A method, apparatus, and medium for automatic testing of sensitivity of a wideband radio frequency system

Abstract

The application discloses a kind of broadband radio frequency system's sensitivity automatic test method, equipment and medium, it is related to broadband radio frequency test technical field, including, starting test procedure, coordinate multiple calibration equipment and to be measured equipment in wireless networking in same test time slot, through air interface synchronous sampling the spectrum data of each calibration equipment and processing into noise distribution parameter set;According to the predicted value of receiving power, the output power of vector signal generator is dynamically adjusted and the change of bit error rate is synchronously monitored, until the difference of bit error rate and preset target bit error rate threshold meets convergence condition, the receiving power value when converging is recorded as sensitivity test result and test report is generated.The application can realize accurate perception and active compensation to test environment space noise by generating high-precision dynamic noise map and calculating compensation transmission power calibration value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wideband radio frequency test, in particular to a method and device for automatically testing sensitivity of a wideband radio frequency system and a medium. BACKGROUND

[0002] The test of wireless communication device receiving sensitivity is a key link to ensure communication quality and reliability. The traditional test method usually uses a vector signal generator and a spectrum analyzer in an anechoic chamber or a static environment, adjusts the transmitting power point by point manually, and determines the sensitivity threshold by observing the bit error rate change. With the development of the fifth generation mobile communication technology (5G) to millimeter wave and large-scale antenna array, the requirements for test efficiency and accuracy are increasing, and automatic test has gradually become an industry standard configuration.

[0003] However, most of the existing automatic test schemes are based on the assumption of ideal static noise environment, and there is a difference between the test environment construction and the complex electromagnetic environment in the real commercial scene. Especially in the dynamic networking test scene of multiple devices and multiple frequency bands coexisting, the background noise is time-varying and the spatial distribution is uneven, and the traditional method is difficult to realize real-time perception and compensation of the interference of spatial noise fluctuation on the test result, which leads to the decrease of sensitivity test accuracy, the increase of test cycle times and the low overall efficiency, and cannot meet the demand of modern radio frequency fast and accurate test. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a method for automatically testing sensitivity of a wideband radio frequency system to solve the problems of insufficient test accuracy and low efficiency of radio frequency receiving sensitivity test in complex dynamic noise environment.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for automatically testing the sensitivity of a wideband radio frequency system, which comprises: starting a test process; coordinating multiple calibration devices and a device under test in a wireless network to synchronously sample the spectrum data of the calibration devices and process the spectrum data into a set of noise distribution parameters in the same test time slot; uploading the set of noise distribution parameters to a master control end for fusion to generate a high-precision dynamic noise map; extracting the noise power value of the location of the device under test from the high-precision dynamic noise map and obtaining a compensation transmit power calibration value; adjusting the output power of a vector signal generator according to the compensation transmit power calibration value to generate a wideband test signal and transmit the wideband test signal to the device under test, and reading the internal state information of the receiving link of the device under test in real time; inputting the internal state information into a machine learning model to output a predicted value of the receiving power required to reach a target bit error rate under the current test condition; dynamically adjusting the output power of the vector signal generator according to the predicted value of the receiving power and synchronously monitoring the change of the bit error rate until the difference between the bit error rate and a preset target bit error rate threshold meets a convergence condition, recording the receiving power value at the time of convergence as the sensitivity test result and generating a test report.

[0008] As a preferred scheme of the method for automatically testing the sensitivity of the wideband radio frequency system, the step of synchronously sampling the spectrum data of the calibration devices and processing the spectrum data into a set of noise distribution parameters comprises the following specific steps:

[0009] The master control end starts the test process, collects the spatial position coordinates of the calibration devices, and establishes nanosecond-level time synchronization with the calibration devices and the device under test through a precise time protocol to allocate the same sampling window of the same time slot;

[0010] The calibration devices perform radio frequency sampling using the local oscillator frequency and the sampling rate in the same sampling window of the same time slot to obtain sampling data;

[0011] The sampling data is windowed and processed to generate the spectrum data of the calibration devices through fast Fourier transform;

[0012] The spectrum data of the calibration devices are fitted with Gaussian distribution to extract noise mean parameters and noise variance parameters;

[0013] The spatial position coordinates of the calibration devices, the noise mean parameters and the noise variance parameters are combined to generate a set of noise distribution parameters.

[0014] As a preferred scheme of the method for automatically testing the sensitivity of the wideband radio frequency system, the step of generating a high-precision dynamic noise map comprises the following specific steps:

[0015] The master control end receives the set of noise distribution parameters and performs standardization preprocessing to generate a set of standardized noise distribution parameters;

[0016] Based on the standardized noise distribution parameter set, the Kriging spatial interpolation algorithm is used to calculate the noise mean estimation value and noise variance estimation value of any position in the test area, and initial noise distribution data is formed;

[0017] The initial noise distribution data is input into the time Kalman filter for time dimension filtering and optimization, and the optimized noise distribution data is generated.

[0018] The optimized noise distribution data is subjected to multi-resolution fusion processing to generate a high-precision dynamic noise map.

[0019] As a preferred scheme of the method for automatically testing the sensitivity of the wideband radio frequency system, wherein the steps for calculating the compensation transmit power calibration value are as follows,

[0020] The spatial position coordinates of the device under test are collected, the high-precision dynamic noise map is queried, and the noise mean estimation value and noise variance estimation value of the position where the device under test is located are obtained.

[0021] Based on the noise mean estimation value and noise variance estimation value, the noise power value is calculated using the noise power synthesis algorithm.

[0022] Taking the spatial position coordinates of the device under test as the center, the spatial data points and noise parameters in the preset neighborhood range are extracted from the high-precision dynamic noise map to form a local noise parameter set.

[0023] According to the local noise parameter set, the spatial uncertainty weight factor is calculated, and the noise power value is corrected to obtain an initial compensation power calibration value.

[0024] The amplitude limit and time smoothing processing are performed on the initial compensation power calibration value to obtain the compensation transmit power calibration value.

[0025] As a preferred scheme of the method for automatically testing the sensitivity of the wideband radio frequency system, wherein the internal state information of the receiving link of the device under test is read in real time, and the specific steps are as follows,

[0026] The compensation transmit power calibration value is converted through the SCPI protocol format to generate an instrument control instruction.

[0027] The vector signal generator adjusts the output power according to the instrument control instruction, and generates a wideband test signal through direct digital frequency synthesis and I / Q modulation algorithm.

[0028] The device under test demodulates the wideband test signal through the internal receiving link to generate internal state information, which is read in real time through the diagnostic interface.

[0029] As a preferred scheme of the method for automatically testing the sensitivity of the wideband radio frequency system, wherein: the internal state information comprises an automatic gain control voltage, a channel estimation coefficient and an equalizer tap weight.

[0030] As a preferred scheme of the method for automatically testing the sensitivity of the wideband radio frequency system, wherein: the output corresponds to a predicted value of the received power required to reach a target bit error rate under current test conditions, and the specific steps are as follows,

[0031] The automatic gain control voltage, the channel estimation coefficient and the equalizer tap weight in the internal state information are combined with historical state data to construct a spatio-temporal joint feature tensor;

[0032] The spatio-temporal joint feature tensor is input into a machine learning model to extract a deep spatio-temporal feature vector;

[0033] According to the deep spatio-temporal feature vector, a posterior probability distribution of the predicted value of the received power is calculated through Bayesian optimization reasoning, and the predicted value of the received power required to reach the target bit error rate is output.

[0034] As a preferred scheme of the method for automatically testing the sensitivity of the wideband radio frequency system, wherein: the received power value at the time of convergence is recorded as the sensitivity test result and a test report is generated, and the specific steps are as follows,

[0035] The initial output power of the vector signal generator is set based on the predicted value of the received power;

[0036] The bit error rate data is monitored in real time, and a power control strategy is dynamically generated and the output power of the vector signal generator is adjusted according to the difference between the bit error rate data and the preset target bit error rate threshold;

[0037] According to the adjusted output power, the bit error rate change of the device under test is monitored synchronously, and the bit error rate difference between the new bit error rate data of the device under test and the preset target bit error rate threshold is calculated;

[0038] It is judged whether the bit error rate difference meets the preset convergence determination parameter, and when it meets, the current received power value is recorded as the sensitivity test result;

[0039] According to the sensitivity test result and the entire test process data, a test report is automatically generated.

[0040] In a second aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the method for automatically testing the sensitivity of the wideband radio frequency system according to the first aspect of the present application.

[0041] In a third aspect, the present application provides a computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements any step of the method for automatic sensitivity test of a wideband radio frequency system according to the first aspect of the present application.

[0042] The present application has the advantages that: by generating a high-precision dynamic noise map and calculating a compensation transmit power calibration value, accurate perception and active compensation of the test environment spatial noise can be achieved. By fusing multi-source noise data to construct a visual electromagnetic environment model, the power pre-adjustment of the transmit signal has environmental perception capability, so that the receive power of the device under test can be quickly approximated to the vicinity of the real sensitivity threshold in the initial stage of the test, reducing the iteration number and time required for fine power adjustment. The test error caused by environmental noise fluctuation is effectively suppressed, and the accuracy and reliability of the sensitivity test result are improved, laying a solid foundation for the acceleration convergence of the entire automated test process and improving the test efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0044] Fig. 1 The flowchart for the method for automatic sensitivity test of a wideband radio frequency system.

[0045] Fig. 2 The flowchart for generating a set of noise distribution parameters.

[0046] Fig. 3 The flowchart for generating a high-precision dynamic noise map.

[0047] Fig. 4 The flowchart for sensitivity test closed-loop control. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0049] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0050] Second, the "one embodiment" or "an embodiment" referred to herein can include a particular feature, structure, or characteristic. The various embodiments appearing at different places in this specification can not all refer to the same embodiment or to the same implementations or alternatives of the present application.

[0051] Referring to Figs. 1-4 For one embodiment of the present application, the embodiment provides a method for automatically testing the sensitivity of a wideband radio frequency system, comprising the following steps:

[0052] S1, start the test process, coordinate multiple calibration devices and devices to be tested in the wireless networking in the same test time slot, and synchronize the sampling of the spectrum data of each calibration device through the air interface and process it into a set of noise distribution parameters.

[0053] S1.1: the master control end starts the test process, collects the spatial position coordinates of each calibration device, and establishes nanosecond-level time synchronization with each calibration device and the device to be tested through the precision time protocol, and distributes the sampling window of the same time slot;

[0054] Specifically, the master control end sends a start command, initializes the test process, triggers each device to enter the preparation state, obtains the spatial position coordinates of each calibration device through indoor positioning, records the three-dimensional coordinate values of each calibration device, including the east coordinate, the north coordinate and the height coordinate;

[0055] Use the precision time protocol to synchronize the time with all calibration devices and devices to be tested, obtain the clock offset and delay by exchanging timing messages, adjust the local clock of each device, and achieve nanosecond-level time synchronization;

[0056] According to the synchronized time, a fixed same time slot is allocated as a sampling window, the start time and duration of the sampling window are specified, and each device is notified.

[0057] S1.2: each calibration device uses the local oscillator frequency and the sampling rate to perform radio frequency sampling in the sampling window of the same time slot to obtain sampling data;

[0058] Specifically, after each calibration device receives the sampling window instruction from the master control end, it starts the radio frequency receiving chain at the specified start time. The calibration device uses a pre-set local oscillator frequency and sampling rate to perform radio frequency sampling. The local oscillator frequency is generated by a local oscillator, and the sampling rate is set by an analog-to-digital converter. During the radio frequency sampling process, the in-phase and quadrature components are collected, analog radio frequency signals are generated, and the analog radio frequency signals are converted into sampling data through the local oscillator down-conversion and analog-to-digital converter sampling.

[0059] It should be noted that the preset local oscillator frequency determines the center frequency according to the radio frequency band range supported by the to-be-tested device, and the example value is 2.4GHz, which ensures that the sampling covers the working range of the target frequency band; the preset sampling rate needs to meet the Nyquist sampling theorem, that is, the sampling rate is greater than or equal to 2 times the signal bandwidth. Example: if the test signal bandwidth is 100MHz, the minimum sampling rate needs to be 200MSa / s to avoid spectral aliasing and signal distortion.

[0060] S1.3: Window processing is performed on the sampling data, and frequency spectrum data of each calibration device is generated by fast Fourier transform;

[0061] Specifically, a window function is applied to the sampling data, the window function is selected from a Hanning window, a Hamming window or a Blackman window, and fast Fourier transform is performed on the windowed sampling data. The number of points of the fast Fourier transform matches the length of the sampling data, and the fast Fourier transform directly generates a frequency domain complex spectrum. The frequency domain complex spectrum is converted into an amplitude spectrum, and the amplitude spectrum is used as the frequency spectrum data of each calibration device.

[0062] S1.4: Gaussian distribution fitting is performed on the frequency spectrum data of each calibration device to extract noise mean parameter and noise variance parameter;

[0063] Specifically, each frequency point amplitude value in the frequency spectrum data of each calibration device is converted into a corresponding frequency spectrum data power value, the power value of the frequency spectrum data of each calibration device is used as a sample, and a Gaussian distribution fitting is performed by using a maximum likelihood estimation method. The noise mean parameter and the noise variance parameter representing the noise power distribution are directly obtained through the fitting process. The noise mean parameter represents the concentration trend of the frequency spectrum data power value, and the noise variance parameter represents the dispersion degree of the frequency spectrum data power value.

[0064] S1.5: The spatial position coordinates, noise mean parameters and noise variance parameters of each calibration device are combined to generate a noise distribution parameter set.

[0065] Specifically, each calibration device uses its own spatial position coordinates, noise mean parameters and noise variance parameters to pack into a data packet. The data packet of each calibration device is transmitted to the host end by wireless or wired communication. The host end receives the data packets of all calibration devices, and combines the data packets of all calibration devices into a noise distribution parameter set.

[0066] S2, upload the noise distribution parameter set to the host end for fusion to generate a high-precision dynamic noise map.

[0067] S2.1: The host end receives the noise distribution parameter set and performs standardization preprocessing to generate a standardized noise distribution parameter set;

[0068] Specifically, the master end receives the noise distribution parameter set through the communication link, determines the arithmetic mean of all noise mean parameters as the mean reference benchmark, determines the standard deviation of all noise mean parameters as the mean dispersion measure, the difference between the noise mean parameter of each calibration device and the mean reference benchmark as the intermediate mean reference benchmark, and the ratio between the intermediate mean reference benchmark and the mean dispersion measure as the standardized noise mean parameter;

[0069] determines the arithmetic mean of all variance mean parameters as the variance reference benchmark, determines the standard deviation of all noise variance parameters as the variance dispersion measure, the difference between the noise variance parameter of each calibration device and the variance reference benchmark as the intermediate variance reference benchmark, and the ratio between the intermediate variance reference benchmark and the variance dispersion measure as the standardized noise variance parameter;

[0070] The spatial position coordinates remain unchanged, and the standardized noise mean parameters, the standardized noise variance parameters, and the spatial position coordinates of all calibration devices are combined to form the standardized noise distribution parameter set.

[0071] S2.2: Based on the standardized noise distribution parameter set, the Kriging spatial interpolation algorithm is used to calculate the noise mean estimate value and the noise variance estimate value at any position in the test area to form the initial noise distribution data.

[0072] Specifically, interpolation is performed on all grid points in the test area, and the noise mean estimate value and the noise variance estimate value in the test area after interpolation are integrated to form the initial noise distribution data.

[0073] The Kriging spatial interpolation algorithm is used to calculate the noise mean estimate value and the noise variance estimate value at any position in the test area, and the expression is:

[0074] ;

[0075] ;

[0076] In the formula, represents the noise mean estimate value, represents the target position coordinates, represents the total number of calibration devices, represents the calibration device index, represents the interpolation weight of the th calibration device for the target position coordinates, represents the standardized noise mean parameter, represents the position coordinates of the th calibration device, represents the noise variance estimate value, represents the standardized noise variance parameter.

[0077] It should be noted that, , and are dimensionless, the output and are dimensionless, keeping the dimension consistent.

[0078] S2.3: input the initial noise distribution data into the time Kalman filter for time dimension filtering and optimization, and generate the optimized noise distribution data;

[0079] Specifically, the initial noise distribution data is configured as the state vector of the time Kalman filter, and the state vector is composed of the noise mean estimate value and the noise variance estimate value of each grid point arranged in order;

[0080] The time Kalman filter performs state prediction operation and state update operation recursively according to discrete time steps, the state prediction operation is based on the previous time state vector and the state transition matrix to generate the state prediction value at the current time, and the state update operation fuses the state prediction value at the current time with the corresponding value in the initial noise distribution data at the current time to generate the posterior state estimate value, and the posterior state estimate value of all grid points constitutes the optimized noise distribution data.

[0081] S2.4: perform multi-resolution fusion processing on the optimized noise distribution data to generate a high-precision dynamic noise map.

[0082] Specifically, the optimized noise distribution data is divided according to the spatial resolution level, the original grid point noise mean estimate value and noise variance estimate value are retained to form the base layer, the base layer grid points are merged according to adjacent rows and columns to form the middle layer with halved resolution, the noise mean estimate value of the middle layer is obtained by averaging the original values of the merged regions, and the noise variance estimate value is obtained by selecting the maximum value of the original values of the merged regions;

[0083] The middle layer is merged according to adjacent rows and columns to form a high layer with halved resolution, a tower fusion algorithm is used to perform hierarchical fusion, the high layer is expanded to the middle layer resolution to form a virtual middle layer by bilinear interpolation, the middle layer and the virtual middle layer are superimposed at the corresponding grid points to form a fusion middle layer, the fusion middle layer is expanded to the base layer resolution to form a virtual base layer by bilinear interpolation, and the base layer and the virtual base layer are superimposed at the corresponding grid points to form a fusion base layer, which is used as a high-precision dynamic noise map.

[0084] S3, extract the noise power value of the position of the to-be-tested device from the high-precision dynamic noise map, and obtain the compensation transmit power calibration value.

[0085] S3.1: collect the spatial position coordinates of the to-be-tested device, query the high-precision dynamic noise map, and obtain the noise mean estimate value and the noise variance estimate value of the position of the to-be-tested device;

[0086] Specifically, the spatial position coordinates of the to-be-tested device are obtained through indoor positioning, the coordinate system of the spatial position coordinates of the to-be-tested device is completely consistent with the coordinate system used by the calibration device, the spatial position coordinates of the to-be-tested device are matched with the grid point positions of the high-precision dynamic noise map, the grid point closest to the spatial position coordinates of the to-be-tested device in the high-precision dynamic noise map is located, and the noise mean estimate value and the noise variance estimate value are directly read from the closest grid point.

[0087] S3.2: Based on the noise mean estimate value and the noise variance estimate value, the noise power value is calculated by using a noise power synthesis algorithm.

[0088]

[0089] In the formula, the noise power value is represented, a physical unit conversion fixed constant is represented, a 1 / 10 scale of the noise mean estimate value is represented, is converted into a milliwatt unit, a fluctuation contribution adjustment coefficient is represented, a 1 / 10 scale of the noise variance estimate value is represented, is converted into a milliwatt unit.

[0090] It should be noted that the fluctuation contribution adjustment coefficient is set according to the noise fluctuation characteristics of the test scene, and an example value is 0.3.

[0091] S3.3: Taking the spatial position coordinates of the to-be-tested device as the center, spatial data points and noise parameters in a preset neighborhood range are extracted from the high-precision dynamic noise map to form a local noise parameter set.

[0092] Specifically, the collected spatial position coordinates of the to-be-tested device are taken as the center of a circle, a circular area is defined by a preset neighborhood range radius, all grid points of the high-precision dynamic noise map are traversed, the Euclidean distance between the spatial position coordinates of each grid point and the spatial position coordinates of the to-be-tested device is calculated by using the Euclidean distance formula, the grid points whose Euclidean distances do not exceed the preset neighborhood range radius are screened, the spatial position coordinates, the noise mean estimate value and the noise variance estimate value of the grid points whose Euclidean distances do not exceed the preset neighborhood range radius are read from the high-precision dynamic noise map, and the spatial position coordinates, the noise mean estimate value and the noise variance estimate value of each grid point constitute a grid point data packet. The grid point data packets of all screened grid points are combined to form a local noise parameter set.

[0093] ​​​It should be noted that the preset neighborhood range refers to a spherical space region with the spatial position coordinate of the to-be-tested device as the center and a preset neighborhood range radius as the radius, the preset neighborhood range is set based on the spatial resolution of the high-precision dynamic noise map, and the preset neighborhood range radius is set based on the physical size of the to-be-tested device and the spatial correlation of the test scene. For example, the value is 0.5 meters to 2 meters.

[0094] S3.4: According to the local noise parameter set, the spatial uncertainty weight factor is calculated, and the noise power value is corrected to obtain the initial compensation power calibration value.

[0095] Specifically, the noise variance estimation values of all grid points in the local noise parameter set are weighted and averaged by the spatial uncertainty weight factor to generate a weighted average noise variance. The product of the preset spatial uncertainty correction coefficient and the weighted average noise variance is used as the spatial uncertainty correction amount, and the spatial uncertainty correction amount is added to the noise power value to generate the initial compensation power calibration value.

[0096] It should be noted that the preset spatial uncertainty correction coefficient is set according to the spatial interpolation error characteristics of the high-precision dynamic noise map and the sensitivity of the to-be-tested device receiving link. For example, the value is 0.1.

[0097] According to the local noise parameter set, the spatial uncertainty weight factor is calculated, and the expression is:

[0098] ;

[0099] In the formula, denotes the spatial uncertainty weight factor of the th neighborhood grid point, denotes the neighborhood grid point index, denotes the spatial position coordinate of the to-be-tested device, denotes the spatial position coordinate of the th neighborhood grid point, denotes a small positive number.

[0100] It should be noted that , and are dimensionless, and the final output is dimensionless, keeping the dimension consistent.

[0101] S3.5: The amplitude limit and timing smoothing processing are performed on the initial compensation power calibration value to obtain the compensation transmit power calibration value.

[0102] Specifically, the minimum compensation power calibration value threshold and the maximum compensation power calibration value threshold are set, and the relationship between the initial compensation power calibration value and the minimum compensation power calibration value threshold and the maximum compensation power calibration value threshold is compared.

[0103] If the initial compensation power calibration value is less than the minimum compensation power calibration value threshold, the initial compensation power calibration value is set to the minimum compensation power calibration value threshold, if the initial compensation power calibration value is greater than the maximum compensation power calibration value threshold, the initial compensation power calibration value is set to the maximum compensation power calibration value threshold, otherwise the initial compensation power calibration value remains unchanged;

[0104] The time sequence smoothing is performed on the amplitude limiting processed compensation power calibration value, the previous time compensation transmit power calibration value is obtained, the current amplitude limiting processed compensation power calibration value is weighted and fused with the previous time compensation transmit power calibration value, the preset smoothing factor is used as the weighted fusion weight, and the compensation transmit power calibration value is generated.

[0105] It should be noted that the minimum compensation power calibration value threshold is set based on the minimum detectable power of the to-be-tested device receiving link, and an example value is -90, which is the lower limit of the typical receiver sensitivity, to ensure that the signal is higher than the noise floor; the maximum compensation power calibration value threshold is set based on the maximum input power tolerance of the to-be-tested device receiving link, and an example value is -30, which is the maximum input power limit value (such as the saturation point of LNA / ADC) of a common radio frequency front end, to protect the radio frequency front end from saturation; the preset smoothing factor is set based on the time-varying fluctuation period of the test environment noise, and an example value is 0.2, under the typical noise fluctuation period (such as hundreds of milliseconds), the weight 0.2 can effectively suppress short-term fluctuations (such as burst interference), while retaining long-term trends (such as device temperature drift), and the response speed and stability are balanced.

[0106] S4, the output power of the vector signal generator is adjusted according to the compensation transmit power calibration value, a wideband test signal is generated and transmitted to the to-be-tested device, and the internal state information of the to-be-tested device receiving link is read in real time.

[0107] S4.1: The compensation transmit power calibration value is converted into an instrument control instruction through an SCPI protocol format;

[0108] Specifically, the power setting command word of the SCPI protocol is used as the instruction prefix, the power unit symbol of the SCPI protocol is used as the instruction suffix, the numerical part of the compensation transmit power calibration value, the power setting command word and the power unit symbol are combined into a complete instruction string, and the complete instruction string is used as the instrument control instruction.

[0109] S4.2: The vector signal generator adjusts the output power according to the instrument control instruction, and generates a wideband test signal through direct digital frequency synthesis and I / Q modulation algorithm;

[0110] Specifically, the vector signal generator receives and parses the instrument control instruction, adjusts the gain of the internal power amplifier according to the power setting value in the instrument control instruction, generates a phase sequence by stepping the frequency control word through the phase accumulator of the direct digital frequency synthesis, performs real-time addressing on the phase sequence through a preset sine / cosine lookup table, generates an in-phase component and a quadrature component, modulates the in-phase component and the quadrature component to a radio frequency carrier through an I / Q modulation algorithm, mixes the in-phase component with a local oscillator cosine signal, mixes the quadrature component with a local oscillator sine signal, adds the mixing results to generate a radio frequency signal, and outputs a wideband test signal after the radio frequency signal is amplified by a power amplifier.

[0111] It should be noted that the preset sine / cosine lookup table is a discrete sine / cosine function value table (such as a quantization value mapping 0-360° phase to 4096 points) pre-stored in the ROM, and the phase sequence is used as an address of the lookup table to directly index the corresponding in-phase component and quadrature component in the sine / cosine lookup table.

[0112] S4.3: The device under test demodulates the wideband test signal through the internal receiving link, generates internal state information, and reads in real time through the diagnostic interface.

[0113] Specifically, the internal receiving link of the device under test receives the wideband test signal, adjusts the signal amplitude of the wideband test signal through an automatic gain control circuit, generates a radio frequency signal, inputs the radio frequency signal into a mixer, generates an intermediate frequency signal through frequency down-conversion, suppresses high-frequency components through an anti-aliasing filter to generate a filtered intermediate frequency signal, inputs the filtered intermediate frequency signal into an analog-to-digital converter for sampling and quantization coding at a preset sampling rate to generate a digital intermediate frequency signal sequence, generates a baseband in-phase signal and a baseband quadrature signal through quadrature demodulation of the digital intermediate frequency signal sequence, eliminates phase offset through carrier synchronization and channel response recovery to generate a synchronized baseband in-phase signal and a synchronized baseband quadrature signal, and performs signal demodulation on the synchronized baseband in-phase signal and the synchronized baseband quadrature signal to generate internal state information, which is continuously read by the device under test through a hardware debugging protocol interface at the same time interval.

[0114] S4.4: The internal state information includes an automatic gain control voltage, a channel estimation coefficient, and an equalizer tap weight.

[0115] Specifically, in the signal demodulation process, the automatic gain control circuit generates an automatic gain control voltage, the channel estimation operation generates a channel estimation coefficient, and the adaptive equalizer generates an equalizer tap weight.

[0116] It should be noted that the automatic gain control voltage refers to an analog voltage value output by the automatic gain control circuit, which is used to adjust the amplification multiple of the variable gain amplifier to stabilize the received signal amplitude.

[0117] Channel estimation coefficient is a complex array representing the frequency response characteristics of the wireless channel, generated by least square estimation algorithm, used to compensate for signal phase and amplitude distortion;

[0118] Equalizer tap weight refers to the real coefficient sequence of the finite impulse response filter in the time domain equalizer, used to suppress inter-symbol interference, and the length is determined by the device specification.

[0119] S5, input the internal state information into the machine learning model, and output the predicted value of the required receiving power to achieve the target bit error rate under the current test condition.

[0120] S5.1: Combine the automatic gain control voltage, channel estimation coefficient and equalizer tap weight in the internal state information with the historical state data to construct a spatio-temporal joint feature tensor;

[0121] Specifically, the automatic gain control voltage is expanded into a fixed-length vector, the channel estimation coefficient is decomposed into a real part vector and an imaginary part vector, and the zero operation is performed on the channel estimation coefficient and the equalizer tap weight with different lengths to align the length, arranged in time frame order, each time frame contains three channel data, channel one is the expanded automatic gain control voltage vector, channel two is the real part vector of the channel estimation coefficient, channel three is the imaginary part vector of the channel estimation coefficient, and channel four is the equalizer tap weight vector. All time frames are stacked to form a spatio-temporal joint feature tensor.

[0122] S5.2: Input the spatio-temporal joint feature tensor into the machine learning model to extract deep spatio-temporal feature vectors;

[0123] It should be noted that the pre-training process of the machine learning model is as follows: collect historical spatio-temporal joint feature tensors as samples and corresponding historical receiving power measured values, construct a three-dimensional convolutional neural network architecture, including an alternating sequence of three-dimensional convolutional layers, nonlinear activation layers and three-dimensional maximum pooling layers, and connect a fully connected layer at the end. The loss function is defined as the mean square error of the predicted receiving power and the historical receiving power measured value, the network parameters are iteratively optimized using the gradient descent algorithm, the gradient of the loss function with respect to the network parameters is calculated, the network parameters are updated in the opposite direction of the gradient, and the iteration process is repeated until the loss function converges. Save the network parameters to obtain the pre-trained machine learning model.

[0124] Specifically, the spatio-temporal joint feature tensor is input into the machine learning model, the machine learning model performs a three-dimensional convolution operation on the spatio-temporal joint feature tensor using a three-dimensional convolution kernel, the three-dimensional convolution result is converted by a nonlinear activation function to generate a primary feature map, the primary feature map is reduced in spatial size by a three-dimensional maximum pooling operation, and the three-dimensional convolution operation, the nonlinear activation function conversion and the three-dimensional maximum pooling operation are repeated until the time dimension is reduced to a single frame and the spatial size reaches a predefined basic size, a three-dimensional feature structure is generated, the three-dimensional feature structure is flattened into a single-dimensional high-dimensional feature sequence, and the single-dimensional high-dimensional feature sequence is linearly transformed by a fully connected layer to generate a deep spatio-temporal feature vector.

[0125] It should be noted that the predefined basic size is set based on the network architecture of the machine learning model, and an example value is: 256 channels x 4 height x 4 width.

[0126] S5.3: According to the deep spatio-temporal feature vector, the posterior probability distribution of the received power prediction value is calculated by Bayesian optimization inference, and the received power prediction value required to reach the target bit error rate is output.

[0127] Specifically, the deep spatio-temporal feature vector is used to query the historical data set of Bayesian optimization inference, the historical data set contains historical deep spatio-temporal feature vectors and corresponding historical received power measured values, the similarity between the deep spatio-temporal feature vector and the historical deep spatio-temporal feature vector is compared using a radial basis function kernel, and the posterior probability distribution mean value is generated by weighted fusion of the historical received power measured values according to the similarity; the historical deep spatio-temporal feature vector and the historical received power measured value are used as autocorrelation values, and the similarity between the deep spatio-temporal feature vector and the historical deep spatio-temporal feature vector is used as cross-correlation values, and the posterior probability distribution variance is generated using the autocorrelation values and the cross-correlation values, the posterior probability distribution is subject to a normal distribution with the posterior probability distribution mean value as the central tendency and the posterior probability distribution variance as the uncertainty measure, and the expected improvement maximum point of the posterior probability distribution is solved with the target bit error rate as the constraint condition, and the received power value corresponding to the expected improvement maximum point is taken as the received power prediction value required to reach the target bit error rate.

[0128] ;

[0129] In the formula, P(x) represents a probability distribution function, P(x) represents a received power prediction value, P(x) represents a deep spatio-temporal feature vector, P(x) represents a posterior probability distribution of a received power prediction value, P(x) represents a posterior probability distribution subject to a normal distribution, P(x) represents a central tendency of a posterior probability distribution, P(x) represents an uncertainty measure of a posterior probability distribution.

[0130] S6, dynamically adjusting the output power of the vector signal generator based on the received power prediction value and synchronously monitoring the bit error rate change until the difference between the bit error rate and the preset target bit error rate threshold meets the convergence condition, recording the received power value at the time of convergence as the sensitivity test result and generating a test report.

[0131] S6.1: setting the initial output power of the vector signal generator based on the received power prediction value;

[0132] Specifically, the received power prediction value is taken as the power setting value, and the power setting value is taken as the initial output power of the vector signal generator.

[0133] S6.2: Real-time monitoring of bit error rate data, dynamically generating power control strategy and adjusting the output power of the vector signal generator according to the difference between the bit error rate data and the preset target bit error rate threshold;

[0134] Specifically, the to-be-tested device obtains real-time bit error rate data by counting the number of error bits and the total number of transmission bits per unit time through the physical layer baseband, and takes the difference between the bit error rate data and the preset target bit error rate threshold as the bit error rate difference absolute value.

[0135] If the bit error rate difference absolute value is greater than the preset first threshold, a fixed step power adjustment strategy is generated, if the bit error rate difference absolute value is between the preset first threshold and the preset second threshold, a proportional adjustment power adjustment strategy is generated, and if the bit error rate difference absolute value is less than the preset second threshold, a fine-tuning power adjustment strategy is generated. After each strategy is generated, a new power instruction string is generated in accordance with the SCPI protocol format, and the vector signal generator executes the new power instruction string to adjust the output power.

[0136] It should be noted that the preset target bit error rate threshold is set based on the communication standard specification of the to-be-tested device, and an example value is 0.000001. The value 0.000001 is the bit error rate threshold of typical high-reliability communication (such as WiFi 6 / 5G) (such as the theoretical value when SNR≥15dB under QPSK modulation), and a value lower than 0.000001 will affect the data transmission integrity (such as video stream stuttering, TCP retransmission surge); the preset first threshold is set based on the order of magnitude of the target bit error rate, and an example value is 0.001. When the bit error rate deteriorates to 0.001 (such as SNR<8dB), it is in a near-outage state (BER sharp rise curve inflection point), and the power needs to be adjusted significantly (fixed step) to quickly escape the dangerous area; the preset second threshold is set based on the convergence accuracy requirement, and an example value is 0.00001. If the second threshold is too small (such as <0.000001), the power fine-tuning is easy to be disturbed by noise and cause oscillation, and if it is too large (such as >0.0001), the convergence speed is slow.

[0137] Fixed step power adjustment strategy refers to the output power of the vector signal generator is adjusted by a fixed amount (such as ±1 decibel milliwatt);

[0138] Proportional adjustment power adjustment strategy refers to the output power of the vector signal generator is adjusted by a fixed amount (such as ±1 decibel milliwatt);

[0139] Fine adjustment power adjustment strategy refers to the output power of the vector signal generator is adjusted by a small amount (such as ±0.1 decibel milliwatt).

[0140] S6.3: According to the adjusted output power, the bit error rate change of the device under test is monitored synchronously, and the bit error rate difference between the new bit error rate data of the device under test and the preset target bit error rate threshold is calculated;

[0141] Specifically, after the vector signal generator completes the output power adjustment, the device under test re-counts the number of error bits and the total number of transmission bits per unit time after a preset stabilization time, generates a new real-time bit error rate, and calculates the difference between the new real-time bit error rate and the preset target bit error rate threshold as the bit error rate difference.

[0142] It should be noted that the preset stabilization time is set based on the signal locking time of the receiving link of the device under test and the minimum frame synchronization time required by the communication protocol, and the example value is 15 milliseconds.

[0143] S6.4: Determine whether the bit error rate difference meets the preset convergence determination parameter, and record the current received power value as the sensitivity test result when it meets the preset convergence determination parameter;

[0144] It should be noted that the preset convergence determination parameter includes a convergence threshold and a minimum stable time length; the convergence threshold is set based on the allowable fluctuation range of the target bit error rate, and the example value is: when the target bit error rate is 10 -6 , take 10 -7 , and the target bit error rate statistics need to meet the law of large numbers. When the target bit error rate is 10 -6 , at least 10 -7 bits of transmission are required to obtain effective statistics; the minimum stable time length is set based on the minimum effective time window of the physical layer error statistics of the device under test, and the example value is: 100 milliseconds for Wi-Fi 6E devices, and the PPDU transmission and block confirmation mechanism of Wi-Fi 6E requires ≥100ms to complete a complete frame exchange.

[0145] Specifically, the bit error rate difference is compared with the convergence threshold in the preset convergence determination parameter, and whether the duration that the bit error rate difference is less than the convergence threshold exceeds the preset minimum stable time length is monitored. Only when the bit error rate difference is less than the convergence threshold and the duration is greater than the minimum stable time length, the current output power value of the vector signal generator is read, and the current output power value is recorded as the sensitivity test result.

[0146] S6.5: According to the sensitivity test results and the data of the entire test process, automatically generate a test report.

[0147] Specifically, the sensitivity test results, the noise distribution parameter set, the key frame data of the high-precision dynamic noise map, the power adjustment time record, the bit error rate change, the to-be-tested device model and the test frequency band bandwidth parameter, the compensation transmission power calibration value time sequence, and the received power prediction value sequence are structured and organized according to a pre-defined report template, the result chapter is filled with the sensitivity test result values, the noise environment chapter is embedded with the noise distribution parameter set and the high-precision dynamic noise map, the power control chapter is inserted with the power adjustment and bit error rate change comparison chart, and the device parameter chapter lists the device model and the frequency band bandwidth; and the test report is generated.

[0148] It should be noted that the pre-defined report template structured organization refers to classified filling of data according to a fixed chapter framework.

[0149] The embodiment also provides a computer device suitable for the sensitivity automatic test method of the wideband radio frequency system, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the sensitivity automatic test method of the wideband radio frequency system proposed in the above embodiment.

[0150] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (near field communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball, or touchpad arranged on the shell of the computer device. In addition, the input device can be an external keyboard, touchpad, or mouse, etc.

[0151] The embodiment also provides a storage medium on which a computer program is stored, the program being executed by a processor to implement the method for automatically testing sensitivity of a wideband radio frequency system as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0152] To sum up, the application can realize accurate perception and active compensation of the spatial noise of a test environment by generating a high-precision dynamic noise map and calculating a compensation transmit power calibration value. By fusing multi-source noise data to construct a visual electromagnetic environment model, the power pre-adjustment of a transmit signal has environment perception capability, so that the receive power of a device under test can be quickly approximated to the vicinity of a real sensitivity threshold in the initial stage of testing, thereby reducing the iteration number and time required for fine power adjustment. The test error caused by environmental noise fluctuation is effectively suppressed, and the accuracy and reliability of the sensitivity test result are improved, which lays a solid foundation for the acceleration and convergence of the entire automated test process and improves the test efficiency.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application but not limit the application. Although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, and all of them should be covered in the scope of the claims of the application.

Claims

1. A method for automatic testing of sensitivity of a wideband radio frequency system, characterized by: The application relates to a wireless network test method and device. The application comprises the following steps: The master end receives the noise distribution parameter set and carries out standardization pretreatment to generate a standardized noise distribution parameter set. Based on the standardized noise distribution parameter set, a Kriging spatial interpolation algorithm is adopted to calculate noise mean estimation values and noise variance estimation values of any position in a test area to form initial noise distribution data. The initial noise distribution data is input into a time series Kalman filter to carry out time dimension filtering and optimization to generate optimized noise distribution data. The optimized noise distribution data is subjected to multi-resolution fusion processing to generate a high-precision dynamic noise map. The noise power value of the position of the device under test is extracted from the high-precision dynamic noise map, and a compensation transmission power calibration value is obtained. According to the compensation transmission power calibration value, the output power of the vector signal generator is adjusted, a wideband test signal is generated and transmitted to the device under test, and the internal state information of the receiving link of the device under test is read in real time. The internal state information is input into a machine learning model, and a receiving power prediction value required to reach a target bit error rate under the current test condition is output. According to the receiving power prediction value, the output power of the vector signal generator is dynamically adjusted, and the bit error rate is monitored synchronously until the difference between the bit error rate and the preset target bit error rate threshold meets the convergence condition. The application comprises the following steps:

2. The method of automatically testing the sensitivity of a wideband radio frequency system of claim 1, wherein: The master end starts the test process, collects the spatial position coordinates of the calibration devices, and establishes nanosecond-level time synchronization with the calibration devices and the device under test through a precise time protocol to distribute the sampling window of the same time slot. The calibration devices carry out radio frequency sampling through local oscillation frequency and sampling rate in the sampling window of the same time slot to obtain sampling data. The sampling data is subjected to window processing, and the frequency spectrum data of the calibration devices are generated through fast Fourier transform. The frequency spectrum data of the calibration devices are subjected to Gaussian distribution fitting to extract noise mean parameters and noise variance parameters. The spatial position coordinates, noise mean parameters and noise variance parameters of the calibration devices are combined to generate a noise distribution parameter set. The application comprises the following steps:

3. The method of automatically testing the sensitivity of a wideband radio frequency system of claim 1, wherein: The spatial position coordinates of the device under test are collected, the high-precision dynamic noise map is queried, and noise mean estimation values and noise variance estimation values of the position of the device under test are obtained. Based on the noise mean estimation values and the noise variance estimation values, a noise power synthesis algorithm is used to calculate the noise power value. The spatial data points and noise parameters in a preset neighborhood range are extracted from the high-precision dynamic noise map with the spatial position coordinates of the device under test as the center to form a local noise parameter set. ​ According to the local noise parameter set, the spatial uncertainty weight factor is calculated, and the noise power value is corrected to obtain an initial compensation power calibration value; The initial compensation power calibration value is subjected to amplitude limiting and timing smoothing processing to obtain a compensation transmit power calibration value.

4. The method of automatically testing the sensitivity of a wideband radio frequency system of claim 1, wherein: The internal state information of the receiving link of the device under test is read in real time, and the specific steps are as follows, The compensation transmit power calibration value is converted through the SCPI protocol format to generate an instrument control instruction; The vector signal generator adjusts the output power according to the instrument control instruction, generates a wideband test signal through direct digital frequency synthesis and I / Q modulation algorithm; The device under test demodulates the wideband test signal through the internal receiving link, generates internal state information, and reads it in real time through the diagnostic interface.

5. The method of automatically testing the sensitivity of a wideband radio frequency system of claim 1, wherein: The internal state information includes automatic gain control voltage, channel estimation coefficient and equalizer tap weight.

6. The method of automatically testing the sensitivity of a wideband radio frequency system of claim 1, wherein: The output corresponds to the predicted value of the receive power required to reach the target bit error rate under the current test condition, and the specific steps are as follows, The automatic gain control voltage, channel estimation coefficient and equalizer tap weight in the internal state information are combined with the historical state data to construct a spatio-temporal joint feature tensor; The spatio-temporal joint feature tensor is input into a machine learning model to extract a deep spatio-temporal feature vector; According to the deep spatio-temporal feature vector, the posterior probability distribution of the receive power prediction value is calculated through Bayesian optimization reasoning, and the predicted value of the receive power required to reach the target bit error rate is output.

7. The method of automatically testing the sensitivity of a wideband radio frequency system of claim 1, wherein: According to the receive power prediction value, the output power of the vector signal generator is dynamically adjusted and the bit error rate is monitored in real time until the difference between the bit error rate and the preset target bit error rate threshold meets the convergence condition, and the receive power value at the time of convergence is recorded as the sensitivity test result and a test report is generated, and the specific steps are as follows, Based on the receive power prediction value, the initial output power of the vector signal generator is set; Real-time monitoring of bit error rate data, dynamic generation of power control strategy and adjustment of the output power of the vector signal generator according to the difference between the bit error rate data and the preset target bit error rate threshold; According to the adjusted output power, the bit error rate change of the device under test is monitored synchronously, and the new bit error rate data of the device under test and the bit error rate difference between the preset target bit error rate threshold are calculated; Determine whether the bit error rate difference meets the preset convergence determination parameter, and record the current receive power value as the sensitivity test result when it meets the requirement; According to the sensitivity test result and the entire test process data, a test report is automatically generated.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: The processor executes the computer program to realize the steps of the sensitivity automatic test method of the wideband radio frequency system of any one of claims 1-7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the sensitivity automatic test method of the wideband radio frequency system of any one of claims 1-7.

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