CN115801518A - Frequency offset estimation method and device based on probability distribution statistics and computer equipment - Google Patents
Frequency offset estimation method and device based on probability distribution statistics and computer equipment Download PDFInfo
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Abstract
The application relates to a frequency offset estimation method, a frequency offset estimation device and computer equipment based on probability distribution statistics. The method comprises the following steps: receiving a communication signal, and obtaining the frequency phase difference of each sampling point in the communication signal by performing sliding autocorrelation calculation on the communication signal; selecting the frequency phase difference of a preset sampling point from the communication signal, and performing cyclic mean square error calculation to obtain a root mean square value of each cyclic calculation moment; visually displaying the sampling curve corresponding to the root mean square value at each cycle calculation moment, and confirming abnormal sampling points in the sampling curve; the confidence degree of the sampling curve is adjusted to enable the abnormal sampling points to be out of the confidence degree interval so as to eliminate the abnormal sampling points; and performing sliding autocorrelation calculation on the communication signals with the abnormal sampling points removed, and outputting a frequency offset value. The method can realize frequency offset estimation in severe environment.
Description
Technical Field
The present application relates to the field of wireless communications technologies, and in particular, to a frequency offset estimation method and apparatus based on probability distribution statistics, and a computer device.
Background
In wireless communication, the most classical frequency error estimation method is based on autocorrelation of repeated training sequences or correlation operation with local sequences to obtain phase differences of all sampling points of the training sequences, and finally, the phase differences are averaged to eliminate noise to obtain the frequency error of the signal.
However, in actual wireless communication, frequency is unstable due to problems such as station heating and crystal oscillation instability, which eventually causes the frequency offset of the receiving end of the wireless communication system to the channel to be inaccurate.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a frequency offset estimation method, device and computer apparatus based on probability distribution statistics.
A frequency offset estimation method based on probability distribution statistics, the method comprising:
receiving a communication signal, and obtaining the frequency phase difference of each sampling point in the communication signal by performing sliding autocorrelation calculation on the communication signal;
selecting the frequency phase difference of a preset sampling point from the communication signal, and performing cyclic mean square error calculation to obtain a root mean square value of each cyclic calculation moment;
visually displaying a sampling curve corresponding to the root mean square value at each cycle calculation moment, and confirming abnormal sampling points in the sampling curve;
eliminating abnormal sampling points by adjusting the confidence degree of the sampling curve to enable the abnormal sampling points to be out of the confidence degree interval;
and performing sliding autocorrelation calculation on the communication signals with the abnormal sampling points removed, and outputting a frequency offset value.
In one embodiment, the communication signals with the abnormal sampling points removed are subjected to sliding autocorrelation calculation, mean value calculation is carried out, a frequency phase difference mean value is obtained, and the frequency phase difference mean value is output as a frequency offset value.
A frequency deviation estimation device based on probability distribution statistics comprises:
the phase difference calculation module is used for receiving a communication signal and obtaining the frequency phase difference of each sampling point in the communication signal by performing sliding autocorrelation calculation on the communication signal;
the root mean square calculation module is used for selecting the frequency phase difference of a preset sampling point from the communication signal and carrying out cyclic mean square deviation calculation to obtain a root mean square value at each cyclic calculation moment;
the abnormal point removing module is used for visually displaying the sampling curve corresponding to the root mean square value at each cycle calculation moment and confirming the abnormal sampling point in the sampling curve; eliminating abnormal sampling points by adjusting the confidence degree of the sampling curve to enable the abnormal sampling points to be out of the confidence degree interval;
and the frequency offset estimation module is used for performing sliding autocorrelation calculation on the communication signal without the abnormal sampling points and outputting a frequency offset value.
In one embodiment, the frequency offset estimation module is further configured to perform sliding autocorrelation calculation on the communication signal from which the abnormal sampling points are removed, perform mean value calculation to obtain a frequency phase difference mean value, and output the frequency phase difference mean value as the frequency offset value.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
receiving a communication signal, and obtaining the frequency phase difference of each sampling point in the communication signal by performing sliding autocorrelation calculation on the communication signal;
selecting the frequency phase difference of a preset sampling point from the communication signal, and performing cyclic mean square error calculation to obtain a root mean square value of each cyclic calculation moment;
visually displaying a sampling curve corresponding to the root mean square value at each cycle calculation moment, and confirming abnormal sampling points in the sampling curve;
eliminating abnormal sampling points by adjusting the confidence degree of the sampling curve to enable the abnormal sampling points to be out of the confidence degree interval;
and performing sliding autocorrelation calculation on the communication signals with the abnormal sampling points removed, and outputting a frequency offset value.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
receiving a communication signal, and performing sliding autocorrelation calculation on the communication signal to obtain a frequency phase difference of each sampling point in the communication signal;
selecting the frequency phase difference of a preset sampling point from the communication signal, and performing cyclic mean square error calculation to obtain a root mean square value at each cyclic calculation moment;
visually displaying a sampling curve corresponding to the root mean square value at each cycle calculation moment, and confirming abnormal sampling points in the sampling curve;
eliminating abnormal sampling points by adjusting the confidence degree of the sampling curve to enable the abnormal sampling points to be out of the confidence degree interval;
and performing sliding autocorrelation calculation on the communication signals with the abnormal sampling points removed, and outputting a frequency offset value.
In order to solve the problem of system instability, after the frequency phase difference is obtained through calculation, the root mean square value of the frequency phase difference is obtained through calculation, abnormal points can be confirmed from the probability distribution through visual display of the root mean square value corresponding to a sampling curve, the abnormal points are eliminated, and accurate frequency deviation values are obtained through recalculation. The method is suitable for an unstable system, and the estimation of the frequency offset value in the severe environment is realized.
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FIG. 1 is a diagram illustrating correlation calculation in the prior art;
FIG. 2 is a flow chart illustrating a frequency offset estimation method based on probability distribution statistics in an embodiment;
FIG. 3 is a schematic view of a stabilization system in one embodiment;
FIG. 4 is a schematic illustration of a distribution curve in one embodiment;
FIG. 5 is a schematic illustration of an embodiment of an unsteady system;
FIG. 6 is a block diagram illustrating an exemplary apparatus for frequency offset estimation based on probability distribution statistics;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In conventional techniques, such as Error! As shown in Reference source not found, the training sequence is a 16-point cycle (fig. 1 only marks 4 points for schematic), the frequency estimation is performed by performing correlation calculation with a point a and a point a delayed by 16 points, and when the system transmits frequency offset, there is a phase difference between the point a and the point a delayed by 16 points.
The phase difference calculated between each point and the correlation value after 16 points are repeated is recorded as Δ F, and the existing frequency estimation algorithm is to average the correlation values of multiple repeated sequences to finally obtain a relatively stable frequency error. Namely:
the above method for estimating frequency and phase difference based on the average algorithm may have little problem when the system frequency offset is stable, but when the system frequency offset is unstable, the average method may introduce a large error.
In the frequency offset stabilizing system, the phase difference of continuous repetition points is a relatively stable arithmetic progression, so that the phase difference of continuous sampling points can be averaged to obtain the true average frequency phase difference of the whole sequence.
When the error frequency is unstable, the frequency error of the sampling point will not be more concentrated and stable arithmetic progression, but the frequency error calculated by some sampling points is severely deviated. This causes a problem that the frequency error finally calculated by averaging is very large, resulting in a frequency error estimation bias that is too large.
In one embodiment, as shown in fig. 1, a frequency offset estimation method based on probability distribution statistics is provided, which includes the following steps:
and 102, receiving the communication signal, and performing sliding autocorrelation calculation on the communication signal to obtain the frequency phase difference of each sampling point in the communication signal.
And 104, selecting the frequency phase difference of a preset sampling point from the communication signal, and performing cyclic mean square error calculation to obtain a root mean square value at each cyclic calculation moment.
And 106, visually displaying the sampling curve corresponding to the root mean square value at each cycle calculation moment, and confirming abnormal sampling points in the sampling curve.
And step 108, adjusting the confidence coefficient of the sampling curve to enable the abnormal sampling points to be outside the confidence coefficient interval so as to eliminate the abnormal sampling points.
And step 110, performing sliding autocorrelation calculation on the communication signal without the abnormal sampling points, and outputting a frequency offset value.
In order to solve the problem of system instability, the frequency offset estimation method based on probability distribution statistics calculates the root mean square value of the frequency phase difference after calculating the frequency phase difference, and can confirm abnormal points from the probability distribution through the visual display of the root mean square value corresponding to the sampling curve, so that the abnormal points are removed, and the accurate frequency offset value is obtained through recalculation. The method is suitable for an unstable system, and the estimation of the frequency offset value in the severe environment is realized.
It should be noted that the present invention can solve the above problem, because when the system frequency offset is unstable, the distribution of the frequency offset values of multiple sampling points is greatly different from the normal distribution under the standard gaussian noise condition, that is, the deviation of individual points is particularly serious, thereby causing the calculation deviation of the average value.
In one embodiment, the communication signals with the abnormal sampling points removed are subjected to sliding autocorrelation calculation, mean value calculation is carried out to obtain a frequency phase difference mean value, and the frequency phase difference mean value is output as a frequency offset value.
Specifically, as shown in fig. 3, the rms value of the phase difference is relatively stable and hardly changes in a stable system, and when normal distribution analysis is performed, the root mean square distribution of the phase difference is concentrated near the mean value, as shown in fig. 4, the normal mean value is concentrated in the middle of the probability distribution curve.
As shown in fig. 5, in the unstable system, the distribution of the root mean square of the phase difference deviates from the central probability region of the mean, and as shown in fig. 4, the probability confidence interval of the normal distribution (for example, the confidence interval is 95% in the general case) can be adjusted to 80%, 70%, etc., and the points outside the confidence interval can be eliminated.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 6, there is provided a frequency offset estimation apparatus based on probability distribution statistics, including: a phase difference calculation module 602, a root mean square calculation module 604, an outlier rejection module 606, and a frequency offset estimation module 608, wherein:
a phase difference calculation module 602, configured to receive a communication signal, and perform sliding autocorrelation calculation on the communication signal to obtain a frequency phase difference of each sampling point in the communication signal;
a root-mean-square calculation module 604, configured to select a frequency phase difference of a predetermined sampling point from the communication signal, and perform cyclic mean-square calculation to obtain a root-mean-square value at each cyclic calculation time;
an abnormal point removing module 606, configured to visually display a sampling curve corresponding to the root mean square value at each cycle calculation time, and confirm an abnormal sampling point in the sampling curve; eliminating abnormal sampling points by adjusting the confidence degree of the sampling curve to enable the abnormal sampling points to be out of the confidence degree interval;
and the frequency offset estimation module 608 is configured to perform sliding autocorrelation calculation on the communication signal from which the abnormal sampling points are removed, and output a frequency offset value.
In one embodiment, the frequency offset estimation module 608 is further configured to perform sliding autocorrelation calculation on the communication signal without the abnormal sampling point, perform mean value calculation to obtain a frequency phase difference mean value, and output the frequency phase difference mean value as a frequency offset value.
For the specific limitation of the frequency offset estimation apparatus based on probability distribution statistics, reference may be made to the above limitation of the frequency offset estimation method based on probability distribution statistics, and details are not repeated here. All or part of the modules in the frequency offset estimation device based on probability distribution statistics can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile 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 operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a frequency offset estimation method based on probability distribution statistics. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, is adapted to carry out the steps of the method of the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (6)
1. A frequency offset estimation method based on probability distribution statistics is characterized by comprising the following steps:
receiving a communication signal, and performing sliding autocorrelation calculation on the communication signal to obtain a frequency phase difference of each sampling point in the communication signal;
selecting the frequency phase difference of a preset sampling point from the communication signal, and performing cyclic mean square error calculation to obtain a root mean square value of each cyclic calculation moment;
visually displaying a sampling curve corresponding to the root mean square value at each cycle calculation moment, and confirming abnormal sampling points in the sampling curve;
eliminating abnormal sampling points by adjusting the confidence degree of the sampling curve to enable the abnormal sampling points to be out of the confidence degree interval;
and performing sliding autocorrelation calculation on the communication signals with the abnormal sampling points removed, and outputting a frequency offset value.
2. The frequency offset estimation method based on probability distribution statistics of claim 1, wherein the performing sliding autocorrelation calculation on the communication signal without abnormal sampling points and outputting a frequency offset value comprises:
and performing sliding autocorrelation calculation on the communication signals without the abnormal sampling points, performing mean value calculation to obtain a frequency phase difference average value, and outputting the frequency phase difference average value as a frequency offset value.
3. A frequency deviation estimation device based on probability distribution statistics is characterized by comprising:
the phase difference calculation module is used for receiving a communication signal and obtaining the frequency phase difference of each sampling point in the communication signal by performing sliding autocorrelation calculation on the communication signal;
the root mean square calculation module is used for selecting the frequency phase difference of a preset sampling point from the communication signal and carrying out cyclic mean square deviation calculation to obtain a root mean square value at each cyclic calculation moment;
the abnormal point removing module is used for visually displaying the sampling curve corresponding to the root mean square value at each cycle calculation moment and confirming the abnormal sampling point in the sampling curve; eliminating abnormal sampling points by adjusting the confidence degree of the sampling curve to enable the abnormal sampling points to be out of the confidence degree interval;
and the frequency offset estimation module is used for performing sliding autocorrelation calculation on the communication signal without the abnormal sampling points and outputting a frequency offset value.
4. The apparatus of claim 3, wherein the frequency offset estimation module is further configured to perform sliding autocorrelation calculation on the communication signal without the abnormal sampling point, perform mean value calculation to obtain a mean value of frequency phase differences, and output the mean value of frequency phase differences as the frequency offset value.
5. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of claim 1 or 2 when executing the computer program.
6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of claim 1 or 2.
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