CN110912844B - Channel estimation optimization method based on big data analysis - Google Patents

Channel estimation optimization method based on big data analysis Download PDF

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CN110912844B
CN110912844B CN201911193690.6A CN201911193690A CN110912844B CN 110912844 B CN110912844 B CN 110912844B CN 201911193690 A CN201911193690 A CN 201911193690A CN 110912844 B CN110912844 B CN 110912844B
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channel estimation
maximum doppler
calculating
frequency shift
shift value
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CN110912844A (en
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周斌
翟志刚
于伟
陆犇
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Shanghai Hanxun Information Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2689—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation
    • H04L27/2695—Link with other circuits, i.e. special connections between synchronisation arrangements and other circuits for achieving synchronisation with channel estimation, e.g. determination of delay spread, derivative or peak tracking

Abstract

The invention relates to a channel estimation optimization method based on big data analysis, which comprises the following steps: step S1, selecting a channel model and building a simulation platform; step S2, fitting a curve to obtain a function relation f (S) of the maximum Doppler frequency shift value; step S3, calculating the real-time maximum Doppler frequency shift value
Figure DDA0002294197410000011
And step S4, channel estimation. The invention can track the change of the channel in real time, improve the accuracy of channel estimation and reduce the error rate of the OFDM communication system.

Description

Channel estimation optimization method based on big data analysis
Technical Field
The invention relates to the field of wireless communication, in particular to a channel estimation optimization method based on big data analysis.
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, maximum delay spread, etc. In the prior art, an empirical value or a directly calculated frequency offset value is generally used as a maximum doppler frequency shift value, and the maximum doppler frequency shift value is often greatly different from a real-time maximum doppler frequency shift value. However, when the maximum doppler shift value is estimated inaccurately, it will cause the time-domain interpolation of the channel response to be inaccurate, and further cause the error rate of the whole hardware communication system to be higher, and the performance to be seriously deteriorated.
Disclosure of Invention
The invention provides a channel estimation optimization method based on big data analysis, which solves the problems of high error rate and serious performance deterioration of the whole hardware communication system caused by inaccurate estimation of a maximum Doppler frequency shift value in the prior art.
The invention provides a channel estimation optimization method based on big data analysis, which comprises the following steps:
step S1, selecting a channel model according to an actual communication scene, and building a simulation platform;
step S2, fitting a function relation f (S) of the maximum Doppler frequency shift value in a curve in the simulation platform set up in the step S1;
step S3, calculating the real-time maximum Doppler frequency shift value by using the function relation f (S) obtained in the step S2
Figure BDA0002294197390000021
Step S4, the maximum Doppler frequency shift value obtained in the step S3
Figure BDA0002294197390000023
As a parameter, channel estimation is performed.
The step S2 includes:
step S21, presetting n maximum Doppler frequency shift values in the simulation platform, and marking as [ d ]1,d2,…dn]；
Step S22, for each maximum Doppler frequency shift value d in the step S21i(i 1, 2.. n) m subframes are collected, and the frequency offset value of each subframe is calculated
Figure BDA0002294197390000024
Obtaining m frequency offset values;
step S23, calculating statistics S of m frequency offset values in the step S22iA maximum Doppler frequency shift value diCorresponding to a statistic siForming a data point(s)i,di) Finally, n data points [(s) are obtained1,d1),(s2,d2),…(sn,dn)]；
Step S24, obtaining a functional relation f (S) between the maximum doppler shift value d and the statistic S by curve fitting, and pre-storing f (S) into an actual hardware system.
The step S3 includes:
step S31, real hardware system collects each sub-frame data in real time, takes a certain SNR threshold t, when the sub-frame SNR is larger than t dB, calculates the frequency deviation value of the sub-frame
Figure BDA0002294197390000025
And caching;
step S32, judging whether the frequency offset number buffered by the actual hardware system reaches m, if not, using the maximum value of the buffered frequency offset value as the maximum Doppler frequency shift value
Figure BDA0002294197390000022
Proceeding to step S4; if m is reached, the process proceeds to step S33;
step S33, calculating statistic S of m cached frequency offset values, wherein the statistic S in the step is the same as the statistic S in the step S23;
step S34, calculating the maximum Doppler frequency shift value according to the function relation f (S) pre-stored in the step S24
Figure BDA0002294197390000031
Calculating the frequency offset value comprises:
step S5, taking the pilot frequency of two rows of OFDM symbols in the sub-frame, and calculating the channel response of the two rows of pilot frequency, and recording as h1＝[h1,0,h1,1,…,h1,N-1]T，h2＝[h2,0,h2,1,…,h2,N-1]TWherein, N is the number of the pilot frequency of one column of OFDM symbols;
step S6, calculating phase difference
Figure BDA0002294197390000032
Wherein, angle () represents the range of the argument (-pi, pi);
step S7, calculating frequency offset
Figure BDA0002294197390000033
Wherein f issIs the baseband sampling frequency, and M is the number of sample points apart between two columns of OFDM symbols.
The statistic siStandard deviations were used.
The statistic s is taken as the standard deviation.
When the actual communication scenario in step S1 is an urban environment, an ETU channel model is adopted.
When the actual communication scenario in step S1 is a rural environment, an RA channel model is used.
The method for obtaining f (S) in step S24 is as follows: according to the data point [(s)1,d1),(s2,d2),…(sn,dn)]Determining the type of the fitting function, then correcting each parameter of the function f(s), and finally obtaining a function relation.
The method for correcting each parameter of the function f(s) adopts an interpolation method or a least square method.
The method comprises the steps of acquiring a plurality of sub-frame data of maximum Doppler frequency shift by using off-line simulation, fitting a curve relation between frequency offset statistics and the maximum Doppler frequency shift, then acquiring a certain amount of sub-frame data in real time in an actual hardware communication system, calculating by using a pre-stored curve relation to obtain a maximum Doppler frequency shift value, and selecting a curve fitting value or a maximum value of cached sub-frame frequency offset as a parameter of channel estimation according to whether the number of cached sub-frames of the system reaches a specified value or not. The invention can track the change of the channel in real time, improve the accuracy of channel estimation and reduce the error rate of the OFDM communication system.
Drawings
Fig. 1 is a schematic diagram of an OFDM time-frequency matrix.
Fig. 2 is a flow chart of a channel estimation optimization method according to the present invention.
Fig. 3 is a flow chart of curve fitting in step S2 according to the present invention.
Fig. 4 is a flowchart for calculating the maximum doppler frequency shift value in real time in step S3 according to the present invention.
Fig. 5 is a system block diagram of a channel estimation optimization method according to the present invention.
Figure 6 is a maximum doppler shift curve fit graph according to one example of the invention.
Fig. 7 to 10 are graphs comparing the error rate of the communication system obtained by the channel estimation optimization method and the direct frequency offset calculation method according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The channel estimation optimization method provided by the invention is applied to OFDM channel estimation, and as shown in FIG. 2, the method comprises the following steps:
and step S1, selecting a channel model according to the actual communication scene, and building a simulation platform. For example, if the actual operation scenario of the wireless system is in an urban environment, an ETU channel model provided by 3GPP is selected for simulation; if the network is in the rural environment, the RA channel model is used for simulation.
Step S2, in the simulation platform set up in step S1, a function relation f (S) of the maximum doppler frequency shift value is curve-fitted.
Step S3, calculating the real-time maximum Doppler frequency shift value by using the function relation f (S) obtained in step S2
Figure BDA0002294197390000041
Step S4, the maximum Doppler frequency shift value obtained in step S3
Figure BDA0002294197390000042
As a parameter, channel estimation is performed.
As shown in fig. 3, step S2 includes:
step S21, presetting n maximum Doppler frequency shift values in the simulation platform, and marking as [ d ]1,d2,…dn]。
Step S22, for each maximum Doppler frequency shift value d in step S21i(i 1, 2.. n) m subframes are collected, and the frequency offset value of each subframe is calculated
Figure BDA0002294197390000043
M frequency offset values are obtained.
Step S23, calculating statistic S of m frequency offset values in step S22iA maximum Doppler frequency shift value diCorresponding to a statistic siForming a data point(s)i,di) Finally, n data points [(s) are obtained1,d1),(s2,d2),…(sn,dn)]. In different scenarios, the most suitable statistic, such as standard deviation, variance or other suitable statistic, may be selected according to the actual simulation result. Under the scene of certain maximum Doppler frequency shift, the frequency offset values calculated by sampling obey certain distribution, and the statistic quantity of the frequency offset values is stableIn (1).
Step S24, obtaining a functional relation f (S) between the maximum doppler shift value d and the statistic S by curve fitting, and pre-storing f (S) into an actual hardware system. The specific method for obtaining f(s) is as follows: according to the data point [(s)1,d1),)s2,d2),…(sn,dn)]The type of the fitting function is roughly determined, and then parameters of the function f(s) are corrected by an interpolation method or a least square method and the like, so that the optimal function f(s) is finally obtained. The results can be obtained by using a plurality of curve fitting methods according to the data points obtained by simulation, and then the method with the optimal effect is selected.
As shown in fig. 4, step S3 includes:
step S31, real hardware system collects each sub-frame data in real time, takes a certain SNR threshold t, when the sub-frame SNR is larger than t dB, calculates the frequency deviation value of the sub-frame
Figure BDA0002294197390000053
And buffered.
Step S32, judging whether the frequency offset number buffered by the actual hardware system reaches m, if not, using the maximum value of the buffered frequency offset value as the maximum Doppler frequency shift value
Figure BDA0002294197390000051
Proceeding to step S4; if m is reached, the process proceeds to step S33.
In step S33, a statistic S of the m buffered frequency offset values is calculated, which is the same as the statistic in step S23. For example, if the statistical amount in step S23 is the standard deviation, the standard deviation of the m frequency offset values is calculated in this step.
Step S34, calculating the maximum Doppler frequency shift value according to the function relation f (S) pre-stored in step S24
Figure BDA0002294197390000052
The principle of the frequency offset calculation in the above steps S22 and S31 is as follows:
two arbitrary columns of OFDM symbolsCorresponding to the same phase difference theta 2 pi f of channel response at subcarrierδτ, where τ is the OFDM symbol time interval for the two columns of pilots. In the formula, theta can be calculated through channel response, tau is known, and therefore frequency offset f can be calculatedδ。
The actual channel is subject to multipath rayleigh fading and noise, so that various random changes exist in the channel response phases at two columns of OFDM pilots, and the inherent frequency offset of the system can be obtained only by averaging the random phase changes. The method adopted by the invention is to carry out weighted summation on the pilot frequency response difference of the corresponding positions of two rows of pilot frequencies, and specifically comprises the following steps:
step S5, taking the pilot frequency of two rows of OFDM symbols in the sub-frame, and calculating the channel response of the two rows of pilot frequency, and recording as h1＝[h1,0,h1,1,…,h1,N-1]T，h2＝[h2,0,h2,1,…,h2,N-1]TWherein, N is the number of the pilots in one column of the OFDM symbols.
Step S6, calculating phase difference
Figure BDA0002294197390000061
Wherein, angle () represents the argument range (- π, π).
Step S7, calculating frequency offset
Figure BDA0002294197390000062
Wherein f issIs the baseband sampling frequency, and M is the number of sample points apart between two columns of OFDM symbols.
The system block diagram of the channel estimation optimization method according to the invention is shown in fig. 5, the invention caches the frequency offset of the latest received subframe in real time, calculates the maximum doppler frequency shift value of the current frame, and can track the change of the channel in real time, thereby obviously improving the accuracy of channel estimation and reducing the error rate of the OFDM communication system.
The invention is further illustrated by the following specific example.
In this example, 1/3 rate turbo coding, 64QAM constellation mapping, and 4-transmit 4-receive antenna pattern are selected.
First, according to step S1, since the actual operation scenario of this example is in an urban environment, an ETU channel is selected and a simulation platform is built.
Next, according to step S2, the maximum doppler shift number n is preset in the simulation platform as 6, which respectively includes: 200Hz, 500Hz, 800Hz, 1100Hz, 1400Hz, 1800 Hz. The number m of the collected subframes of each maximum Doppler frequency shift value is 1000, and the standard deviation s of the frequency shift of the 1000 subframes is calculatediComprises the following steps: 19. 52, 80, 120, 160, 215. According to the scatter data composed of the standard deviation and the maximum Doppler frequency shift value, fitting a curve chart as shown in FIG. 6, and obtaining a functional relation f(s) of the maximum Doppler frequency shift value and the standard deviation as: d ═ 0.0088s2+10.1777s +10.7857, the functional relation f(s) is pre-stored in the actual hardware system.
Then, according to step S3, the actual hardware system collects each sub-frame in real time, and calculates the frequency offset value of the sub-frame when the sub-frame snr is greater than 10dB, with the 10dB snr as the threshold
Figure BDA0002294197390000065
And buffered. When the frequency deviation number of the buffer memory does not reach 1000, taking the maximum value in the sub-frame frequency deviation values buffered by the system as the maximum Doppler frequency shift value
Figure BDA0002294197390000063
When the frequency deviation number of the buffer memory reaches 1000, the frequency deviation value of the 1000 sub-frames is used for counting the standard deviation, and then the function relation f(s) is used for calculating the maximum Doppler frequency shift value
Figure BDA0002294197390000064
Finally, according to step S4, the maximum Doppler shift estimation value calculated in step S3 is added
Figure BDA0002294197390000071
As one of the parameters, channel estimation is performed.
Fig. 7 to 10 are graphs showing the bit error rate of a communication system obtained by the channel estimation optimization method of the present invention compared with the bit error rate of a communication system obtained by the method of directly calculating the frequency offset.
It can be seen from the figure that when the actual maximum doppler shift is relatively large (such as 1800Hz, 1200Hz, 700Hz), the directly calculated frequency offset error rate is very high, and does not improve much with the increase of the signal-to-noise ratio. The bit error rate obtained by using the statistical fitting real-time estimation method of the invention is rapidly reduced along with the increase of the signal-to-noise ratio. When the actual maximum doppler shift ratio is small, although the error rate obtained by directly calculating the frequency offset method also has a descending trend along with the increase of the signal-to-noise ratio, the signal-to-noise ratio has a huge difference compared with the method of the present invention. Therefore, in the ground-space channel with high speed, high carrier frequency and large Doppler frequency shift, the channel estimation method has good performance and strong adaptability.
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 (8)

1. A channel estimation optimization method based on big data analysis is characterized by comprising the following steps:
step S1, selecting a channel model according to an actual communication scene, and building a simulation platform;
step S2, fitting a function relation f (S) of the maximum Doppler frequency shift value in a curve in the simulation platform set up in the step S1; the method comprises the following steps:
step S21, presetting n maximum Doppler frequency shift values in the simulation platform, and marking as [ d ]1，d2，...dn]；
Step S22, for each maximum Doppler frequency shift value d in the step S21i(i 1, 2.. n) m subframes are collected, and the frequency offset value of each subframe is calculated
Figure FDA0003455162030000011
Obtaining m frequency offset values;
step S23, calculating statistics S of m frequency offset values in the step S22iA maximum Doppler frequency shift value diCorresponding to a statistic siForming a data point(s)i，di) Finally, n data points [(s) are obtained1，d1)，(s2，d2)，…(sn，dn)]；
Step S24, obtaining a function relation f (S) of the maximum Doppler frequency shift value d and the statistic S by a curve fitting method, and pre-storing f (S) into an actual hardware system;
step S3, calculating the real-time maximum Doppler frequency shift value by using the function relation f (S) obtained in the step S2
Figure FDA0003455162030000012
The method comprises the following steps:
step S31, real hardware system collects each sub-frame data in real time, takes a certain SNR threshold t, when the sub-frame SNR is larger than t dB, calculates the frequency deviation value of the sub-frame
Figure FDA0003455162030000016
And caching;
step S32, judging whether the frequency offset number buffered by the actual hardware system reaches m, if not, using the maximum value of the buffered frequency offset value as the maximum Doppler frequency shift value
Figure FDA0003455162030000013
Proceeding to step S4; if m is reached, the process proceeds to step S33;
step S33, calculating statistic S of m cached frequency offset values, wherein the statistic S in the step is the same as the statistic S in the step S23;
step S34, calculating the maximum Doppler frequency shift value according to the function relation f (S) pre-stored in the step S24
Figure FDA0003455162030000014
Step S4, the maximum Doppler frequency shift value obtained in the step S3
Figure FDA0003455162030000015
As a parameter, channel estimation is performed.
2. The channel estimation optimization method of claim 1, wherein calculating the frequency offset value comprises:
step S5, taking the pilot frequency of two rows of OFDM symbols in the sub-frame, and calculating the channel response of the two rows of pilot frequency, and recording as h1＝[h1，0，h1，1，…，h1，N-1]T，h2＝[h2，0，h2，1，…，h2，N-1]TWherein, N is the number of the pilot frequency of one column of OFDM symbols;
step S6, calculating phase difference
Figure FDA0003455162030000021
Wherein, angle () represents the range of the argument (-pi, pi);
step S7, calculating frequency offset
Figure FDA0003455162030000022
Wherein f issIs the baseband sampling frequency, and M is the number of sample points apart between two columns of OFDM symbols.
3. The channel estimation optimization method of claim 1, wherein the statistic siStandard deviations were used.
4. The channel estimation optimization method of claim 1, wherein the statistic s is a standard deviation.
5. The channel estimation optimization method according to claim 1, wherein an ETU channel model is adopted when the actual communication scenario in step S1 is an urban environment.
6. The channel estimation optimization method according to claim 1, wherein an RA channel model is used when the actual communication scenario in step S1 is a rural environment.
7. The channel estimation optimization method of claim 1, wherein the step S24 is derived by the method of f (S): according to the data point [(s)1，d1)，(s2，d2)，...(sn，dn)]Determining the type of the fitting function, then correcting each parameter of the function f(s), and finally obtaining a function relation.
8. The channel estimation optimization method of claim 7, wherein the method of modifying each parameter f(s) is interpolation or least square method.
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