Disclosure of Invention
To solve the problems in the prior art, a first object of the present invention is to provide a signal analyzer adaptive ACLR measurement method, including:
acquiring a signal to be detected;
signal tuning, namely acquiring the frequency band of a signal to be detected, and setting the center frequency to the frequency of the signal to be detected;
capturing signal characteristics by using a preset signal identification model, and acquiring the signal type of a signal to be detected based on the signal characteristics;
Acquiring the measurement parameters of ACLR under the signal type from a preset parameter library;
dynamically adjusting gain allocation of the signal receiving link;
Collecting and counteracting the noise of the instrument, and restoring the offset channel state of the signal to be detected;
and obtaining and outputting an ACLR measurement result.
Specifically, the method for adjusting the gain allocation of the signal receiving link comprises the following steps:
establishing a search network in a reference level range by a preset step, acquiring a reference value of a reference level in a comparison table based on the input power of a signal, optimizing a search path, and acquiring a base reference level;
And on the basis of the initial scanning time, adjusting the scanning time T, wherein the calculation formula of the scanning time T is that T=T0×an,T0 is the initial scanning time, n is the adjustment times, a is a real number larger than 1, an ACLR measurement result is obtained in real time after each adjustment, the variation of the ACLR between two adjacent scans is calculated, and whether the variation of the ACLR is smaller than a threshold value or not is judged until the variation of the ACLR is smaller than the threshold value.
Specifically, the signal characteristics include center frequency, occupied bandwidth, sampling rate, modulation mode, analysis bandwidth, symbol rate and signal-to-noise ratio.
Specifically, the measurement parameters recorded in the parameter library include the number of carriers, the carrier integral bandwidth, the carrier interval, the offset number, the offset integral bandwidth, the offset frequency, the number of gap channels, the gap channel integral bandwidth and the gap channel offset frequency.
Specifically, the signal identification model comprises four convolutional layers, one LSTM layer and two fully-connected deep neural networks,
The convolution layer extracts local characteristics of signals and converts signal data into a series of characteristic diagrams;
the LSTM layer captures the long-term dependency and short-term dependency of each signal characteristic in the signal according to the time sequence characteristics of the input signal, and converts the extracted local characteristic of the signal into a state vector containing the time sequence characteristic;
The fully-connected deep neural network comprises an input layer, a first hidden layer, a second hidden layer and an output layer, wherein the input layer acquires a state vector containing time sequence features, the first hidden layer and the second hidden layer are used for carrying out deep feature extraction and converting the state vector into probability quality of different signals, and the class predicted value of a signal to be identified is output to obtain the signal type of the signal to be detected.
Specifically, before the reference level is adjusted, whether the used signal analyzer supports the phase noise optimization function or not and whether the pre-amplifier option is provided or not are sequentially judged, and if so, the corresponding function or option is started.
A second object of the present invention is to provide an intelligent adaptive ACLR measurement system of a signal analyzer, and an adaptive ACLR measurement method using the signal analyzer, including:
The signal acquisition module is used for acquiring signals to be detected;
The signal tuning module is used for acquiring the frequency band of the signal to be detected and setting the center frequency to the frequency of the signal to be detected;
The signal characteristic recognition module is provided with a preset signal recognition model, captures signal characteristics and obtains the signal type of the signal to be detected based on the signal characteristics;
The measurement configuration module is provided with measurement parameters of ACLR under different signal types, and the measurement parameters of ACLR under the signal type to which the signal to be measured belongs are matched;
the gain distribution adjusting module is used for dynamically adjusting the gain distribution of the signal receiving link;
the self-adaptive instrument calibration module is used for collecting and counteracting the noise of the instrument and restoring the offset channel state of the signal to be measured;
and the ACLR calculation module is used for obtaining the measurement result of the ACLR.
A third object of the present invention is to provide a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements a signal analyzer adaptive ACLR measurement method.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the received signal characteristics, the invention adaptively adjusts the gain distribution of the receiving link suitable for the signal, ensures that the signal analyzer always measures the ACLR in an optimal state, obviously improves the accuracy of the ACLR measurement and improves the measurement efficiency.
2. The invention is based on intelligent recognition algorithm, and by extracting the characteristics of the received signals, the invention matches the measurement scenes of different current communication protocols and automatically sets the measurement parameters in the scenes, thereby improving the measurement efficiency.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
The application provides a signal analyzer self-adaptive ACLR measuring method, wherein ACLR (adjacent channel power leakage ratio) is the ratio of main channel power to adjacent channel power, and is used for evaluating the interference quantity of a signal on an adjacent channel, reflecting the interference performance of a certain frequency channel signal on the adjacent signal and the interval signal in a system, and is one of key parameters for evaluating the interference degree of a transmitting signal in an adjacent frequency band, especially important in a wireless communication system with limited frequency spectrum resources and multiple coexisting signals. The application adjusts the gain distribution of the signal according to the signal type by analyzing the characteristics of the received signal, optimizes the measurement result by self-adaptive noise cancellation algorithm and the like, and increases the accuracy and convenience of ACLR measurement, and comprises the following specific steps:
s1, obtaining a signal to be detected.
After the signal analyzer acquires the signal to be measured, the intelligent self-adaptive ACLR measurement flow can be selectively activated and started.
S2, signal tuning, namely acquiring the frequency band of the signal to be detected, and setting the center frequency to the frequency of the signal to be detected.
Through signal tuning, according to the frequency characteristic of the measured signal, through adjusting the internal parameter of the analyzer, the signal can be correctly received and processed, and the signal can be accurately captured and analyzed by setting the central frequency of the signal analyzer to the frequency of the signal, and the signal can be maximally received and processed by setting the central frequency of the signal analyzer to the frequency of the signal, so that the accuracy and reliability of signal identification are improved.
S3, grabbing signal characteristics by using a preset signal identification model, and acquiring signal types according to the signal characteristics of the signal to be detected.
On the basis of the second step, the acquired signals are input into a preset signal identification model, the signal identification model acquires the signal type of the current signal by grabbing signal characteristics, the signal characteristics comprise center frequency, occupied bandwidth, sampling rate, modulation mode, analysis bandwidth, symbol rate and signal-to-noise ratio, and the preset signal identification model judges the signal type of the current signal by identifying the signal characteristics.
The preset signal identification model comprises four layers of convolution layers, one layer of LSTM layer and two layers of fully-connected Deep Neural Network (DNN), wherein an input signal is sequentially input into the convolution layers, local characteristics of the signal are extracted, signal data are converted into a series of characteristic diagrams, the LSTM layer captures the time sequence characteristics of the input signal, long-term dependence and short-term dependence of each signal characteristic in the signal, the extracted local characteristics of the signal are converted into state vectors containing the time sequence characteristics, the fully-connected deep neural network comprises an input layer, a first hidden layer, a second hidden layer and an output layer, the input layer acquires the state vectors containing the time sequence characteristics, the first hidden layer and the second hidden layer are used for carrying out depth characteristic extraction and converting the state vectors into probability quality of different signals, and the class predicted values of the signals to be identified are output to obtain the signal types of the signals to be detected.
Specifically, the convolution layers are four layers, the channel numbers are 128, 256, 160 and 80 respectively, the convolution kernels are 1×3, 2×3, 1×3 and 1×3 respectively, 60 calculation units are arranged in the LSTM layers, and the 2 layers of fully-connected deep neural networks are 128 nodes and 11 nodes respectively.
When the signal identification model is trained, firstly, enough frequency spectrum data under various signal types are acquired through acquisition, label classification is carried out, and then the acquired data are subjected to pretreatment such as denoising, maximum and minimum normalization and the like so as to improve the processing capacity of the neural network. The acquired data was divided into training set (70%), validation set (15%) and test set (15%) for batch model training.
S4, acquiring the measurement parameters of the ACLR under the signal type from a preset parameter library.
The method comprises the steps that the signal type obtained based on the signal identification model obtains relevant measurement parameters of ACLR under the signal type in a preset parameter library and is set in a signal analyzer, each different signal type corresponds to different measurement parameters, the signal type is obtained through the signal identification model, the measurement scene of different current communication protocols is matched, the measurement parameters under the scene are automatically set, and the measurement efficiency is improved.
The parameter library comprises the following parameters of carrier number, carrier integral bandwidth, carrier interval, offset number, offset integral bandwidth, offset frequency, gap channel number, gap channel integral bandwidth and gap channel offset frequency.
S5, dynamically adjusting gain distribution of the signal receiving link.
After the measurement parameters are determined, the gain allocation of the signal receiving link is dynamically adjusted in real time by referring to the current ACLR measurement result, and ACLR of the measured signal is measured in an optimal state. The invention realizes the dynamic adjustment of the gain distribution of the receiving link by analyzing key parameters affecting the noise of the instrument and adopting the channel mixing, the adjustment reference level and the scanning time.
S5.1, judging whether the used signal analyzer supports the phase noise optimization function, if so, starting phase noise optimization, and if not, turning to S5.2.
S5.2, judging whether the used signal analyzer is provided with a pre-amplifier option, if so, turning on the pre-amplifier, and if not, turning on to S5.3.
S5.3, establishing a search network in a preset step within the range of the reference level, acquiring a reference value of the reference level in a comparison table based on the input power of the signal, and optimizing a search path to obtain a base reference level.
The reference level is the maximum voltage value which can be processed by an analog-digital converter in the signal analyzer, and the adjustment of the reference level is to automatically adjust gain distribution according to the strength of the signal and the setting of the reference level in the adjustment of the display range of the signal analyzer so as to ensure that the signal can be correctly displayed and provide a reference for the measurement of ACLR.
In order to quickly and accurately acquire the datum reference level, the application acquires the reference value of the reference level based on the signal input power and adjusts the signal based on the reference value.
The search network is built in a preset step within the range of the reference level, the step amount is preferably 0.5dB, in the search process, the deterioration effect of the reference level on the ACLR and the search efficiency are considered, meanwhile, the hardware limitation is considered, if the reference level is set too high, the overload of the intermediate frequency amplifier can be caused, the signal quality is further affected, and therefore, the search path is optimized by inquiring the comparison table on the premise that the ACLR index is improved and the intermediate frequency overload does not occur. The comparison table is summarized by the input power of the signal and the corresponding proper reference level, and the input power of the signal and the corresponding reference level reference value are recorded in the comparison table.
The reference level reference value closest to the input power of the current signal is obtained by inquiring the comparison table, the reference level is adjusted in a stepping mode on the basis of the reference level reference value, unnecessary searching is avoided, searching efficiency is improved, the purpose of optimizing a searching path is achieved, and the proper reference level is finally obtained.
And S5.4, adjusting the scanning time T on the basis of the initial scanning time T0, wherein the calculation formula of the scanning time T is T=T0×an, T0 is the initial scanning time, n is the adjustment times, a is a real number larger than 1, an ACLR measurement result is obtained in real time after each adjustment, the variation of ACLR between two adjacent scans is calculated, and whether the variation of the ACLR is smaller than a threshold value or not is judged until the variation of the ACLR is smaller than the threshold value.
The scan time is increased by an exponential, e.g., 5, increment of the scan time on a per-scan basis based on an initial scan time, e.g., based on a default value for the instrument. The scanning time is the time required for scanning a full frequency range once, and the scanning accuracy can be improved by extending the scanning time. At least one adjustment is performed on the scanning time T by t=t0×an, n is the adjustment number, and a is a real number greater than 1. Considering that the influence effect of the scanning time on the measurement result is in a decreasing state along with the increase of the scanning time, acquiring the current ACLR measurement result in real time after each adjustment, calculating the variation of ACLR between two adjacent scans, and judging whether the variation of ACLR is smaller than a threshold value or not until the variation of ACLR is smaller than the threshold value, wherein the threshold value is preferably 0.02dB.
S6, collecting and counteracting the noise of the instrument, and restoring the offset channel state of the signal to be detected;
In order to reduce the interference of instrument noise to the measurement result, an adaptive instrument noise calibration technology (noise cancellation technology) is adopted, and the offset channel state of the measured signal is restored to the greatest extent possible by collecting the noise of the instrument and canceling the noise, so that the ACLR measurement accuracy is improved.
S7, obtaining and outputting an ACLR measurement result.
The ACLR obtaining process is a function of the signal analyzer itself, and is not described in detail in the prior art.
The invention also provides an intelligent self-adaptive ACLR measurement system of the signal analyzer, which comprises:
The signal acquisition module is used for acquiring signals to be detected;
The signal tuning module is used for acquiring the frequency band of the signal to be detected and setting the center frequency to the frequency of the signal to be detected;
The signal characteristic recognition module is provided with a preset signal recognition model, captures signal characteristics and obtains the signal type of the signal to be detected based on the signal characteristics;
The measurement configuration module is provided with measurement parameters of ACLR under different signal types, and the measurement parameters of ACLR under the signal type to which the signal to be measured belongs are matched;
the gain distribution adjusting module is used for dynamically adjusting the gain distribution of the signal receiving link;
the self-adaptive instrument calibration module is used for collecting and counteracting the noise of the instrument and restoring the offset channel state of the signal to be measured;
and the ACLR calculation module is used for obtaining the measurement result of the ACLR.
It will be appreciated that the above modules correspond to the various steps in the adaptive ACLR measurement method of the signal analyzer. The operations, features and beneficial effects described above for the method are equally applicable to the signal analyzer and the modules contained therein, and are not described here again.
Another embodiment of the invention is a storage medium that is a computer-readable storage medium having stored thereon computer instructions that can be stored on a memory and that when executed by one or more processors, thereby implementing the adaptive ACLR measurement method of a signal analyzer as described in the method embodiments above.
The modules, if implemented as software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or partly in the form of a software product or all or part of the technical solution, which is stored in a storage medium, comprising instructions for causing a signal analyzer to perform all or part of the steps of the method according to the embodiments of the present invention.
The memory is used for storing program codes. The Memory may be a circuit with a Memory function, such as RAM (Random-Access Memory), FIFO (FIRSTIN FIRST Out), etc., without physical form in the integrated circuit. Or the memory may be a physical form of memory, such as a memory stick, TF card (Trans-FLASH CARD), smart media card (SMART MEDIA CARD), secure digital card (secure digitalcard), flash memory card (FLASH CARD), or other storage device. The memory may be in data communication with the processor via 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 signal analyzer hardware and software resources, supporting the execution of signal analyzer-based adaptive ACLR measurement programs, as well as other software and/or programs. The network communication module is used for realizing communication among all components in the memory and communication with other hardware and software in the signal analyzer.
The processor may include one or more microprocessors, digital processors. The processor may call program code stored in the memory to perform the relevant functions. The processor is also called a central processing Unit (CPU, central Processing Unit), is a very large scale integrated circuit, and is an operation Core (Core) and a Control Unit (Control Unit).
While the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modifications or substitutions do not depart from the spirit of the embodiments.