CN103117966B - Channel estimation method based on dynamic pilot frequency adjustment - Google Patents

Abstract

The invention discloses a channel estimation method based on dynamic pilot adjustment, which at least includes the following steps: acquiring a currently injected data frame; performing pilot pattern analysis on the data frame; performing channel estimation according to the pilot pattern analysis result , and obtain the MSE value; perform MSE statistical analysis on the MSE value of the data frame; judge whether the data frame needs to adjust the pilot pattern; when the pilot pattern needs to be adjusted, use the pilot pattern adjustment algorithm to determine the pilot Pattern adjustment strategy, and call the pilot pattern in the pilot pattern library for adjustment. Using the method can obtain higher estimation accuracy than the traditional fixed pilot pattern channel estimation method, thereby effectively improving the overall transmission performance of the communication system in the satellite mobile multipath channel environment.

Description

A Channel Estimation Method Based on Dynamic Pilot Adjustment

technical field

The invention belongs to the technical field of mobile communication, and relates to a method for realizing channel estimation by dynamically adjusting the density of pilot patterns.

Background technique

The new generation of mobile communication technology represented by OFDM system is changing with each passing day. Orthogonal Frequency Division Multiplexing (OFDM) system is a multi-carrier communication system, which has a higher spectrum utilization rate than general systems and strong anti-interference performance. These advantages make it widely used in terrestrial wireless mobile communications, and in The satellite mobile communication system has also received more and more attention. However, the harsh satellite transmission environment such as multipath fading, multipath delay, Doppler frequency offset and other channel interference factors in the wireless channel seriously affect the performance of the system, causing differences in the amplitude and phase of the received signal. This effect is mainly reflected in the fact that it will cause deflection (multipath delay) and distortion (multipath fading) of the symbol constellation diagram, thereby affecting the phase and amplitude of the received signal, resulting in certain bit errors; in the actual system In this case, when the mobile receiving station is in the middle and high-speed mobile situation (including the relative movement between the satellite and the ground target due to factors such as orbital drift and the local movement of the ground mobile vehicle receiver, etc.), in addition to the above-mentioned multipath fading channel interference factors In addition, the impact of Doppler frequency offset on the received signal should also be considered. The interference of Doppler frequency offset on data symbols is mainly reflected in the fact that it will cause the phase of the symbol constellation to rotate dynamically, and the rotation speed will vary with the phase error. The increase and speed up, resulting in a large number of bit errors, thereby increasing the bit error rate of the system. In order to eliminate all kinds of negative effects caused by channel interference to the greatest extent, at the receiving end, the channel response of data symbols must be effectively estimated to improve system performance, which is the problem of channel estimation.

At present, the method to solve the channel estimation problem is generally to insert various pilot information into the data frame to track and estimate the dynamic change of the channel response. Most of the pilot information is based on a pilot structure with a fixed pattern configuration. Usually, the satellite mobile communication broadcast channel is a transmission channel with time/frequency selective fading characteristics, and the interference factors such as multipath fading, multipath delay and Doppler frequency deviation of the channel are changing all the time. Especially in the mobile multipath fading channel environment with deep fading and fast fading, this change is more severe. At this time, any channel estimation method based on a fixed pilot pattern cannot effectively track and estimate the complex and variable channel response of the data symbols in real time. The channel estimation method designed according to a specific pilot pattern mode usually It can only adapt to a single channel environment. Existing channel estimation methods generally have the problem of being unable to track the channel response in a large range of dynamic fluctuations in time, which reduces the accuracy of channel estimation and increases the bit error rate of the system.

Contents of the invention

The technical problem of the present invention is: aiming at the deficiencies of the prior art, a channel estimation method based on dynamic pilot adjustment is proposed, and this method can obtain higher estimation accuracy than the traditional fixed pilot pattern channel estimation method, thereby The overall transmission performance of the communication system in the satellite mobile multipath channel environment is effectively improved.

Technical solution of the present invention:

A channel estimation method based on dynamic pilot adjustment, at least including the following steps:

Get the currently injected data frame;

Performing pilot pattern analysis on the data frame;

Perform channel estimation according to the analysis result of the pilot pattern, and obtain an MSE value;

Carry out MSE statistical analysis to the MSE value of described data frame; Described MSE value carries out statistical analysis and is used to obtain the historical statistical data maximum value MAX (MSE) of MSE, the historical statistical data minimum value MIN (MSE) of MSE, the mathematics of MSE The variance σ(MSE) of expected E(MSE) and MSE, and the number of times MaxC1 exceeding MAX(MSE), the number of times MinC2 lower than MIN(MSE), and the number of times σ(MSE) increased VarRMSC3;

judging whether the data frame needs to adjust the pilot pattern;

When the pilot pattern needs to be adjusted, a pilot pattern adjustment strategy is determined using a pilot pattern adjustment algorithm, and the pilot pattern in the pilot pattern library is called for adjustment; the pilot pattern adjustment algorithm includes the following steps:

According to the statistical value obtained by the statistical analysis of the MSE value, the following judgments are made:

If the MSE value of the current data frame exceeds MAX (MSE) or is less than MIN (MSE), then execute the first branch: then judge whether MaxC1 exceeds the threshold value of MaxC1; Otherwise, execute the second branch: judge whether VarRMSC3 exceeds the gate of VarRMSC3 limit value;

In the first branch, if MaxC1 exceeds the threshold value of MaxC1, then adjust the pilot interval; otherwise, judge whether MinC2 exceeds the threshold value of MinC2; in the second branch, if VarRMSC3 exceeds the threshold value of VarRMSC3 limit, adjust the pilot spacing; otherwise, do not adjust the pilot;

In the first branch, if MinC2 exceeds the threshold value of MinC2, adjust the pilot pattern; otherwise, do not adjust the pilot.

Advantage of the present invention compared with prior art:

The present invention designs the pilot pattern as a variable structure, realizes more accurate tracking and estimation of the channel response through dynamic adjustment, designs a dynamic adjustment algorithm of the pilot pattern, and establishes a dynamic pilot pattern containing various density patterns. The frequency pattern library expands the channel estimation method based on the pilot system.

The present invention uses a dynamic pilot pattern adjustment mechanism to obtain better channel estimation accuracy than the traditional static/fixed pilot pattern method. Under the same signal-to-noise ratio (SNR=10-20dB), statistical research on simulation data shows that, Compared with the traditional method, the method of the invention improves the channel estimation accuracy (MSE) by about 1-2 orders of magnitude on average, and has a performance improvement of about 8-9 dB.

The present invention adopts a dynamic pilot pattern adjustment mechanism, so that the dynamic pilot method can save about 20% of system subcarrier frequency resources at most compared with the traditional static pilot method, and the saved subcarrier frequency resources can be used by data subcarriers, thereby significantly The utilization rate of system frequency resources is improved. The method of the invention optimizes the system sub-carrier frequency resources while improving the performance of the channel estimation precision MSE.

Description of drawings

Fig. 1 is a flow chart of the method of the present invention;

Fig. 2 is a flowchart of the dynamic adjustment algorithm of the pilot pattern of the present invention;

Fig. 3 is a kind of dynamic pilot pattern structure of adjusting the density of pilot frequency according to the present invention;

Fig. 4 is a kind of dynamic pilot pattern structure of adjusting sparse pilot density of the present invention;

Figure 5 is a comparison of channel estimation MSE performance under the COST-207 channel model;

Figure 6 is a comparison of channel estimation MSE performance under the SFN-4 channel model;

Fig. 7 is the variation curve of the dynamic pilot density (interval) under the COST-207 channel model;

Fig. 8 is the variation curve of the dynamic pilot density (interval) under the SFN-4 channel model;

Detailed ways

As shown in FIG. 1 , the main modules of a channel estimation method based on dynamic pilot adjustment in the present invention include: an MSE statistical analysis module, a pilot pattern adjustment algorithm module, and a pilot pattern library module.

As shown in Figure 1, the MSE (mean square error) statistical analysis module is responsible for analyzing and processing the current and past MSE data information, so as to make a judgment on whether to adjust the pilot pattern, and order how to adjust the pilot pattern in the next frame. The density of the frequency pattern.

During the data information processing process of the MSE statistical analysis module, the historical statistical information of MSE should be considered, which mainly includes the following four parameters:

a) The maximum value of historical statistical data of MSE, namely MAX(MSE);

b) The minimum value of historical statistical data of MSE, namely; MIN(MSE);

c) The mathematical expectation of MSE, that is, the mean E(MSE), is calculated as follows:

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; MSE</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; MSE</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}$

d) The variance of MSE, that is, the MSE of MSE, that is, the mean square error σ(MSE), is calculated as follows:

$\mathrm{<span\; class="notranslate">\; \&sigma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; MSE</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; MSE</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; MSE</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

$<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; MSE</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; MSE</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

In the formula, M represents the length of the observation sample space.

In addition to the above four statistical parameters, the following three influencing factors should also be considered when making a decision on whether to adjust the pilot pattern:

a) The number of times exceeding MAX(MSE) is recorded as MaxC1;

b) The number of times lower than MIN(MSE) is denoted as MinC2;

c) The number of σ (MSE) increases, denoted as VarRMSC3.

The design of these three parameters is to consider some additional constraints in practical engineering applications to avoid frequent adjustment of pilot pattern density due to drastic changes in the channel environment.

As shown in FIG. 2 , in the pilot pattern adjustment algorithm of the present invention, MaxC1TH, MinC2TH and VarRMSC3TH are defined to represent the thresholds of MaxC1, MinC2 and VarRMSC3 respectively. After calculating the MSE, MAX(MSE), MIN(MSE), E(MSE) and σ(MSE) of a data frame, these parameters are comprehensively evaluated. If the MSE (MSE _m ) of the current frame exceeds the fluctuation limit of historical data, then execute the right branch judgment, and compare the number of times MaxC1 exceeding the upper limit, if it is greater than MaxC1TH, then the pilot pattern of the next frame will be tuned, that is, from the pilot Select a new pilot pattern from the frequency pattern library to replace the existing pilot pattern. Define L to represent the sparseness of the pilot pattern, and the larger the value of L, the sparser the pattern pilot interval, and vice versa, the denser the pattern pilot interval.

As shown in Figure 3, a large number of pilot patterns containing multiple density configurations are stored in the pilot pattern library of the present invention. Here, a dynamic pilot pattern adjustment structure for adjusting the density of pilot patterns is provided. In this structure, the current data frame L=8, and the next frame L=4.

If it is less than MaxC1TH, further compare the number of times MinC2 lower than the lower bound, and if it is greater than MinC2TH, then adjust the pilot pattern of the next frame. Figure 4 shows a dynamic pilot pattern adjustment structure for adjusting the density of the pilot pattern , the current data frame L=8, and the next frame L=16.

If it is less than MinC2TH, the pilot pattern density of the next frame will not be adjusted, that is, the pilot pattern of the next frame is still consistent with the current frame.

If the MSE of the current frame does not exceed the fluctuation limit of the historical data, then execute the left branch judgment, and further compare the σ(MSE) increase times VarRMSC, if it is less than VarRMSC3TH, then the pilot pattern density of the next frame will not be adjusted, otherwise , to cipher the pilot pattern of the next frame.

In practical engineering applications, the number of pilot patterns in the pilot pattern library is always limited. Therefore, if all the pilot patterns in the pilot library have been exhausted after repeated adjustments, the execution of the above dynamic adjustment algorithm will be terminated. Continue running with the current pattern. Assuming that the channel environment has been deteriorating and the MSE has been rising, then according to the above rules, the pilot interval should be adjusted continuously until the pilot interval is adjusted to the densest. If the deterioration of MSE cannot be curbed at this time, then keep this interval. Until the channel environment improves (MSE decreases), and vice versa, this is a mechanism to prevent algorithmic deadlock.

Further, the channel estimation accuracy of the dynamic pilot channel estimation method of the present invention is compared with the traditional static/fixed pilot pattern channel estimation method.

Simulate two kinds of satellite mobile multimedia broadcast receiving environments, namely COST207 typical urban environment channel and SFN-4 broadcast single frequency network channel, two representative mobile multipath channels for simulation test, among which COST207/TU-6 mainly Simulate the receiving environment of urban mobile multipath fading. Considering the characteristics of shadow fading of tall buildings in the city, the number of multipaths is set to 6, the maximum multipath delay distance ranges from 1 to 2 kilometers, and the maximum relative moving speed of the receiving end is 120km/h; SFN -4 broadcast single frequency network channel mainly simulates the mobile multipath fading receiving environment in a wider area. Considering the characteristics of mobile receiving environments such as highways and high-speed moving objects (high-speed trains), set the number of multipaths to 4, and the maximum multipath delay When the distance is within 10 kilometers, the maximum relative moving speed of the receiving end is 300km/h.

As shown in Figures 5 and 6, in the COST207/TU-6 channel environment, when the SNR of the system using the traditional static pilot channel estimation method is 20dB, the MSE is in the order of 10 ^-2 , while the channel using the dynamic pilot pattern The estimation method system can reduce the MSE to less than 0.001 under the same situation, which is about 1 to 2 orders of magnitude higher than the estimation accuracy of the traditional method; correspondingly, in the SFN-4 channel environment, the new method can reduce the MSE to a minimum of 10 Between ^-2 and 10 ^-3 , compared with the estimation accuracy of the traditional static pilot method, there is also about 8-9dB performance improvement, obviously this improvement is very considerable. Even in the case of low SNR (SNR=0-10dB), although it is seriously affected by noise, the analysis shows that the channel estimation accuracy at this time still has about 4-5dB gain compared with the traditional method.

As shown in Figures 7 and 8, no matter in the COST207/TU-6 channel environment or in the SFN-4 channel environment, the mean value (La _ave ) of pilot pattern density fluctuations is better than that of the traditional static pilot channel estimation method The standard pilot sparseness typical value L=8 specified in is larger, and its average value can be expanded to 9.6 and 8.2 respectively, which means that the dynamic pilot method can save about 20% of the system subcarrier frequency compared with the traditional static pilot method Resources, the saved subcarrier frequency resources can be used by data subcarriers to transmit entity data, thus significantly improving the utilization rate of system frequency resources.

The new method of the invention realizes the optimization of system sub-carrier frequency resources while improving the channel estimation precision MSE performance.

Parts not described in detail in the present invention belong to the common knowledge of those skilled in the art.

Claims (1)

1., based on a channel estimation methods for dynamic pilot adjustment, it is characterized in that, at least comprise the following steps:

Obtain the Frame of current injection;

Pilot frequency design parsing is carried out to described Frame;

Carry out channel estimating according to described pilot frequency design analysis result, and obtain MSE value;

MSE statistical analysis is carried out to the MSE value of described Frame; The analysis of described MSE Data-Statistics for obtaining the variances sigma (MSE) of the historical statistical data maximum MAX (MSE) of MSE, the historical statistical data minimum value MIN (MSE) of MSE, the mathematic expectaion E (MSE) of MSE and MSE, and more than MAX (MSE) number of times MaxC1, raise number of times VarRMSC3 lower than MIN (MSE) number of times MinC2 and σ (MSE);

Judge that described Frame is the need of adjustment pilot frequency design;

When needs adjust described pilot frequency design, utilize pilot frequency design adjustment algorithm determination pilot frequency design adjustable strategies, and the pilot frequency design called in pilot frequency design storehouse adjusts; Described pilot frequency design adjustment algorithm comprises the following steps:

Make the following judgment according to the statistical value that described MSE Data-Statistics analysis obtains:

If the MSE value of current data frame is more than MAX (MSE) or be less than MIN (MSE), then perform the first branch: judge MaxC1 whether more than the threshold value of MaxC1; Otherwise, perform the second branch: judge VarRMSC3 whether more than the threshold value of VarRMSC3;

In described first branch, if MaxC1 is more than the threshold value of MaxC1, then adjust close pilot tone spacing; Otherwise, judge MinC2 whether more than the threshold value of MinC2; In described second branch, if VarRMSC3 is more than the threshold value of VarRMSC3, then adjust close pilot tone spacing; Otherwise, do not adjust pilot tone;

In described first branch, if MinC2 is more than the threshold value of MinC2, then adjusts and dredge pilot frequency design; Otherwise, do not adjust pilot tone.