A 5G antenna with integrated electromagnetic shielding and band detection

Abstract

The present invention discloses a 5G antenna integrating electromagnetic shielding and band detection, belonging to the field of 5G antennas. The present invention adaptively adjusts the antenna frequency band according to the resolved signal bandwidth type and bandwidth characteristics, sends the transmission signal according to the adjusted antenna frequency band, and intelligently adjusts the input or output bandwidth by detecting bandwidth transformation. When detecting bandwidth transformation, an intelligent resolution system is used to intelligently resolve whether the signal is a usable transmission signal by intelligently resolving whether it is a stable bandwidth or a clutter bandwidth, and adjusts the antenna frequency band according to the intelligent resolution bandwidth to achieve adaptive bandwidth regulation and variable stable communication. A deep learning network model is set to perform noise reduction processing on the transmission signal to avoid the influence of noise on the signal resolution process, thereby further improving the accuracy of signal resolution.

Description

Technical Field

[0001] The present invention belongs to the field of 5G antennas, and specifically relates to a 5G antenna that integrates electromagnetic shielding and band detection. Background Art

[0002] Nowadays, with the sharp increase in network traffic of electronic devices, 5G communication technology that can send or receive a large amount of information has become an important part in the field of communication. Using signals in the frequency band (e.g., about 3 GHz or higher) used for 5G mobile communication networks allows the wavelength of the signal to be shortened in millimeters, so that the bandwidth can be used more widely. This means that to send or receive a large amount of information, conventional communication technology using signals in a frequency band lower than that of 5G communication technology is still required. For example, in addition to the antenna module for 5G communication, the electronic device may also include WiFi communication and Bluetooth communication using a frequency band of 6 GHz or lower. However, when performing 5G communication, the antenna is often interfered by abnormal clutter, causing 5G communication to be blocked. It is impossible to comprehensively consider the abnormal clutter situation and the actual situation of 5G communication to adjust the antenna frequency band, resulting in unstable communication. The above problems exist in the prior art;

[0003] At the same time, a Chinese patent with the authorization announcement number CN110247173B discloses a wearable device and a smart watch, including a first 5G antenna, a second 5G antenna, a main body and a wearable part, wherein the main body includes a middle frame plate and a metal frame, the metal frame is connected to the periphery of the middle frame plate, the metal frame includes a first frame body and a second frame body relative to each other, the first frame body is provided with a first slit, the first slit separates the first frame body to form a first radiator and a second radiator, the first radiator and the second radiator are both used to send and receive 5G signals, and the wearable part is connected to the main body. The first radiator and the second radiator are formed by opening a first slit on the metal frame, and the metal frame is used as part of the antenna to send and receive signals, which makes the appearance of the wearable device shiny while avoiding the electromagnetic shielding caused by the metal material, and significantly improves the communication effect. At the same time, the two 5G antennas form a multi-input-multi-output antenna structure, which can maintain a good communication effect even if the user blocks the antenna;

[0004] The above patents have the problem raised by this background technology: when the prior art is performing 5G communication, the antenna is often interfered by abnormal clutter, causing 5G communication to be obstructed. It is impossible to comprehensively consider the abnormal clutter situation and the actual situation of 5G communication to adjust the antenna frequency band, resulting in unstable communication. In order to solve these problems, this application designs a 5G antenna that integrates electromagnetic shielding and band detection. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a 5G antenna that integrates electromagnetic shielding and band detection. The present invention receives the signal to be transmitted, and imports the transmitted signal after noise reduction into the intelligent resolution model to resolve the signal bandwidth type and bandwidth characteristics, and adaptively adjusts the antenna frequency band according to the resolved signal bandwidth type and bandwidth characteristics, sends the transmission signal according to the adjusted antenna frequency band, and intelligently adjusts the input or output bandwidth by detecting bandwidth transformation. When detecting bandwidth transformation, the intelligent resolution system is referenced to intelligently resolve whether the signal is a usable transmission signal by intelligently resolving whether it is a stable bandwidth or a clutter bandwidth, and adjusts the antenna frequency band according to the intelligent resolution bandwidth to realize adaptive bandwidth adjustment and variable stable communication. The transmission signal is denoised by setting a deep learning network model to avoid the influence of noise on the signal resolution process, thereby further improving the accuracy of signal resolution.

[0006] To achieve the above object, the present invention provides the following technical solution: a 5G antenna integrating electromagnetic shielding and band detection, which includes the following component modules:

[0007] A signal receiving module, used for receiving a signal to be transmitted;

[0008] The signal denoising module is used to denoise the signal to be transmitted through the constructed neural network model;

[0009] A signal intelligent discrimination module is used to import the transmitted signal after noise reduction into the intelligent discrimination model to discern the signal bandwidth type and bandwidth characteristics;

[0010] An antenna frequency band analysis module is used to adaptively adjust the antenna frequency band according to the resolved signal bandwidth type and bandwidth characteristics;

[0011] A signal sending module, used for sending the transmission signal according to the adjusted antenna frequency band;

[0012] The control module is used to control the operation of the signal receiving module, the signal noise reduction module, the signal intelligent resolution module, the antenna frequency band analysis module and the signal sending module.

[0013] As a preferred technical solution for a 5G antenna integrating electromagnetic shielding and band detection, the signal noise reduction module includes an electric signal noise acquisition unit, a magnetic signal noise acquisition unit and a neural network noise reduction module, wherein the electric signal noise acquisition unit is used to collect the electric field noise frequency and intensity data during the signal transmission process, the magnetic signal noise acquisition unit is used to collect the magnetic field noise frequency and intensity data during the signal transmission process, and the neural network noise reduction module performs noise reduction processing on the signal to be transmitted based on the constructed neural network model;

[0014] As a preferred technical solution for a 5G antenna integrating electromagnetic shielding and band detection, the noise reduction model in the signal noise reduction module includes the following specific steps:

[0015] S11, obtaining historical real signal information, historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by a signal receiving module, constructing a deep learning neural network model whose input is historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by a signal receiving module, and whose output is real signal information;

[0016] S12, dividing the extracted historical real signal information, historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by the signal receiving module into 70% parameter training sets and 30% parameter test sets; inputting 70% parameter training sets into the deep learning neural network model for training to obtain an initial deep learning neural network model; using 30% parameter test sets to test the initial deep learning neural network model, and outputting an initial deep learning neural network model that meets the highest judgment accuracy of the preset real signal information as the deep learning neural network model, wherein the output strategy formula of a specific neuron in the deep learning neural network model is: ,in is the output of n+1 layer s-term neurons, is the connection weight between neuron i in the nth layer and neuron s in the n+1th layer, represents the output of neuron i in layer n, represents the bias of the linear relationship between neuron i in the nth layer and neuron s in the n+1th layer, Represents the Sigmoid activation function;

[0017] S13, obtaining the transmission signal after noise reduction;

[0018] As a preferred technical solution for a 5G antenna integrating electromagnetic shielding and band detection, the intelligent resolution model in the signal intelligent resolution module includes the following specific contents:

[0019] S21. Divide the transmitted signal after noise reduction into signals of several time periods according to the time length, obtain the baseband signal frequency data and the highest frequency component data of each time period of the transmitted signal after noise reduction, import the baseband signal frequency data and the highest frequency component data of each time period of the corresponding signal and the baseband signal frequency data and the highest frequency component data of the 5G signal into the 5G signal recognition value calculation formula to calculate the 5G signal recognition value, wherein the 5G signal recognition value calculation formula of the transmitted signal after noise reduction is: , where T is the length of the time period, is the frequency data of the baseband signal and the data range of the highest frequency component at the tth moment, is the baseband signal frequency data and the highest frequency component data range of the 5G signal, dt is the time integral, is the intersection of two ranges, is the union of two ranges, and P() is the length of the range in parentheses;

[0020] S22, comparing the calculated 5G signal recognition value of the transmitted signal after noise reduction with the set 5G signal recognition threshold, if the calculated 5G signal recognition value of the transmitted signal after noise reduction is greater than or equal to the set 5G signal recognition threshold, it means that the transmitted signal after noise reduction is an available 5G signal bandwidth; if the calculated 5G signal recognition value of the transmitted signal after noise reduction is less than the set 5G signal recognition threshold, it means that the transmitted signal after noise reduction is an unusable clutter signal bandwidth;

[0021] As an optimal technical solution for a 5G antenna integrating electromagnetic shielding and band detection, the antenna frequency band analysis module includes a 5G signal bandwidth analysis unit and an antenna frequency band adaptive adjustment unit, wherein the 5G signal bandwidth analysis unit is used to analyze the bandwidth of the acquired 5G signal to obtain the optimal bandwidth for stable transmission of the 5G signal, and the antenna frequency band adaptive adjustment unit is used to adaptively adjust the antenna frequency band according to the optimal bandwidth for stable transmission of the 5G signal.

[0022] As a preferred technical solution for a 5G antenna integrating electromagnetic shielding and band detection, a 5G signal bandwidth analysis strategy is run in the 5G signal bandwidth analysis unit, wherein the 5G signal bandwidth analysis strategy includes the following specific steps:

[0023] S31. Obtain the baseband signal frequency data and the highest frequency component data of each time period of the transmitted signal after noise reduction, and obtain the bandwidth belonging to the 5G signal frequency in the baseband signal frequency data and the highest frequency component data range of each time period, and substitute the signal strength of the baseband signal frequency data and the highest frequency component data range of each time period, and the bandwidth belonging to the 5G signal frequency in the baseband signal frequency data and the highest frequency component data range of each time period into the optimal bandwidth analysis value calculation formula to calculate the optimal bandwidth analysis value of the stable transmission of the 5G signal in the corresponding time period, wherein the optimal bandwidth analysis value calculation formula for the j time period is: ,Aj is the 5G signal strength at time j, s is the set bandwidth, N is the number of clutter at time j, Bdj is the dth clutter strength at time j, f is the adjustment coefficient, the range of the adjustment coefficient is 1 to 2, and the adjustment coefficient is obtained according to the accuracy required for adjustment. The optimal bandwidth for stable transmission of 5G signals is obtained when the maximum set bandwidth range of Zj is set to 5G signal.

[0024] As an optimal technical solution for a 5G antenna integrating electromagnetic shielding and band detection, the antenna frequency band adaptive adjustment unit includes an optimal bandwidth acquisition component and an antenna frequency band adjustment component, wherein the optimal bandwidth acquisition component is used to obtain the optimal bandwidth for stable transmission of 5G signals, and the antenna frequency band adjustment component is used to adjust the antenna frequency band in real time to the corresponding optimal bandwidth for stable transmission of 5G signals.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention receives a signal to be transmitted, imports the transmitted signal after noise reduction into an intelligent discrimination model to discriminate the type and characteristics of signal bandwidth, adaptively adjusts the antenna frequency band according to the resolved signal bandwidth type and bandwidth characteristics, sends the transmitted signal according to the adjusted antenna frequency band, and intelligently adjusts the input or output bandwidth by detecting bandwidth transformation. When detecting bandwidth transformation, the intelligent discrimination system is used to identify whether the signal is an available transmission signal by intelligently distinguishing whether it is a stable bandwidth or a clutter bandwidth, and adjusts the antenna frequency band according to the intelligently distinguished bandwidth to realize adaptive bandwidth adjustment and variable stable communication.

[0027] The present invention performs noise reduction processing on the transmitted signal by setting a deep learning network model, thereby avoiding the influence of noise on the signal resolution process and further improving the accuracy of signal resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a schematic diagram of the overall framework of a 5G antenna with integrated electromagnetic shielding and band detection according to the present invention;

[0029] Figure 2This is a schematic diagram of a 5G antenna signal noise reduction module framework integrating electromagnetic shielding and band detection according to the present invention;

[0030] Figure 3 This is a schematic diagram of the framework of a 5G antenna signal antenna frequency band analysis module that integrates electromagnetic shielding and band detection in the present invention. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0032] Example 1

[0033] The technical problem solved by this embodiment is: in the prior art, when performing 5G communication, the antenna is often interfered by abnormal clutter, resulting in obstruction of 5G communication, and the antenna frequency band cannot be adjusted by comprehensively considering the abnormal clutter situation and the actual situation of 5G communication, resulting in unstable communication;

[0034] To solve the above problems, please refer to Figure 1 The present invention provides a preferred embodiment: Figure 1 As shown, a 5G antenna integrating electromagnetic shielding and band detection includes the following components:

[0035] A signal receiving module, used for receiving a signal to be transmitted;

[0036] A signal intelligent discrimination module is used to import the transmitted signal after noise reduction into the intelligent discrimination model to discern the signal bandwidth type and bandwidth characteristics;

[0037] An antenna frequency band analysis module is used to adaptively adjust the antenna frequency band according to the resolved signal bandwidth type and bandwidth characteristics;

[0038] A signal sending module, used for sending the transmission signal according to the adjusted antenna frequency band;

[0039] A control module, used to control the operation of a signal receiving module, a signal noise reduction module, a signal intelligent discrimination module, an antenna frequency band analysis module and a signal sending module;

[0040] A further improvement in this embodiment is that the signal noise reduction module includes an electric signal noise acquisition unit, a magnetic signal noise acquisition unit and a neural network noise reduction module, wherein the electric signal noise acquisition unit is used to collect the electric field noise frequency and intensity data during the signal transmission process, the magnetic signal noise acquisition unit is used to collect the magnetic field noise frequency and intensity data during the signal transmission process, and the neural network noise reduction module performs noise reduction processing on the signal to be transmitted based on the constructed neural network model;

[0041] A further improvement in this embodiment is that the intelligent resolution model in the signal intelligent resolution module includes the following specific contents:

[0042] S21. Divide the transmitted signal after noise reduction into signals of several time periods according to the time length, obtain the baseband signal frequency data and the highest frequency component data of each time period of the transmitted signal after noise reduction, import the baseband signal frequency data and the highest frequency component data of each time period of the corresponding signal and the baseband signal frequency data and the highest frequency component data of the 5G signal into the 5G signal recognition value calculation formula to calculate the 5G signal recognition value, wherein the 5G signal recognition value calculation formula of the transmitted signal after noise reduction is: , where T is the length of the time period, is the frequency data of the baseband signal and the data range of the highest frequency component at the tth moment, is the baseband signal frequency data and the highest frequency component data range of the 5G signal, dt is the time integral, is the intersection of two ranges, is the union of two ranges, and P() is the length of the range in parentheses;

[0043] S22, comparing the calculated 5G signal recognition value of the transmitted signal after noise reduction with the set 5G signal recognition threshold, if the calculated 5G signal recognition value of the transmitted signal after noise reduction is greater than or equal to the set 5G signal recognition threshold, it means that the transmitted signal after noise reduction is an available 5G signal bandwidth; if the calculated 5G signal recognition value of the transmitted signal after noise reduction is less than the set 5G signal recognition threshold, it means that the transmitted signal after noise reduction is an unusable clutter signal bandwidth;

[0044] In this way, by comprehensively analyzing and calculating the transmission bandwidth characteristics of the transmitted signal after noise reduction and the 5G signal, the available 5G signal bandwidth and the unavailable clutter signal bandwidth can be accurately classified.

[0045] A further improvement in this embodiment is that the 5G signal discrimination threshold is obtained as follows: 5000 groups of signal data of different time lengths are obtained to classify whether they are 5G signals, the 5000 groups of signal data of different time lengths are substituted into the 5G signal discrimination value calculation formula to calculate the 5G signal discrimination value, and the calculated 5G signal discrimination value and the classification result of the 5G signal are imported into the fitting software to output the 5G signal discrimination threshold that meets the maximum classification accuracy;

[0046] A further improvement in this embodiment is that the antenna frequency band analysis module includes a 5G signal bandwidth analysis unit and an antenna frequency band adaptive adjustment unit, wherein the 5G signal bandwidth analysis unit is used to analyze the bandwidth of the acquired 5G signal to obtain the optimal bandwidth for stable transmission of the 5G signal, and the antenna frequency band adaptive adjustment unit is used to adaptively adjust the antenna frequency band according to the optimal bandwidth for stable transmission of the 5G signal.

[0047] A further improvement in this embodiment is that a 5G signal bandwidth analysis strategy is executed in the 5G signal bandwidth analysis unit, wherein the 5G signal bandwidth analysis strategy includes the following specific steps:

[0048] S31. Obtain the baseband signal frequency data and the highest frequency component data of each time period of the transmitted signal after noise reduction, and obtain the bandwidth belonging to the 5G signal frequency in the baseband signal frequency data and the highest frequency component data range of each time period, and substitute the signal strength of the baseband signal frequency data and the highest frequency component data range of each time period, and the bandwidth belonging to the 5G signal frequency in the baseband signal frequency data and the highest frequency component data range of each time period into the optimal bandwidth analysis value calculation formula to calculate the optimal bandwidth analysis value of the stable transmission of the 5G signal in the corresponding time period, wherein the optimal bandwidth analysis value calculation formula for the j time period is: ,Aj is the 5G signal strength at time j, s is the set bandwidth, N is the number of clutter at time j, Bdj is the dth clutter strength at time j, f is the adjustment coefficient, the range of the adjustment coefficient is 1 to 2, and the adjustment coefficient is obtained according to the accuracy required for adjustment. The optimal bandwidth for stable transmission of 5G signals is obtained when the maximum set bandwidth range of Zj is set to 5G signal.

[0049] A further improvement in this embodiment is that the antenna frequency band adaptive adjustment unit includes an optimal bandwidth acquisition component and an antenna frequency band adjustment component, wherein the optimal bandwidth acquisition component is used to acquire the optimal bandwidth for stable transmission of 5G signals, and the antenna frequency band adjustment component is used to adjust the antenna frequency band in real time to the optimal bandwidth for stable transmission of the corresponding 5G signals;

[0050] It should be noted that in this embodiment, the advantages of this embodiment over the prior art are: receiving the signal to be transmitted, importing the transmitted signal after noise reduction into the intelligent resolution model to resolve the signal bandwidth type and bandwidth characteristics, adaptively adjusting the antenna frequency band according to the resolved signal bandwidth type and bandwidth characteristics, sending the transmission signal according to the adjusted antenna frequency band, and intelligently adjusting the input or output bandwidth by detecting bandwidth changes. When detecting bandwidth changes, the intelligent resolution system is referenced to intelligently resolve whether the signal is a usable transmission signal by intelligently resolving whether it is a stable bandwidth or a clutter bandwidth, and adjusting the antenna frequency band according to the intelligent resolution bandwidth to achieve adaptive bandwidth adjustment and variable stable communication.

[0051] Example 2

[0052] This embodiment adds a signal noise reduction module to the embodiment 1. The specific content of this embodiment is: a 5G antenna integrating electromagnetic shielding and band detection, which includes the following components:

[0053] A signal receiving module, used for receiving a signal to be transmitted;

[0054] The signal denoising module is used to denoise the signal to be transmitted through the constructed neural network model;

[0055] A signal intelligent discrimination module is used to import the transmitted signal after noise reduction into the intelligent discrimination model to discern the signal bandwidth type and bandwidth characteristics;

[0056] An antenna frequency band analysis module is used to adaptively adjust the antenna frequency band according to the resolved signal bandwidth type and bandwidth characteristics;

[0057] A signal sending module, used for sending the transmission signal according to the adjusted antenna frequency band;

[0058] A control module, used to control the operation of a signal receiving module, a signal noise reduction module, a signal intelligent discrimination module, an antenna frequency band analysis module and a signal sending module;

[0059] A further improvement in this embodiment is that the signal noise reduction module includes an electric signal noise acquisition unit, a magnetic signal noise acquisition unit and a neural network noise reduction module, wherein the electric signal noise acquisition unit is used to collect the electric field noise frequency and intensity data during the signal transmission process, the magnetic signal noise acquisition unit is used to collect the magnetic field noise frequency and intensity data during the signal transmission process, and the neural network noise reduction module performs noise reduction processing on the signal to be transmitted based on the constructed neural network model;

[0060] A further improvement in this embodiment is that the noise reduction model in the signal noise reduction module includes the following specific steps:

[0061] S11, obtaining historical real signal information, historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by a signal receiving module, constructing a deep learning neural network model whose input is historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by a signal receiving module, and whose output is real signal information;

[0062] The following is a simplified example showing how to implement a basic deep learning neural network model using C:

[0063] #include<stdio.h>

[0064] #include<stdlib.h>

[0065] #include<cuda_runtime.h>

[0066] #include<tensorflow / core / public / session.h>

[0067] #include<tensorflow / core / platform / env.h>

[0068] / / Define some constants needed by the neural network

[0069] #define BATCH_SIZE 32

[0070] #define INPUT_SIZE 1024 / / Number of features of input data

[0071] #define OUTPUT_SIZE 1024 / / Number of features of output data

[0072] #define NUM_EPOCHS 100

[0073] / / Device configuration

[0074] struct DeviceConfig {

[0075] const char* device_type;

[0076] int device_id;

[0077] };

[0078] / / Create a simple convolutional neural network

[0079] tensorflow::Status CreateConvolutionalNetwork(tensorflow::Session*session,

[0080] struct DeviceConfigdevice_config) {

[0081] / / Create a computational graph

[0082] tensorflow::GraphDef graph_def;

[0083] / / Load the pre-trained model

[0084] tensorflow::Status load_status = tensorflow::ReadBinaryProto(

[0085] tensorflow::Env::Default(), "path / to / your / model.pb",&graph_def);

[0086] if (!load_status.ok()) {

[0087] return tensorflow::Status(tensorflow::error::Code::NOT_FOUND,

[0088] load_status.message());

[0089] }

[0090] / / Create a session

[0091] tensorflow::SessionOptions options;

[0092] options.config.mutable_gpu_options()->set_allow_growth(true);

[0093] if (device_config.device_type == "gpu") {

[0094] options.config.mutable_gpu_options()->set_visible_device_list(

[0095] std::to_string(device_config.device_id));

[0096] }

[0097] tensorflow::Session* sess;

[0098] status = tensorflow::NewSession(options,&sess);

[0099] if (!status.ok()) {

[0100] return status;

[0101] }

[0102] / / Import the computation graph into the session

[0103] status = sess->Create(graph_def);

[0104] if (!status.ok()) {

[0105] return status;

[0106] }

[0107] return tensorflow::Status::OK();

[0108] }

[0109] int main() {

[0110] / / Initialize device configuration

[0111] struct DeviceConfig device_config = {.device_type = "gpu",.device_id = 0};

[0112] / / Create a TensorFlow session and computation graph

[0113] tensorflow::Session* session;

[0114] tensorflow::Status status = tensorflow::NewSession(tensorflow::SessionOptions(),&session);

[0115] if (!status.ok()) {

[0116] printf("Error creating session: %s\n", status.ToString().c_str());

[0117] return 1;

[0118] }

[0119] / / Call the above function to create a convolutional neural network

[0120] status = CreateConvolutionalNetwork(session, device_config);

[0121] if (!status.ok()) {

[0122] printf("Error creating network: %s\n", status.ToString().c_str());

[0123] return 1;

[0124] }

[0125] / / There should be data preprocessing and model training steps here

[0126] / / ...

[0127] / / Finally, close the TensorFlow session

[0128] session->Close();

[0129] delete session;

[0130] return 0;

[0131] }

[0132] This code is just a framework, which needs to be filled with data preprocessing, model training, and inference parts according to actual conditions;

[0133] S12, dividing the extracted historical real signal information, historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by the signal receiving module into 70% parameter training sets and 30% parameter test sets; inputting 70% parameter training sets into the deep learning neural network model for training to obtain an initial deep learning neural network model; using 30% parameter test sets to test the initial deep learning neural network model, and outputting an initial deep learning neural network model that meets the highest judgment accuracy of the preset real signal information as the deep learning neural network model, wherein the output strategy formula of a specific neuron in the deep learning neural network model is: ,in is the output of n+1 layer s-term neurons, is the connection weight between neuron i in the nth layer and neuron s in the n+1th layer, represents the output of neuron i in layer n, represents the bias of the linear relationship between neuron i in the nth layer and neuron s in the n+1th layer, Represents the Sigmoid activation function;

[0134] S13: Obtain the transmission signal after noise reduction.

[0135] In this way, by constructing a deep learning network model, the transmitted signal is subjected to noise reduction processing, the influence of noise on the signal resolution process is avoided, and the accuracy of signal resolution is further improved;

[0136] A further improvement in this embodiment is that the intelligent resolution model in the signal intelligent resolution module includes the following specific contents:

[0137] S21. Divide the transmitted signal after noise reduction into signals of several time periods according to the time length, obtain the baseband signal frequency data and the highest frequency component data of each time period of the transmitted signal after noise reduction, import the baseband signal frequency data and the highest frequency component data of each time period of the corresponding signal and the baseband signal frequency data and the highest frequency component data of the 5G signal into the 5G signal recognition value calculation formula to calculate the 5G signal recognition value, wherein the 5G signal recognition value calculation formula of the transmitted signal after noise reduction is: , where T is the length of the time period, is the frequency data of the baseband signal and the data range of the highest frequency component at the tth moment, is the baseband signal frequency data and the highest frequency component data range of the 5G signal, dt is the time integral, is the intersection of two ranges, is the union of two ranges, and P() is the length of the range in parentheses;

[0138] S22. Compare the calculated 5G signal recognition value of the transmitted signal after noise reduction with the set 5G signal recognition threshold. If the calculated 5G signal recognition value of the transmitted signal after noise reduction is greater than or equal to the set 5G signal recognition threshold, it means that the transmitted signal after noise reduction is an available 5G signal bandwidth. If the calculated 5G signal recognition value of the transmitted signal after noise reduction is less than the set 5G signal recognition threshold, it means that the transmitted signal after noise reduction is an unusable noise signal bandwidth.

[0139] In this way, by comprehensively analyzing and calculating the transmission bandwidth characteristics of the transmitted signal after noise reduction and the 5G signal, the available 5G signal bandwidth and the unavailable clutter signal bandwidth can be accurately classified.

[0140] A further improvement in this embodiment is that the 5G signal discrimination threshold is obtained as follows: 5000 groups of signal data of different time lengths are obtained to classify whether they are 5G signals, the 5000 groups of signal data of different time lengths are substituted into the 5G signal discrimination value calculation formula to calculate the 5G signal discrimination value, and the calculated 5G signal discrimination value and the classification result of the 5G signal are imported into the fitting software to output the 5G signal discrimination threshold that meets the maximum classification accuracy;

[0141] A further improvement in this embodiment is that the antenna frequency band analysis module includes a 5G signal bandwidth analysis unit and an antenna frequency band adaptive adjustment unit, wherein the 5G signal bandwidth analysis unit is used to analyze the bandwidth of the acquired 5G signal to obtain the optimal bandwidth for stable transmission of the 5G signal, and the antenna frequency band adaptive adjustment unit is used to adaptively adjust the antenna frequency band according to the optimal bandwidth for stable transmission of the 5G signal.

[0142] A further improvement in this embodiment is that a 5G signal bandwidth analysis strategy is executed in the 5G signal bandwidth analysis unit, wherein the 5G signal bandwidth analysis strategy includes the following specific steps:

[0143] S31. Obtain the baseband signal frequency data and the highest frequency component data of each time period of the transmitted signal after noise reduction, and obtain the bandwidth belonging to the 5G signal frequency in the baseband signal frequency data and the highest frequency component data range of each time period, and substitute the signal strength of the baseband signal frequency data and the highest frequency component data range of each time period, and the bandwidth belonging to the 5G signal frequency in the baseband signal frequency data and the highest frequency component data range of each time period into the optimal bandwidth analysis value calculation formula to calculate the optimal bandwidth analysis value of the stable transmission of the 5G signal in the corresponding time period, wherein the optimal bandwidth analysis value calculation formula for the j time period is: ,Aj is the 5G signal strength at time j, s is the set bandwidth, N is the number of clutter at time j, Bdj is the dth clutter strength at time j, f is the adjustment coefficient, the range of the adjustment coefficient is 1 to 2, and the adjustment coefficient is obtained according to the accuracy required for adjustment. The optimal bandwidth for stable transmission of 5G signals is obtained when the maximum set bandwidth range of Zj is set to 5G signal.

[0144] A further improvement in this embodiment is that the antenna frequency band adaptive adjustment unit includes an optimal bandwidth acquisition component and an antenna frequency band adjustment component, wherein the optimal bandwidth acquisition component is used to obtain the optimal bandwidth for stable transmission of 5G signals, and the antenna frequency band adjustment component is used to adjust the antenna frequency band in real time to the corresponding optimal bandwidth for stable transmission of 5G signals.

[0145] The advantage of this embodiment over embodiment 1 is that by setting up a deep learning network model to perform noise reduction processing on the transmitted signal, the influence of noise on the signal resolution process is avoided, thereby further improving the accuracy of signal resolution.

[0146] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A 5G antenna integrating electromagnetic shielding and band detection, characterized in that: It includes the following specific steps: A signal receiving module, used for receiving a signal to be transmitted; The signal denoising module is used to denoise the signal to be transmitted through the constructed neural network model; A signal intelligent discrimination module is used to import the transmitted signal after noise reduction into the intelligent discrimination model to discern the signal bandwidth type and bandwidth characteristics; An antenna frequency band analysis module is used to adaptively adjust the antenna frequency band according to the resolved signal bandwidth type and bandwidth characteristics; A signal sending module, used for sending the transmission signal according to the adjusted antenna frequency band; The control module is used to control the operation of the signal receiving module, the signal noise reduction module, the signal intelligent resolution module, the antenna frequency band analysis module and the signal sending module; the intelligent resolution model in the signal intelligent resolution module includes the following specific contents: S21, divide the transmitted signal after noise reduction into several time periods according to the time length, obtain the baseband signal frequency data and the highest frequency component data of each time period of the transmitted signal after noise reduction, import the baseband signal frequency data and the highest frequency component data of each time period of the corresponding signal and the baseband signal frequency data and the highest frequency component data of the 5G signal into the 5G signal recognition value calculation formula to calculate the 5G signal recognition value, wherein the 5G signal recognition value calculation formula of the transmitted signal after noise reduction is: , where T is the length of the time period, is the frequency data of the baseband signal and the data range of the highest frequency component at the tth moment, is the baseband signal frequency data and the highest frequency component data range of the 5G signal, dt is the time integral, is the intersection of two ranges, is the union of two ranges, and P() is the length of the range in parentheses; The intelligent resolution model also includes the following specific steps: S22. Compare the calculated 5G signal recognition value of the transmitted signal after noise reduction with the set 5G signal recognition threshold. If the calculated 5G signal recognition value of the transmitted signal after noise reduction is greater than or equal to the set 5G signal recognition threshold, it means that the transmitted signal after noise reduction is an available 5G signal bandwidth. If the calculated 5G signal recognition value of the transmitted signal after noise reduction is less than the set 5G signal recognition threshold, it means that the transmitted signal after noise reduction is an unavailable clutter signal bandwidth. The antenna frequency band analysis module includes a 5G signal bandwidth analysis unit and an antenna frequency band adaptive adjustment unit, wherein the 5G signal bandwidth analysis unit is used to analyze the bandwidth of the acquired 5G signal to obtain the optimal bandwidth for stable transmission of the 5G signal, and the antenna frequency band adaptive adjustment unit is used to adaptively adjust the antenna frequency band according to the optimal bandwidth for stable transmission of the 5G signal. The 5G signal bandwidth analysis unit runs a 5G signal bandwidth analysis strategy, wherein the 5G signal bandwidth analysis strategy includes the following specific steps: S31, obtaining the baseband signal frequency data and the highest frequency component data of each time period of the transmitted signal after noise reduction, obtaining the bandwidth belonging to the 5G signal frequency in the baseband signal frequency data and the highest frequency component data range of each time period, substituting the signal strength of the baseband signal frequency data and the highest frequency component data range of each time period, and the bandwidth belonging to the 5G signal frequency in the baseband signal frequency data and the highest frequency component data range of each time period into the optimal bandwidth analysis value calculation formula to calculate the optimal bandwidth analysis value of the stable transmission of the 5G signal in the corresponding time period, wherein the optimal bandwidth analysis value calculation formula for the j time period is: ,Aj is the 5G signal strength at time j, s is the set bandwidth, N is the number of clutter at time j, Bdj is the dth clutter strength at time j, and f is the adjustment coefficient. The optimal bandwidth for stable transmission of 5G signals is obtained when the bandwidth range with the maximum setting of Zj is set.

2. A 5G antenna with integrated electromagnetic shielding and band detection as claimed in claim 1, characterized in that: The signal noise reduction module includes an electric signal noise acquisition unit, a magnetic signal noise acquisition unit and a neural network noise reduction module, wherein the electric signal noise acquisition unit is used to collect the electric field noise frequency and intensity data during the signal transmission process, the magnetic signal noise acquisition unit is used to collect the magnetic field noise frequency and intensity data during the signal transmission process, and the neural network noise reduction module performs noise reduction processing on the signal to be transmitted based on the constructed neural network model; the noise reduction model in the signal noise reduction module includes the following specific steps: S11, obtaining historical real signal information, historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by a signal receiving module, constructing a deep learning neural network model whose input is historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by a signal receiving module, and whose output is real signal information; S12, dividing the extracted historical real signal information, historical electric field noise frequency and intensity data, historical magnetic field noise frequency and intensity data, and signal information received by the signal receiving module into 70% parameter training sets and 30% parameter test sets; inputting 70% parameter training sets into the deep learning neural network model for training to obtain an initial deep learning neural network model; using 30% parameter test sets to test the initial deep learning neural network model, and outputting an initial deep learning neural network model that meets the highest judgment accuracy of the preset real signal information as the deep learning neural network model, wherein the output strategy formula of a specific neuron in the deep learning neural network model is: ,in is the output of n+1 layer s-term neurons, is the connection weight between neuron i in the nth layer and neuron s in the n+1th layer, represents the output of neuron i in layer n, represents the bias of the linear relationship between neuron i in the nth layer and neuron s in the n+1th layer, Represents the Sigmoid activation function; S13: Obtain the transmission signal after noise reduction.

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