CN104022996B - Channel estimation-based timing synchronization method for orthogonal frequency division multiplexing (OFDM) system - Google Patents

Abstract

The invention discloses a method for timing synchronization of an OFDM system based on channel estimation, which belongs to the field of communication technology and mainly solves the problem of timing errors in the system when the first path of a multipath fading channel is not the strongest path, and the probability of missed detection of the existing fine synchronization algorithm Big problem, this method locates the path with the strongest energy on the basis of rough synchronization, fine synchronization uses the least squares algorithm to find the channel impulse response, and judges the location of each path through the energy change in the sliding window based on its cyclic right shift characteristic. And the minimum or adaptive threshold method is used to detect the first path, which can reduce the probability of missed detection of accurate symbol synchronization and effectively improve the timing performance of the system.

Description

A Timing Synchronization Method for OFDM System Based on Channel Estimation

technical field

The invention relates to OFDM system timing synchronization technology, in particular to a channel estimation-based OFDM system timing synchronization method, which belongs to the field of communication technology.

Background technique

OFDM (Orthogonal Frequency Division Multiplexing, Orthogonal Frequency Division Multiplexing technology) is widely used in high-speed wireless transmission systems due to its high spectrum utilization rate and strong anti-multipath fading capability. Timing synchronization is one of the key issues in OFDM systems, and there have been a lot of researches on it.

In the prior art, most people solve the peak platform phenomenon of timing measurement by improving the training symbol structure and using related characteristics, and at the same time eliminate the influence of sub-peaks, so that the timing is more accurate. However, its coarse synchronization algorithms are all based on energy detection. In a multipath fading channel, they can be synchronized on the strongest path. If the first path is not the strongest path, the DFT window will lag behind, thereby introducing inter-symbol interference. In the literature, the fine synchronization algorithm is used to locate the first path, so as to solve the above problems.

Literature [1]: Kishore CN, Reddy V.A technique for dominant path delay estimation in an OFDM system and its application to frame synchronization in OFDM mode of WMAN[J].EURASIP Journal on Wireless Communications andNetworking,2006,2006(2):18-18 .Discloses a timing synchronization algorithm for OFDM systems, which fully analyzes the channel impulse response should satisfy the cyclic right shift characteristic in the fine synchronization process, and describes how to use the least square method (LeastSquare, LS algorithm) for channel estimation, to obtain Channel impulse response function, but it uses the sliding window to calculate the impulse response energy sum, and uses its maximum value to determine the position of the first path, but it has a peak platform, and the probability of false detection is high.

Literature [2]: Yang F, Zhang X. Robust Time-Domain FineSymbol Synchronization for OFDM-Based Packet Transmission Using CAZAC Preamble [C]. MILCOM Military Communications Conferen-ce, 2013: 436-440. Also disclosed an OFDM system timing Synchronization algorithm, which we refer to as "Yang method" for short in the following text, it uses CAZAC sequence with zero autocorrelation characteristic of banner, eliminates side peaks, and makes the threshold range adjustable, but when the first path fading is seriously lower than the threshold, it cannot be realized Accurate detection.

Contents of the invention

In view of the above-mentioned defects, the present invention proposes a method for timing synchronization of OFDM systems based on channel estimation. On the basis of rough synchronization positioning and guiding the strongest path, the method uses LS algorithm to obtain channel impulse response, and then sets the energy summation The sliding window is used, and the energy change value based on the window is used to realize the accurate positioning of the first path, so as to complete the symbol timing synchronization.

In order to achieve the above object, the concrete technical scheme adopted in the present invention is as follows:

A kind of OFDM system timing synchronization method based on channel estimation, its key is to carry out according to the following steps:

Step S1, using the coarse synchronization algorithm to obtain the coarse synchronization timing point d _c ;

Step S2, use the least squares method to perform channel estimation to obtain the channel impulse response function

Step S3, according to the formula Calculate the energy sum of the sliding window impulse response starting from the dth receiving end data, where N _g ' is the length of the sliding window, and A is the number of even subcarriers;

Step S4, according to formula e (d)=X (d+1)-X (d) calculates the difference e (d) of adjacent sliding window impulse response energy sum;

Step S5, determine the timing deviation according to the position of the first negative peak value of e(d)

Step S6, according to Calculate the position d _s of the system timing synchronization point.

As an implementation, the minimum value method is used to determine the timing deviation in step S5 First determine the point with the smallest e(d) in the range of 1≤d≤A as d', and then follow Compute timing offset.

As another implementation, in step S5, the adaptive threshold method is used to determine the timing deviation

First, follow the Determine the discriminant threshold of e(d), where δ is a preset constant;

Next, compare e(d) with -Threshold to find the position of the first negative peak, and set it to Path1;

Finally, follow Compute timing offset.

Specifically, the preset constant δ=2.

Preferably, the coarse synchronization algorithm described in step S1 is performed in the following manner:

Step S1-1: According to Calculate the timing metric value of the dth receiving data, where:

r(d) is the d-th receiving data; c(i) is the i-th sending data starting from the cyclic prefix;

Step S1-2: Translating the position with the largest timing metric value by N _g to obtain the coarse synchronization timing point d _c , where N _g is the length of the cyclic prefix.

Combined with specific application scenarios, the number of DFT transformation points used by the system is N=256, the cyclic prefix N _g =64, the number of even subcarriers A=N/2=128, and the sliding window length N _g '=N _g +1=65 .

Notable effect of the present invention is:

In the case that the first path is not the strongest path, the present invention proposes a timing synchronization method based on channel estimation. On the basis of coarse synchronization to the strongest path, fine synchronization first obtains the channel impulse response according to the LS algorithm, and then Based on its cyclic right-shifting characteristics, the first path is accurately located by using the minimum value or adaptive threshold method through the energy change in the sliding window. The minimum method improves the detection probability, but there are still large missed detections. While the adaptive threshold method for different channels can accurately locate the first path, this method can reduce the probability of missed detection of symbol synchronization and improve the timing performance of the system.

Description of drawings

Fig. 1 is method step figure of the present invention;

Fig. 2 is the channel amplitude characteristic figure of OFDM system under different situations;

Fig. 3 is the energy value in the sliding window and the variation between adjacent values;

Fig. 4 is the energy change diagram in the sliding window;

Figure 5 is a comparative analysis diagram of the missed detection probability of various algorithms under the SUI1 channel;

Figure 6 is a comparative analysis diagram of the missed detection probability of various algorithms under the SUI2 channel;

Figure 7 is a comparative analysis diagram of the MSE results of various algorithms under the SUI1 channel;

Figure 8 is a comparative analysis diagram of the MSE results of various algorithms under the SUI2 channel.

detailed description

Below in conjunction with accompanying drawing and embodiment the present invention will be further described:

As shown in Figure 1, a kind of OFDM system timing synchronization method based on channel estimation is carried out according to the following steps:

Step S1, using the coarse synchronization algorithm to obtain the coarse synchronization timing point d _c ;

First construct two repeated sequences through the Schmidl algorithm, let C(2k) be the PN sequence on the even subcarrier in the frequency domain, C(2k+1)=0, where 0≤k≤A-1, then the training sequence at the sending end ( excluding CP) can be expressed as The specific steps of coarse synchronization include:

Step S1-1: According to Calculate the timing metric value of the dth receiving data, where:

r(d) is the d-th receiving data; c(i) is the i-th sending data starting from the cyclic prefix;

Step S1-2: Translate the position with the largest timing metric value by N _g to obtain the coarse synchronization timing point d _c , where N _g is the length of the cyclic prefix. In a multipath fading channel, the coarse synchronization timing point d _c is the maximum energy path delay.

Step S2, use the least squares method to perform channel estimation to obtain the channel impulse response function

Since the preamble sequence is a repeated structure at both ends, it has the characteristic of cyclic right shift. Based on this characteristic, the energy change in the sliding window of cyclic left shift can be used to find out the position of the first path through the minimum value method or adaptive threshold method. Specifically For the process, please refer to the literature [1] in the background technology.

In order to verify the above characteristics, Figure 2 shows the computer simulation results applied to the SUI channel. Wherein the first path is the strongest path and the second path is the strongest path respectively represent the corresponding channel amplitudes when there is no timing deviation and there is a timing deviation τ in coarse synchronization.

It can be seen from Figure 2 that when there is a timing deviation in the coarse synchronization, that is, the second path is the strongest path, the amplitude of the first path of the channel will be cyclically shifted to the right by τ correspondingly, thus satisfying the cyclic right shift characteristic.

Step S3, for the impulse response window of the channel, set a cyclically left sliding window starting from the second half to calculate its energy sum. When the first path is the strongest path, with the movement of the sliding window, the energy in the sliding window increases sequentially by the first path, the second path...the last path. When the i-th (i>=2) path is the path with the strongest energy, the paths of the impulse response of the channel have cyclically shifted to the right, and the i-th path...the last path is increased accordingly, and then the first path, The 2nd path, until the i-1th path. Then you can follow the formula:

Calculate the energy sum of the sliding window impulse response starting from the dth receiving end data, where N _g ' is the length of the sliding window, and A is the number of even subcarriers;

Step S4, in order to observe the rising edge and the falling edge more clearly, that is to determine the position of each path, calculate the energy sum of the impulse response of adjacent sliding windows according to the formula e(d)=X(d+1)-X(d) difference e(d);

From the above analysis, it can be seen that the position of the first path is the first negative peak of the energy change e(d) in the sliding window.

Therefore, in step S5, determine the timing deviation according to the position of the first negative peak of e(d)

Finally, step S6, according to Calculate the position d _s of the system timing synchronization point.

As method A:

Utilize the minimum value method to determine the timing deviation in step S5 First determine the point with the smallest e(d) in the range of 1≤d≤A as d', and then follow Compute timing offset.

In order to unify the detection method, the minimum value in the e(d) window is detected. When the coarse synchronization timing is accurate, that is, the first path is the strongest path, add X(end)*9/10 after X(end) (X(end) is the value of the last value in the sliding window), artificially generate a falling edge.

The above analysis is specifically applied to the SUI channel model, where N _g ′=N _g +1, A=128, N _g =64, the graphs of X(d) and e(d) are shown in Figure 3, because the SUI channel There are three paths, so when the first path is the strongest path, there are three rising edges and artificially generated falling edges. Detect its minimum value, that is, detect the artificially generated negative peak value (the position after the first path is moved to the right), by the formula It can be seen that at this time When the second path is the strongest path, there are two rising edges and two falling edges. When the first falling edge is coarse synchronization and there is a timing deviation, the position of the first path moves to the right after the cycle, and the second falling edge The edge is an artificially generated negative peak, and finding its minimum value can also correctly detect the position of the first path delay, which is consistent with the previous analysis results.

As method B:

Utilize adaptive threshold method to determine timing deviation in step S5

First, follow the Determine the discrimination threshold of e(d), where δ is a preset constant, and the preset constant δ=2 in the implementation process can also be flexibly set according to the system synchronization performance requirements, considering that the energy is mainly concentrated in each path, and the The energy value is much larger than other points, so the method of averaging e(d) is adopted. There is a large difference between the calculated average value and other sample points except each path, and it changes with the energy change of the signal at the receiving end;

Next, compare e(d) with -Threshold to find the position of the first negative peak, and set it to Path1;

Finally, follow Compute timing offset.

Although the minimum value method in method A improves the detection accuracy, there are still large missed detections, because it only corrects some cases where the strongest path is not the first path.

As shown in Figure 4, when the second path is the strongest path, the first negative peak is the position of the first path after the cycle moves to the right. At this time, the energy of the first path is less than e(end), which exists through the minimum value rule False detection. Similarly, when the third path is the strongest path, the first negative peak is the position of the first path after the cycle moves to the right, and the second negative peak is the position of the second path after the cycle moves to the right. At this time, the energy of the second path is stronger than the energy of the first path, and there are also false detections when passing the minimum value.

The best timing is to accurately find the position of the first path, that is, the first negative peak of e(d). Therefore, method B adopts the method of setting a threshold to improve the detection accuracy, and accurately detects the first path by comparing and judging with the adaptive threshold.

In order to further understand the technical effect of the present invention, with reference to the WiMAX standard, the structure of the training symbol described in the second part is constructed, wherein the number of DFT points N=256, the cyclic prefix N _g =64, the sliding window length N _g '=N _g + 1=65, the number of even-numbered subcarriers A=N/2=128, and the system clock is 20MHz. This paper uses the timing mean square error MSE and the probability of missed detection P _m , that is, the number of frames that have not been precisely timed to the ideal synchronization point and the total number of simulated frames The ratio of numbers is used to test the performance of each algorithm. The channel models used are SUI1 and SUI2, and each result is averaged through 100,000 simulations.

Figure 5 and Figure 6 are the comparison charts of the missed detection probabilities of the four algorithms. Among them, the coarse synchronization method is the coarse synchronization algorithm in the article, the A method is the combination of the coarse synchronization and the fine synchronization in the article, and the minimum value method is used for detection, and the B method is compared with the A method, and the adaptive threshold method is used in the final detection. The Yang method is the method mentioned in the literature [2] in the background technology, which is used as a comparison.

It can be seen from Fig. 5 and Fig. 6 that the missed detection probability of method A, method B and Yang method under SUI1 and SUI2 channels is smaller than that of the coarse synchronization method. This result stems from the fact that in a multipath fading channel, the symbol timing estimated by the coarse synchronization algorithm is the delay of the maximum energy path, while the other three methods use the fine synchronization algorithm to correct the timing deviation from the first path delay point. The performance of Yang method is similar to that of A method. These two algorithms correct some of the timing points where the strongest path is not the first path back to the first path, but there are still missed detections. In method B, the setting of the threshold is related to the energy of the signal and noise, so the detection probability will be affected by the signal-to-noise ratio. Under the low signal-to-noise ratio, the noise energy is large, and some points are affected by the noise and exceed the threshold value. At this time What was taken was not the first path, resulting in missed detection. Therefore, in the case of low signal-to-noise ratio, the detection performance of SUI1 and SUI2 is slightly worse than that of method A and Yang method, but with the increase of signal-to-noise ratio, the detection probability of method B is lower than that of the other three methods, and the performance is better. .

The missed detection probability in Figure 5 and Figure 6 is the ratio of the number of frames that are not precisely timed to the ideal synchronization point to the total number of simulated frames, but for OFDM systems, when the synchronization point falls in the CP, only the phase has a constant offset , which can be compensated by channel estimation. Therefore, this paper further compares the MSE results of each algorithm at the timing point of the SUI1 and SUI2 channels, as shown in Figure 7 and Figure 8.

Observing Figure 7 and Figure 8, it can be seen that the downward trend shown by the MSE curve is consistent with the probability curve of missed detection, which is in line with the expected results. It can be seen from the simulation results that the A method improves the detection accuracy, because it corrects the timing points of some of the strongest paths not being the first path back to the first path by taking the minimum value in the window. The threshold setting of method B is more reasonable and flexible, can accurately find out the position of the first path, and has higher detection accuracy.

Finally, it should be noted that the above detailed description is only a preferred specific embodiment of the present invention. It should be understood that those skilled in the art can make many modifications and changes according to the concept of the present invention without creative efforts. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art shall be within the scope of protection defined by the claims.

Claims (6)

1. a kind of ofdm system time synchronization method based on channel estimation, it is characterised in that follow the steps below：

Step S1, obtains thick synchronization timing point d using thick synchronized algorithm_c；

Step S2, carries out channel estimation using method of least square, obtains channel impulse response function

Step S3, according to formula $X\left(d\right)=\underset{l=0}{\overset{{N_g}^{\′-1}}{\mathrm{\Σ}}}{\left|\hat{h}(d+l)\mathrm{mod}A\right|}^2{{A-N}_g}^{\′}\≤d\≤A$ Calculate d-th receiving end data The sliding window impulse response energy of beginning and wherein N_g' for sliding window length, A for even subcarriers number；

Step S4, calculates the difference e (d) of adjacent sliding window impulse response energy sum according to formula e (d)=X (d+1)-X (d)；

Step S5, determines timing offset according to the position that e (d) first times negative peak is located

Step S6, according toIt is calculated the position d of timing synchronous point_s。

2. a kind of ofdm system time synchronization method based on channel estimation according to claim 1, it is characterised in that：Step Timing offset is determined using minima method in rapid S5Determine that point e (d) minimum in the range of 1≤d≤A is d', is then pressed first According toCalculate timing offset.

3. a kind of ofdm system time synchronization method based on channel estimation according to claim 1, it is characterised in that：Step Timing offset is determined using adaptive threshold method in rapid S5

First, according toDetermine the judgement threshold of e (d), wherein δ is preset constant；

Then, e (d) is made decisions with-Threshold and is compared, found the position that first negative peak is located, be set to Path1；

Finally, according toCalculate timing offset.

4. a kind of ofdm system time synchronization method based on channel estimation according to claim 3, it is characterised in that：Institute State preset constant δ=2.

5. a kind of ofdm system time synchronization method based on channel estimation according to claim 1-4 any one, its It is characterised by, the thick synchronized algorithm described in step S1 is carried out in such a way：

Step S1-1：According toThe timing metric value of d-th receiving end data is calculated, wherein：

$R\left(d\right)=\underset{i=0}{\overset{A-1}{\mathrm{\Σ}}}{\left|r(d+i+A)\right|}^2;P\left(d\right)=\underset{i=0}{\overset{A-1}{\mathrm{\Σ}}}{\left[r(d+i)c\left(i\right)\right]}^{*}\left[r(d+i+A)c\left(i\right)\right],$ R (d) is d Individual receiving end data；C (i) is i-th originating data started from Cyclic Prefix；

Step S1-2：By position translation N that timing metric value is maximum_gObtain thick synchronization timing point d_c, N_gFor the length of Cyclic Prefix Degree.

6. a kind of ofdm system time synchronization method based on channel estimation according to claim 5, it is characterised in that：System DFT transform points N=256 that system is adopted, Cyclic Prefix N_g=64, number A=N/2=128 of even subcarriers, sliding window Length N_g'=N_g+ 1=65.