A method for aclr measurement of 5g nr signal

Abstract

The application discloses a kind of ACLR measurement methods for 5G NR signal, belong to communication system measurement field, including the following steps: input signal waveform file, read bandwidth, sampling rate, data length, resolution bandwidth and scan time information, calculate the absolute power value of main channel signal;Using the method of segmented superposition, the relative power value of adjacent channel is calculated;And using rising edge falling edge detection method detects the starting point and end point of signal.The application adopts the mode of segmented superposition, not only reduces the amount of operation, but also weakens the interference of noise to signal, while compatible with speed and accuracy, meet the test demand of user, improve user experience.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of communication system measurement, and particularly relates to an ACLR measurement method for 5G NR signals. BACKGROUND

[0002] The continuous improvement of user demand for communication prompts operators to strive to expand bandwidth to provide more users with efficient and reliable Internet services. The frame structure of NR (New Radio) has flexibility and diversity, and introduces the concept of flexible time slots, which can be dynamically adjusted for different UEs, can be adjusted to the symbol level, and the Slot type of NR is more, which can meet more scenarios and business types. It can be seen that the NR architecture is complex and is still evolving, which brings new challenges to network and user equipment design and testing. One of the key challenges is how to manage power during signal transmission.

[0003] In 5G digital communication, the power of the transmitted signal leaking into the adjacent channel can interfere with the signal transmission in the adjacent channel, and thus affect the performance of the communication system. The ACLR (Adjacent Channel Leakage Ratio) test can verify whether the working performance of the system transmitter meets the specified limit. In view of the complexity of the key technologies of 5G, it may be challenging for the tester to quickly and correctly implement the ACLR test.

[0004] The traditional ACLR measurement method adopts one-key ACLR measurement method. First, a test signal is generated by running Signal Studio software on an Agilent X series signal analyzer, and then the signal waveform is loaded to a signal generator through LAN or GPIB. The radio frequency output end of the signal generator is connected to the input end of the signal analyzer. The ACLR performance is measured by using a scanning spectrum analyzer. After selecting appropriate parameter configuration, one-key ACLR measurement can be quickly performed. The method is simple, fast and easy to use, but the test performance is not high. Scholars adjust the attenuator by optimizing the signal level requirement on the input mixer, and realize minimum amplitude limiting. Some analyzers can automatically select the attenuation value according to the currently measured signal value, which lays a good foundation for realizing the best measurement range, optimizes the ACLR test result, and steps about 1-2 dB. Later, researchers introduced a resolution bandwidth filter to reduce the resolution bandwidth, which increased the scanning time and thus reduced the scanning speed, and optimized the measurement result. At the same time, the measurement speed is reduced. With the deepening of research, scholars have proposed a noise correction measurement method. When the noise correction is turned on, the analyzer will perform a scan to measure the internal noise at the current center frequency, and the internal noise will be subtracted from the measurement result in the future scan. This method reduces the influence of noise on the signal, improves the ACLR measurement, and optimizes the measurement result. In addition, scholars also take the filter IBW method. This method uses increasingly steep cutoff filters, but this method will reduce the absolute accuracy of power measurement, but it has no adverse effect on the ACLR result.

[0005] In summary, these measurement methods are simple and easy to use, but the general accuracy is not high, and speed and accuracy cannot be simultaneously compatible. When the signal bandwidth is small, the left and right adjacent channel test results have large deviations. How to provide an efficient and accurate ACLR measurement method to meet the test requirements of precision instruments has become a technical problem to be solved. SUMMARY

[0006] In view of the above technical problems in the prior art, the present application provides an ACLR measurement method for 5G NR signals, which is reasonable in design, overcomes the shortcomings of the prior art, and has good effects.

[0007] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0008] An ACLR measurement method for 5G NR signals, comprising the following steps:

[0009] Step 1: input the signal waveform file, read the bandwidth, sampling rate, data length, resolution bandwidth and scanning time information, and calculate the absolute power value of the main channel signal;

[0010] Step 2: Calculate the adjacent channel relative power value by using the method of segmented superposition; and detect the starting point and ending point of the signal by using the rising edge and falling edge detection method.

[0011] Preferably, in step 1, the absolute power value of the main channel signal is calculated, specifically including the following steps:

[0012] Step S11: Read the waveform data input by the user, calculate the length of the waveform data, and obtain the frequency domain data information of the waveform file after performing Fourier transform on the whole data segment;

[0013] Step S12: Move the frequency spectrum of the baseband signal to the vicinity of the carrier frequency by using the spectrum shifting method;

[0014] Step S13: After obtaining the frequency domain information of the whole data segment, calculate the interval position, i.e. the starting position and ending position, of the main channel signal in the whole data segment according to the sampling rate, the bandwidth of the main channel signal, and the number of Fourier transform points, and count the frequency domain values of all points in the interval to obtain the absolute power value of the main channel signal.

[0015] Preferably, in step 2, the adjacent channel relative power value is calculated, specifically including the following steps:

[0016] Step S21: Divide the original signal data into several segments according to the number of Fourier transform points, and use the method of windowing each segment to optimize the frequency spectrum quality;

[0017] Step S22: Perform Fourier transform on each windowed original data segment to obtain the frequency domain information of each original data segment, and perform spectrum shifting;

[0018] Step S23: After obtaining the frequency domain information of each original data segment, count the power value of each signal segment and perform power superposition, divide the superimposed power value by the number of segments to obtain the frequency domain signal after power averaging;

[0019] Step S24: Calculate the interval position, i.e. the starting position and ending position, of the adjacent channel signal in the frequency domain signal after power averaging according to the sampling rate, the bandwidth of the main channel signal, the bandwidth of the adjacent channel signal, and the number of Fourier transform points, count the frequency domain values of all points in the interval to obtain the absolute power value of the adjacent channel signal, and subtract the absolute power value of the main channel signal from the absolute power value of the adjacent channel signal to obtain the relative power value of the adjacent channel.

[0020] Preferably, in step 2, the rising edge detection method specifically includes the following process: take point i as the starting point, calculate the sum of the power values of powerlen points, and the power value is P up1 , take point i+powerlen as the starting point, calculate the sum of the power values of powerlen points, and the power value is P up2 , traverse i until when Pup2 >20*P up1 When the iteration ends, the value of i is recorded as the starting point of the falling edge.

[0021]

[0022]

[0023] The specific process of the falling edge detection method is as follows: taking point i+startpoint as the starting point, the sum of the power values of powerlen points is calculated, and the power value is P down1 , taking point i+startpoint+powerlen as the starting point, the sum of the power values of powerlen points is calculated, and the power value is P down2 , and iterating i until when P down1 >20*P down2 When the iteration ends, the value of i is recorded as the ending point of the falling edge.

[0024]

[0025]

[0026] Wherein, P up1 , P up2 , P down1 , P down2 are the power values of a certain interval of the channel, datalen is the data length, powerlen is the power scanning window length, dataI is the real part of the signal, and dataQ is the imaginary part of the signal.

[0027] The beneficial technical effects brought by the present application are:

[0028] The present application adopts the segmented superposition method, which not only reduces the operation amount, but also weakens the interference of noise on the signal, and at the same time, the speed and accuracy are compatible, the test demand of the user is met, and the user experience is improved. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The flowchart of the method of the present application. DETAILED DESCRIPTION

[0030] The present application will be further described in detail below in combination with the drawings and specific embodiments:

[0031] As shown in the drawings: the ACRL measurement method of the present application specifically comprises the following steps: Figure 1

[0032] ​Step 1: input signal waveform file, read bandwidth, sampling rate, data length, resolution bandwidth, scan time and other information, calculate the absolute power value of the main channel signal; specifically comprising the following steps:

[0033] Step S11: read the waveform data input by the user, calculate the length of the waveform data, and obtain the frequency domain data information of the waveform file after performing Fourier transform on the whole data;

[0034] Step S12: in the form of spectrum shift, the spectrum of the baseband signal is shifted to the vicinity of the carrier frequency, and the anti-interference ability of the signal is improved;

[0035] Step S13: after obtaining the frequency domain information of the whole data, the interval position (start position and end position) of the main channel signal in the whole data is calculated according to the sampling rate, the main channel signal bandwidth and the number of Fourier transform points, and the frequency domain values of all points in the interval are counted, that is, the absolute power value of the main channel signal.

[0036] Step 2: calculate the relative power value of the adjacent channel by using the segmentation and superposition method. Considering the difference between FDD signal and TDD signal characteristics, the present application introduces a rising edge and falling edge detection method to improve the measurement accuracy of FDD signal and TDD signal.

[0037] In step 2, the segmentation and superposition method is used to calculate the relative power value of the adjacent channel, specifically comprising the following steps:

[0038] Step S21: the original signal data is divided into several segments according to the Fourier transform point number, and the windowing method is used to optimize the spectrum quality of each segment to prevent the spectrum from being disorderly;

[0039] Step S22: Fourier transform is performed on each original data after windowing to obtain the frequency domain information of each original data, and spectrum shift is performed to improve the anti-interference ability of the signal;

[0040] Step S23: after obtaining the frequency domain information of each original data, the power value of each segment signal is counted and superimposed, and the superimposed power value is divided by the number of segments, that is, the power-averaged frequency domain signal is obtained;

[0041] Step S24: according to the sampling rate, the main channel signal bandwidth, the adjacent channel signal bandwidth and the Fourier transform point number, the interval position (start position and end position) of the adjacent channel signal in the power-averaged frequency domain signal is calculated, and the frequency domain values of all points in the interval are counted, that is, the absolute power value of the adjacent channel signal is obtained, and the absolute power value is subtracted from the absolute power value of the main channel signal, that is, the relative power value of the adjacent channel is obtained.

[0042] The rising edge and falling edge detection method in step 2 specifically comprises the following steps:

[0043] In order to further improve the accuracy of the calculation, the rising edge and falling edge detection method is used to detect the starting point and the ending point of the signal in consideration of the noise mixed in the original signal.

[0044] Step S31: rising edge detection process: taking point i as the starting point, the sum of the power values of the powerlen points is calculated, and the power value is P up1 , and taking point i+powerlen as the starting point, the sum of the power values of the powerlen points is calculated, and the power value is P up2 , and i is traversed until when P up2 >20*P up1 , the traversal ends, and the value of i is recorded as the starting point of the rising edge startpoint.

[0045]

[0046]

[0047] Step S32: falling edge detection process: taking point i+startpoint as the starting point, the sum of the power values of the powerlen points is calculated, and the power value is P down1 , and taking point i+startpoint+powerlen as the starting point, the sum of the power values of the powerlen points is calculated, and the power value is P down2 , and i is traversed until when P down1 >20*P down2 , the traversal ends, and the value of i is recorded as the ending point of the falling edge.

[0048]

[0049]

[0050] , P up1 , P up2 , P down1 , P down2 are the power values of a certain interval of the channel, datalen is the data length, powerlen is the power scanning window length, dataI is the real part of the signal, and dataQ is the imaginary part of the signal. The data after the starting point position is segmented, and experiments prove that this method can weaken the interference of noise on signal data. In addition, if powerlen is set too large, the starting point and the ending point calculation accuracy is improved, but the operation amount is increased, and if it is set too small, the rising edge and the falling edge position may not be captured.

[0051] Of course, the above description is not a limitation of the present application, and the present application is not limited to the above examples. Changes, modifications, additions or substitutions made by those skilled in the art within the spirit and scope of the present application should also be included in the protection scope of the present application.

Claims

1. A method for ACLR measurement of 5G NR signals, characterized in that: Includes the following steps: Step 1: Input the signal waveform file, read the bandwidth, sampling rate, data length, resolution bandwidth and scan time information, and calculate the absolute power value of the main channel signal; Step 2: Calculate the relative power values ​​of adjacent channels using a segmented superposition method; and detect the start and end points of the signal using a rising and falling edge detection method. Calculating the relative power values ​​of adjacent channels involves the following steps: Step S21: Divide the original signal data into several segments according to the number of Fourier transform points, and use the method of windowing each segment to optimize the spectral quality; Step S22: Perform Fourier transform on each segment of the original data after windowing to obtain the frequency domain information of each segment of the original data, and then perform spectrum shifting; Step S23: After obtaining the frequency domain information of each segment of original data, the power value of each signal segment is counted and the power is superimposed. The superimposed power value is divided by the number of segments to obtain the frequency domain signal after power averaging. Step S24: Based on the sampling rate, the bandwidth of the main channel signal, the bandwidth of the adjacent channel signal, and the number of points in the Fourier transform, calculate the interval position (start position and end position) of the adjacent channel signal in the frequency domain signal after power averaging. Statistically count the frequency domain values ​​of all points within the interval to obtain the absolute power value of the adjacent channel signal. Subtract the absolute power value of the main channel signal from this absolute power value to obtain the relative power value of the adjacent channel.

2. The ACLR measurement method for 5G NR signals according to claim 1, characterized in that: Step 1 involves calculating the absolute power value of the main channel signal, specifically including the following steps: Step S11: Read the waveform data input by the user, calculate the length of the waveform data, and perform a Fourier transform on the entire data segment to obtain the frequency domain data information of the waveform file. Step S12: Use spectrum shifting to shift the spectrum of the baseband signal to near the carrier frequency; Step S13: After obtaining the frequency domain information of the entire data segment, calculate the interval position of the main channel signal in the entire data segment, i.e., the start position and the end position, based on the sampling rate, the bandwidth of the main channel signal, and the number of points of the Fourier transform. Statistically count the frequency domain values ​​of all points within the interval to obtain the absolute power value of the main channel signal.

3. The ACLR measurement method for 5G NR signals according to claim 1, characterized in that: In step 2, the specific process of the rising edge detection method is as follows: Taking point i as the starting point, calculate the sum of the power values ​​of powerlen points, where the power value is P. up1 Then, starting from point i + powerlen, calculate the sum of the power values ​​of powerlen points. This power value is P. up2 Iterate through i until P... up2 >20*P up1 When the traversal ends, the value of i is recorded as the starting point of the rising edge. The specific process of the falling edge detection method is as follows: Taking point i+startpoint as the starting point, calculate the sum of the power values ​​of powerlen points, which is P. down1 Then, taking point i + startpoint + powerlen as the starting point, calculate the sum of the power values ​​of powerlen points, and this power value is P. down2 Iterate through i until P... down1 >20*P down2 When the traversal ends, the value of i is recorded as the end point of the falling edge. Among them, P up1 P up2 P down1 P down2 Let data be the power value of a certain segment of the channel, datalen be the data length, powerlen be the power scanning window length, dataI be the real part of the signal, and dataQ be the imaginary part of the signal.

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