector of the RF detection signal and the local time-frequency semantic joint coding and modulation value of the first RF detection signal, perform point-wise multiplication with the square root of the number of eigenvalues ​​of the local time-frequency semantic joint coding vector of the RF detection signal and the inverse of the local time-frequency semantic joint coding and modulation value of the second RF detection signal, and take the inverse of each eigenvalue to obtain the second RF detection signal local time-frequency semantic joint coding phase conversion vector;

[0082] Subtract the dot product vector of the second RF detection signal local time-frequency semantic joint coding phase conversion vector from the first RF detection signal local time-frequency semantic joint coding phase conversion vector and the weighted hyperparameter to obtain an optimized RF detection signal local time-frequency semantic joint coding vector;

[0083] The optimized local time-frequency semantic joint coding vector of the radio frequency detection signal is input into a decoder-based driving signal generator to obtain the radio frequency ALC driving signal.

[0084] Among them, the local time-frequency semantic joint coding vector of the RF detection signal is recorded as The optimization expression is:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] in, is the local time-frequency semantic joint coding vector of the radio frequency detection signal, represents the set of real numbers, The local time-frequency semantic joint coding vector of the radio frequency detection signal is represented by The eigenvalues ​​at the positions, represents the number of eigenvalues ​​of the local time-frequency semantic joint coding vector of the radio frequency detection signal, represents the local time-frequency semantic joint coding and modulation value of the first radio frequency detection signal, represents the local time-frequency semantic joint coding and modulation value of the second radio frequency detection signal, It means point multiplication by position. It means subtracting by position. It means to calculate the inverse of each eigenvalue of the eigenvector. represents the local time-frequency semantic joint coding phase transformation vector of the first RF detection signal, represents the local time-frequency semantic joint coding phase transformation vector of the second RF detection signal, represents the weighted hyperparameter, Represents the optimized local time-frequency semantic joint coding vector of the RF detection signal.

[0092] In this preferred embodiment, the difference in the difference in modulation representation of the eigenvalue of the local time-frequency semantic joint coding vector of the RF detection signal relative to the feature set of the vector as a whole of the local time-frequency semantic joint coding vector of the RF detection signal is used as semantic change intensity information, and a phase-like transformation corresponding to the position-based intensity modulation is performed through different and modulation representations, so that the aggregation enhancement of semantic change phase perception can enhance the axial aggregation receptive field along the feature aggregation direction by performing a spatial translation operation based on alternating stacking under the scale equalization of the vector set of the local time-frequency semantic joint coding vector of the RF detection signal, thereby enhancing the perception effect of the aggregated semantics of the local time-frequency semantic joint coding vector of the RF detection signal for the detailed temporal semantic changes, thereby improving the expression effect of the local time-frequency semantic joint coding vector of the RF detection signal, and improving its decoder-based driving signal generator to obtain the signal quality and accuracy of the RF ALC driving signal.

[0093] In summary, the above scheme uses artificial intelligence technology based on deep learning to perform fine-grained time-frequency feature analysis on the RF detection signal, captures the local time-frequency domain feature expression of the signal, and optimizes its feature distribution to dig out the deep-level time-frequency characteristics of the RF detection signal, so as to generate the RF ALC drive signal and realize the power control of the RF frequency band signal. In this way, accurate power adjustment of the communication signal can be achieved, thereby ensuring the signal transmission quality, reducing interference and improving system capacity.

[0094] Figure 2 FIG. 1 is a block diagram of a control system for microwave and millimeter wave communication signals according to an exemplary embodiment. Figure 2 As shown, the control system 200 includes:

[0095] A radio frequency detection signal acquisition module 201 is used to acquire a radio frequency detection signal;

[0096] The time-frequency analysis module 202 is used to perform a local scale-based time-frequency analysis on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal;

[0097] A feature distribution enhancement module 203 is used to enhance the feature distribution of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal;

[0098] The driving signal generating module 204 is configured to generate a radio frequency ALC driving signal based on the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

[0099] Optionally, the time-frequency analysis module includes: a wavelet analysis unit, used to perform wavelet analysis on the RF detection signal to obtain a time-frequency diagram of the RF detection signal; and a time-frequency feature extraction unit, used to extract local area time-frequency features of the RF detection signal time-frequency diagram to obtain a sequence of local time-frequency semantic feature vectors of the RF detection signal.

[0100] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 600 suitable for implementing the embodiment of the present application. The terminal device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0101] like Figure 3 As shown, the electronic device 600 may include a processing device 601 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 602 or a program loaded from a storage device 608 into a random access memory 603. Various programs and data required for the operation of the electronic device 600 are also stored in the random access memory 603. The processing device 601, the read-only memory 602, and the random access memory 603 are connected to each other via a bus 604. An input / output interface 605 is also connected to the bus 604.

[0102] Typically, the following devices may be connected to the input / output interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0103] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the read-only memory 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present application are executed.

[0104] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0105] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0106] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0107] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0108] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0109] The modules involved in the embodiments described in this application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the test parameter acquisition module can also be described as a "module for acquiring device test parameters corresponding to the target device".

[0110] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0111] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] Figure 4 FIG. 1 is an application scenario diagram of a method for controlling microwave and millimeter wave communication signals according to an exemplary embodiment. Figure 4 As shown, in this application scenario, first, a radio frequency band signal is generated by a radio frequency divider and multiplier (for example, Figure 4 Then, the acquired radio frequency band signal is input to a server (for example, Figure 4 In the S) shown in , the server is capable of processing the RF frequency band signal based on a control algorithm of a microwave millimeter wave communication signal to generate the RF ALC driving signal.

[0113] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0114] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0115] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims. Regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Claims

1. A method for controlling microwave and millimeter wave communication signals, characterized in that: include: Generate a radio frequency band signal by using a radio frequency divider and multiplier; Performing internal detection on the radio frequency band signal to obtain a radio frequency detection signal; Inputting the radio frequency detection signal to the ALC driving board for signal processing to obtain a radio frequency ALC driving signal; The modulator of the RF frequency divider and multiplier is controlled by the RF ALC driving signal to realize power control of the 250KHz-3GHz RF band, wherein the RF detection signal is input to the ALC driving board for signal processing to obtain the RF ALC driving signal, including: Acquiring the radio frequency detection signal; Performing a local scale-based time-frequency analysis on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal; Performing feature distribution enhancement on the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal; The radio frequency ALC driving signal is generated based on the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

2. The method for controlling microwave and millimeter wave communication signals according to claim 1, characterized in that: Performing a local scale-based time-frequency analysis on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal includes: Performing wavelet analysis on the radio frequency detection signal to obtain a time-frequency diagram of the radio frequency detection signal; The local area time-frequency features of the radio frequency detection signal time-frequency diagram are extracted to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal.

3. The method for controlling microwave and millimeter wave communication signals according to claim 2, characterized in that: Extracting the local area time-frequency features of the radio frequency detection signal time-frequency diagram to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal includes: Slicing the time-frequency diagram of the radio frequency detection signal to obtain a sequence of local time-frequency diagrams of the radio frequency detection signal; Each local time-frequency diagram of the radio frequency detection signal in the sequence of local time-frequency diagrams of the radio frequency detection signal is input into the detection signal local time-frequency feature extractor based on the hole convolutional neural network model to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal.

4. The method for controlling microwave and millimeter wave communication signals according to claim 3, characterized in that: The method of enhancing the feature distribution of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal includes: The sequence of local time-frequency semantic feature vectors of the radio frequency detection signal is input into a reverse screening and strengthening network based on the feature distribution of the anchor center to obtain the sequence of local time-frequency semantic feature vectors of the optimized radio frequency detection signal.

5. The method for controlling microwave and millimeter wave communication signals according to claim 4, characterized in that: Inputting the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal into a reverse screening and strengthening network based on the feature distribution of the anchor center to obtain the sequence of local time-frequency semantic feature vectors of the optimized radio frequency detection signal, comprising: Calculating the anchor center of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain an anchor center feature vector; Calculating the semantic implicit correlation coefficient between each local time-frequency semantic feature vector of the radio frequency detection signal in the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal and the anchor center feature vector to obtain a sequence of semantic implicit correlation coefficients; Calculating the reciprocal of each semantic latent association coefficient in the sequence of semantic latent association coefficients to obtain a sequence of semantic anti-association coefficients; Using a Softmax function to normalize the sequence of semantic anti-correlation coefficients to obtain a sequence of semantic anti-correlation energy coefficients; Using each semantic anti-correlation energy coefficient in the sequence of semantic anti-correlation energy coefficients as a weight, weighting each local time-frequency semantic feature vector of the radio frequency detection signal in the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the inversely suppressed radio frequency detection signal; The position difference between the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signal and the sequence of the local time-frequency semantic feature vectors of the inversely suppressed radio frequency detection signal is calculated to obtain the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

6. The method for controlling microwave and millimeter wave communication signals according to claim 5, characterized in that: Calculating the anchor center of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain an anchor center feature vector includes: The position-based mean vector of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal is calculated to obtain the anchor center feature vector.

7. The method for controlling microwave and millimeter wave communication signals according to claim 6, characterized in that: Calculating the semantic implicit correlation coefficient between each local time-frequency semantic feature vector of the radio frequency detection signal and the anchor center feature vector in the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal to obtain a sequence of semantic implicit correlation coefficients, including: Calculating the inner product between the local time-frequency semantic feature vector of the radio frequency detection signal and the anchor center feature vector to obtain a semantic association factor; Calculating the sum of the squares of each eigenvalue in the local time-frequency semantic feature vector of the radio frequency detection signal and the sum of the squares of each eigenvalue in the anchor center feature vector, and calculating the square root of the sum to obtain a feature distance factor; The semantic association factor is calculated and divided by the feature distance factor to obtain the semantic latent association coefficient.

8. The method for controlling microwave and millimeter wave communication signals according to claim 7, characterized in that: Generating the radio frequency ALC driving signal based on the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal includes: The sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal is input into a decoder-based driving signal generator to obtain the radio frequency ALC driving signal.

9. A control system for microwave and millimeter wave communication signals, characterized in that: include: A radio frequency detection signal acquisition module is used to acquire a radio frequency detection signal; A time-frequency analysis module, used for performing a local scale-based time-frequency analysis on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal; A feature distribution enhancement module, used for enhancing the feature distribution of the sequence of local time-frequency semantic feature vectors of the radio frequency detection signal to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signal; The driving signal generating module is used to generate a radio frequency ALC driving signal based on the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal.

10. The control system of microwave and millimeter wave communication signals according to claim 9, characterized in that: The time-frequency analysis module comprises: A wavelet analysis unit, used for performing wavelet analysis on the radio frequency detection signal to obtain a time-frequency diagram of the radio frequency detection signal; The time-frequency feature extraction unit is used to extract the local area time-frequency features of the radio frequency detection signal time-frequency diagram to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal.

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