CN111064682B - Data statistics-based root mean square delay spread estimation method and system - Google Patents

Data statistics-based root mean square delay spread estimation method and system Download PDF

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CN111064682B
CN111064682B CN201911356944.1A CN201911356944A CN111064682B CN 111064682 B CN111064682 B CN 111064682B CN 201911356944 A CN201911356944 A CN 201911356944A CN 111064682 B CN111064682 B CN 111064682B
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赵宇
于伟
周斌
卜智勇
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Shanghai Hanxun Information Technology Co ltd
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Abstract

The invention relates to a root mean square delay spread estimation method and a system based on data statistics, which comprises the following steps: step S1, a simulation platform is set up, a functional relation f (S) of root mean square time delay expansion is fitted in the simulation platform in a curve mode, and the f (S) is prestored in an actual hardware system; and step S2, acquiring data in real time by the actual hardware system, and calculating the real-time root mean square time delay expansion according to the functional relation f (S) obtained in the step S1. The method and the system disclosed by the invention are applied to OFDM channel estimation, and can accurately estimate the root mean square delay spread, thereby improving the accuracy of channel estimation and reducing the error rate of an OFDM system. In addition, the method and the system are simple to implement and low in complexity.

Description

Data statistics-based root mean square delay spread estimation method and system
Technical Field
The invention relates to the field of wireless communication, in particular to a root mean square delay spread estimation method and system based on data statistics.
Background
OFDM, the mainstream signal modulation and transmission technology of the physical layer of current wireless communication, decomposes a physically high-speed wideband signal into a plurality of parallel low-speed narrowband signals. Since the bandwidth of the narrowband signal is smaller than the coherence bandwidth of the wireless channel, the duration of one OFDM symbol is smaller than the coherence time of the channel.
The OFDM symbols of a whole subframe can be decomposed into a time-frequency matrix as shown in fig. 1. Wherein, the horizontal axis is the time axis of the OFDM symbol, and the length of one square grid represents the duration of one OFDM symbol; the vertical axis is an OFDM subcarrier frequency axis, and the width of one square grid represents the bandwidth of a subcarrier narrowband signal. Since each of the checkered signals experiences slow fading in the time domain and flat fading in the frequency domain, it can be approximately considered that one checkered point can be represented by the same channel response. When such a time-frequency signal reaches the receiving end through the channel, each square undergoes different fading, and the channel estimation is to obtain the channel response of each square.
At present, a method of inserting pilot frequency is used for channel estimation, and the specific method is as follows: the transmitting end inserts pilot symbols (i.e. black boxes in the figure) into some positions of the time-frequency matrix, and since the pilot symbols are known to the transmitting end and the receiving end, when the receiving end receives the pilot symbols, the channel response at the pilot positions can be obtained. Referring to fig. 1, that is, the channel responses at the black squares can be obtained, and then the channel responses of all other white squares in the subframe data time-frequency matrix are obtained by interpolation.
However, the current method for obtaining unknown data by interpolation using known data is wiener filtering, and the estimation accuracy of the method depends heavily on the accuracy of input parameters, such as: maximum doppler shift, average signal-to-noise ratio, root mean square delay spread, etc. In the prior art, an empirical value is generally used as an estimated value of the root mean square delay spread, and the estimated value often has a larger difference with the real-time root mean square delay spread. However, when the root mean square delay spread estimation is inaccurate, the time domain interpolation of the channel response is inaccurate, and further, the error rate of the whole hardware communication system is high, and the performance is seriously deteriorated.
Disclosure of Invention
The invention provides a root mean square delay spread estimation method and system based on data statistics, and solves the problems that in the prior art, the root mean square delay spread estimation is inaccurate, so that the error rate of the whole hardware communication system is high, and the performance is seriously deteriorated.
The invention discloses a root-mean-square delay spread estimation method based on data statistics, which comprises the following steps:
step S1, a simulation platform is set up, a functional relation f (S) of root mean square time delay expansion is fitted in the simulation platform in a curve mode, and the f (S) is prestored in an actual hardware system; and
step S2, the real hardware system collects data in real time, and calculates the real-time RMS time delay expansion according to the function relation f (S) obtained in the step S1
Figure GDA0003455161640000021
Wherein the step S1 includes:
step S11, setting n channel models in the simulation platform;
step S12, calculating the root mean square delay spread of each channel according to the parameters of each channel model in the step S11
Figure GDA0003455161640000022
Step S13, collecting m sub-frames for each channel, and calculating the root mean square time delay expansion tau of each sub-frame by using the pilot frequency of each sub-framejObtaining m root mean square time delay spreads;
step S14, calculating m statistics S of root mean square time delay spread in the step S13iObtaining n data points
Figure GDA0003455161640000023
Step S15, for the data point in the step S14
Figure GDA0003455161640000024
Performing curve fitting to obtain the root mean square delay spread of the channel
Figure GDA0003455161640000025
And statistic siFunctional relationship f(s).
The step S2 includes:
step S21, the actual hardware system collects each sub-frame data of the channel in real time, calculates the root mean square time delay expansion tau of each sub-framej' and buffer;
step S22, judging whether the root mean square time delay expansion number of the actual hardware system buffer reaches m, if not, using the average value of the buffer time delay expansion as the real time root mean square time delay expansion
Figure GDA0003455161640000026
Finishing; if m is reached, the process proceeds to step S23;
step S23, calculating m statistics S of the cached root mean square time delay spread, wherein the statistics in the step S are the same as the statistics in the step S14;
step S24, calculating the real-time RMS time delay spread according to the function relationship f (S) pre-stored in the step S1
Figure GDA0003455161640000031
In the step S12, root mean square delay spread of each channel is calculated
Figure GDA0003455161640000032
The formula of (1) is:
Figure GDA0003455161640000033
wherein the content of the first and second substances,
Figure GDA0003455161640000034
τ is the time delay, Ac(τ) is the gain for the time delay.
Statistic S in the step S14iStandard deviation or mean values are used.
The statistic s in step 23 is standard deviation or average value.
A rms delay spread estimation system based on data statistics, comprising:
the simulation platform module obtains a function relation f(s) of root-mean-square delay spread through curve fitting; and
data acquisition and computation hardware module, f(s) prestoring the hardware module and computing therefrom real-time RMS time delay spread
Figure GDA0003455161640000035
The simulation platform module comprises:
-a data set calculation module in which n channel models are set and n data points are acquired; and
-a curve fitting module in which said n data points are curve fitted to obtain a channel root mean square delay spread
Figure GDA0003455161640000036
And statistic siFunctional relationship f(s).
The data acquisition and calculation hardware module comprises:
-a data acquisition module for acquiring a pilot for each sub-frame of the channel; and
-a calculation module for calculating a real-time root mean square time delay spread
Figure GDA0003455161640000037
The data acquisition module comprises:
-a pilot sequence extraction module; and
-a pilot location channel response location calculation module.
The calculation module comprises:
-a delay spread calculation module for calculating the root mean square delay spread τ of each sub-frame of said channelj’；
-a delay spread buffer module for buffering the root mean square delay spread τ of each sub-frame of the channelj' and judging whether the buffer number reaches a specified value, if not, taking the average value of the buffer delay expansion as the real-time root mean square delay expansion
Figure GDA0003455161640000041
If yes, entering into real-time root mean square time delay expansion
Figure GDA0003455161640000042
A calculation module; and
-real time rms delay spread
Figure GDA0003455161640000043
The calculation module calculates statistic s of the cached root mean square time delay expansion, and calculates real-time root mean square time delay expansion according to a function relation f(s) prestored by the simulation platform module
Figure GDA0003455161640000044
In the OFDM system with the multipath effect, the curve relation between the time delay expansion statistic and the actual root-mean-square time delay expansion is fitted by using off-line simulation and is prestored in an actual hardware system. Then, a certain amount of subframe data is collected in real time by an actual hardware communication system, statistics is calculated, root mean square delay spread is calculated by using a pre-stored curve relation, and a curve fitting value or a buffered subframe delay spread average value is selected to be used as a channel estimation parameter according to whether the number of the operated subframes reaches a specified value or not. The method and the system disclosed by the invention are applied to OFDM channel estimation, and can accurately estimate the root mean square delay spread, thereby improving the accuracy of channel estimation and reducing the error rate of an OFDM system. In addition, the method and the system are simple to implement and low in complexity.
Drawings
Fig. 1 is a schematic diagram of an OFDM time-frequency matrix.
Fig. 2 is a flow chart of a method of rms delay spread estimation in accordance with the present invention.
Fig. 3 is a flow chart of curve fitting in step S3 according to the present invention.
Fig. 4 is a flow chart of calculating the root mean square delay spread in real time in step S4 according to the present invention.
FIG. 5 is a block diagram of an emulation platform system in accordance with the present invention.
FIG. 6 is a block diagram of a data acquisition and computing system in accordance with the present invention.
Fig. 7 is a comparison graph of simulated bit errors obtained by the rms delay spread estimation method according to the present invention and the conventional fixed value method.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The invention relates to a root mean square delay spread estimation method based on data statistics, which is applied to OFDM channel estimation and mainly comprises two parts of off-line simulation, curve fitting and hardware real-time data acquisition and calculation. Namely, as shown in fig. 2, includes:
step S1, a simulation platform is set up, a functional relation f (S) of root mean square time delay expansion is fitted in the simulation platform in a curve mode, and the f (S) is prestored in an actual hardware system; and
step S2, the real hardware system collects data in real time and calculates the real time RMS time delay expansion according to the function relation f (S) obtained in step S1
Figure GDA0003455161640000051
Fig. 3 shows an off-line simulation and curve fitting flowchart of step S1, which includes:
step S11, setting n different types of typical channel models in the simulation platform;
step S12, according to the time delay of each channel model in step S11, calculating the root mean square time delay spread of each channel according to the formula (1)
Figure GDA0003455161640000052
Figure GDA0003455161640000053
Wherein the content of the first and second substances,
Figure GDA0003455161640000054
τ is the time delay, Ac(τ) is the gain for the time delay.
Step S13, collecting m sub-frames for each channel, calculating root mean square time delay expansion tau of each sub-frame by using pilot frequency of each sub-framejAnd m root mean square delay spreads are obtained.
Step S14, due to the m τjThe values obey a certain probability distribution, so that their statistic s can be calculatediTo derive data points for each channel
Figure GDA0003455161640000055
Further obtaining n data points of n channels
Figure GDA0003455161640000056
Statistic siStandard deviation or mean may be used.
Step S15, for the data point in step S14
Figure GDA0003455161640000057
Performing curve fitting to obtain the root mean square delay spread of the channel
Figure GDA0003455161640000058
And statistic siFunctional relationship f(s).
The hardware real-time data acquisition calculation flowchart is shown in fig. 4, and includes:
step S21, the actual hardware system collects each sub-frame data of the channel in real time, and calculates the root mean square time delay expansion tau of each sub-frame by using the pilot frequency of each sub-framej' and buffer;
step S22, judging whether the root mean square time delay expansion number of the actual hardware system buffer reaches m, if not, using the average value of the buffer time delay expansion as the real time root mean square time delay expansion
Figure GDA0003455161640000059
Finishing; if m is reached, the process proceeds to step S23;
step S23, calculating m statistics S of the cached root mean square time delay spread, wherein the statistics in the step S are the same as the statistics in the step S14; for example, if the statistical amount in step S14 adopts the standard deviation, the standard deviation of m frequency offset values is calculated in this step; if the statistical quantity in step S14 is an average value, the average value of m frequency offset values is calculated in this step.
Step S24, calculating the real-time RMS time delay spread according to the function relationship f (S) pre-stored in the step S15
Figure GDA0003455161640000061
The method calculates the root mean square delay spread, and takes the root mean square delay spread as the parameter of channel estimation, thereby obviously improving the accuracy of channel estimation and reducing the error rate of the OFDM system.
The invention relates to a root-mean-square delay spread estimation system based on data statistics, which comprises:
the simulation platform module obtains a function relation f(s) of root-mean-square delay spread through curve fitting; and a data acquisition and computation hardware module, f(s) prestoring the hardware module and computing therefrom a real-time RMS time delay spread
Figure GDA0003455161640000062
As shown in fig. 5, the simulation platform module specifically includes: the device comprises a data setting calculation module and a curve fitting module. The simulation platform module system flow is as follows: setting n channel models in a simulation platform, and calculating the root mean square delay spread of each channel
Figure GDA0003455161640000063
Secondly, m sub-frames are collected for each channel, and the root mean square time delay expansion tau of each sub-frame is calculated by utilizing the pilot frequency of each sub-framejAnd m root mean square delay spreads are obtained. Then m statistics s of root mean square delay spread are calculatediObtaining n data points; finally, curve fitting is carried out on the n data points, so that the root mean square time delay expansion of the channel is obtained
Figure GDA0003455161640000064
And statistic siFunctional relationship f(s).
As shown in fig. 6, the data acquisition and calculation hardware module includes: the device comprises a data acquisition module and a calculation module. The data acquisition module comprises a pilot frequency sequence extraction module and a pilot frequency position channel response position calculation module, and the calculation module comprises a time delay expansion calculation module, a time delay expansion cache module and a real-time root-mean-square time delay expansion
Figure GDA0003455161640000065
And a calculation module.
The data acquisition and calculation hardware module system flow is as follows: acquisition of each channel in a pilot sequence extraction moduleThe pilot frequency of the subframe, divide and get the signal channel response of the pilot frequency place with the pilot frequency that the receiving end knows and pilot frequency received actually in the signal channel response position calculation module of pilot frequency position; secondly, calculating the root mean square delay spread tau of each subframe of the channel in a delay spread calculation modulej'; then, the root mean square delay spread tau of each sub-frame of the channel is buffered in a delay spread buffer modulej' and judging whether the buffer number reaches m, if not, using the average value of the buffer delay expansion as the real-time root mean square delay expansion
Figure GDA0003455161640000066
If m is reached, then enter into real time RMS time delay expansion
Figure GDA0003455161640000067
A calculation module; last time root mean square delay spread
Figure GDA0003455161640000068
Calculating statistic s of cached root mean square time delay expansion in a calculating module, and calculating real-time root mean square time delay expansion according to a function relation f(s) prestored in the simulation platform module
Figure GDA0003455161640000071
The data collection and computing hardware modules of the present invention are further described below by way of a specific example.
In this example, 1 transmit and 1 receive antenna mode, ETU channel, doppler spread of 0Hz and 16QAM modulation are selected, and the value of m is 500.
During the computation, the actual hardware system collects each sub-frame in real time. And when the number of the subframes is less than 500, taking the average value of the subframe delay spread buffered by the system as the final root mean square delay spread. And when the subframe number is 500 subframes, counting the standard deviation by using the delay spread of the 500 subframes in front of the current subframe, and calculating the root mean square delay spread required by the channel estimation of the current subframe by using the curve relation between the standard deviation and offline training fitting.
According to the inventionA comparison graph of simulated bit errors obtained by the rms delay spread estimation method and the conventional fixed value method is shown in fig. 7. ETU channel delay τ ═ 050120200230500160023005000]1e-9s, corresponding gain Ac(τ)＝[-1 -1 -1 0 0 0 -3 -5 -7]dB, and a normalized value for the baseband period of 60. As can be seen from the figure, conventional τ is usedrmsMethod for taking fixed value, when the taken fixed value and actual taurmsWhen the values are very different, the error code curve appears on a platform along with the increase of the signal-to-noise ratio, and the performance is very poor. The method of the invention can track the change of the channel in real time, and the error rate is rapidly reduced along with the increase of the signal-to-noise ratio. Therefore, the method of the invention can accurately measure taurmsAnd estimating, thereby improving the performance of channel estimation.
The above embodiments are merely preferred embodiments of the present invention, which are not intended to limit the scope of the present invention, and various changes may be made in the above embodiments of the present invention. All simple and equivalent changes and modifications made according to the claims and the content of the specification of the present application fall within the scope of the claims of the present patent application. The invention has not been described in detail in order to avoid obscuring the invention.

Claims (11)

1. A root mean square delay spread estimation method based on data statistics is characterized by comprising the following steps:
step S1, a simulation platform is set up, a functional relation f (S) of root mean square time delay expansion is fitted in the simulation platform in a curve mode, and the f (S) is prestored in an actual hardware system;
step S2, the real hardware system collects data in real time, and calculates the real-time RMS time delay expansion according to the function relation f (S) obtained in the step S1
Figure FDA0003455161630000011
2. The rms delay spread estimation method according to claim 1, wherein said step S1 includes:
step S11, setting n channel models in the simulation platform;
step S12, calculating the root mean square delay spread of each channel according to the parameters of each channel model in the step S11
Figure FDA0003455161630000012
Step S13, collecting m sub-frames for each channel, and calculating the root mean square time delay expansion tau of each sub-frame by using the pilot frequency of each sub-framejObtaining m root mean square time delay spreads;
step S14, calculating m statistics S of root mean square time delay spread in the step S13iObtaining n data points
Figure FDA0003455161630000013
Step S15, for the data point in the step S14
Figure FDA0003455161630000014
Performing curve fitting to obtain the root mean square delay spread of the channel
Figure FDA0003455161630000015
And statistic siFunctional relationship f(s).
3. The rms delay spread estimation method according to claim 2, wherein the rms delay spread of each channel is calculated in step S12
Figure FDA0003455161630000016
The formula of (1) is:
Figure FDA0003455161630000017
wherein the content of the first and second substances,
Figure FDA0003455161630000018
τ is the time delay, Ac(τ) is the gain for the time delay.
4. The rms delay spread estimation method according to claim 2, wherein the statistic S in step S14iStandard deviation or mean values are used.
5. The rms delay spread estimation method according to claim 1, wherein said step S2 includes:
step S21, the actual hardware system collects each sub-frame data of the channel in real time, calculates the root mean square time delay expansion tau of each sub-framej' and buffer;
step S22, judging whether the root mean square time delay expansion number of the actual hardware system buffer reaches m, if not, using the average value of the buffer time delay expansion as the real time root mean square time delay expansion
Figure FDA0003455161630000021
Finishing; if m is reached, the process proceeds to step S23;
step S23, calculating m statistics S of the cached root mean square time delay spread, wherein the statistics in the step S are the same as the statistics in the step S14;
step S24, calculating the real-time RMS time delay spread according to the function relationship f (S) pre-stored in the step S1
Figure FDA0003455161630000022
6. A RMS delay spread estimation method according to claim 5, characterized in that the statistic S in step S23 is the standard deviation or the average.
7. A rms delay spread estimation system based on data statistics, comprising:
-a simulation platform module, which obtains a functional relation f(s) of root mean square delay spread by curve fitting; and
-a data acquisition and computation hardware module, said f(s) being pre-stored in said hardware module and computing therefrom a real-time rms delay spread
Figure FDA0003455161630000023
8. The system of claim 7, wherein the simulation platform module comprises:
-a data set calculation module in which n channel models are set and n data points are acquired; and
-a curve fitting module in which said n data points are curve fitted to obtain a channel root mean square delay spread
Figure FDA0003455161630000024
And statistic siFunctional relationship f(s).
9. The system of claim 7, wherein the data collection and computation hardware module comprises:
-a data acquisition module for acquiring a pilot for each sub-frame of the channel; and
-a calculation module for calculating a real-time root mean square time delay spread
Figure FDA0003455161630000025
10. The system of claim 9, wherein the data collection module comprises:
-a pilot sequence extraction module; and
-a pilot location channel response location calculation module.
11. The system of claim 9, wherein the computing module comprises:
-a delay spread calculation module for calculating the root mean square delay spread τ of each sub-frame of said channelj’；
-a delay spread buffer module for buffering the root mean square delay spread τ of each sub-frame of the channelj' and judging whether the buffer number reaches a specified value, if not, taking the average value of the buffer delay expansion as the real-time root mean square delay expansion
Figure FDA0003455161630000031
If yes, entering into real-time root mean square time delay expansion
Figure FDA0003455161630000032
A calculation module; and
-real time rms delay spread
Figure FDA0003455161630000033
The calculation module calculates statistic s of the cached root mean square time delay expansion, and calculates real-time root mean square time delay expansion according to a function relation f(s) prestored by the simulation platform module
Figure FDA0003455161630000034
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