Scrambling code generator polynomial blind identification method of direct sequence spread scrambling signal

Abstract

The invention discloses a blind recognition method for a scrambling code generator polynomial of a direct spread scrambling signal, which is not limited by the number of stages of the generator polynomial, can recognize any high-order generator polynomial, and can fully utilize a storage space through reasonable K value setting. Moreover, the blind recognition method for the scrambling code generator polynomial of the direct spread scrambling signal, provided by the invention, has robustness for frequency offset, does not limit the order of the recognizable generator polynomial, and needs a relatively short spread spectrum scrambling sequence, so that the blind recognition method has very strong practicability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, and particularly relates to a blind identification method of scrambling code generation polynomial of direct sequence spread scrambling signal. BACKGROUND

[0002] Direct sequence spread spectrum (DSSS) technology is one of the commonly used communication technologies in modern communication technology system (Yan Linbin. DS-CDMA signal blind estimation technology research[D]. University of Electronic Science and Technology, 2017). The basic principle is that at the signal sending end, the information code sequence is multiplied by a high-rate spread spectrum code sequence to improve the transmission rate of the information code sequence, so as to achieve the effect of expanding the signal spectrum and reducing the signal power spectral density. Direct sequence spread scrambling signal refers to the randomization processing of the sequence after spreading at the bit layer in order to change the statistical characteristics of the transmission sequence and improve the information bit confidentiality. At the receiving end, the de-scrambling of the received signal must be completed first, and then the signal can be processed subsequently. The direct sequence spread scrambling signal studied in the embodiment refers to the signal that uses Walsh sequence or OVSF sequence to spread the information code sequence, and then uses Gold sequence to scramble. For non-cooperative receivers, the spread spectrum sequence number used by the sending signal and the Gold sequence generation polynomial and the register initial state are unknown, which makes it very difficult to analyze and process the direct sequence spread scrambling signal. At present, there are relevant researches on the identification of the spread spectrum sequence number and the Gold sequence register initial state, and the identification of the register initial state is based on the identification of the scrambling code generation polynomial. Therefore, in non-cooperative communication, how to complete the blind identification of the scrambling code generation polynomial of the direct sequence spread scrambling signal has important significance for subsequent signal processing.

[0003] For the identification of the generating polynomial, the prior art (Yang Zhongli, Liu Yukong. Research on the synthesis algorithm of self-synchronous scrambling sequence [J]. Information technology, 2005 (02): 30-32) and the prior art (Wu Wenjun, Huang Zhiping, Tang Guilin, Liu Chunwu. Fast recovery of code sequence containing error [J]. Ordnance industry, 2009, 30 (08): 1134-1138) use the iterative relationship of linear shift register to construct an error equation, and then use Walsh-Hadamard transform to solve the error equation to determine the generating polynomial, which has the advantages of high identification accuracy and easy-to-understand principle. But its disadvantage is that the higher the order is, the more the calculation amount of the algorithm increases exponentially, and the generating polynomial of Gold sequence is high, which is almost difficult for hardware to bear. The prior art (Yuan Ye. Blind identification of linear scrambling [D]. University of Electronic Science and Technology, 2013) proposes an identification algorithm based on the distance of probability distribution, which is to analyze the mathematical model of the descrambling process, and obtains the conclusion that the value of the bit group corresponding to the scrambling sequence and the generating polynomial is constrained by the source sequence, and the value has unbalancedness, and accordingly the generating polynomial of the scrambling code is obtained. The disadvantage of the algorithm is that the order and the number of the generating polynomial need to be known in advance, which is not practical in engineering. In addition, the above algorithms all have high requirements for the unbalance degree of the source, and for the direct spread spectrum signal, the 0 and 1 bits of the spread spectrum sequence are completely balanced, which leads to the failure of the above algorithms.

[0004] For the direct spread spectrum signal, the scrambling code used is Gold sequence, and the order of the corresponding generating polynomial is also high. If the traditional identification algorithm is used to identify the scrambling code number, whether the algorithm calculation amount increases exponentially or the source unbalance degree is 0, which leads to the failure of the algorithm, or the influence of the frequency offset introduced in the signal propagation process, all of which will lead to the difficulty of the application of the traditional algorithm. In summary, in non-cooperative communication, it is difficult to blindly identify the scrambling code parameters of the intercepted direct spread spectrum scrambled signal. The present application proposes a blind identification method of the scrambling code generating polynomial of the direct spread spectrum scrambled signal in the background of non-cooperative communication. SUMMARY

[0005] In order to solve the limitations and defects of the prior art, the present application provides a blind identification method of the scrambling code generating polynomial of the direct spread spectrum scrambled signal, which comprises:

[0006] The direct spread spectrum scrambled signal sent by the sending end is modeled, and the expression is as follows:

[0007] T(k)=S n (k)(β I X I (k)+jβ Q X Q (k)),k=1,2,3,...

[0008] wherein β I is the gain of the I channel, β Q is the gain of the Q channel, X I (k) is the sequence after the I channel bit is spread by a Walsh sequence or an OVSF sequence, X Q (k) is the sequence after the Q channel bit is spread by a Walsh sequence or an OVSF sequence, S n (k) is a Gold sequence used for scrambling, n is the serial number of the Gold sequence, and j is an imaginary unit;

[0009] The direct spread scrambling signal received by the receiving end is modeled, and the expression is as follows:

[0010] T rec (k) = e jθ βS n (k)(β I X I (k) + jβ Q X Q (k)) + n(k)

[0011] wherein θ is a phase offset caused by an initial phase and a frequency offset, n(k) is a noise component, β is a channel gain, β I is the signal gain of the I channel, β Q is the signal gain of the Q channel;

[0012] The sequence M rec (k) is obtained, and the expression of the sequence M rec (k) is as follows:

[0013]

[0014] wherein w re (k) is a noise component, is a shift sequence of an original Gold sequence;

[0015] The bit sequence of the sequence M rec (k) determined as 01 is defined as a sequence R rec (k);

[0016] The Gold sequence generation polynomial degree is 2L, and the sequence R rec (k) satisfies a (N-2Lx2L+1)-dimensional error-containing equation group, wherein N is the length of the sequence R rec (k), and 2L is the generation polynomial degree of the Gold sequence;

[0017] The solution of the error-containing equation group is a vector [c 2L c 2L-1 ... c11]T wherein c i ,0

[0018] Optionally, the expression of the error-containing equation set is as follows:

[0019]

[0020] Optionally, the step of obtaining the sequence M rec (k) comprises:

[0021] According to a preset pre-processing method, the received signal T rec (k) is pre-processed.

[0022] Optionally, the step of pre-processing the received signal T rec (k) according to the preset pre-processing method comprises:

[0023] The received signal T rec (k) is subjected to conjugate difference and two times decimation processing.

[0024] Optionally, the method further comprises:

[0025] The real part of the processed signal is selected to obtain the sequence M rec (k).

[0026] Optionally, the method further comprises:

[0027] The coefficients of the generating polynomial are solved based on a W-H analysis method of matrix updating.

[0028] Optionally, the step of solving the coefficients of the generating polynomial based on the W-H analysis method of matrix updating comprises:

[0029] The N-2L row coefficients of the error-containing equation set are regarded as N-2L bit groups of 2L+1 bits, each bit group has 2 2L +1 value conditions, a vector S with a dimension of 1x2 2L+1 is constructed, the vector S is used to count the number of occurrences of each bit group in the error-containing equation set, the same bit groups are accumulated, and the accumulated values are stored in the decimal number position corresponding to the vector S;

[0030] A Hadamard matrix H with a dimension of 2 K is constructed, wherein K<2L+1, which is used to represent the dimension of the matrix, the matrix H is used to iteratively generate a matrix with a dimension of 2 2L+1 x 2 K , the vector S and the matrix with a dimension of 2 2L+1 x 2 KThe product of the matrices is part of the Walsh spectrum, where the values ​​of K and L are used to ensure that the dimension is 2. 2L+1 ×2 K The matrix can be stored;

[0031] Traverse i=2 2L-K ,...,2 2L+1-K -1, convert the decimal number i being iterated over into a binary vector R of dimension 2L+1-K. i Read the vector R sequentially from the least significant bit to the most significant bit. i The bits, when the vector R i When the bit is 0, the matrix H is updated to H = [H; H], and when the vector R i When a bit is 1, update the matrix H to H = [H; -H], until the vector R is completely read. i After reading all bits of the vector R, i After all bits are processed, the matrix H is updated to a dimension 2. 2L+1 ×2 K matrix H i The vector S and the matrix H i Multiplying them together yields the vector W. i ;

[0032] All vectors W i Combining them in order yields a dimension of 1×2. 2L The vector W0, with dimensions 1×2 2L The vector 0 is combined with the vector W0 to obtain the final vector W, which is the Walsh spectrum. The binary vector corresponding to the maximum value index of the vector W is obtained, and the binary vector corresponding to the maximum value index of the vector W is the coefficient of the generator polynomial.

[0033] Optionally, the value of K is 13.

[0034] Optionally, the value of L is 7.

[0035] Optionally, the value of N can be 1200 or 2400.

[0036] The present invention has the following beneficial effects:

[0037] Compared to existing technologies, the blind identification method for scrambling generator polynomials of direct-sequence spread spectrum (DSSS) scrambling signals provided by this invention is not limited by the order of the generator polynomial and can identify generator polynomials of any high order. A reasonable K value setting can fully utilize storage space. Furthermore, the blind identification method for scrambling generator polynomials of DSS scrambling signals provided by this invention is robust to frequency offset, has no limitation on the order of the identifiable generator polynomial, and requires a relatively short spreading scrambling sequence, thus possessing strong practicality. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A flow chart of W-H algorithm based on matrix updating is provided for the first embodiment of the present application.

[0039] Fig. 2(a) is a diagram showing the identification accuracy of the generated polynomial when N=1200 according to the first embodiment of the present application.

[0040] Fig. 2(b) is a diagram showing the identification accuracy of the generated polynomial when N=2400 according to the first embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the skilled in the art better understand the technical solutions of the present application, the method for blind identification of the scrambling code generated polynomial of the direct spread scrambling signal provided by the present application is described in detail below with reference to the accompanying drawings.

[0042] Embodiment I

[0043] The present embodiment provides a method for blind identification of the scrambling code generated polynomial of the direct spread scrambling signal. The method is directed to the signals which are spread by Walsh sequence or OVSF sequence and then scrambled by Gold sequence. In the presence of frequency offset, the method completes the blind identification of the Gold sequence generated polynomial.

[0044] According to the relationship between the signal spread gain and the period length of the spread sequence, the direct spread signal can be divided into short code direct spread signal and long code direct spread signal. The direct spread signal with equal spread gain and spread sequence period is called short code direct spread signal; the direct spread signal with smaller spread gain than the spread sequence period is called long code direct spread signal. The present embodiment mainly studies the short code direct spread signal.

[0045] The direct spread scrambling signal at the transmitting end is modeled as follows:

[0046] T(k) = S n (k)(β I X I (k) + jβ Q X Q (k)), k = 1, 2, 3,...

[0047] wherein β I and β Q are the gains of the I and Q signals respectively, X I (k) and X Q (k) are the sequences after the Walsh sequence or OVSF sequence spreading of the I and Q bits respectively, S n (k) is the Gold sequence used for scrambling, and n is the serial number of the Gold sequence. The definition of the Gold sequence is the prior art, and will not be described herein again. The identification of the generated polynomial thereof is the blind identification target of the present embodiment.

[0048] The received spread spectrum scrambling signal is modeled as follows:

[0049] T rec (k) = e jθ βS n (k)(β I X I (k) + jβ Q X Q (k)) + n(k)

[0050] where θ is the initial phase and the phase offset caused by the frequency offset, n(k) is the noise component, β is the channel gain, β I is the signal gain of the I channel, and β Q is the signal gain of the Q channel.

[0051] To eliminate the influence of the spread spectrum code, the initial phase of the received signal, and the frequency offset on the phase of the received signal, the received data T rec (k) is first preprocessed to obtain the shift sequence of the original Gold sequence. In this embodiment, the received data is subjected to conjugate difference and two times decimation processing by using a preset preprocessing method, and then the real part of the processed signal is taken to obtain M rec (k):

[0052]

[0053] where w re (k) is the noise component, and M is the shift sequence of the original Gold sequence. For the convenience of subsequent analysis, the bit sequence in which M rec (k) is determined as 01 is defined as R rec (k).

[0054] Before blind recognition of the scrambling code generation polynomial, this embodiment first obtains the degree of the Gold sequence generation polynomial as 2L. Further, the sequence R rec (k) satisfies the following error-containing equation group:

[0055]

[0056] The above equation is an (N-2Lx2L+1)-dimensional error-containing equation group, and as many equations as possible satisfy the above equation. The vector [c 2L c 2L-1 ... c1 1] T is called the solution of this error-containing equation group, and the solution is the coefficient of the generation polynomial.

[0057] Based on the above equation, in order to obtain the coefficients of the generator polynomial, the traditional WH analysis method needs to first statistically analyze the bit states of the faulty equation system and store them in a 1×2... 2L+1 In a 2-dimensional vector S, generate a vector of dimension 2. 2L+1 The Hadamard matrix is ​​then used. Subsequently, the Walsh spectral coefficients obtained by multiplying the vector S by the Hadamard matrix are used to calculate the polynomial coefficients, but this is impossible for generator polynomials with high series. To address the problem of the Hadamard matrix being too large to store and compute, this embodiment uses 2... 2L+1 The Hadamard matrix is ​​segmented into several smaller matrices of lower dimension to facilitate storage and computation. Walsh spectral coefficients are then calculated piecewise based on these smaller matrices, and finally combined to form the final Walsh spectral coefficients. For the above purpose, this embodiment proposes a matrix-updated WH analysis method to solve for the generator polynomial coefficients.

[0058] Figure 1 The flowchart of the WH algorithm based on matrix update provided in Embodiment 1 of the present invention is as follows:

[0059] a. Based on sequence R rec (k) Construct a system of equations containing errors.

[0060] b. Consider the N-2L row coefficients of the faulty system of equations as N-2L groups of 2L+1 bits each, where each group may have 2... 2L +1 Consider the possible values. Construct a 1×2... 2L+1 A dimensional vector S is used to count the number of times each bit group appears in the system of equations. The cumulative value of the same bit group is stored in the corresponding decimal number position of S.

[0061] c. Construct a 2 K A 2-dimensional Hadamard matrix H, where K < 2L+1, is used to iteratively generate a 2-dimensional Hadamard matrix. 2L+1 ×2 K A small matrix of dimension S, the product of which is a vector S and this matrix constitutes a portion of the final desired Walsh spectrum. The values ​​of K and L should guarantee 2... 2L+1 ×2 K A matrix of a certain dimension can be stored.

[0062] d. Traverse i=2 2L-K ,...,2 2L+1-K -1, for a decimal number i in a certain traversal, convert it into a binary vector R of dimension 2L+1-K. i Read R sequentially from least significant bit to most significant bit. iThe initial matrix H is updated to H = [H; H] when a certain bit is 0, and to H = [H; -H] when it is 1, until the vector R is completely read. i All bits are read. After reading all bits, the H matrix is ​​updated to a matrix with dimension 2. 2L+1 ×2 K matrix H i Finally, using vector S and matrix H i Multiplying them together yields the vector W. i .

[0063] e. Put all vectors W i Combining them in sequence yields a 1×2 dimension. 2L The vector W0. A vector with dimension 1×2... 2L Combining the zero vector with the W0 vector yields the final vector W, which is the desired Walsh spectrum. The binary vector corresponding to the maximum value index of vector W is the coefficient of the generator polynomial.

[0064] It is worth noting that in step d, the reason why parameter i changes from 2... 2L-K Start iterating up to 2 2L+1-K The reason for using -1 instead of starting the iteration from 0 is that the highest bit of the generator polynomial coefficients is 1, and the decimal number corresponding to the generator polynomial coefficients must be located in the latter half of the vector W index. Therefore, the first half of the vector W can be skipped. Additionally, to fully utilize the simulation storage space and simultaneously speed up execution, the value of K is set to 13 during simulation.

[0065] Compared to the traditional WH algorithm, the matrix-updated WH analysis method proposed in this embodiment is no longer limited by the number of generator polynomial series. It can identify generator polynomials of any high order, and a reasonable K value setting can also ensure full utilization of storage space, making it highly practical for engineering applications.

[0066] This embodiment uses a Gold sequence of order L=7 for simulation, and the generator polynomial of the preferred pair of m sequences is x. 7 +x 3 +1 and x 7 +x 3 +x 2 +x+1. Generate a spread spectrum signal of length N, spread using either Walsh or OVSF codes, with the spreading code chosen randomly. Then, scramble the spread signal by multiplying it with an equal-length Gold sequence, setting the symbol rate to 1 Msps. Pass the scrambled spread signal through an AWGN channel, introducing a frequency offset. Set the signal-to-noise ratio to -2 to 4 dB, and the sequence length N to 1200 and 2400, respectively. Add frequency offsets of 0 kHz, 1 kHz, and 2 kHz for each signal-to-noise ratio. Perform 500 Monte Carlo simulations.

[0067] Figure 2(a) shows the accuracy of generator polynomial recognition when N=1200 according to Embodiment 1 of the present invention. Figure 2(b) shows the accuracy of generator polynomial recognition when N=2400 according to Embodiment 1 of the present invention. The received signal is preprocessed according to the above process, and then the generator polynomial is recognized according to the above process. Based on the blind recognition scheme designed in this embodiment, the simulation results of the recognition accuracy of Gold code generator polynomials are shown in Figure 2(a) and Figure 2(b).

[0068] As shown in the figure above, the recognition accuracy of the direct-sequence spread spectrum (DSSS) scrambling signal generator polynomial blind recognition algorithm gradually increases with the increase of the signal-to-noise ratio (SNR). With a spread spectrum sequence length of 1200 and an SNR of 3dB, the recognition accuracy can reach over 95%; with a spread spectrum sequence length of 2400 and an SNR of 3dB, the recognition accuracy can reach over 100%. This demonstrates that increasing the sequence length can improve the algorithm's recognition accuracy to some extent. Furthermore, comparing the results under different frequency offsets shows that the frequency offset has almost no impact on the recognition accuracy of this algorithm.

[0069] In summary, the blind identification method for scrambling generator polynomials of direct-spread scrambling signals provided in this embodiment is robust to frequency offset, has no limitation on the order of the identifiable generator polynomial, and requires a short spread spectrum scrambling sequence, thus possessing strong practicality.

[0070] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A blind identification method using a scrambling code generator polynomial for direct-sequence spread spectrum scrambling signals, characterized in that, include: The direct-sequence spread spectrum (DSSS) scrambling signal transmitted by the transmitter is modeled as follows: T(k)=S n (k)(β I X I (k)+jβ Q X Q (k)),k=1,2,3,... Where, β I β is the gain of the I-channel signal. Q X is the gain of the Q-channel signal. I (k) is the sequence of I-way bits after being spread by a Walsh sequence or an OVSF sequence, X Q (k) is the sequence of Q-way bits after being spread by a Walsh sequence or an OVSF sequence, S n (k) is the Gold sequence used for scrambling, n is the index of the Gold sequence, and j is the imaginary unit; The direct-sequence spread spectrum (DSSS) scrambling signal received at the receiver is modeled as follows: T rec (k)=e jθ βS n (k)(β I X I (k)+jβ Q X Q (k))+n(k) Where θ is the phase offset caused by the initial phase and frequency offset, n(k) is the noise component, and β is the channel gain. I β is the signal gain of path I. Q The signal gain of the Q-path; Obtain sequence M rec (k), the sequence M rec The expression for (k) is as follows: Among them, w re (k) represents the noise component. This is a shifted sequence of the original Gold sequence; The sequence M rec (k) The sequence of bits with a decision of 0 or 1 is defined as sequence R. rec (k); The Gold sequence generator polynomial series is obtained as 2L, and the sequence R is obtained. rec (k) satisfies a fault-containing system of (N-2L×2L+1)-dimensional equations, where N is the sequence R. rec The length of (k) is 2L, which is the generating polynomial series of the Gold sequence; The solution to the system of equations containing errors is obtained as a vector [c 2L c 2L-1 ...c1 1] T , where c i ,0<i≤2L represents the coefficient factors of the generator polynomial, and T represents the transpose of the vector.

2. The blind identification method for scrambling code generator polynomials of direct-sequence spread scrambling signals according to claim 1, characterized in that, The expression for the faulty system of equations is as follows:

3. The blind identification method for scrambling codes generated by the generator polynomial of a direct-sequence spread scrambling signal according to claim 2, characterized in that, The obtained sequence M rec (k) is preceded by: The received signal T is processed according to a preset preprocessing method. rec (k) Perform preprocessing.

4. The blind identification method for scrambling code generator polynomial of direct-sequence spread scrambling signal according to claim 3, characterized in that, The received signal T is processed according to a preset preprocessing method. rec (k) The preprocessing steps include: For the received signal T rec (k) Perform conjugate difference and double downsampling processing.

5. The blind identification method for scrambling code generator polynomials of direct-sequence spread scrambling signals according to claim 4, characterized in that, Also includes: Selecting the real part of the processed signal yields the sequence M. rec (k).

6. The blind identification method for scrambling code generator polynomials of direct-sequence spread scrambling signals according to claim 5, characterized in that, Also includes: The coefficients of the generator polynomial are solved using the WH analysis method based on matrix update.

7. The blind identification method for scrambling code generator polynomials of direct-sequence spread scrambling signals according to claim 6, characterized in that, The steps for solving the coefficients of the generator polynomial using the WH analysis method based on matrix update include: The N-2L row coefficients of the faulty equation system can be considered as N-2L groups of 2L+1 bits each, with each bit group having 2 2L+1 The possible values ​​are constructed with a dimension of 1×2. 2L+1 The vector S is used to count the number of times each bit group appears in the system of error-containing equations, accumulate the same bit group, and store the accumulated value in the decimal number position corresponding to the vector S. Construction dimension is 2 K The Hadamard matrix H, where K < 2L+1, is used to represent the dimension of the matrix. This matrix H is used to iteratively generate a matrix of dimension 2. 2L+1 ×2 K The matrix, wherein the vector S is a matrix of dimension 2 2L+1 ×2 K The product of the matrices is part of the Walsh spectrum, where the values ​​of K and L are used to ensure that the dimension is 2. 2L+1 ×2 K The matrix can be stored; Traverse i=2 2L-K ,...,2 2L+1-K -1, convert the decimal number i being iterated over into a binary vector R of dimension 2L+1-K. i Read the vector R sequentially from the least significant bit to the most significant bit. i The bits, when the vector R i When the bit is 0, the matrix H is updated to H = [H; H], and when the vector R i When a bit is 1, update the matrix H to H = [H; -H], until the vector R is completely read. i After reading all bits of the vector R, i After all bits are processed, the matrix H is updated to a dimension 2. 2L+1 ×2 K matrix H i The vector S and the matrix H i Multiplying them together yields the vector W. i ; All vectors W i Combining them in order yields a dimension of 1×2. 2L The vector W0, with dimensions 1×2 2L The vector 0 is combined with the vector W0 to obtain the final vector W, which is the Walsh spectrum. The binary vector corresponding to the maximum value index of the vector W is obtained, and the binary vector corresponding to the maximum value index of the vector W is the coefficient of the generator polynomial.

8. The blind identification method for scrambling code generator polynomials of direct-sequence spread scrambling signals according to claim 7, characterized in that, The value of K is 13.

9. The blind identification method for scrambling code generator polynomials of direct-sequence spread scrambling signals according to claim 8, characterized in that, The value of L is 7.

10. The blind identification method for scrambling code generator polynomials of direct-sequence spread scrambling signals according to claim 9, characterized in that, The value of N is 1200 or 2400.

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