CN118971849A - A control method and system for microwave millimeter wave communication signals - Google Patents
A control method and system for microwave millimeter wave communication signals Download PDFInfo
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Abstract
本申请涉及一种微波毫米波通信信号的控制方法及系统，涉及微波毫米波通信技术领域。该方法包括：通过射频分频倍频器产生射频频段信号；对所述射频频段信号进行内部检波以得到射频检波信号；将所述射频检波信号输入至ALC驱动板进行信号处理以得到射频ALC驱动信号。然后采用基于深度学习的人工智能技术对射频检波信号进行细粒度的时频特征分析，捕捉到信号的各个局部时频域特征表达，并通过对其特征分布优化，以挖掘出射频检波信号的深层次时频特性，以此来生成射频ALC驱动信号，实现对射频频段信号的功率控制，可以实现对通信信号的精确功率调节，从而能够确保信号传输质量，减少干扰以及提高系统容量。
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
The application relates to the technical field of microwave millimeter wave communication, in particular to a control method and a control system of microwave millimeter wave communication signals.
Background
In the current wireless communication technology, microwave and millimeter wave communication is widely applied to 5G, 6G and future wireless communication systems due to the advantages of large bandwidth, high speed and the like. Control of microwave millimeter wave communication signals is critical to ensure reliable, efficient and interference-free wireless communication. However, due to the high frequency characteristics of millimeter wave signals, power control is more challenging, such as severe signal attenuation, large path loss, and multipath propagation effects.
Because the power accuracy and dynamic range of the signal output by the signal generator are limited by the frequency range, the conventional power control method is generally based on fixed frequency points or narrow frequency bands, so that the conventional power control method has limitations in processing ultra-wideband signals, dynamic spectrum environments and multi-band compatibility, and power discontinuity is easily generated due to multi-band splicing, so that the signal quality is reduced and the interference is increased. Furthermore, in current microwave millimeter wave communication systems, automatic level control (Automatic Level Control, ALC) is a key technique for maintaining signal power stability, especially during signal detection and frequency conversion. However, the conventional ALC method mainly relies on a fixed reference point or average power estimation, is weak in adaptability to time-varying channel conditions and complex radio frequency environments, and is difficult to capture and suppress the time-frequency characteristic variation of the signal in real time, thereby affecting the performance of communication.
Therefore, an optimized control method and system for microwave millimeter wave communication signals are needed to solve the above technical problems.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present application provides a control method for microwave millimeter wave communication signals, the method comprising:
Generating a radio frequency band signal through a radio frequency division frequency 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 driving board for signal processing to obtain a radio frequency ALC driving signal;
The modulator of the radio frequency division frequency multiplier is controlled by the radio frequency ALC driving signal to realize the power control of the radio frequency band of 250KHz-3GHz, wherein the radio frequency detection signal is input to an ALC driving board for signal processing to obtain the radio frequency ALC driving signal, and the method comprises the following steps:
Acquiring the radio frequency detection signal;
Performing time-frequency analysis based on local scale 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 reinforcement on the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signals;
And generating the radio frequency ALC driving signal based on the sequence of the optimized radio frequency detection signal local time-frequency semantic feature vector.
Optionally, performing 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, including: performing wavelet analysis on the radio frequency detection signals to obtain a time-frequency chart of the radio frequency detection signals; and extracting the local area time-frequency characteristics of the time-frequency diagram of the radio frequency detection signal to obtain the sequence of the local time-frequency semantic characteristic vector of the radio frequency detection signal.
Optionally, extracting the local area time-frequency characteristic of the time-frequency chart of the radio frequency detection signal to obtain the sequence of the local time-frequency semantic characteristic vector of the radio frequency detection signal includes: performing time-frequency diagram segmentation on 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; and respectively inputting each radio frequency detection signal local time-frequency diagram in the sequence of the radio frequency detection signal local time-frequency diagram into a detection signal local time-frequency characteristic extractor based on a cavity convolutional neural network model to obtain the sequence of the radio frequency detection signal local time-frequency semantic characteristic vector.
Optionally, performing feature distribution reinforcement on the sequence of the 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, including: inputting the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal into a feature distribution reverse screening strengthening network based on an anchoring center to obtain the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
Optionally, inputting the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signal into a feature distribution reverse screening strengthening network based on an anchoring center to obtain the sequence of the local time-frequency semantic feature vectors of the optimized radio frequency detection signal, which comprises the following steps: calculating an anchoring center of the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain an anchoring center feature vector; calculating semantic hidden association coefficients between each radio frequency detection signal local time-frequency semantic feature vector and the anchoring center feature vector in the sequence of the radio frequency detection signal local time-frequency semantic feature vectors to obtain a sequence of semantic hidden association numbers; calculating the reciprocal of each semantic hidden association coefficient in the sequence of semantic hidden association numbers to obtain a sequence of semantic anti-association numbers; normalizing the sequence of the semantic anti-correlation coefficient by using a Softmax function to obtain a sequence of semantic anti-correlation energy coefficients; taking each semantic anti-correlation energy coefficient in the sequence of semantic anti-correlation energy coefficients as a weight, and weighting each radio frequency detection signal local time-frequency semantic feature vector in the sequence of radio frequency detection signal local time-frequency semantic feature vectors to obtain a sequence of inverse inhibition radio frequency detection signal local time-frequency semantic feature vectors; and calculating the difference according to the position between the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal and the sequence of the local time-frequency semantic feature vector of the reverse suppression radio frequency detection signal so as to obtain the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
Optionally, calculating an 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: and calculating the position-wise mean value vector of the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain the anchoring center feature vector.
Optionally, calculating a semantic hidden association coefficient between each radio frequency detection signal local time-frequency semantic feature vector and the anchoring center feature vector in the sequence of radio frequency detection signal local time-frequency semantic feature vectors to obtain a sequence of semantic hidden association numbers, including: calculating the inner product between the local time-frequency semantic feature vector of the radio frequency detection signal and the anchoring center feature vector to obtain a semantic association factor; calculating the addition result between the square sum of all the eigenvalues in the local time-frequency semantic eigenvector of the radio frequency detection signal and the square sum of all the eigenvalues in the anchoring center eigenvector, and calculating the evolution of the addition result to obtain a characteristic distance factor; and calculating the semantic association factor divided by the feature distance factor to obtain the semantic hidden association coefficient.
Optionally, generating the radio frequency ALC driving signal based on the sequence of the optimized radio frequency detection signal local time-frequency semantic feature vector includes: and inputting the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal into a decoder-based driving signal generator to obtain the radio frequency ALC driving signal.
In a second aspect, the present application provides a control system for microwave millimeter wave communication signals, the system comprising:
The radio frequency detection signal acquisition module is used for acquiring radio frequency detection signals;
the time-frequency analysis module is used for carrying out time-frequency analysis based on local scale on the radio frequency detection signals so as to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signals;
the characteristic distribution strengthening module is used for strengthening characteristic distribution of the sequence of the local time-frequency semantic characteristic vectors of the radio frequency detection signals so as to obtain the sequence of the local time-frequency semantic characteristic vectors of the optimized radio frequency detection signals;
And the driving signal generation module is used for generating a radio frequency ALC driving signal based on the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
Optionally, the time-frequency analysis module includes: the wavelet analysis unit is used for carrying out wavelet analysis on the radio frequency detection signals to obtain a time-frequency diagram of the radio frequency detection signals; the time-frequency characteristic extraction unit is used for extracting the time-frequency characteristic of the local area of the time-frequency diagram of the radio frequency detection signal so as to obtain the sequence of the local time-frequency semantic characteristic vector of the radio frequency detection signal.
The application has at least the following technical effects: by adopting the technical scheme, the radio frequency band signal is generated through the radio frequency division frequency multiplier; performing internal detection on the radio frequency band signal to obtain a radio frequency detection signal; inputting the radio frequency detection signals to an ALC driving board for signal processing to obtain radio frequency ALC driving signals, then adopting an artificial intelligence technology based on deep learning to perform fine-granularity time-frequency characteristic analysis on the radio frequency detection signals, capturing each local time-frequency domain characteristic expression of the signals, and optimizing the characteristic distribution to mine deep time-frequency characteristics of the radio frequency detection signals so as to generate the radio frequency ALC driving signals, thereby realizing power control on radio frequency band signals, realizing accurate power adjustment on communication signals, ensuring signal transmission quality, reducing interference and improving system capacity.
Additional features and advantages of the application will be set forth in the detailed description which follows.
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The above and other features, advantages and aspects of embodiments of the present application will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale. In the drawings:
Fig. 1 is a flowchart illustrating a control method of a microwave millimeter wave communication signal according to an exemplary embodiment.
Fig. 2 is a block diagram illustrating a control system for microwave millimeter wave communication signals in accordance with an exemplary embodiment.
Fig. 3 is a block diagram of an electronic device, according to an example embodiment.
Fig. 4 is an application scenario diagram illustrating a control method of a microwave millimeter wave communication signal according to an exemplary embodiment.
Reference numerals: 200. a control system; 201. a radio frequency detection signal acquisition module; 202. a time-frequency analysis module; 203. a feature distribution strengthening module; 204. a driving signal generation module; 600. an electronic device; 601. a processing device; 602. a read-only memory; 603. a random access memory; 604. a bus; 605. an input/output interface; 606. an input device; 607. an output device; 608. a storage device; 609. a communication device.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the application is susceptible of embodiment in the drawings, it is to be understood that the application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the application. It should be understood that the drawings and embodiments of the application are for illustration purposes only and are not intended to limit the scope of the present application.
It should be understood that the various steps recited in the method embodiments of the present application may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the application is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is 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. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the devices in the embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of such messages or information.
In order to solve the above problems, the present application provides a control method and system for microwave millimeter wave communication signals, which generates radio frequency band signals through a radio frequency division frequency multiplier; performing internal detection on the radio frequency band signal to obtain a radio frequency detection signal; and inputting the radio frequency detection signal to an ALC driving board for signal processing to obtain a radio frequency ALC driving signal. Then, adopting artificial intelligence technology based on deep learning to perform fine-grained time-frequency characteristic analysis on the radio frequency detection signals, capturing each local time-frequency domain characteristic expression of the signals, and optimizing the characteristic distribution so as to mine deep time-frequency characteristics of the radio frequency detection signals, thereby generating radio frequency ALC driving signals and realizing power control on radio frequency band signals. Thus, accurate power adjustment of communication signals can be realized, so that signal transmission quality can be ensured, interference can be reduced, and system capacity can be improved.
The following describes specific embodiments of the present application in detail with reference to the drawings.
Fig. 1 is a flowchart illustrating a control method of a microwave millimeter wave communication signal according to an exemplary embodiment, and as shown in fig. 1, the method includes:
s101, generating a radio frequency band signal through a radio frequency division frequency multiplier;
step S102, carrying out internal detection on the radio frequency band signal to obtain a radio frequency detection signal;
Step S103, inputting the radio frequency detection signal to an ALC driving board for signal processing to obtain a radio frequency ALC driving signal;
And controlling a modulator of the radio frequency division frequency multiplier through the radio frequency ALC driving signal to realize power control of a radio frequency band of 250KHz-3GHz, wherein step S103 is to input the radio frequency detection signal to an ALC driving board for signal processing to obtain the radio frequency ALC driving signal, and comprises the following steps:
Step S1031, obtaining the radio frequency detection signal;
S1032, performing 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;
step S1033, carrying out feature distribution reinforcement on the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal to obtain a sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal;
Step S1034, generating the radio frequency ALC driving signal based on the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
The application provides a control method of microwave millimeter wave communication signals, which comprises the following steps: generating a radio frequency band signal through a radio frequency division 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 driving board for signal processing to obtain a radio frequency ALC driving signal; and controlling a modulator of the radio frequency division frequency multiplier through the radio frequency ALC driving signal so as to realize power control of a radio frequency band of 250KHz-3 GHz. That is, the microwave millimeter wave signal covering a wide frequency band is generated by the radio frequency division frequency multiplier, and the microwave millimeter wave frequency band power control in a large dynamic range is realized by the ALC driving plate, so that the power discontinuity caused by multi-band splicing is avoided. Among them, since the conventional ALC driving board mainly depends on a fixed reference point or average power estimation, it is weak to adapt to time-varying channel conditions and complex radio frequency environments, and it is difficult to capture and suppress the time-frequency characteristic variation of the signal in real time, thereby affecting the performance of communication.
In this regard, the technical concept of the application is to perform fine-grained time-frequency characteristic analysis on the radio frequency detection signal by adopting an artificial intelligence technology based on deep learning, capture each local time-frequency domain characteristic expression of the signal, and mine the deep time-frequency characteristic of the radio frequency detection signal by optimizing the characteristic distribution thereof, so as to generate a radio frequency ALC driving signal, realize power control on the radio frequency band signal, and realize accurate power adjustment on the communication signal, thereby ensuring the signal transmission quality, reducing interference and improving the system capacity.
Based on this, in the technical scheme of the application, firstly, the radio frequency detection signal is acquired. The radio frequency detection signal is derived from a signal in a radio frequency band, and the power intensity of the radio frequency signal is intuitively revealed through detection processing. By carrying out deep analysis on the radio frequency detection signal, accurate regulation and control on the signal power of the radio frequency band can be realized based on the signal characteristics of the radio frequency detection signal.
The radio frequency detection signal is then considered to be a complex time-varying signal that is non-stationary, i.e. its statistical properties change over time. Therefore, in order to accurately describe the time-varying characteristics of the radio frequency detection signal, in the technical scheme of the application, wavelet analysis is further performed on the radio frequency detection signal so as to reveal the distribution condition of the radio frequency detection signal at different times and frequencies, thereby obtaining a time-frequency diagram of the radio frequency detection signal. Compared with traditional Fourier transform and other methods, the wavelet analysis has higher precision and resolution in processing the non-stationary signal, and can effectively describe the time-frequency characteristic of the radio frequency detection signal.
In one embodiment of the present application, 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, including: performing wavelet analysis on the radio frequency detection signals to obtain a time-frequency chart of the radio frequency detection signals; and extracting the local area time-frequency characteristics of the time-frequency diagram of the radio frequency detection signal to obtain the sequence of the local time-frequency semantic characteristic vector of the radio frequency detection signal.
Then, considering that the radiofrequency detection signal may include a plurality of time-frequency characteristic components, directly processing the radiofrequency detection signal time-frequency diagram as a whole may not fully capture local dynamic variation characteristics of the signal, and may cause an increase in processing complexity due to an excessive data volume. Therefore, in the technical scheme of the application, the time-frequency diagram of the radio frequency detection signal is further subjected to time-frequency diagram segmentation, and is decomposed into a series of partial time-frequency diagrams, so that the sequence of the partial time-frequency diagrams of the radio frequency detection signal is obtained. The local time-frequency diagram of each radio frequency detection signal corresponds to the time-frequency characteristic representation of the radio frequency detection signal in a specific time-frequency window, so that the complexity of subsequent processing is reduced, and meanwhile, time-frequency detailed information of the radio frequency detection signal can be effectively reserved.
In one embodiment of the present application, extracting the local area time-frequency characteristic of the time-frequency chart of the radio frequency detection signal to obtain the sequence of the local time-frequency semantic characteristic vector of the radio frequency detection signal includes: performing time-frequency diagram segmentation on 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; and respectively inputting each radio frequency detection signal local time-frequency diagram in the sequence of the radio frequency detection signal local time-frequency diagram into a detection signal local time-frequency characteristic extractor based on a cavity convolutional neural network model to obtain the sequence of the radio frequency detection signal local time-frequency semantic characteristic vector.
Secondly, a cavity convolution neural network model with excellent performance in image feature extraction is adopted to extract time-frequency features of each radio frequency detection signal local time-frequency image in the sequence of the radio frequency detection signal local time-frequency image, the special network structure and convolution mode of the cavity convolution neural network are utilized to carry out sensitive capturing and effective extraction on the time-frequency features of each radio frequency detection signal local time-frequency image so as to capture key time-frequency features such as signal intensity, frequency distribution and the like of the radio frequency detection signal, and therefore a sequence of radio frequency detection signal local time-frequency semantic feature vectors is obtained, and finer feature description is provided for the radio frequency detection signal.
Next, considering that the time-frequency characteristic representation of different location areas has different importance in the time-frequency chart of the radio frequency detection signal, the conventional characteristic extraction method may not effectively distinguish the differences between the characteristics, thereby causing redundancy or loss of the characteristics. Therefore, in the technical scheme of the application, the feature screening strengthening network based on the feature distribution reverse screening strengthening network of the anchoring center is further adopted to conduct feature screening strengthening on the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal so as to obtain the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal. The feature distribution reverse screening strengthening network can identify key time-frequency features and irrelevant features according to feature distribution in the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals by introducing an anchoring center mechanism, and conduct feature strengthening on the irrelevant features through a reverse screening strengthening process, and further remove redundant features by calculating the difference between the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals and the irrelevant features, so that feature purification and optimization are achieved, and feature distinguishing and expression capacity is further improved.
In one embodiment of the present application, the feature distribution reinforcement is performed on the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signal to obtain the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signal, including: inputting the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal into a feature distribution reverse screening strengthening network based on an anchoring center to obtain the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
Further, in one embodiment of the present application, inputting the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signal into a reverse screening strengthening network based on the feature distribution of the anchoring center to obtain the sequence of the local time-frequency semantic feature vectors of the optimized radio frequency detection signal includes: calculating an anchoring center of the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain an anchoring center feature vector; calculating semantic hidden association coefficients between each radio frequency detection signal local time-frequency semantic feature vector and the anchoring center feature vector in the sequence of the radio frequency detection signal local time-frequency semantic feature vectors to obtain a sequence of semantic hidden association numbers; calculating the reciprocal of each semantic hidden association coefficient in the sequence of semantic hidden association numbers to obtain a sequence of semantic anti-association numbers; normalizing the sequence of the semantic anti-correlation coefficient by using a Softmax function to obtain a sequence of semantic anti-correlation energy coefficients; taking each semantic anti-correlation energy coefficient in the sequence of semantic anti-correlation energy coefficients as a weight, and weighting each radio frequency detection signal local time-frequency semantic feature vector in the sequence of radio frequency detection signal local time-frequency semantic feature vectors to obtain a sequence of inverse inhibition radio frequency detection signal local time-frequency semantic feature vectors; and calculating the difference according to the position between the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal and the sequence of the local time-frequency semantic feature vector of the reverse suppression radio frequency detection signal so as to obtain the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
Still further, in one embodiment of the present application, calculating an 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: and calculating the position-wise mean value vector of the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain the anchoring center feature vector.
Still further, in one embodiment of the present application, calculating the semantic hidden correlation coefficient between each radio frequency detection signal local time-frequency semantic feature vector and the anchor center feature vector in the sequence of radio frequency detection signal local time-frequency semantic feature vectors to obtain the sequence of semantic hidden correlation relations comprises: calculating the inner product between the local time-frequency semantic feature vector of the radio frequency detection signal and the anchoring center feature vector to obtain a semantic association factor; calculating the addition result between the square sum of all the eigenvalues in the local time-frequency semantic eigenvector of the radio frequency detection signal and the square sum of all the eigenvalues in the anchoring center eigenvector, and calculating the evolution of the addition result to obtain a characteristic distance factor; and calculating the semantic association factor divided by the feature distance factor to obtain the semantic hidden association coefficient.
Specifically, the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal is processed by the following feature distribution inverse screening formula to obtain the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal, wherein the feature distribution inverse screening formula is as follows:
wherein, Is a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal,、、AndThe first radio frequency detection signal local time-frequency semantic feature vector, the second radio frequency detection signal local time-frequency semantic feature vector and the first radio frequency detection signal local time-frequency semantic feature vector in the sequence of the radio frequency detection signal local time-frequency semantic feature vectors respectivelyLocal time-frequency semantic feature vector sum of radio frequency detection signalsLocal time-frequency semantic feature vectors of the radio frequency detection signals,The value of (1) is the number of the local time-frequency semantic feature vectors of the radio frequency detection signal in the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signal,Represents the firstThe first of local time-frequency semantic feature vectors of the radio frequency detection signalsThe characteristic value of the individual position is used,In order to anchor the central feature vector,For the j-th eigenvalue of the anchor center eigenvector,Representing the length of the local time-frequency semantic feature vector of the radio frequency detection signal,Represents the firstSemantic hidden correlation coefficients between local time-frequency semantic feature vectors of the radio frequency detection signals and the anchoring center feature vectors,Is the firstThe number of semantic anti-association coefficients,Is the firstThe semantics are inversely related to the energy coefficients,Represents an exponential function with a base of a natural constant,Is a sequence of reverse-restrained radio frequency detection signal local time-frequency semantic feature vectors,、、AndThe first reverse inhibition RF detection signal local time-frequency semantic feature vector, the second reverse inhibition RF detection signal local time-frequency semantic feature vector and the second reverse inhibition RF detection signal local time-frequency semantic feature vector in the sequence of the reverse inhibition RF detection signal local time-frequency semantic feature vector are respectivelyLocal time-frequency semantic feature vector sum of reverse inhibition radio frequency detection signalsA local time-frequency semantic feature vector of the reverse suppressed radio frequency detection signal,Is a sequence of optimizing the local time-frequency semantic feature vector of the radio frequency detection signal,、、AndThe first optimized radio frequency detection signal local time-frequency semantic feature vector, the second optimized radio frequency detection signal local time-frequency semantic feature vector and the first optimized radio frequency detection signal local time-frequency semantic feature vector are respectivelyLocal time-frequency semantic feature vector sum of optimized radio frequency detection signalsOptimizing the local time-frequency semantic feature vector of the radio frequency detection signal.
And then, inputting the sequence of the local time-frequency semantic feature vectors of the optimized radio frequency detection signal into a decoder-based driving signal generator to obtain a radio frequency ALC driving signal. The decoding regression capability of the decoder is utilized to perform feature learning and nonlinear mapping on the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal, and the radio frequency ALC driving signal which is highly matched with the time-frequency characteristic of the radio frequency detection signal is reconstructed through the decoding process, so that the real-time power requirement of the radio frequency signal is accurately reflected. And the working state of the microwave frequency multiplier is adjusted in real time according to the intensity and the characteristics of the radio frequency ALC driving signal, so that the dynamic and fine adjustment of radio frequency signal power is realized, and the power fluctuation caused by the channel condition change and the radio frequency environment complexity is effectively restrained, thereby improving the stability and the capacity of a communication system.
In one embodiment of the present application, generating the radio frequency ALC driving signal based on the sequence of the optimized radio frequency detection signal local time-frequency semantic feature vectors includes: and inputting the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal into a decoder-based driving signal generator to obtain the radio frequency ALC driving signal.
Particularly, in the technical scheme of the application, each radio frequency detection signal local time-frequency semantic feature vector in the sequence of the radio frequency detection signal local time-frequency semantic feature vectors respectively represents the radio frequency detection local time-frequency image semantic feature. In the process of inputting the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal into an anchor center-based feature distribution inverse screening strengthening network, the anchor center-based feature distribution inverse screening strengthening network takes a global average value vector of the local time-frequency semantic feature vector of the radio frequency detection signal as an anchor center to carry out feature distribution inverse screening strengthening on the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal, so that effective components in the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal can be strengthened and invalid components in the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal can be restrained. However, the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal is taken as the whole feature distribution, and the local complementary signal is lost due to the attention weight mismatch, so that the fine granularity semantic perception aggregation expression effect of the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal taken as the whole feature distribution is expected to be improved.
Inputting the sequence of the optimized radio frequency detection signal local time-frequency semantic feature vector into a decoder-based driving signal generator to obtain the radio frequency ALC driving signal, comprising:
cascading the sequence of the optimized radio frequency detection signal local time-frequency semantic feature vector to obtain a radio frequency detection signal local time-frequency semantic joint coding vector;
Calculating the sum of absolute values of all characteristic values of the local time-frequency semantic joint coding vectors of the radio frequency detection signals to obtain local time-frequency semantic joint coding and modulation values of the first radio frequency detection signals, and calculating square roots of square sums of all characteristic values of the local time-frequency semantic joint coding vectors of the radio frequency detection signals to obtain local time-frequency semantic joint coding and modulation values of the second radio frequency detection signals;
After carrying out point subtraction on the local time-frequency semantic joint coding vector of the radio frequency detection signal and the local time-frequency semantic joint coding and modulation value of the second radio frequency detection signal, respectively carrying out point multiplication on the local time-frequency semantic joint coding vector of the radio frequency detection signal, the number of characteristic values of the local time-frequency semantic joint coding vector of the radio frequency detection signal and the inverse of the local time-frequency semantic joint coding and modulation value of the first radio frequency detection signal, and taking the inverse of each characteristic value to obtain a local time-frequency semantic joint coding phase conversion vector of the first radio frequency detection signal;
After carrying out point subtraction on the local time-frequency semantic joint coding vector of the radio frequency detection signal and the local time-frequency semantic joint coding and modulation value of the first radio frequency detection signal, respectively carrying out point multiplication on the square root of the number of the characteristic values of the local time-frequency semantic joint coding vector of the radio frequency detection signal and the reciprocal of the local time-frequency semantic joint coding and modulation value of the second radio frequency detection signal, and taking the reciprocal of each characteristic value to obtain a local time-frequency semantic joint coding phase conversion vector of the second radio frequency detection signal;
subtracting the local time-frequency semantic joint code phase conversion vector point of the first radio frequency detection signal from the point multiplication vector of the second radio frequency detection signal to obtain an optimized local time-frequency semantic joint code vector of the radio frequency detection signal;
And inputting the optimized local time-frequency semantic joint coding vector of the radio frequency detection signal into a decoder-based driving signal generator to obtain the radio frequency ALC driving signal.
Wherein, the local time-frequency semantic joint coding vector of the radio frequency detection signal is recorded asThe optimization of (c) is expressed as:
wherein, A vector is encoded for local time-frequency semantic association of the radio frequency detection signal,Represents a set of real numbers,Representing the local time-frequency semantic joint coding vector of the radio frequency detection signalThe characteristic value of the individual position is used,The number of eigenvalues representing the local time-frequency semantic joint coding vector of the radio frequency detection signal,Representing local time-frequency semantic joint coding and modulation values of the first radio frequency detection signal,Representing the local time-frequency semantic joint coding and modulation values of the second radio frequency detection signal,Representing the multiplication by the position point,Indicating that the subtraction is performed by position,Representing the reciprocal of each eigenvalue of the calculated eigenvector,Representing a local time-frequency semantic joint code phase conversion vector of the first radio frequency detection signal,Representing a local time-frequency semantic joint code phase conversion vector of the second radio frequency detection signal,The weighted super-parameter is represented by a weighted super-parameter,And (5) representing the optimized local time-frequency semantic joint coding vector of the radio frequency detection signal.
In the preferred embodiment, the difference of the feature value of the local time-frequency semantic joint coding vector of the radio frequency detection signal relative to the difference of the different and modulation representation of the vector integral feature set of the local time-frequency semantic joint coding vector of the radio frequency detection signal is used as semantic change intensity information, and the phase-like conversion corresponding to the position-based intensity modulation is performed through the different and modulation representation form, so that the aggregation enhancement of semantic change phase perception can promote the axial aggregation receptive field along the feature aggregation direction through the space translation operation based on alternate stacking under the vector set scale balance of the local time-frequency semantic joint coding vector of the radio frequency detection signal, thereby promoting the perception effect of the aggregation semantic of the local time-frequency semantic joint coding vector of the radio frequency detection signal on the detail time sequence semantic change, promoting the expression effect of the local time-frequency semantic joint coding vector of the radio frequency detection signal, and promoting the signal quality and the accuracy of the radio frequency ALC driving signal based on the driving signal generator of the decoder.
In summary, by adopting the above scheme, the time-frequency characteristic analysis of fine granularity is performed on the radio frequency detection signal by adopting the artificial intelligence technology based on deep learning, each local time-frequency domain characteristic expression of the signal is captured, and the deep time-frequency characteristic of the radio frequency detection signal is mined by optimizing the characteristic distribution of the local time-frequency domain characteristic expression, so that the radio frequency ALC driving signal is generated, and the power control of the radio frequency band signal is realized. Thus, accurate power adjustment of communication signals can be realized, so that signal transmission quality can be ensured, interference can be reduced, and system capacity can be improved.
Fig. 2 is a block diagram illustrating a control system for microwave millimeter wave communication signals in accordance with an exemplary embodiment. As shown in fig. 2, the control system 200 includes:
a radio frequency detection signal acquisition module 201, configured to acquire a radio frequency detection signal;
the time-frequency analysis module 202 is configured to perform time-frequency analysis based on local scale on the radio frequency detection signal to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signal;
The feature distribution strengthening module 203 is configured to strengthen feature distribution of the sequence of the 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 204 is configured to generate a radio frequency ALC driving signal based on the sequence of the local time-frequency semantic feature vectors of the optimized radio frequency detection signal.
Optionally, the time-frequency analysis module includes: the wavelet analysis unit is used for carrying out wavelet analysis on the radio frequency detection signals to obtain a time-frequency diagram of the radio frequency detection signals; the time-frequency characteristic extraction unit is used for extracting the time-frequency characteristic of the local area of the time-frequency diagram of the radio frequency detection signal so as to obtain the sequence of the local time-frequency semantic characteristic vector of the radio frequency detection signal.
Referring now to fig. 3, a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present application is shown. The terminal device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the application.
As shown in fig. 3, the electronic device 600 may include a processing means 601 (e.g., a central processing unit, a graphics processor, etc.) that may perform various appropriate actions and processes according to programs stored in a read-only memory 602 or loaded from a storage means 608 into a random access memory 603. In the random access memory 603, various programs and data necessary for the operation of the electronic device 600 are also stored. The processing means 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.
In general, the following devices may be connected to the input/output interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 609, or from storage device 608, or from read only memory 602. The above-described functions defined in the method of the embodiment of the present application are performed when the computer program is executed by the processing means 601.
The computer readable medium of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText TransferProtocol ), 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"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. The name of the module is not limited to the module itself in some cases, and for example, the test parameter obtaining module may also be described as "a module for obtaining the device test parameter corresponding to the target device".
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: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Fig. 4 is an application scenario diagram illustrating a control method of a microwave millimeter wave communication signal according to an exemplary embodiment. As shown in fig. 4, in this application scenario, first, a radio frequency band signal is generated by a radio frequency division multiplier (e.g., C as illustrated in fig. 4); the acquired radio frequency band signal is then input into a server (e.g., S as illustrated in fig. 4) deployed with a control algorithm for microwave millimeter wave communication signals, wherein the server is capable of processing the radio frequency band signal based on the control algorithm for microwave millimeter wave communication signals to generate the radio frequency ALC drive signal.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present application is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims. The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Claims (10)
1. A method for controlling microwave millimeter wave communication signals, comprising:
Generating a radio frequency band signal through a radio frequency division frequency 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 driving board for signal processing to obtain a radio frequency ALC driving signal;
The modulator of the radio frequency division frequency multiplier is controlled by the radio frequency ALC driving signal to realize the power control of the radio frequency band of 250KHz-3GHz, wherein the radio frequency detection signal is input to an ALC driving board for signal processing to obtain the radio frequency ALC driving signal, and the method comprises the following steps:
Acquiring the radio frequency detection signal;
Performing time-frequency analysis based on local scale 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 reinforcement on the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain a sequence of optimized local time-frequency semantic feature vectors of the radio frequency detection signals;
And generating the radio frequency ALC driving signal based on the sequence of the optimized radio frequency detection signal local time-frequency semantic feature vector.
2. The method for controlling microwave millimeter wave communication signals according to claim 1, wherein performing time-frequency analysis based on local scale on the radio frequency detection signals to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signals comprises:
Performing wavelet analysis on the radio frequency detection signals to obtain a time-frequency chart of the radio frequency detection signals;
And extracting the local area time-frequency characteristics of the time-frequency diagram of the radio frequency detection signal to obtain the sequence of the local time-frequency semantic characteristic vector of the radio frequency detection signal.
3. The method for controlling microwave millimeter wave communication signals according to claim 2, wherein extracting the local area time-frequency characteristics of the time-frequency diagram of the radio frequency detection signal to obtain the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signal comprises:
performing time-frequency diagram segmentation on 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;
And respectively inputting each radio frequency detection signal local time-frequency diagram in the sequence of the radio frequency detection signal local time-frequency diagram into a detection signal local time-frequency characteristic extractor based on a cavity convolutional neural network model to obtain the sequence of the radio frequency detection signal local time-frequency semantic characteristic vector.
4. The method for controlling microwave millimeter wave communication signals according to claim 3, wherein the step of performing feature distribution reinforcement on the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain the sequence of the optimized local time-frequency semantic feature vectors of the radio frequency detection signals comprises:
inputting the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal into a feature distribution reverse screening strengthening network based on an anchoring center to obtain the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
5. The method for controlling microwave millimeter wave communication signals according to claim 4, wherein inputting the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals into the inverse screening strengthening network based on the feature distribution of the anchoring center to obtain the sequence of the local time-frequency semantic feature vectors of the optimized radio frequency detection signals comprises:
Calculating an anchoring center of the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain an anchoring center feature vector;
Calculating semantic hidden association coefficients between each radio frequency detection signal local time-frequency semantic feature vector and the anchoring center feature vector in the sequence of the radio frequency detection signal local time-frequency semantic feature vectors to obtain a sequence of semantic hidden association numbers;
calculating the reciprocal of each semantic hidden association coefficient in the sequence of semantic hidden association numbers to obtain a sequence of semantic anti-association numbers;
Normalizing the sequence of the semantic anti-correlation coefficient by using a Softmax function to obtain a sequence of semantic anti-correlation energy coefficients;
Taking each semantic anti-correlation energy coefficient in the sequence of semantic anti-correlation energy coefficients as a weight, and weighting each radio frequency detection signal local time-frequency semantic feature vector in the sequence of radio frequency detection signal local time-frequency semantic feature vectors to obtain a sequence of inverse inhibition radio frequency detection signal local time-frequency semantic feature vectors;
and calculating the difference according to the position between the sequence of the local time-frequency semantic feature vector of the radio frequency detection signal and the sequence of the local time-frequency semantic feature vector of the reverse suppression radio frequency detection signal so as to obtain the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
6. The method according to claim 5, wherein calculating an 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 comprises:
And calculating the position-wise mean value vector of the sequence of the local time-frequency semantic feature vectors of the radio frequency detection signals to obtain the anchoring center feature vector.
7. The method according to claim 6, wherein calculating the semantic hidden correlation coefficient between each radio frequency detection signal local time-frequency semantic feature vector and the anchor center feature vector in the sequence of radio frequency detection signal local time-frequency semantic feature vectors to obtain the sequence of semantic hidden correlation numbers comprises:
calculating the inner product between the local time-frequency semantic feature vector of the radio frequency detection signal and the anchoring center feature vector to obtain a semantic association factor;
Calculating the addition result between the square sum of all the eigenvalues in the local time-frequency semantic eigenvector of the radio frequency detection signal and the square sum of all the eigenvalues in the anchoring center eigenvector, and calculating the evolution of the addition result to obtain a characteristic distance factor;
and calculating the semantic association factor divided by the feature distance factor to obtain the semantic hidden association coefficient.
8. The method of claim 7, wherein generating the radio frequency ALC drive signal based on the sequence of optimized radio frequency detection signal local time-frequency semantic feature vectors comprises:
And inputting the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal into a decoder-based driving signal generator to obtain the radio frequency ALC driving signal.
9. A control system for microwave millimeter wave communication signals, comprising:
The radio frequency detection signal acquisition module is used for acquiring radio frequency detection signals;
the time-frequency analysis module is used for carrying out time-frequency analysis based on local scale on the radio frequency detection signals so as to obtain a sequence of local time-frequency semantic feature vectors of the radio frequency detection signals;
the characteristic distribution strengthening module is used for strengthening characteristic distribution of the sequence of the local time-frequency semantic characteristic vectors of the radio frequency detection signals so as to obtain the sequence of the local time-frequency semantic characteristic vectors of the optimized radio frequency detection signals;
And the driving signal generation module is used for generating a radio frequency ALC driving signal based on the sequence of the local time-frequency semantic feature vector of the optimized radio frequency detection signal.
10. The control system of microwave millimeter wave communication signals according to claim 9, wherein said time-frequency analysis module comprises:
The wavelet analysis unit is used for carrying out wavelet analysis on the radio frequency detection signals to obtain a time-frequency diagram of the radio frequency detection signals;
The time-frequency characteristic extraction unit is used for extracting the time-frequency characteristic of the local area of the time-frequency diagram of the radio frequency detection signal so as to obtain the sequence of the local time-frequency semantic characteristic vector of the radio frequency detection signal.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002043962A (en) * | 2000-07-19 | 2002-02-08 | Ntt Docomo Inc | Receiving method and receiver in communication system |
| US20060089112A1 (en) * | 2004-10-23 | 2006-04-27 | Pedro Irazoqui-Pastor | Wireless neural data acquisition system |
| CN104596369A (en) * | 2014-12-26 | 2015-05-06 | 北京理工大学 | SVM-based continuous wave Doppler radio fuse frequency-swept jamming prevention method |
| CN115754469A (en) * | 2023-01-05 | 2023-03-07 | 湖南红船科技有限公司 | Weak signal detection and extraction method, system, equipment, medium and terminal for unmanned aerial vehicle |
| US20230317096A1 (en) * | 2021-09-16 | 2023-10-05 | Tencent Technology (Shenzhen) Company Limited | Audio signal processing method and apparatus, electronic device, and storage medium |
-
2024
- 2024-09-27 CN CN202411354458.7A patent/CN118971849B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002043962A (en) * | 2000-07-19 | 2002-02-08 | Ntt Docomo Inc | Receiving method and receiver in communication system |
| US20060089112A1 (en) * | 2004-10-23 | 2006-04-27 | Pedro Irazoqui-Pastor | Wireless neural data acquisition system |
| CN104596369A (en) * | 2014-12-26 | 2015-05-06 | 北京理工大学 | SVM-based continuous wave Doppler radio fuse frequency-swept jamming prevention method |
| US20230317096A1 (en) * | 2021-09-16 | 2023-10-05 | Tencent Technology (Shenzhen) Company Limited | Audio signal processing method and apparatus, electronic device, and storage medium |
| CN115754469A (en) * | 2023-01-05 | 2023-03-07 | 湖南红船科技有限公司 | Weak signal detection and extraction method, system, equipment, medium and terminal for unmanned aerial vehicle |
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