A multi-user spreading code acquisition method based on multi-time series joint detection

Abstract

A multi-user spreading code acquisition method based on multi-time series joint detection belongs to the field of spread spectrum communication technology. First, the present invention performs correlation operations on the spread spectrum signal at the receiving end and uses an accumulation algorithm to reduce the influence of noise on the signal. Then, it calculates the characteristic information required for signal acquisition. By combining the dimension of time-domain difference, new characteristics are given to the signal from the perspective of the change rate, breaking through the problem that traditional acquisition methods cannot represent relatively complex signals, and enabling code acquisition of relatively complex signals. By using multi-time series joint detection for code acquisition at the receiver, the duration of interference can be reduced, and the resistance of the spread spectrum communication system to strong interference can be effectively improved. By utilizing the correlation of multi-time series signal characteristics and combining multi-user detection methods, the interference between multi-users can be reduced. The present invention is applicable to the communication field, improving the accuracy and robustness of the multi-user spread spectrum communication system and enhancing the communication quality.

Description

Technical Field

[0001] The present invention relates to a multi-user spreading code acquisition method based on multi-time series joint detection, and belongs to the technical field of spread spectrum communication. Technical Background

[0002] With the rapid development of wireless communication technology, people have put forward higher requirements for the reliability and security of communication, especially in multi-user communication systems. As a commonly used security means in multi-user communication systems, spread spectrum communication systems are widely used in military communication, satellite communication, mobile communication and other fields due to their good security and confidentiality. Among them, code acquisition technology is an important part of spread spectrum technology and also an important factor restricting the development of spread spectrum technology.

[0003] Spread spectrum code acquisition technology usually includes two main steps: search and decision. The search process is to find a possible user code sequence by matching with a known code sequence. The decision process is to compare the correlation peak obtained by the search with a preset threshold value to determine whether the user code sequence exists in the received signal. Multi-user spread spectrum communication generally uses code division multiple access (CDMA, Code Division Multiple Access) technology, and its core is to achieve multi-user communication on the same frequency band simultaneously through code division multiplexing. Correspondingly, at the receiving end, it is necessary to capture the corresponding spread spectrum code sequence through sampling and matched filtering. With the development of CDMA technology, multi-user spread spectrum communication code acquisition technology has also been continuously improved and optimized. In recent years, a series of new code acquisition technologies have emerged, such as code acquisition technology based on the maximum likelihood method, code acquisition technology based on the tree search algorithm, and code acquisition technology based on wavelet transform, etc. With the continuous growth of the demand for high-speed and large-capacity communication, the requirement for anti-jamming performance of spread spectrum communication in code acquisition is increasing day by day, and the existing technologies have gradually been unable to meet its strong anti-jamming requirements.

[0004] In an actual spread spectrum communication system, since the peak value of the spread spectrum signal is usually several times or even more than ten times higher than the average value, this will cause problems such as saturation or distortion of some circuits of the receiver, resulting in code acquisition failure. In this regard, the traditional method is to use the peak-to-average power ratio (PAPR) for code acquisition, which can avoid the appearance of high-amplitude peaks to ensure that the receiver can accurately capture the code sequence. However, with the development of mobile communication technology, the traditional code acquisition technology has gradually been unable to meet the requirements of high accuracy, so it is necessary to add new features to participate in the decision of code acquisition. Summary of the Invention

[0005] Aiming at the problem that traditional code acquisition technologies have deficiencies in anti-interference ability and accuracy, the main purpose of the present invention is to provide a multi-user spread spectrum code acquisition method based on multi-time series joint detection. By spreading each path of signals with different pseudo-codes at the sending end, when the receiving end performs code acquisition, it adopts multi-time series joint detection and combines time-domain differential information for code acquisition processing, which can reduce the duration of interference and improve the anti-interference ability of the spread spectrum communication system and the accuracy of code acquisition.

[0006] The object of the present invention is achieved through the following technical solutions:

[0007] A multi-user spread spectrum code acquisition method based on multi-time series joint detection disclosed by the present invention first performs correlation operations on the spread spectrum signals at the receiving end and uses an accumulation algorithm to reduce the influence of noise on the signals, and then calculates the characteristic information required for signal acquisition. On the basis of traditional code acquisition, combined with time-domain differential information, another dimensional information characteristic is given to complex signals. The joint detection is carried out using the characteristic information of different time series to reduce the duration of interference, thereby improving the anti-interference ability of code acquisition. At the same time, the accuracy of code acquisition is improved by using the correlation of the signal characteristics of different time series. To improve the anti-interference ability of the spread spectrum communication system and the accuracy of code acquisition.

[0008] A multi-user spread spectrum code acquisition method based on multi-time series joint detection disclosed by the present invention includes the following steps:

[0009] Step 1: Use the FFT algorithm to perform correlation operations on the spread spectrum signals and quickly calculate the correlation results.

[0010] If the input sequence is s(n), the locally generated pseudo-code sequence is l(n), and the pseudo-code period length is L, then the correlation operation result r(n) of the sequence and the local PN code can be expressed as

[0011]

[0012] It can be seen from the above formula that the computational complexity of directly calculating the correlation is proportional to the square of L. When the value of L is very large, the computational complexity will increase significantly. For this reason, first, according to the relationship between the correlation operation and the convolution operation, combined with the property that the time-domain convolution is equal to the frequency-domain product, the FFT fast convolution is used for the operation.

[0013]

[0014] Let S(k) = FFT(s(n)), L(k) = FFT(l(n)), then:

[0015]

[0016] In the formula, Denotes circular convolution, IFFT denotes inverse fast Fourier transform, and * denotes complex conjugate.

[0017] Using the FFT fast capture algorithm, it is possible to shift in the frequency domain to replace multiplication in the time domain, greatly reducing multiplication resources and achieving fast calculation of correlation results.

[0018] Step 2: Use the accumulation algorithm to reduce the impact of noise on the signal and improve the success rate of code capture under low signal-to-noise ratio.

[0019] In an actual communication system, the signal-to-noise ratio is relatively low, and the signal will be submerged in noise. The correlation operation within one period cannot capture the phase of the pseudo-code and the carrier frequency offset. Therefore, it is necessary to combine other algorithms to reduce the impact of noise on the signal. Among them, the most commonly used method is to use the accumulation algorithm. When the receiver is in a low signal-to-noise ratio environment, the received signal is very weak and cannot effectively detect the signal. By accumulating the correlation results of adjacent K pseudo-code periods, the signal-to-noise ratio of the correlation output R(m) of the FFT parallel search can be increased.

[0020]

[0021]

[0022] The above two equations are the expressions of coherent accumulation and non-coherent accumulation respectively. Since the absolute value is taken for the noise part during the non-coherent accumulation process, the gain effect is not as good as that of coherent accumulation, but the loss of non-coherent accumulation can be obtained from the detection probability and false alarm probability.

[0023] For the coherent accumulation method, its gain is:

[0024] G c (k) = 10lg(k)

[0025] In the formula, G c is the coherent accumulation gain, and k is the number of accumulation segments, that is, the number of points.

[0026] For the non-coherent accumulation method, its gain is:

[0027] G i (k) = G c (k) - G

[0028] In the formula, G i is the non-coherent accumulation gain; k is the number of accumulation segments, that is, the number of points; G is the square loss, and its calculation method is as follows:

[0029]

[0030]

[0031]

[0032] G = SNR SQ -SNR coh

[0033] where I and Q are the correlation values of two orthogonal paths respectively; n I and n Q represent the noise signals on the I-branch and Q-branch respectively; is the noise power; V 2 is the power of the correlation signal; V n is the value of the non-coherent integration in the absence of the signal; V(V n ) represents the variance of V n ; E(V) and E(V n ) represent the expected values of V and V n respectively; P 2 is the autocorrelation power of the signal under test,; SNR SQ is the signal-to-noise ratio under non-coherent integration, SNR coh is the signal-to-noise ratio after coherent integration.

[0034] Step 3: Combine the time-domain differential information to obtain the characteristics required for capture, reduce the duration of interference, and improve the anti-interference ability of capture.

[0035] Calculate the characteristics required for the captured signal, the peak-to-average power ratio PAPR of the signal, that is, the ratio of the maximum power to the average power of the signal R sum (n):

[0036]

[0037] According to the PAPR obtained above, perform time-series division in units of the capture window, and combine the time-domain differential information to find the characteristics of the signal, the change rate of PAPR ΔPAPR:

[0038]

[0039] In the formula, Δ is the minimum difference between two time series, and its value is equal to the sequence length of the spreading codeword.

[0040] It can be seen from the above formula that ΔPAPR is the change rate characteristic of two time series, and its interference duration is at most 2 time series. Compared with traditional code capture, the accumulation of interference signals over the entire time series is shortened, thereby reducing the influence of interference signals on signal characteristics and improving the anti-interference ability of the system.

[0041] Step 4: Use multi-time series joint testing for capture to improve the anti-interference ability and accuracy of capture.

[0042] Obtain the conditional probability \(F\) that \(\Delta PAPR \lt p_0\) under the condition that the PAPR reaches the maximum value, and use this probability as the judgment basis for capture.

[0043] \(F = f(\Delta PAPR \lt p_0|\max(PAPR))\)

[0044] \(p_0\) is a relatively small threshold set by the communication system, and the theoretical value of \(F\) approaches 1. When the value of \(F\) is greater than the set threshold, it is used as the basis for capture; otherwise, it cannot be used as the basis for capture. At the same time, the frame data is retransmitted or error correction processing is performed to improve the anti-interference ability and accuracy of capture.

[0045] Beneficial effects:

[0046] 1. A multi-user spreading code acquisition method based on multi-time series joint test disclosed by the present invention combines the dimension of time-domain difference, endows the signal with new features from the perspective of the change rate, breaks through the problem that traditional acquisition methods cannot characterize relatively complex signals, and can realize code acquisition of relatively complex signals.

[0047] 2. Compared with the traditional code acquisition method of single-time series hypothesis test, the multi-user spreading code acquisition method based on multi-time series joint test disclosed by the present invention can reduce the duration of interference and effectively improve the resistance ability of the spread spectrum communication system to strong interference by using the multi-time series joint test for code acquisition by the receiver.

[0048] 3. The multi-user spreading code acquisition method based on multi-time series joint test disclosed by the present invention utilizes the correlation of multi-time series signal features, combines with the multi-user detection method, reduces the interference between multi-users, and thus improves the accuracy and robustness of the multi-user spread spectrum communication system. Description of the drawings

[0049] Figure 1 is a flowchart of a multi-user spreading code acquisition method based on multi-time series joint test disclosed by the present invention;

[0050] Figure 2 is a schematic diagram of the m-sequence pseudo-code generation structure in this embodiment;

[0051] Figure 3 is a flowchart of the FFT fast acquisition algorithm in this embodiment;

[0052] Figure 4 is a schematic diagram of the accumulated acquisition correlation peak in this embodiment;

[0053] Figure 5 is a diagram of the PAPR calculation result in this embodiment;

[0054] Figure 6This is the partial result diagram of ΔPAPR1 in this embodiment. Detailed implementation manners

[0055] In order to enable those skilled in the art to more deeply understand the implementation idea of the solution of the present invention, the technical solutions in the embodiments of the present invention will be carefully and clearly described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation cases obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0056] The following describes the specific steps of the embodiments of the present invention in combination with specific scenarios:

[0057] There are 3 users at the sending end, using the BPSK modulation method. The sending sequence of each user is a random sequence of 0s and 1s with a length of N. The m-sequence with L = 127 is used for direct spread spectrum, and the length of one symbol is 127. As Figure 2 shown, the m-sequence is generated by a shift register.

[0058] A multi-user spread spectrum code acquisition method based on multi-time series joint test disclosed in this embodiment, as Figure 1 shown, the specific implementation steps are as follows:

[0059] Step 1: Use the FFT algorithm to implement the correlation operation of the spread spectrum signal.

[0060] The input sequence is s(n), n = 1, 2,..., 127. According to the m-sequence with the locally generated pseudo-code period length of L = 127, denoted as l(n), the operation result of the correlation between the sequence and the local PN code is

[0061]

[0062] Among them, j is the index of each user, j = 1, 2, 3. According to the relationship between the correlation operation and the convolution operation, the following formula is usually used for calculation in the actual system. The specific implementation block diagram is as Figure 3 shown.

[0063]

[0064] In the formula, represents circular convolution, S(k) = FFT(s(n)), L(k) = FFT(l(n)), IFFT represents the inverse fast Fourier transform, and * represents complex conjugate.

[0065] According to the above calculation process, the relevant values of 3 users can be calculated respectively. In the case of a relatively high signal-to-noise ratio, each user's relevant value should have a relatively high correlation peak, while other values are generally much smaller than the correlation peak value.

[0066] Step 2: Use the accumulation algorithm and the non-coherent accumulation method to accumulate the correlation results of adjacent two pseudo-code periods, so as to increase the signal-to-noise ratio of the correlation output R(m) of the FFT parallel search. The schematic diagram of capturing the correlation peak is as Figure 4 shown.

[0067]

[0068] That is, the accumulated relevant value of User 1 is:

[0069]

[0070] The accumulated relevant value of User 2 is:

[0071]

[0072] The accumulated relevant value of User 3 is:

[0073]

[0074] In this example, the coherent accumulation method is adopted, and its gain is:

[0075] G c (k) = 10lg(k)

[0076] In the formula, G c is the coherent accumulation gain, and k is the number of accumulation segments (number of points).

[0077] As can be seen from the above, k = 2, so the coherent accumulation gain G c (k) = 3dB.

[0078] Step 3: Calculate the characteristics required to capture the signal, the peak-to-average power ratio (PAPR) of the signal, that is, the ratio of the maximum power to the average power of the correlation value R sum (n), specifically as follows:

[0079]

[0080] According to the above formula, the PAPR of 3 users can be obtained respectively, denoted as PAPR i , then the PAPR of User 1 is:

[0081]

[0082] Similarly, the PAPR of the other two users can be obtained, and some of the results are as Figure 5。The variation trend of the PAPR value is basically the same as the relevant value under the condition of relatively high signal-to-noise ratio, only the amplitude is different. The PAPR is the normalization of the relevant cumulative value, which is beneficial to the processing and implementation of the actual hardware system.

[0083] According to the PAPR obtained above, the shape of the PAPR in the time domain is an equidistant pulse sequence. The time series is divided by the interval between every two peaks as the unit, and the characteristics of the signal are obtained by combining the time domain differential information. The change rate ΔPAPR of the PAPR is calculated as follows:

[0084]

[0085] Here, Δ = 127.

[0086] Combined with this example, the change rates of the PAPR of 3 users are obtained respectively, denoted as ΔPAPR j , where j = 1, 2, 3. That is:

[0087]

[0088] Step 4: Obtain the conditional probability that ΔPAPR < p0 under the condition that the PAPR reaches the maximum value, and use this probability as the decision basis for capture.

[0089] F = f(ΔPAPR < p0|max(PAPR))

[0090] p0 is a relatively small value, which can be set and modified according to the actual situation. Taking User 1 as an example, after calculation, f1(ΔPAPR1 < 0.01) > 99.9%, so the threshold of User 1 can be set to 0.01 here, and some of its results are as Figure 6 . The same method can be used to process User 2 and User 3. It is found through calculation that the vast majority of the values of F remain near 1, and only less than 0.1% of the values are close to 0, which is also completely in line with the theory. Therefore, it can be considered that the capture value of 0.1% is unreliable and needs to be discarded or marked for subsequent data retransmission or error correction processing. From the above results, it can be seen that using multi-time series joint detection is beneficial to improving the accuracy and success rate of code capture.

[0091] The above specific description further details the purpose and technical solution of the invention. It should be understood that the above is only a specific embodiment of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-user spreading code acquisition method based on multi-time series joint detection, characterized in that: It includes the following steps: Step 1: Perform correlation operations on the spread-spectrum signal using the FFT algorithm to quickly calculate the correlation results. Step 2: Use the accumulation algorithm to reduce the influence of noise on the signal and improve the success rate of code acquisition under low signal-to-noise ratios. Step 3: Combine the time-domain differential information to obtain the characteristics required for acquisition, reduce the duration of interference, and improve the anti-interference ability of acquisition. Calculate the features required to capture the signal, the peak-to-average power ratio PAPR of the signal, that is, the ratio of the maximum power to the average power of the signal R sum (n): According to the PAPR obtained above, perform time-series division in units of acquisition windows, and combine the time-domain differential information to find the characteristics of the signal, the change rate ΔPAPR of PAPR: In the formula, Δ is the minimum difference between two time series, and its value is numerically equal to the sequence length of the spreading codeword. ΔPAPR is the change rate characteristic of two time series, and its interference duration is at most 2 time series. Compared with traditional code acquisition, the accumulation of interference signals over the entire time series is shortened, thereby reducing the influence of interference signals on signal characteristics and improving the anti-interference ability of the system. Step 4: Use multi-time-series joint detection for acquisition to improve the anti-interference ability and accuracy of acquisition. Obtain the conditional probability F that ΔPAPR < p0 under the condition that PAPR reaches the maximum value, and use this probability as the decision basis for acquisition. F = f(ΔPAPR < p0|max(PAPR)) p0 is a threshold set by the communication system, and the theoretical value of F approaches 1; when the F value is greater than the set threshold, it is used as the basis for acquisition; otherwise, it cannot be used as the basis for acquisition. At the same time, retransmit the frame data or perform error correction processing to improve the anti-interference ability and accuracy of acquisition.

2. The multi-user spreading code acquisition method based on multi-time series joint detection according to claim 1, wherein: The implementation method of Step 1 is as follows. The input sequence is s(n), the locally generated pseudo-code sequence is l(n), and the pseudo-code period length is L. Then the operation result r(n) of the correlation between the sequence and the local PN code can be expressed as It can be seen from the above formula that the computational complexity of directly calculating the correlation is proportional to the square of L; when the value of L is very large, the computational complexity will increase significantly; for this, first, according to the relationship between the correlation operation and the convolution operation, combined with the property that the time-domain convolution is equal to the frequency-domain product, use the FFT fast convolution for operation. Let S(k) = FFT(s(n)), L(k) = FFT(l(n)), then: wherein represents circular convolution, IFFT represents inverse fast Fourier transform, and * represents complex conjugate; Using the FFT fast acquisition algorithm, frequency-domain shifting can be used to replace time-domain multiplication, greatly reducing the multiplication resources and realizing the fast calculation of correlation results.

3. The multi-user spreading code acquisition method based on multi-time series joint test according to claim 2, characterized in that: The implementation method of Step 2 is as follows. When the receiver is in a low signal-to-noise ratio environment, the received signal is very weak and the signal cannot be effectively detected; by accumulating the correlation results of adjacent K pseudo-code periods, the signal-to-noise ratio of the correlation output R(n) of the FFT parallel search can be increased. The above two formulas are the expressions of coherent accumulation and non-coherent accumulation respectively. Since the absolute value of the noise part is taken first in the non-coherent accumulation process, the gain effect is not as good as that of coherent accumulation, but the loss of non-coherent accumulation can be obtained from the detection probability and false alarm probability. For the coherent accumulation method, its gain is: G c G(k) = 10 lg(k) where G c is the coherent accumulation gain, and k is the number of accumulation segments, i.e., the number of points; And for the non-coherent accumulation method, its gain is: G i G(k) = G c G(k) - G Where G i is the incoherent accumulation gain; k is the number of accumulation segments, i.e., the number of points; G is the square loss, and its calculation method is as follows: G = SNR SQ -SNR coh where I and Q are the two orthogonal correlation values respectively; n I and n Q represent the noise signals on the I branch and the Q branch respectively; is the noise power; V 2 is the power of the correlation signal; V n is the value of the non-coherent integration in the absence of the signal; V(V n ) represents the variance of V n ; E(V) and E(V n ) represent the expected values of V and V n respectively; P 2 is the autocorrelation power of the signal under test; SNR SQ is the signal-to-noise ratio under non-coherent integration, and SNR coh is the signal-to-noise ratio after coherent integration.

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