Signal analyzer adaptive ACLR measurement method and system, and medium

Abstract

The invention provides a self-adaptive ACLR measurement method of a signal analyzer. The method comprises the following steps: acquiring a to-be-measured signal; signal tuning: obtaining the frequency band of the to-be-tested signal, and setting the center frequency to the frequency of the to-be-tested signal; capturing signal features by using a preset signal identification model, and obtaining the signal type of the to-be-detected signal based on the signal features; acquiring measurement parameters of the ACLR under the signal type in a preset parameter library; dynamically adjusting gain distribution of a signal receiving link; acquiring and counteracting the noise of the instrument, and restoring the offset channel state of the signal to be detected; and calculating and outputting a measurement result of the ACLR. On the basis of an intelligent identification algorithm, by extracting features of received signals, matching measurement scenes of different current communication protocols and automatically setting measurement parameters in the scenes, measurement efficiency is improved, gain distribution suitable for receiving links of the signals is adjusted in a self-adaptive mode, it is ensured that a signal analyzer always measures the ACLR in the optimal state, and the measurement accuracy of the ACLR is improved. The measurement efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ACLR measurement, and in particular to a signal analyzer adaptive ACLR measurement method, system and medium. Background Art

[0002] In wireless communication systems, the spectrum and frequency band selection of signals are crucial, as the use of frequency bands directly affects the communication quality and the overall performance of the system. The spectrum shows electromagnetic waves within a specific frequency range, and wireless communication systems usually operate in multiple frequency bands to achieve effective two-way communication. However, this also raises a common problem: adjacent channel interference (ACI). ACI is interference caused by signal leakage or excessive proximity of adjacent frequency bands, which may seriously affect the performance of the system and the reliability of data transmission. In order to evaluate and control this interference, communication experts usually rely on the measurement of adjacent channel leakage ratio (ACLR). ACLR is a key indicator that quantifies the degree of leakage of a signal outside its assigned frequency band, thereby helping to evaluate the interference to adjacent frequency bands.

[0003] In wireless communication systems, adjacent channel leakage ratio (ACLR) is one of the important indicators for evaluating the performance of communication systems. Signal analyzer is the main tool for measuring ACLR, which can accurately measure and analyze various aspects of the spectrum. However, on the one hand, in current broadband signals and multi-carrier systems, there are often multiple frequency components, which are more likely to cause signal attenuation and distortion during the propagation process, making ACLR measurement more complicated. On the other hand, the existing ACLR measurement method relies on the filtering performance and gain allocation of the signal receiving link, but because the filtering performance and gain allocation are relatively fixed and cannot be adjusted adaptively, they cannot be flexibly applied to signals with different bandwidths, powers and spectrum characteristics.

[0004] Traditional ACLR measurement methods generally rely on the filtering performance and gain allocation of the signal receiving link. However, since the gain allocation is relatively fixed and cannot be adjusted adaptively, it cannot be flexibly applied to signals with different bandwidths, powers, and spectral characteristics. When faced with broadband and high dynamic range signals, there are limitations in measurement accuracy and efficiency. With the increasing demand for measurement accuracy, how to perform ACLR measurements efficiently and accurately has become a technical problem that needs to be solved in the field of signal analyzers. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the first object of the present invention is to provide a signal analyzer adaptive ACLR measurement method, comprising:

[0006] Get the signal to be tested;

[0007] Signal tuning, obtaining the frequency band of the signal to be tested, and setting the center frequency to the frequency of the signal to be tested;

[0008] Use the preset signal recognition model to capture signal features, and obtain the signal type of the signal to be tested based on the signal features;

[0009] Obtain the measurement parameters of ACLR under the signal type in the preset parameter library;

[0010] Dynamically adjust the gain allocation of the signal receiving link;

[0011] Collect and offset the instrument's own noise to restore the offset channel state of the signal to be measured;

[0012] Obtain and output the ACLR measurement result.

[0013] Specifically, the signal receiving link gain allocation adjustment method is:

[0014] A search network is established with a preset step within the reference level range, and a reference value of the reference level is obtained from a comparison table based on the input power of the signal, the search path is optimized, and a reference reference level is obtained;

[0015] Based on the initial scanning time, adjust the scanning time T. The calculation formula of scanning time T is: T=T0×a n , T0 is the initial scanning time, n is the number of adjustments, a is a real number greater than 1, and the ACLR measurement results are obtained in real time after each adjustment and the ACLR change between two adjacent scans is calculated to determine whether the ACLR change is less than the threshold until the ACLR change is less than the threshold.

[0016] Specifically, the signal characteristics include center frequency, occupied bandwidth, sampling rate, modulation mode, analysis bandwidth, symbol rate, and signal-to-noise ratio.

[0017] Specifically, the measurement parameters recorded in the parameter library include the number of carriers, carrier integrated bandwidth, carrier spacing, number of offsets, offset integrated bandwidth, offset frequency, number of gap channels, gap channel integrated bandwidth, and gap channel offset frequency.

[0018] Specifically, the signal recognition model includes four convolutional layers, one LSTM layer, and two fully connected deep neural networks.

[0019] The convolution layer extracts local features of the signal and converts the signal data into a series of feature maps;

[0020] The LSTM layer inputs the time series characteristics of the signal, captures the long-term dependency and short-term dependency of each signal feature in the signal, and converts the extracted local features of the signal into a state vector containing the time series features;

[0021] The fully connected deep neural network includes an input layer, a first hidden layer, a second hidden layer and an output layer. The input layer obtains a state vector containing time series features, performs deep feature extraction and conversion into probability masses of different signals through the first hidden layer and the second hidden layer, outputs a category prediction value of the signal to be identified, and obtains the signal type of the signal to be tested.

[0022] Specifically, before adjusting the reference level, determine in turn whether the signal analyzer used supports the phase noise optimization function and whether it has a preamplifier option. If supported, enable the corresponding function or option.

[0023] A second object of the present invention is to provide an intelligent adaptive ACLR measurement system for a signal analyzer, using the adaptive ACLR measurement method for the signal analyzer, comprising:

[0024] A signal acquisition module, used for acquiring a signal to be tested;

[0025] A signal tuning module is used to obtain the frequency band of the signal to be tested and set the center frequency to the frequency of the signal to be tested;

[0026] A signal feature recognition module, in which a preset signal recognition model is set to capture signal features and obtain the signal type of the signal to be tested based on the signal features;

[0027] A measurement configuration module, in which measurement parameters of ACLR under different signal types are set to match the measurement parameters of ACLR under the signal type to which the signal to be measured belongs;

[0028] A gain allocation adjustment module, used for dynamically adjusting the gain allocation of a signal receiving link;

[0029] Adaptive instrument calibration module, used to collect and offset the instrument's own noise and restore the offset channel state of the signal to be measured;

[0030] The ACLR calculation module is used to obtain the ACLR measurement result.

[0031] A third objective of the present invention is to provide a storage medium, wherein the storage medium is a computer-readable storage medium, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, an adaptive ACLR measurement method of a signal analyzer is implemented.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention adaptively adjusts the gain allocation of the receiving link applicable to the signal according to the received signal characteristics, ensuring that the signal analyzer always measures ACLR in an optimal state, significantly improving the accuracy of ACLR measurement and enhancing measurement efficiency.

[0034] 2. The present invention is based on an intelligent recognition algorithm, which extracts the characteristics of received signals, matches the measurement scenarios of different current communication protocols, and automatically sets the measurement parameters in the scenario, thereby improving measurement efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of ACLR measurement of the present invention;

[0036] Figure 2 This is a gain allocation adjustment flow chart of the present invention. DETAILED DESCRIPTION

[0037] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0038] The present invention provides an adaptive ACLR measurement method for a signal analyzer. ACLR (adjacent channel power leakage ratio) is the ratio of the main channel power to the adjacent channel power. It is used to evaluate the amount of interference generated by a signal on an adjacent channel, reflecting the interference performance of a frequency channel signal in the system to adjacent signals and spaced signals. It is one of the key parameters for evaluating the degree of interference of a transmitted signal in an adjacent frequency band, and is particularly important in wireless communication systems with limited spectrum resources and multiple signals coexisting. This application analyzes the characteristics of the received signal, adjusts the gain allocation of the signal according to the signal type, and optimizes the measurement results through algorithms such as adaptive noise cancellation, thereby increasing the accuracy and convenience of ACLR measurement. The specific steps are as follows:

[0039] S1, obtain the signal to be tested.

[0040] After the signal analyzer obtains the signal to be measured, you can choose to activate and start the intelligent adaptive ACLR measurement process.

[0041] S2, signal tuning, obtains the frequency band of the signal to be tested, and sets the center frequency to the frequency of the signal to be tested.

[0042] Through signal tuning, according to the frequency characteristics of the signal under test, by adjusting the internal parameters of the analyzer to ensure that the signal can be correctly received and processed, and setting the center frequency of the signal analyzer to the frequency of the signal can ensure that the signal is accurately captured and analyzed. By setting the center frequency of the signal analyzer to the frequency of the signal, the signal analyzer can receive and process the signal to the maximum extent, thereby improving the accuracy and reliability of signal identification.

[0043] S3, using a preset signal recognition model to capture signal features, and obtaining a signal type according to the signal features of the signal to be tested.

[0044] Based on the second step, the collected signal is input into the preset signal recognition model. The signal recognition model obtains the signal type of the current signal by capturing the signal characteristics. The signal characteristics include center frequency, occupied bandwidth, sampling rate, modulation mode, analysis bandwidth, symbol rate, and signal-to-noise ratio. The preset signal recognition model determines the signal type of the current signal by identifying the above signal characteristics.

[0045] The preset signal recognition model includes four convolutional layers, one LSTM layer, and two fully connected deep neural networks (DNN). The input signal is input into the convolutional layers in sequence to extract the local features of the signal and convert the signal data into a series of feature maps; the LSTM layer uses the time series characteristics of the input signal to capture the long-term and short-term dependencies of each signal feature in the signal, and converts the extracted local features of the signal into a state vector containing time series features; the fully connected deep neural network includes an input layer, a first hidden layer, a second hidden layer, and an output layer. The input layer obtains a state vector containing time series features, performs deep feature extraction through the first hidden layer and the second hidden layer and converts it into probability masses of different signals, outputs the category prediction value of the signal to be identified, and obtains the signal type of the signal to be tested.

[0046] Specifically, there are four convolutional layers with 128, 256, 160, and 80 channels, respectively, and the sizes of the convolution kernels are 1×3, 2×3, 1×3, and 1×3, respectively. There are 60 computing units in the LSTM layer and 2 layers of fully connected deep neural networks with 128 nodes and 11 nodes, respectively.

[0047] When training the signal recognition model, firstly, we collect enough spectrum data of various signal types and classify the labels. Then, we perform preprocessing such as denoising and maximum and minimum normalization on the acquired data to improve the processing capacity of the neural network. The collected data is divided into a training set (70%), a validation set (15%), and a test set (15%) for batch model training.

[0048] S4, obtaining the measurement parameters of ACLR under the signal type in a preset parameter library.

[0049] Based on the signal type obtained by the signal recognition model, the relevant measurement parameters of ACLR under the signal type are obtained in the preset parameter library and set in the signal analyzer. Each different signal type corresponds to different measurement parameters. The signal type is obtained through the signal recognition model, and the measurement scenarios of different current communication protocols are matched and the measurement parameters under the scenario are automatically set to improve measurement efficiency.

[0050] The parameter library includes the following parameters: number of carriers, carrier integrated bandwidth, carrier spacing, number of offsets, offset integrated bandwidth, offset frequency, number of gap channels, gap channel integrated bandwidth, and gap channel offset frequency.

[0051] S5, dynamically adjust the gain allocation of the signal receiving link.

[0052] After the measurement parameters are determined, the gain distribution of the signal receiving link is adjusted dynamically in real time with reference to the current ACLR measurement results, so as to measure the ACLR of the measured signal in the best state. The present invention realizes the dynamic adjustment of the gain distribution of the receiving link by analyzing the key parameters affecting the instrument noise, adopting channel mixing, adjusting the reference level and the scanning time.

[0053] S5.1, determine whether the signal analyzer used supports the phase noise optimization function. If it does, enable the phase noise optimization. If not, go to S5.2.

[0054] S5.2, determine whether the signal analyzer used has a preamplifier option. If so, turn on the preamplifier. If not, go to S5.3.

[0055] S5.3, establishing a search network with a preset step within the reference level range, obtaining a reference value of the reference level in a comparison table based on the input power of the signal, optimizing the search path, and obtaining a benchmark reference level.

[0056] The reference level is the maximum voltage value that the analog-to-digital converter inside the signal analyzer can process. Adjusting the reference level is to adjust the display range of the signal analyzer. According to the signal strength and the reference level setting, the gain distribution is automatically adjusted to ensure that the signal can be displayed correctly, providing a benchmark for ACLR measurement.

[0057] In order to quickly and accurately obtain the benchmark reference level, the present application obtains a reference value of the reference level based on the signal input power, and adjusts the signal based on the reference value.

[0058] A search network is established with a preset step within the reference level range, and the step size is preferably 0.5dB. During the search process, the deterioration effect of the reference level on ACLR and the search efficiency are taken into account, and hardware limitations are also taken into account. If the reference level is set too high, it may cause the intermediate frequency amplifier to overload, thereby affecting the signal quality. Therefore, the search path is optimized by querying the comparison table under the premise of improving the ACLR index and preventing intermediate frequency overload. The comparison table is obtained by summarizing the signal input power and the corresponding appropriate reference level. The signal input power and the corresponding reference level reference value are recorded in the comparison table.

[0059] By obtaining the input power of the current signal, obtaining the reference level reference value closest to the current signal input power by querying the comparison table, and stepping the reference level based on the reference level reference value, unnecessary searches can be avoided, the search efficiency can be improved, the purpose of optimizing the search path can be achieved, and finally a suitable reference level can be obtained.

[0060] S5.4, based on the initial scanning time T0, adjust the scanning time T. The calculation formula of scanning time T is: T=T0×a n , where T0 is the initial scan time, n is the number of adjustments, and a is a real number greater than 1. After each adjustment, the ACLR measurement results are obtained in real time and the change in ACLR between two adjacent scans is calculated to determine whether the change in ACLR is less than the threshold, until the change in ACLR is less than the threshold.

[0061] Based on the initial scan time, such as the default value of the instrument, the scan time is multiplied exponentially on each scan, such as increasing the scan time by 5 times. The scan time is the time required to scan the full frequency range. By extending the scan time, the scan accuracy can be improved. Take T = T0 × a n The scanning time T is adjusted at least once, where n is the number of adjustments and a is a real number greater than 1. Considering that the effect of the scanning time on the measurement result decreases as it increases, the current ACLR measurement result is obtained in real time after each adjustment and the ACLR change between two adjacent scans is calculated to determine whether the ACLR change is less than a threshold, until the ACLR change is less than the threshold, and the threshold is preferably 0.02dB.

[0062] S6, collects and offsets the instrument's own noise to restore the offset channel state of the signal to be measured;

[0063] In order to reduce the interference of instrument noise on the measurement results, adaptive instrument noise calibration technology (noise cancellation technology) is used to collect the instrument's own noise and cancel it out, so as to restore the offset channel state of the measured signal as much as possible and improve the ACLR measurement accuracy.

[0064] S7, obtaining and outputting the ACLR measurement result.

[0065] The process of obtaining ACLR is a function of the signal analyzer itself, which belongs to the prior art and will not be described in detail here.

[0066] The present invention also provides an intelligent adaptive ACLR measurement system for a signal analyzer, comprising:

[0067] A signal acquisition module, used for acquiring a signal to be tested;

[0068] A signal tuning module is used to obtain the frequency band of the signal to be tested and set the center frequency to the frequency of the signal to be tested;

[0069] A signal feature recognition module, in which a preset signal recognition model is set to capture signal features and obtain the signal type of the signal to be tested based on the signal features;

[0070] A measurement configuration module, in which measurement parameters of ACLR under different signal types are set to match the measurement parameters of ACLR under the signal type to which the signal to be measured belongs;

[0071] A gain allocation adjustment module, used for dynamically adjusting the gain allocation of a signal receiving link;

[0072] Adaptive instrument calibration module, used to collect and offset the instrument's own noise and restore the offset channel state of the signal to be measured;

[0073] The ACLR calculation module is used to obtain the ACLR measurement result.

[0074] It is understandable that the above modules correspond to the steps in the adaptive ACLR measurement method of the signal analyzer. The operations, features and beneficial effects described above for the method are also applicable to the signal analyzer and the modules contained therein, and will not be repeated here.

[0075] Another embodiment of the present invention is a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer instructions. The computer instructions can be stored in a memory and, when executed by one or more processors, implement the adaptive ACLR measurement method of the signal analyzer as described in the method embodiment above.

[0076] If the module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a signal analyzer to perform all or part of the steps of the method described in each embodiment of the present invention.

[0077] The memory is used to store program code. The memory can be a circuit with storage function in an integrated circuit without a physical form, such as RAM (Random-Access Memory), FIFO (First In First Out), etc. Alternatively, the memory can also be a memory in a physical form, such as a memory stick, TF card (Trans-flash Card), smart media card (smart media card), secure digital card (secure digital card), flash memory card (flash card) and other storage devices. The memory can communicate data with the processor through a communication bus. The memory may include an operating system, a network communication module, and a signal analyzer adaptive ACLR measurement program. The operating system is a program that manages and controls the hardware and software resources of the signal analyzer, and supports the operation of the signal analyzer adaptive ACLR measurement program and other software and / or programs. The network communication module is used to realize the communication between the components inside the memory, as well as the communication with other hardware and software in the signal analyzer.

[0078] The processor may include one or more microprocessors and digital processors. The processor may call the program code stored in the memory to execute related functions. The processor is also called a central processing unit (CPU), which is a large-scale integrated circuit and a computing core (Core) and a control core (Control Unit).

[0079] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A signal analyzer adaptive ACLR measurement method, characterized in that: include: Get the signal to be tested; Signal tuning, obtaining the frequency band of the signal to be tested, and setting the center frequency to the frequency of the signal to be tested; Use the preset signal recognition model to capture signal features, and obtain the signal type of the signal to be tested based on the signal features; Obtain the measurement parameters of ACLR under the signal type in the preset parameter library; Dynamically adjust the gain allocation of the signal receiving link; Collect and offset the instrument's own noise to restore the offset channel state of the signal to be measured; Obtain and output the ACLR measurement result.

2. The signal analyzer adaptive ACLR measurement method according to claim 1, characterized in that: The signal receiving link gain allocation adjustment method is: A search network is established with a preset step within the reference level range, and a reference value of the reference level is obtained from a comparison table based on the input power of the signal, the search path is optimized, and a reference reference level is obtained; Based on the initial scanning time, adjust the scanning time T. The calculation formula of scanning time T is: T=T0×a n , where T0 is the initial scan time, n is the number of adjustments, and a is a real number greater than 1. After each adjustment, the ACLR measurement results are obtained in real time and the change in ACLR between two adjacent scans is calculated to determine whether the change in ACLR is less than the threshold until the change in ACLR is less than the threshold.

3. The signal analyzer adaptive ACLR measurement method according to claim 1, characterized in that: The signal characteristics include center frequency, occupied bandwidth, sampling rate, modulation mode, analysis bandwidth, symbol rate, and signal-to-noise ratio.

4. The signal analyzer adaptive ACLR measurement method according to claim 1, characterized in that: The measurement parameters recorded in the parameter library include the number of carriers, carrier integrated bandwidth, carrier spacing, the number of offsets, offset integrated bandwidth, offset frequency, the number of gap channels, gap channel integrated bandwidth, and gap channel offset frequency.

5. The signal analyzer adaptive ACLR measurement method according to claim 1, characterized in that: The signal recognition model includes four convolutional layers, one LSTM layer, and two fully connected deep neural networks. The convolution layer extracts local features of the signal and converts the signal data into a series of feature maps; The LSTM layer inputs the time series characteristics of the signal, captures the long-term dependency and short-term dependency of each signal feature in the signal, and converts the extracted local features of the signal into a state vector containing the time series features; The fully connected deep neural network includes an input layer, a first hidden layer, a second hidden layer and an output layer. The input layer obtains a state vector containing time series features, performs deep feature extraction and conversion into probability masses of different signals through the first hidden layer and the second hidden layer, outputs a category prediction value of the signal to be identified, and obtains the signal type of the signal to be tested.

6. The signal analyzer adaptive ACLR measurement method according to claim 2, characterized in that: Before adjusting the reference level, determine whether the signal analyzer supports the phase noise optimization function and whether it has a preamplifier option. If so, enable the corresponding function or option.

7. An intelligent adaptive ACLR measurement system for a signal analyzer, using the adaptive ACLR measurement method for a signal analyzer according to any one of claims 1 to 6, characterized in that: include: A signal acquisition module, used for acquiring a signal to be tested; A signal tuning module is used to obtain the frequency band of the signal to be tested and set the center frequency to the frequency of the signal to be tested; A signal feature recognition module, in which a preset signal recognition model is set to capture signal features and obtain the signal type of the signal to be tested based on the signal features; A measurement configuration module, in which measurement parameters of ACLR under different signal types are set to match the measurement parameters of ACLR under the signal type to which the signal to be measured belongs; A gain allocation adjustment module, used for dynamically adjusting the gain allocation of a signal receiving link; Adaptive instrument calibration module, used to collect and offset the instrument's own noise and restore the offset channel state of the signal to be measured; The ACLR calculation module is used to obtain the ACLR measurement result.

8. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the signal analyzer adaptive ACLR measurement method according to any one of claims 1 to 6 is implemented.

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