CN105337635A - Spread spectrum sequence dispreading method and system - Google Patents

Abstract

Translated fromChinese

本发明涉及通信技术领域，具体涉及一种扩频序列解扩方法和系统。该方法根据本地伪随机序列的本原多项式及约束方程组，建立本地伪随机序列的因子图，基于因子图模型完成了扩频序列的码捕获和码跟踪，本发明提出的一种扩频序列解扩方法，能实现基于因子图模型的码捕获和码跟踪，具有能纠正接收码片错误的能力，并且能直接输出每组扩频码对应码元的概率信息，较传统解扩计算方法，本发明提出的解扩计算方法具有的纠正接收码片错误的能力能提高解扩的成功率，并且能直接输出后续译码步骤需要的码元概率信息，能减少后续将解扩结果转化为码元概率信息的步骤。

The invention relates to the field of communication technology, in particular to a method and system for despreading a spread spectrum sequence. According to the original polynomial and constraint equations of the local pseudo-random sequence, the method establishes the factor graph of the local pseudo-random sequence, and completes the code acquisition and code tracking of the spread spectrum sequence based on the factor graph model. A spread spectrum sequence proposed by the present invention The despreading method can realize code capture and code tracking based on the factor graph model, has the ability to correct received chip errors, and can directly output the probability information of each group of spreading codes corresponding to the symbols. Compared with the traditional despreading calculation method, The despreading calculation method proposed by the present invention has the ability to correct received chip errors, can improve the success rate of despreading, and can directly output the symbol probability information required by the subsequent decoding step, and can reduce the subsequent conversion of despreading results into codes. Steps for meta-probability information.

Description

Translated fromChinese

一种扩频序列解扩方法和系统A spread spectrum sequence despreading method and system

技术领域technical field

本发明涉及通信技术领域，具体涉及一种扩频序列解扩方法和系统。The invention relates to the field of communication technology, in particular to a method and system for despreading a spread spectrum sequence.

背景技术Background technique

扩频通信指的是用来传输信息的信号带宽远远大于信息本身的带宽的一种通信模式。由于扩频通信技术具有抗干扰性能好、可进行多址通信、保密性好、抗衰落、抗多径、干扰小等优点，近十余年来，已迅速在民用通信的各个领域得到广泛的应用。直接序列扩频(简称直序扩频)是扩频的一种主要方式，它通过利用高速率的扩频序列在发射端扩展信号的频谱，而在接收端用相同的扩频码序列进行解扩，把展开的扩频信号还原成原来的信号。在直序扩频中，经常被采用的扩频码序列是M序列(通过在m序列后补0得到)。传统直序扩频系统中的解扩方法可以分为两个部分：码捕获与码跟踪；首先通过码捕获确定码相位，再通过码跟踪进一步减少码相位的误差。传统解扩方法具有捕获时间长，捕获后需要进一步进行码跟踪以减少相位误差的缺点，并且传统方法没有利用扩频码各个码片之间的约束关系降低每个码片提供信息的误差。Spread spectrum communication refers to a communication mode in which the signal bandwidth used to transmit information is much larger than the bandwidth of the information itself. Because spread spectrum communication technology has the advantages of good anti-interference performance, multi-access communication, good confidentiality, anti-fading, anti-multipath, and small interference, it has been widely used in various fields of civil communication in the past ten years. application. Direct Sequence Spread Spectrum (Direct Sequence Spread Spectrum for short) is a main method of spread spectrum. It spreads the spectrum of the signal at the transmitting end by using a high-rate spreading sequence, and uses the same spreading code sequence at the receiving end to decompose. Spread, restore the expanded spread spectrum signal to the original signal. In direct-sequence spread spectrum, the frequently used spread spectrum code sequence is M sequence (obtained by adding 0 after the m sequence). The despreading method in the traditional direct-sequence spread spectrum system can be divided into two parts: code acquisition and code tracking; firstly, the code phase is determined through code acquisition, and then the code phase error is further reduced through code tracking. The traditional despreading method has the disadvantages of long acquisition time and the need for further code tracking to reduce the phase error after acquisition, and the traditional method does not use the constraint relationship between each chip of the spreading code to reduce the error of information provided by each chip.

针对传统扩频方法没有利用扩频码各个码片之间的约束关系的问题，2003年，KeithM.Chugg和MingruiZhu在公开的ANewApproachtoRapidPNCodeAcquisitionUsingIterativeMessagePassingTechniques(一种基于消息传递算法的伪随机码捕获方法)中提出了利用因子图模型表示M序列的码片之间的约束关系的基础上的一种基于消息传递算法(IterativeMessagePassingAlgorithm，IMPA)的扩频码捕获方法，相比于传统扩频码捕获方法，该算法能够利用M序列的各个码片之间的约束关系提高扩频码捕获的概率，但是该算法存在收敛速度慢的问题，导致扩频码的检测速度无法令人满意。Aiming at the problem that the traditional spread spectrum method does not utilize the constraint relationship between each chip of the spread spectrum code, in 2003, KeithM.Chugg and MingruiZhu proposed in the public ANewApproachtoRapidPNCodeAcquisitionUsingIterativeMessagePassingTechniques (a pseudo-random code capture method based on the message passing algorithm) A spread spectrum code acquisition method based on the IterativeMessagePassingAlgorithm (IMPA) based on the factor graph model to represent the constraint relationship between the chips of the M sequence. Compared with the traditional spread spectrum code capture method, the algorithm can The probability of capturing the spreading code is improved by using the constraint relationship between each chip of the M sequence, but the algorithm has the problem of slow convergence speed, which makes the detection speed of the spreading code unsatisfactory.

2009年，徐定杰和赵国清等在公开的基于IMPA的伪码快速捕获算法的性能分析和改进中提出基于冗余约束的改进算法，即R-IMPA(RedundancyIMPA，基于消息传递的冗余算法)，该算法通过增加检测节点的约束长度来加快消息传递算法的收敛速度，从而提高检测速度。In 2009, Xu Dingjie and Zhao Guoqing proposed an improved algorithm based on redundancy constraints in the performance analysis and improvement of the public IMPA-based pseudo-code fast capture algorithm, that is, R-IMPA (RedundancyIMPA, a redundancy algorithm based on message passing), The algorithm speeds up the convergence speed of the message passing algorithm by increasing the constraint length of the detection nodes, thereby improving the detection speed.

但是上述以因子图模型为基础的扩频码捕获算法仅仅完成扩频序列的码捕获过程，不能实现基于因子图模型的码跟踪过程，在与系统后续译码部分或码元判决部分进行连接时还需要通过传统解扩方法完成解扩步骤并输出相应的码元信息。However, the above-mentioned spreading code acquisition algorithm based on the factor graph model only completes the code acquisition process of the spreading sequence, and cannot realize the code tracking process based on the factor graph model. It is also necessary to complete the despreading step through the traditional despreading method and output the corresponding symbol information.

发明内容Contents of the invention

本发明所要解决的技术问题是，如何实现基于因子图模型的码跟踪。The technical problem to be solved by the invention is how to realize the code tracking based on the factor graph model.

针对上述问题，本发明提出了一种扩频序列解扩方法，包括：For the problems referred to above, the present invention proposes a method for despreading the spread spectrum sequence, comprising:

步骤S1、根据本地伪随机序列的本原多项式及约束方程组S，建立码元为0对应的本地伪随机序列的第一因子图和码元为1对应的本地伪随机序列的第二因子图；Step S1, according to the original polynomial of the local pseudo-random sequence and the constraint equation set S, establish the first factor graph of the local pseudo-random sequence corresponding to the symbol 0 and the second factor graph of the local pseudo-random sequence corresponding to the symbol 1 ;

步骤S2、根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值和第一因子图，计算码元为0时发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}在满足本地伪随机序列约束方程组S的条件下各码片出现1的概率P₀(x_i＝1|Y,S)和出现0的概率P₀(x_i＝0|Y,S)，其中1≤i≤n，n＞1；Step S2, according to the value of the chip sequence Y={y₁ , y₂ ,...,y_i ,...,y_n } in the received spreading sequence symbol and the first factor graph, calculate when the symbol is 0 The chip sequence X={x₁ ,x₂ ,…,_xi ,…,x_n } sent by the sending end satisfies the probability of 1 in each chip under the condition that the local pseudo-random sequence constraint equation set S is satisfied P₀ (x_i =1|Y,S) and the probability of occurrence of 0 P₀ (_xi =0|Y,S), where 1≤i≤n, n>1;

根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值和第二因子图，计算码元为1时发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}在满足本地伪随机序列约束方程组S的条件下各码片出现1的概率P₁(x_i＝1|Y,S)和出现0的概率P₁(x_i＝0|Y,S)；According to the value of the chip sequence Y={y₁ , y₂ ,…,y_i ,…,y_n } in the received spreading sequence symbol and the second factor diagram, when the symbol is 1, the sender sends The chip sequence X={x₁ , x₂ ,...,_xi ,...,x_n }, under the condition of satisfying the local pseudo-random sequence constraint equation set S, the probability of 1 appearing in each chip P₁ (_xi =1 |Y,S) and the probability of occurrence of 0 P₁ (_xi = 0|Y,S);

步骤S3、根据第一因子图输出的P₀(x_i＝1|Y,S)和P₀(x_i＝0|Y,S)，确定发送端发送的码片序列中的第i个码片与本地伪随机序列的第i个码片数值相等的概率P₀(z_i1)和P₀(z_i2)；Step S3, according to P₀ (_xi =1|Y,S) and P₀ (_xi =0|Y,S) output by the first factor graph, determine the i-th code in the chip sequence sent by the sending end The probability P₀ (z_i1 ) and P₀ (z_i2 ) that the chip is equal to the i-th chip of the local pseudo-random sequence;

根据第二因子图输出的P₁(x_i＝1|Y,S)和P₁(x_i＝0|Y,S)，确定发送端发送的码片序列中的第i个码片与本地伪随机序列的第i个码片数值相等的概率P₁(z_i1)和P₁(z_i2)，According to P₁ (_xi =1|Y,S) and P₁ (_xi =0|Y,S) output by the second factor graph, determine the i-th chip in the chip sequence sent by the sending end and the local The probability P₁ (z_i1 ) and P₁ (z_i2 ) that the value of the ith chip of the pseudo-random sequence is equal,

其中P_x(z_i1)为码元为x时发送端发送的第i个码片与本地伪随机序列i个码片皆为0的概率，P_x(z_i2)为码元为x时发送端发送的第i个码片与本地伪随机序列i个码片皆为1的概率，x＝0或1；Among them, P_x (z_i1 ) is the probability that the i-th chip sent by the sender when the symbol is x and the i-chip of the local pseudo-random sequence are all 0, and P_x (z_i2 ) is the probability that the i-th chip sent by the sender is 0 when the symbol is x. The probability that the ith chip sent by the end and the i chip of the local pseudo-random sequence are all 1, x=0 or 1;

步骤S4、根据本地伪随机序列码片长度建立码元为0对应的第一图结构和码元为1对应的第二图结构；Step S4, according to the chip length of the local pseudo-random sequence, establish the first graph structure corresponding to the symbol 0 and the second graph structure corresponding to the symbol 1;

步骤S5、将根据第一因子图确定的P₀(z_i1)和P₀(z_i2)作为第一图结构第一层计算节点的输入，根据所述第一图结构，对P₀(D)进行运算，计算扩频前码元为0的概率P(D＝0)其中p₀(z_i)为P₀(z_i1)或P₀(z_i2)；Step S5, taking P₀ (z_i1 ) and P₀ (z_i2 ) determined according to the first factor graph as the input of the calculation node of the first layer of the first graph structure, and according to the first graph structure, for P₀ (D ) to calculate the probability P(D=0) that the symbol before spreading is 0 p₀ (z_i ) is P₀ (z_i1 ) or P₀ (z_i2 );

将根据第二因子图确定的P₁(z_i1)和P₁(z_i2)作为第二图结构第一层计算节点的输入，根据所述第二图结构，对P₁(D)进行运算，计算扩频前码元为1的概率P(D＝1)，其中p₁(z_i)为P₁(z_i1)或P₁(z_i2)；Taking P₁ (z_i1 ) and P₁ (z_i2 ) determined according to the second factor graph as the input of the first layer calculation node of the second graph structure, and performing operations on P₁ (D) according to the second graph structure , calculate the probability P(D=1) that the symbol before spreading is 1, where p₁ (z_i ) is P₁ (z_i1 ) or P₁ (z_i2 );

步骤S6、当p(D＝0)≥p(D＝1)时，判定扩频前码元为0的概率为p(D＝0)，码元为1的概率为1-p(D＝0)；当p(D＝0)<p(D＝1)时，判定扩频前码元为1的概率为p(D＝1)，码元为0的概率为1-p(D＝1)。Step S6, when p(D=0)≥p(D=1), the probability that the symbol before the spread spectrum is determined to be 0 is p(D=0), and the probability that the symbol is 1 is 1-p(D= 0); when p(D=0)<p(D=1), it is determined that the probability that the symbol before spreading is 1 is p(D=1), and the probability that the symbol is 0 is 1-p(D= 1).

优选地，所述步骤S2具体包括：Preferably, the step S2 specifically includes:

步骤S21、根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值，计算发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}中各码片为0的概率P₀(x_i＝0|y_i)和发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}中各码片为1的概率P₀(x_i＝1|y_i)；Step_S21 ._Calculate the chip sequence X_={ The probability P₀ (_xi = 0|y_i ) of each chip in x₁ , x₂ ,…,_xi ,…,x_n } being 0 and the chip sequence X={x₁ ,x₂ ,...,_xi ,...,x_n } the probability P₀ that each chip is 1 (_xi =1|y_i );

步骤S22、将P₀(x_i＝1|y_i)作为第一因子图中与变量y_i对应的变量节点的概率1输入，将P₀(x_i＝0|y_i)作为第一因子图中与变量y_i对应的变量节点的概率0输入，对第二因子图进行迭代计算，得到P₀(x_i＝1|Y,S)和P₀(x_i＝0|Y,S)；Step S22, input P₀ (_xi = 1|y_i ) as the probability 1 of the variable node corresponding to the variable y_i in the first factor graph, and use P₀ (_xi = 0|y_i ) as the first factor The probability 0 of the variable node corresponding to the variable y_i in the graph is input, and the second factor graph is iteratively calculated to obtain P₀ (_xi =1|Y,S) and P₀ (_xi =0|Y,S) ;

将P₁(x_i＝1|y_i)作为第二因子图中与变量y_i对应的变量节点的概率0输入，将P₁(x_i＝0|y_i)作为第二因子图中与变量y_i对应的变量节点的概率1输入，对第二因子图进行迭代计算，得到P₁(x_i＝1|Y,S)和P₁(x_i＝0|Y,S)。P₁ (_xi = 1|y_i ) is input as the probability 0 of the variable node corresponding to the variable y_i in the second factor graph, and P₁ (_xi = 0|y_i ) is input as the probability 0 of the variable node in the second factor graph. The probability 1 of the variable node corresponding to the variable y_i is input, and the second factor graph is iteratively calculated to obtain P₁ (_xi =1|Y,S) and P₁ (_xi =0|Y,S).

优选地，所述步骤S3具体包括：Preferably, the step S3 specifically includes:

当本地伪随机序列第i个码片为0时，选取所述第一因子图第i个变量节点输出的P₀(x_i＝0|Y,S)作为P₀(z_i1)，当本地伪随机序列第i个码片为1时，选取所述第一因子图第i个变量节点输出的P₀(x_i＝1|Y,S)作为P₀(z_i2)；When the i-th chip of the local pseudo-random sequence is 0, select P₀ (x_i =0|Y,S) output by the i-th variable node of the first factor graph as P₀ (z_i1 ), when the local When the i-th chip of the pseudo-random sequence is 1, select P₀ (x_i =1|Y,S) output by the i-th variable node of the first factor graph as P₀ (z_i2 );

当本地伪随机序列第i个码片为1时，选取所述第二因子图第i个变量节点输出的P₁(x_i＝0|Y,S)作为P₁(z_i1)，当本地伪随机序列第i个码片为1时，选取所述第二因子图第i个变量节点输出的P₁(x_i＝1|Y,S)作为P₁(z_i2)。When the i-th chip of the local pseudo-random sequence is 1, select P₁ (x_i =0|Y,S) output by the i-th variable node of the second factor graph as P₁ (z_i1 ), when the local When the i-th chip of the pseudo-random sequence is 1, select P₁ (_xi =1|Y,S) output by the i-th variable node of the second factor graph as P₁ (z_i2 ).

优选地，根据本地伪随机序列的长度，确定所述第一图结构和所述第二图结构皆为相同的树结构：Preferably, according to the length of the local pseudo-random sequence, it is determined that both the first graph structure and the second graph structure are the same tree structure:

当本地伪随机序列的长度n＝2^k时，树结构的第一层有2^k-1个2输入或节点，第j层有2^k-j个2输入或节点，第j层的第i个节点的两对输入分别与第j-1层的与两个节点的输出连接，其中，1≤i≤2^k-j，1＜j≤k，k＞1；When the length of the local pseudo-random sequence is n=2^k , the first layer of the tree structure has 2^k-1 2-inputs or nodes, the j-th layer has 2^kj 2-inputs or nodes, and the i-th node of the j-th layer The two pairs of inputs are respectively compared with the j-1th layer's and The output connection of two nodes, where, 1≤i≤2^kj , 1<j≤k, k>1;

当本地伪随机序列的长度n＝2^k+b时，树结构的第一层有2^k-1个或节点，其中前b个节点为3输入或节点，其余为2输入或节点；第j层有2^k-j个2输入或节点，第j层的第i个节点的两对输入分别与第j-1层的与两个节点的输出端连接，其中，1≤b≤2^k-1；When the length of the local pseudo-random sequence n=^2k +b, the first layer of the tree structure has^2k-1 or nodes, wherein the first b nodes are 3 input or nodes, and the rest are 2 input or nodes; the jth The layer has 2^kj 2-inputs or nodes, the i-th node of the j-th layer The two pairs of inputs are respectively compared with the j-1th layer's and The output terminals of the two nodes are connected, where 1≤b≤2^k-1 ;

当本地伪随机序列的长度n＝2^k+2^k-1+a时，树结构的第一层有2^k-1+2^k-2个或节点，其中前b个为3输入或节点，其余为2输入或节点；第二层有2^k-2个3输入或节点；第m层有2^k-m个2输入或节点；树结构的第二层中，第n个节点的三对输入分别与第一层的第3n-2、3n-1和3n个节点的输出连接；第m层的第q个节点的两对输入分别与第m-1层的与两个节点的输出端连接，其中1≤a＜2^k-1，2<m≤k，1≤n≤2^k-2，1≤q≤2^k-m。When the length of the local pseudo-random sequence n=^2k +^2k-1 +a, the first layer of the tree structure has^2k-1 +^2k-2 or nodes, of which the first b are 3 input or nodes, The rest are 2 inputs or nodes; the second layer has 2^k-2 3 inputs or nodes; the mth layer has 2^km 2 inputs or nodes; in the second layer of the tree structure, the three pairs of inputs of the nth node are respectively Connect to the output of the 3n-2, 3n-1, and 3nth nodes of the first layer; the qth node of the mth layer The two pairs of inputs are respectively related to the m-1th layer and The output terminals of the two nodes are connected, where 1≤a<2^k-1 , 2<m≤k, 1≤n≤2^k-2 , 1≤q≤2^km .

一种扩频序列解扩系统，包括：A spreading sequence despreading system, comprising:

因子图建立模块，用于根据本地伪随机序列的本原多项式及约束方程组S，建立码元为0对应的本地伪随机序列的第一因子图和码元为1对应的本地伪随机序列的第二因子图；The factor graph building module is used to establish the first factor graph of the local pseudo-random sequence corresponding to 0 and the local pseudo-random sequence corresponding to 1 according to the original polynomial of the local pseudo-random sequence and the set of constraint equations S. second factor graph;

第一运算模块，用于根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值和第一因子图，计算码元为0时发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}在满足本地伪随机序列约束方程组S的条件下各码片出现1的概率P₀(x_i＝1|Y,S)和出现0的概率P₀(x_i＝0|Y,S)，其中1≤i≤n，n＞1；根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值和第二因子图，计算码元为1时发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}在满足本地伪随机序列约束方程组S的条件下各码片出现1的概率P₁(x_i＝1|Y,S)和出现0的概率P₁(x_i＝0|Y,S)；The first operation module is used to calculate the code according to the value of the chip sequence Y={y₁ , y₂ ,...,y_i ,...,y_n } in the received spreading sequence symbol and the first factor diagram When the element is 0, the chip sequence X={x₁ ,x₂ ,…,_xi ,…,x_n } sent by the sending end satisfies the probability of 1 in each chip under the condition of satisfying the local pseudo-random sequence constraint equation set S P₀ (_xi =1|Y,S) and the probability of 0 appearing P₀ (_xi =0|Y,S), where 1≤i≤n, n>1; according to the received spreading sequence symbols The value of the chip sequence Y={y₁ , y₂ ,...,y_i ,...,y_n } and the second factor graph, calculate the chip sequence X={x 1 sent by the sender when the symbol is₁ ,x₂ ,…,_xi ,…,x_n} Under the condition of satisfying the local_pseudo -random sequence constraint equations S Probability P₁ (_xi = 0|Y,S);

确定模块，用于根据第一因子图输出的P₀(x_i＝1|Y,S)和P₀(x_i＝0|Y,S)，确定发送端发送的码片序列中的第i个码片与本地伪随机序列的第i个码片数值相等的概率P₀(z_i1)和P₀(z_i2)；根据第二因子图输出的P₁(x_i＝1|Y,S)和P₁(x_i＝0|Y,S)，确定发送端发送的码片序列中的第i个码片与本地伪随机序列的第i个码片数值相等的概率P₁(z_i1)和P₁(z_i2)，A determining module, configured to determine the i-th chip sequence sent by the sending end according to P₀ (_xi =1|Y,S) and P₀ (_xi =0|Y,S) output by the first factor graph The probability P₀ (z_i1 ) and P₀ (z_i2 ) that a chip is equal to the value of the i-th chip of the local pseudo-random sequence; P₁ (_xi =1|Y,S ) and P₁ (_xi = 0|Y, S), determine the probability that the i-th chip in the chip sequence sent by the sender is equal to the value of the i-th chip in the local pseudo-random sequence P₁ (z_i1 ) and P₁ (z_i2 ),

其中P_x(z_i1)为码元为x时发送端发送的第i个码片与本地伪随机序列i个码片皆为0的概率，P_x(z_i2)为码元为x时发送端发送的第i个码片与本地伪随机序列i个码片皆为1的概率，x＝0或1；Among them, P_x (z_i1 ) is the probability that the i-th chip sent by the sender when the symbol is x and the i-chip of the local pseudo-random sequence are all 0, and P_x (z_i2 ) is the probability that the i-th chip sent by the sender is 0 when the symbol is x. The probability that the ith chip sent by the end and the i chip of the local pseudo-random sequence are all 1, x=0 or 1;

图结构建立模块，用于根据本地伪随机序列码片长度建立码元为0对应的第一图结构和码元为1对应的第二图结构；A graph structure building module, used to establish a first graph structure corresponding to a symbol of 0 and a second graph structure corresponding to a symbol of 1 according to the length of the local pseudo-random sequence chip;

第二运算模块，用于将根据第一因子图确定的P₀(z_i1)和P₀(z_i2)作为第一图结构第一层计算节点的输入，根据所述第一图结构，对P₀(D)进行运算，计算扩频前码元为0的概率P(D＝0)其中p₀(z_i)为P₀(z_i1)或P₀(z_i2)；将根据第二因子图确定的P₁(z_i1)和P₁(z_i2)作为第二图结构第一层计算节点的输入，根据所述第二图结构，对P₁(D)进行运算，计算扩频前码元为1的概率P(D＝1)，其中p₁(z_i)为P₁(z_i1)或P₁(z_i2)；The second operation module is configured to use P₀ (z_i1 ) and P₀ (z_i2 ) determined according to the first factor graph as the input of the calculation node of the first layer of the first graph structure, and according to the first graph structure, to P₀ (D) carries out operation, calculates the probability P(D=0) that symbol before spreading is 0 wherein p₀ (z_i ) is P₀ (z_i1 ) or P₀ (z_i2 ); P₁ (z_i1 ) and P₁ (z_i2 ) determined according to the second factor graph are used as the first layer of the second graph structure Calculate the input of the node, according to the second graph structure, perform operations on P₁ (D), and calculate the probability P(D=1) that the symbol before spreading is 1, wherein p₁ (z_i ) is P₁ (z_i1 ) or P₁ (z_i2 );

码元概率判定模块，用于当p(D＝0)≥p(D＝1)时，判定扩频前码元为0的概率为p(D＝0)，码元为1的概率为1-p(D＝0)；当p(D＝0)<p(D＝1)时，判定扩频前码元为1的概率为p(D＝1)，码元为0的概率为1-p(D＝1)。Symbol probability determination module, for when p(D=0)≥p(D=1), the probability that the symbol before the spread spectrum is determined to be 0 is p(D=0), and the probability that the symbol is 1 is 1 -p(D=0); when p(D=0)<p(D=1), it is determined that the probability that the symbol before spreading is 1 is p(D=1), and the probability that the symbol is 0 is 1 -p(D=1).

优选地，所述第一运算模块具体包括：Preferably, the first computing module specifically includes:

第一子计算模块，用于根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值，计算发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}中各码片为0的概率P₀(x_i＝0|y_i)和发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}中各码片为1的概率P₀(x_i＝1|y_i)；The first sub-calculation module is used to calculate the code sent by the sending end according to the value of the chip sequence Y={y₁ , y₂ ,...,y_i ,...,y_n } in the received spreading sequence symbol Chip sequence X={x₁ ,x₂ ,...,_xi ,...,x_n } the probability P₀ (_xi =0|y_i ) of each chip being 0 and the chip sequence X sent by the sender= Probability P₀ that each chip in {x₁ , x₂ ,...,_xi ,...,x_n } is 1 (_xi =1|y_i );

第二子计算模块，用于将P₀(x_i＝1|y_i)作为第一因子图中与变量y_i对应的变量节点的概率1输入，将P₀(x_i＝0|y_i)作为第一因子图中与变量y_i对应的变量节点的概率0输入，对第二因子图进行迭代计算，得到P₀(x_i＝1|Y,S)和P₀(x_i＝0|Y,S)；The second sub-calculation module is used to input P₀ (_xi = 1|y_i ) as the probability 1 of the variable node corresponding to the variable y_i in the first factor graph, and input P₀ (_xi = 0|y_i ) is input as the probability 0 of the variable node corresponding to the variable y_i in the first factor graph, and iteratively calculates the second factor graph to obtain P₀ (_xi =1|Y,S) and P₀ (_xi =0 |Y,S);

第三子计算模块，用于将P₁(x_i＝1|y_i)作为第二因子图中与变量y_i对应的变量节点的概率0输入，将P₁(x_i＝0|y_i)作为第二因子图中与变量y_i对应的变量节点的概率1输入，对第二因子图进行迭代计算，得到P₁(x_i＝1|Y,S)和P₁(x_i＝0|Y,S)。The third sub-calculation module is used to input P₁ (_xi = 1|y_i ) as the probability 0 of the variable node corresponding to the variable y_i in the second factor graph, and input P₁ (_xi = 0|y_i ) is input as the probability 1 of the variable node corresponding to the variable y_i in the second factor graph, and iteratively calculates the second factor graph to obtain P₁ (_xi =1|Y,S) and P₁ (_xi =0 |Y, S).

优选地，所述确定模块具体包括：Preferably, the determination module specifically includes:

第一子确定模块，用于当本地伪随机序列第i个码片为0时，选取所述第一因子图第i个变量节点输出的P₀(x_i＝0|Y,S)作为P₀(z_i1)，当本地伪随机序列第i个码片为1时，选取所述第一因子图第i个变量节点输出的P₀(x_i＝1|Y,S)作为P₀(z_i2)；The first sub-determining module is used to select P₀ (_xi =0|Y,S) output by the i-th variable node of the first factor graph when the i-th chip of the local pseudo-random sequence is 0 as P₀ (z_i1 ), when the i-th chip of the local pseudo-random sequence is 1, select P₀ (_xi =1|Y,S) output by the i-th variable node of the first factor graph as P₀ ( z_i2 );

第二子确定模块，用于当本地伪随机序列第i个码片为1时，选取所述第二因子图第i个变量节点输出的P₁(x_i＝0|Y,S)作为P₁(z_i1)，当本地伪随机序列第i个码片为1时，选取所述第二因子图第i个变量节点输出的P₁(x_i＝1|Y,S)作为P₁(z_i2)。The second sub-determination module is used to select P₁ (_xi =0|Y, S) output by the i-th variable node of the second factor graph when the i-th chip of the local pseudo-random sequence is 1 as P₁ (z_i1 ), when the i-th chip of the local pseudo-random sequence is 1, select P₁ (_xi =1|Y,S) output by the i-th variable node of the second factor graph as P₁ ( z_i2 ).

优选地，所述第一图结构和所述第二图结构皆为相同的树结构：Preferably, both the first graph structure and the second graph structure are the same tree structure:

当本地伪随机序列的长度n＝2^k时，树结构的第一层有2^k-1个2输入或节点，第j层有2^k-j个2输入或节点，第j层的第i个节点的两对输入分别与第j-1层的与两个节点的输出连接，其中，1≤i≤2^k-j，1＜j≤k，k＞1；When the length of the local pseudo-random sequence is n=2^k , the first layer of the tree structure has 2^k-1 2-inputs or nodes, the j-th layer has 2^kj 2-inputs or nodes, and the i-th node of the j-th layer The two pairs of inputs are respectively compared with the j-1th layer's and The output connection of two nodes, where, 1≤i≤2^kj , 1<j≤k, k>1;

当本地伪随机序列的长度n＝2^k+b时，树结构的第一层有2^k-1个或节点，其中前b个节点为3输入或节点，其余为2输入或节点；第j层有2^k-j个2输入或节点，第j层的第i个节点的两对输入分别与第j-1层的与两个节点的输出端连接，其中，1≤b≤2^k-1；When the length of the local pseudo-random sequence is n=^2k +b, the first layer of the tree structure has^2k-1 or nodes, wherein the first b nodes are 3 input or nodes, and the rest are 2 input or nodes; the jth The layer has 2^kj 2-inputs or nodes, the i-th node of the j-th layer The two pairs of inputs are respectively compared with the j-1th layer's and The output terminals of the two nodes are connected, where 1≤b≤2^k-1 ;

当本地伪随机序列的长度n＝2^k+2^k-1+a时，树结构的第一层有2^k-1+2^k-2个或节点，其中前b个为3输入或节点，其余为2输入或节点；第二层有2^k-2个3输入或节点；第m层有2^k-m个2输入或节点；树结构的第二层中，第n个节点的三对输入分别与第一层的第3n-2、3n-1和3n个节点的输出连接；第m层的第q个节点的两对输入分别与第m-1层的与两个节点的输出端连接，其中1≤a＜2^k-1，2<m≤k，1≤n≤2^k-2，1≤q≤2^k-m。When the length of the local pseudo-random sequence n=^2k +^2k-1 +a, the first layer of the tree structure has^2k-1 +^2k-2 or nodes, of which the first b are 3 input or nodes, The rest are 2 inputs or nodes; the second layer has 2^k-2 3 inputs or nodes; the mth layer has 2^km 2 inputs or nodes; in the second layer of the tree structure, the three pairs of inputs of the nth node are respectively Connect to the output of the 3n-2, 3n-1, and 3nth nodes of the first layer; the qth node of the mth layer The two pairs of inputs are respectively related to the m-1th layer and The output terminals of the two nodes are connected, where 1≤a<2^k-1 , 2<m≤k, 1≤n≤2^k-2 , 1≤q≤2^km .

本发明提出的一种扩频序列解扩方法，能实现基于因子图模型的码捕获和码跟踪，具有能纠正接收码片错误的能力，并且能直接输出每组扩频码对应码元的概率信息。较传统解扩计算方法，本发明提出的解扩计算方法具有的纠正接收码片错误的能力能提高解扩的成功率，并且能直接输出后续译码步骤需要的码元概率信息，能减少后续将解扩结果转化为码元概率信息的步骤。A spread spectrum sequence despreading method proposed by the present invention can realize code acquisition and code tracking based on the factor graph model, has the ability to correct received chip errors, and can directly output the probability of each group of spread spectrum codes corresponding to symbols information. Compared with the traditional despreading calculation method, the despreading calculation method proposed by the present invention has the ability to correct received chip errors, can improve the success rate of despreading, and can directly output the symbol probability information required by the subsequent decoding steps, which can reduce the subsequent A step of converting the despreading result into symbol probability information.

附图说明Description of drawings

图1为本发明一实施例提供的一种扩频序列解扩方法流程示意图；Fig. 1 is a schematic flow chart of a spreading sequence despreading method provided by an embodiment of the present invention;

图2为本发明一实施例提供的码元为0对应的因子图的结构示意图；FIG. 2 is a schematic structural diagram of a factor graph corresponding to a symbol of 0 provided by an embodiment of the present invention;

图3为本发明一实施例提供的当本地伪随机序列的长度n＝2^k时码元为0对应的树结构示意图；Fig. 3 is the tree structure diagram corresponding to symbol 0 when the length n=^2k of the local pseudo-random sequence provided by an embodiment of the present invention;

图4为本发明一实施例提供的当本地伪随机序列的长度n＝2^k+b时码元为0对应的树结构示意图；FIG. 4 is a schematic diagram of a tree structure corresponding to a symbol of 0 when the length of the local pseudo-random sequence n=^2k +b provided by an embodiment of the present invention;

图5为本发明一实施例提供的当本地伪随机序列的长度n＝2^k+2^k-1+a时码元为0对应的树结构示意图；FIG. 5 is a schematic diagram of a tree structure corresponding to a symbol of 0 when the length of the local pseudo-random sequence n=^2k +^2k-1 +a provided by an embodiment of the present invention;

图6为本发明另一实施例提供的一种扩频序列解扩系统示意框图。Fig. 6 is a schematic block diagram of a spreading sequence despreading system provided by another embodiment of the present invention.

具体实施方式detailed description

为了能够更清楚地理解本发明的上述目的、特征和优点，下面结合附图和具体实施方式对本发明进行进一步的详细描述。需要说明的是，在不冲突的情况下，本申请的实施例及实施例中的特征可以相互组合。In order to understand the above-mentioned purpose, features and advantages of the present invention more clearly, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是，本发明还可以采用其他不同于在此描述的其他方式来实施，因此，本发明的保护范围并不受下面公开的具体实施例的限制。In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described here. Therefore, the protection scope of the present invention is not limited by the specific details disclosed below. EXAMPLE LIMITATIONS.

图1为本发明一实施例提供的一种扩频序列解扩方法流程示意图。参见图1，该方法包括：FIG. 1 is a schematic flowchart of a method for despreading a spreading sequence provided by an embodiment of the present invention. Referring to Figure 1, the method includes:

步骤S1、根据本地伪随机序列的本原多项式及约束方程组S，建立码元为0对应的本地伪随机序列的第一因子图和码元为1对应的本地伪随机序列的第二因子图；Step S1, according to the original polynomial of the local pseudo-random sequence and the constraint equation set S, establish the first factor graph of the local pseudo-random sequence corresponding to the symbol 0 and the second factor graph of the local pseudo-random sequence corresponding to the symbol 1 ;

步骤S2、根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值和第一因子图，计算码元为0时发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}在满足本地伪随机序列约束方程组S的条件下各码片出现1的概率P₀(x_i＝1|Y,S)和出现0的概率P₀(x_i＝0|Y,S)，其中1≤i≤n，n＞1；Step S2, according to the value of the chip sequence Y={y₁ , y₂ ,...,y_i ,...,y_n } in the received spreading sequence symbol and the first factor graph, calculate when the symbol is 0 The chip sequence X={x₁ ,x₂ ,…,_xi ,…,x_n } sent by the sending end satisfies the probability of 1 in each chip under the condition that the local pseudo-random sequence constraint equation set S is satisfied P₀ (x_i =1|Y,S) and the probability of occurrence of 0 P₀ (_xi =0|Y,S), where 1≤i≤n, n>1;

根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值和第二因子图，计算码元为1时发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}在满足本地伪随机序列约束方程组S的条件下各码片出现1的概率P₁(x_i＝1|Y,S)和出现0的概率P₁(x_i＝0|Y,S)；According to the value of the chip sequence Y={y₁ , y₂ ,…,y_i ,…,y_n } in the received spreading sequence symbol and the second factor diagram, when the symbol is 1, the sender sends The chip sequence X={x₁ , x₂ ,...,_xi ,...,x_n }, under the condition of satisfying the local pseudo-random sequence constraint equation set S, the probability of 1 appearing in each chip P₁ (_xi =1 |Y,S) and the probability of occurrence of 0 P₁ (_xi = 0|Y,S);

步骤S3、根据第一因子图输出的P₀(x_i＝1|Y,S)和P₀(x_i＝0|Y,S)，确定发送端发送的码片序列中的第i个码片与本地伪随机序列的第i个码片数值相等的概率P₀(z_i1)和P₀(z_i2)；Step S3, according to P₀ (_xi =1|Y,S) and P₀ (_xi =0|Y,S) output by the first factor graph, determine the i-th code in the chip sequence sent by the sending end The probability P₀ (z_i1 ) and P₀ (z_i2 ) that the chip is equal to the i-th chip of the local pseudo-random sequence;

根据第二因子图输出的P₁(x_i＝1|Y,S)和P₁(x_i＝0|Y,S)，确定发送端发送的码片序列中的第i个码片与本地伪随机序列的第i个码片数值相等的概率P₁(z_i1)和P₁(z_i2)，According to P₁ (_xi =1|Y,S) and P₁ (_xi =0|Y,S) output by the second factor graph, determine the i-th chip in the chip sequence sent by the sending end and the local The probability P₁ (z_i1 ) and P₁ (z_i2 ) that the value of the ith chip of the pseudo-random sequence is equal,

其中P_x(z_i1)为码元为x时发送端发送的第i个码片与本地伪随机序列i个码片皆为0的概率，P_x(z_i2)为码元为x时发送端发送的第i个码片与本地伪随机序列i个码片皆为1的概率，x＝0或1；Among them, P_x (z_i1 ) is the probability that the i-th chip sent by the sender when the symbol is x and the i-chip of the local pseudo-random sequence are all 0, and P_x (z_i2 ) is the probability that the i-th chip sent by the sender is 0 when the symbol is x. The probability that the ith chip sent by the end and the i chip of the local pseudo-random sequence are all 1, x=0 or 1;

步骤S4、根据本地伪随机序列码片长度建立码元为0对应的第一图结构和码元为1对应的第二图结构；Step S4, according to the chip length of the local pseudo-random sequence, establish the first graph structure corresponding to the symbol 0 and the second graph structure corresponding to the symbol 1;

步骤S5、将根据第一因子图确定的P₀(z_i1)和P₀(z_i2)作为第一图结构第一层计算节点的输入，根据所述第一图结构，对P₀(D)进行运算，计算扩频前码元为0的概率P(D＝0)其中p₀(z_i)为P₀(z_i1)或P₀(z_i2)；Step S5, taking P₀ (z_i1 ) and P₀ (z_i2 ) determined according to the first factor graph as the input of the calculation node of the first layer of the first graph structure, and according to the first graph structure, for P₀ (D ) to calculate the probability P(D=0) that the symbol before spreading is 0 p₀ (z_i ) is P₀ (z_i1 ) or P₀ (z_i2 );

将根据第二因子图确定的P₁(z_i1)和P₁(z_i2)作为第二图结构第一层计算节点的输入，根据所述第二图结构，对P₁(D)进行运算，计算扩频前码元为1的概率P(D＝1)，其中p₁(z_i)为P₁(z_i1)或P₁(z_i2)；Taking P₁ (z_i1 ) and P₁ (z_i2 ) determined according to the second factor graph as the input of the first layer calculation node of the second graph structure, and performing operations on P₁ (D) according to the second graph structure , calculate the probability P(D=1) that the symbol before spreading is 1, where p₁ (z_i ) is P₁ (z_i1 ) or P₁ (z_i2 );

步骤S6、当p(D＝0)≥p(D＝1)时，判定扩频前码元为0的概率为p(D＝0)，码元为1的概率为1-p(D＝0)；当p(D＝0)<p(D＝1)时，判定扩频前码元为1的概率为p(D＝1)，码元为0的概率为1-p(D＝1)。Step S6, when p(D=0)≥p(D=1), the probability that the symbol before the spread spectrum is determined to be 0 is p(D=0), and the probability that the symbol is 1 is 1-p(D= 0); when p(D=0)<p(D=1), it is determined that the probability that the symbol before spreading is 1 is p(D=1), and the probability that the symbol is 0 is 1-p(D= 1).

不难理解的是，步骤S5中的P₀(D)为码元0内码片相位正确的概率，等于码元0内所有码片与本地伪随机码序列对应位置码片相同的概率之积；P₁(D)为码元1内码片相位正确的概率，等于码元1内所有码片与本地伪随机码序列对应位置码片相同的概率之积。It is not difficult to understand that P₀ (D) in step S5 is the probability that the phase of the chip in symbol 0 is correct, which is equal to the product of the probability that all the chips in symbol 0 are the same as the corresponding position of the local pseudo-random code sequence ; P₁ (D) is the probability that the phase of the chip in symbol 1 is correct, which is equal to the product of the probability that all the chips in symbol 1 are the same as the corresponding position chips of the local pseudo-random code sequence.

其中，所述本地伪随机序列可以为M序列。Wherein, the local pseudo-random sequence may be an M sequence.

以M序列为例，所述步骤S1具体包括以下步骤：Taking the M sequence as an example, the step S1 specifically includes the following steps:

步骤S11、将M序列对应的本原多项式作为约束方程的左边，令其等于0，构建约束方程组；假设M序列长度为n，本原多项式的最高次幂为m，约束方程组中约束方程的个数为n-m-1个。Step S11, use the primitive polynomial corresponding to the M sequence as the left side of the constraint equation, make it equal to 0, and construct a constraint equation system; assuming that the length of the M sequence is n, the highest power of the primitive polynomial is m, and the constraint equation in the constraint equation system The number of is n-m-1.

以15位M序列进行举例说明：假设15位M序列为：[111101011001000]，15位M序列的本原多项式为x⁴+x+1，其构成的约束方程组如下：Take the 15-digit M-sequence as an example: Assume that the 15-digit M-sequence is: [111101011001000], the primitive polynomial of the 15-digit M-sequence is x⁴ +x+1, and its constrained equations are as follows:

$\left\{\begin{array}{c}{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>4</span>4</span>}}<span><span>+</span>+</span>{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>1</span>1</span>}}<span><span>+</span>+</span>{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>0</span>0</span>}}<span><span>=</span>=</span>\mathrm{<span><span>0</span>0</span>}\\ {\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>5</span>5</span>}}<span><span>+</span>+</span>{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>+</span>+</span>{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>1</span>1</span>}}<span><span>=</span>=</span>\mathrm{<span><span>0</span>0</span>}\\ {\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>6</span>6</span>}}<span><span>+</span>+</span>{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>3</span>3</span>}}<span><span>+</span>+</span>{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>2</span>2</span>}}<span><span>=</span>=</span>\mathrm{<span><span>0</span>0</span>}\\ <span><span>.</span>.</span>\\ <span><span>.</span>.</span>\\ <span><span>.</span>.</span>\\ {\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>15</span>15</span>}}<span><span>+</span>+</span>{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>12</span>12</span>}}<span><span>+</span>+</span>{\mathrm{<span><span>x</span>x</span>}}_{\mathrm{<span><span>11</span>11</span>}}<span><span>=</span>=</span>\mathrm{<span><span>0</span>0</span>}\end{array}\right.$

步骤S12、建立与约束方程组对应的因子图，以步骤S11得到的约束方程组中的每一个方程作为因子图的校验节点，M序列对应的n个变量作为变量节点，将变量节点与包含其对应变量的方程对应的校验节点连接，完成所有连接后得到M序列对应的因子图，其中码元为0对应的因子图如图2所示，码元为1对应的因子图结构与码元为0对应的因子图结构相同，只是输入量不同，在此不再赘述。Step S12, set up the factor graph corresponding to the constraint equations, use each equation in the constraint equations obtained in step S11 as the check node of the factor graph, and the n variables corresponding to the M sequence as the variable nodes, and combine the variable nodes with the The check node connection corresponding to the equation of the corresponding variable is completed. After all the connections are completed, the factor graph corresponding to the M sequence is obtained. The factor graph corresponding to the code element 0 is shown in Figure 2, and the factor graph structure corresponding to the code element 1 is the same as the code The structure of the factor graph corresponding to 0 is the same, but the input amount is different, so I won’t go into details here.

由上述技术方案可知，本发明提出的一种扩频序列解扩方法，能实现基于因子图模型的码捕获和码跟踪，具有能纠正接收码片错误的能力，并且能直接输出每组扩频码对应码元的概率信息。较传统解扩计算方法，本发明提出的解扩计算方法具有的纠正接收码片错误的能力能提高解扩的成功率，并且能直接输出后续译码步骤需要的码元概率信息，能减少后续将解扩结果转化为码元概率信息的步骤。It can be seen from the above-mentioned technical scheme that a kind of spreading sequence despreading method proposed by the present invention can realize code acquisition and code tracking based on factor graph model, has the ability to correct received chip errors, and can directly output each group of spread spectrum The code corresponds to the probability information of the symbol. Compared with the traditional despreading calculation method, the despreading calculation method proposed by the present invention has the ability to correct received chip errors, can improve the success rate of despreading, and can directly output the symbol probability information required by the subsequent decoding steps, which can reduce subsequent A step of converting the despreading result into symbol probability information.

优选地，所述步骤S2具体包括：Preferably, the step S2 specifically includes:

步骤S21、根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值，计算发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}中各码片为0的概率P₀(x_i＝0|y_i)和发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}中各码片为1的概率P₀(x_i＝1|y_i)；Step_S21 ._Calculate the chip sequence X_={ The probability P₀ (_xi = 0|y_i ) of each chip in x₁ , x₂ ,…,_xi ,…,x_n } being 0 and the chip sequence X={x₁ ,x₂ ,...,_xi ,...,x_n } the probability P₀ that each chip is 1 (_xi =1|y_i );

步骤S22、将P₀(x_i＝1|y_i)作为第一因子图中与变量y_i对应的变量节点的概率1输入，将P₀(x_i＝0|y_i)作为第一因子图中与变量y_i对应的变量节点的概率0输入，对第二因子图进行迭代计算，得到P₀(x_i＝1|Y,S)和P₀(x_i＝0|Y,S)；Step S22, input P₀ (_xi = 1|y_i ) as the probability 1 of the variable node corresponding to the variable y_i in the first factor graph, and use P₀ (_xi = 0|y_i ) as the first factor The probability 0 of the variable node corresponding to the variable y_i in the graph is input, and the second factor graph is iteratively calculated to obtain P₀ (_xi =1|Y,S) and P₀ (_xi =0|Y,S) ;

将P₁(x_i＝1|y_i)作为第二因子图中与变量y_i对应的变量节点的概率0输入，将P₁(x_i＝0|y_i)作为第二因子图中与变量y_i对应的变量节点的概率1输入，对第二因子图进行迭代计算，得到P₁(x_i＝1|Y,S)和P₁(x_i＝0|Y,S)。P₁ (_xi = 1|y_i ) is input as the probability 0 of the variable node corresponding to the variable y_i in the second factor graph, and P₁ (_xi = 0|y_i ) is input as the probability 0 of the variable node in the second factor graph. The probability 1 of the variable node corresponding to the variable y_i is input, and the second factor graph is iteratively calculated to obtain P₁ (_xi =1|Y,S) and P₁ (_xi =0|Y,S).

可选地，对因子图进行迭代计算采用和积算法。Optionally, the iterative calculation of the factor graph adopts the sum-product algorithm.

优选地，所述步骤S3具体包括：Preferably, the step S3 specifically includes:

当本地伪随机序列第i个码片为0时，选取所述第一因子图第i个变量节点输出的P₀(x_i＝0|Y,S)作为P₀(z_i1)，当本地伪随机序列第i个码片为1时，选取所述第一因子图第i个变量节点输出的P₀(x_i＝1|Y,S)作为P₀(z_i2)；When the i-th chip of the local pseudo-random sequence is 0, select P₀ (x_i =0|Y,S) output by the i-th variable node of the first factor graph as P₀ (z_i1 ), when the local When the i-th chip of the pseudo-random sequence is 1, select P₀ (x_i =1|Y,S) output by the i-th variable node of the first factor graph as P₀ (z_i2 );

当本地伪随机序列第i个码片为1时，选取所述第二因子图第i个变量节点输出的P₁(x_i＝0|Y,S)作为P₁(z_i1)，当本地伪随机序列第i个码片为1时，选取所述第二因子图第i个变量节点输出的P₁(x_i＝1|Y,S)作为P₁(z_i2)。When the i-th chip of the local pseudo-random sequence is 1, select P₁ (x_i =0|Y,S) output by the i-th variable node of the second factor graph as P₁ (z_i1 ), when the local When the i-th chip of the pseudo-random sequence is 1, select P₁ (_xi =1|Y,S) output by the i-th variable node of the second factor graph as P₁ (z_i2 ).

优选地，根据本地伪随机序列的长度，确定所述第一图结构和所述第二图结构皆为相同的树结构：Preferably, according to the length of the local pseudo-random sequence, it is determined that both the first graph structure and the second graph structure are the same tree structure:

当本地伪随机序列的长度n＝2^k时，树结构的最第一层有2^k-1个2输入或节点，第j层有2^k-j个2输入或节点，第j层的第i个节点的两对输入分别与第j-1层的与两个节点的输出连接，其中，1≤i≤2^k-j，1＜j≤k，k＞1；码元为0对应的树结构如图3所示，图3中1≤x＜k，码元为1对应的树结构与码元为0对应的树结构相同，只是输入量不同，在此不再赘述。When the length of the local pseudo-random sequence n=2^k , the first layer of the tree structure has 2^k-1 2-inputs or nodes, the j-th layer has 2^kj 2-inputs or nodes, and the i-th of the j-th layer node The two pairs of inputs are respectively compared with the j-1th layer's and The output connection of two nodes, among them, 1≤i≤2^kj , 1<j≤k, k>1; the tree structure corresponding to code element 0 is shown in Figure 3, in Figure 3 1≤x<k, code The tree structure corresponding to the element being 1 is the same as the tree structure corresponding to the code element being 0, except that the input amount is different, and details will not be repeated here.

当本地伪随机序列的长度n＝2^k+b时，树结构的第一层有2^k-1个或节点，其中前b个节点为3输入或节点，其余为2输入或节点；第j层有2^k-j个2输入或节点，第j层的第i个节点的两对输入分别与第j-1层的与两个节点的输出端连接，其中，1≤b≤2^k-1；码元为0对应的树结构如图4所示，码元为1对应的树结构与码元为0对应的树结构相同，只是输入量不同，在此不再赘述。When the length of the local pseudo-random sequence n=^2k +b, the first layer of the tree structure has^2k-1 or nodes, wherein the first b nodes are 3 input or nodes, and the rest are 2 input or nodes; the jth The layer has 2^kj 2-inputs or nodes, the i-th node of the j-th layer The two pairs of inputs are respectively compared with the j-1th layer's and The output terminals of the two nodes are connected, where 1≤b≤2^k-1 ; the tree structure corresponding to the code element 0 is shown in Figure 4, the tree structure corresponding to the code element 1 and the tree structure corresponding to the code element 0 The same, only the input amount is different, and will not be repeated here.

当本地伪随机序列的长度n＝2^k+2^k-1+a时，树结构的第一层有2^k-1+2^k-2个或节点，其中前b个为3输入或节点，其余为2输入或节点；第二层有2^k-2个3输入或节点；第m层有2^k-m个2输入或节点；树结构的第二层中，第n个节点的三对输入分别与第一层的第3n-2、3n-1和3n个节点的输出连接；第m层的第q个节点的两对输入分别与第m-1层的与两个节点的输出端连接，其中1≤a＜2^k-1，2<m≤k，1≤n≤2^k-2，1≤q≤2^k-m；码元为0对应的树结构如图5所示，码元为1对应的树结构与码元为0对应的树结构相同，只是输入量不同，在此不再赘述。When the length of the local pseudo-random sequence n=^2k +^2k-1 +a, the first layer of the tree structure has^2k-1 +^2k-2 or nodes, of which the first b are 3 input or nodes, The rest are 2 inputs or nodes; the second layer has 2^k-2 3 inputs or nodes; the mth layer has 2^km 2 inputs or nodes; in the second layer of the tree structure, the three pairs of inputs of the nth node are respectively Connect to the output of the 3n-2, 3n-1, and 3nth nodes of the first layer; the qth node of the mth layer The two pairs of inputs are respectively related to the m-1th layer and The output terminals of the two nodes are connected, where 1≤a<2^k-1 , 2<m≤k, 1≤n≤2^k-2 , 1≤q≤2^km ; the tree structure corresponding to code element 0 is shown in the figure As shown in 5, the tree structure corresponding to the code element 1 is the same as the tree structure corresponding to the code element 0, except that the input amount is different, and will not be repeated here.

比如，M序列的长度15＝2³+2^3-1+3，那么采用的树结构为：第一层有2^3-1+2^3-2＝6个或节点，其中前3个为3输入或节点，其余3个为2输入或节点，从左至右可以命名为第二层有2个3输入或节点，从左至右可以命名为第三层有1个2输入或节点，为树结构的第二层中，第1个节点的三对输入的0概率输入分别与第一层的第1、2和3个节点和的0概率输出连接，第二个节点与第一层的第4、5和6个节点和的0概率输出连接；在第三层的节点与和两个节点的0概率输出端连接，节点输出的0概率为码元序列相位正确的概率。该树结构中共有9个或节点，其中5个节点为3输入或节点，其余4个为2输入或节点。For example, the length of the M sequence is 15 = 2³ + 2^3-1 + 3, then the tree structure adopted is: the first layer has 2^3-1 + 2^3-2 = 6 or nodes, of which the first 3 are 3 Input or node, the remaining 3 are 2 input or nodes, from left to right can be named as The second layer has 2 3 inputs or nodes, from left to right can be named as The third layer has 1 2-input or node, for In the second level of the tree structure, the first node The 0-probability input of the three pairs of inputs is connected to the 1st, 2nd and 3rd nodes of the first layer respectively and The 0 probability output connection, the second node with the 4th, 5th and 6th nodes of the first layer and The 0-probability output connection; in the third layer node with and The 0-probability output of two nodes is connected, and the node The output probability of 0 is the probability that the phase of the symbol sequence is correct. There are 9 OR nodes in the tree structure, 5 of which are 3-input OR nodes, and the remaining 4 are 2-input OR nodes.

假设图结构中的基本计算节点的两对输入分别为p_in1(0)，p_in1(1)与p_in2(0)，p_in2(1)，一对输出为p_out(0)，p_out(1)，则计算关系可以表示如下：Assume that the two pairs of inputs of the basic computing nodes in the graph structure are p_in1 (0), p_in1 (1) and p_in2 (0), p_in2 (1), and a pair of outputs are p_out (0), p_out (1), then the calculation relationship can be expressed as follows:

$\left\{\begin{array}{c}{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>o</span>o</span>}\mathrm{<span><span>u</span>u</span>}\mathrm{<span><span>t</span>t</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span><span><span>=</span>=</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>\\ {\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>o</span>o</span>}\mathrm{<span><span>u</span>u</span>}\mathrm{<span><span>t</span>t</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span><span><span>=</span>=</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span><span><span>+</span>+</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span><span><span>+</span>+</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>\end{array}\right.<span><span>.</span>.</span>$

如果图结构中的基本计算节点有三对输入：p_in1(0)，p_in1(1)；p_in2(0)，p_in2(1)；p_in3(0)，p_in3(1)和一对输出p_out(0)，p_out(1)，则其计算关系可以表示如下：：If the basic computing node in the graph structure has three pairs of inputs:_pin1 (0),_pin1 (1);_pin2 (0),_pin2 (1);_pin3 (0),_pin3 (1) and a pair of Output p_out (0), p_out (1), then its calculation relationship can be expressed as follows:

$\left\{\begin{array}{c}{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>o</span>o</span>}\mathrm{<span><span>u</span>u</span>}\mathrm{<span><span>t</span>t</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span><span><span>=</span>=</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>3</span>3</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>\\ {\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>o</span>o</span>}\mathrm{<span><span>u</span>u</span>}\mathrm{<span><span>t</span>t</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span><span><span>=</span>=</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>3</span>3</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span><span><span>+</span>+</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>3</span>3</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span><span><span>+</span>+</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>3</span>3</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>\\ {\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>3</span>3</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span><span><span>+</span>+</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>3</span>3</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span><span><span>+</span>+</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>3</span>3</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span><span><span>+</span>+</span>\\ {\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>1</span>1</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>2</span>2</span>}}<span><span>(</span>(</span>\mathrm{<span><span>1</span>1</span>}<span><span>)</span>)</span>{\mathrm{<span><span>p</span>p</span>}}_{\mathrm{<span><span>i</span>i</span>}\mathrm{<span><span>n</span>no</span>}\mathrm{<span><span>3</span>3</span>}}<span><span>(</span>(</span>\mathrm{<span><span>0</span>0</span>}<span><span>)</span>)</span>\end{array}\right.<span><span>.</span>.</span>$

如图6所示，本发明另一实施例提供的一种扩频序列解扩系统100，包括：As shown in FIG. 6, a spreading sequence despreading system 100 provided by another embodiment of the present invention includes:

因子图建立模块101，用于根据本地伪随机序列的本原多项式及约束方程组S，建立码元为0对应的本地伪随机序列的第一因子图和码元为1对应的本地伪随机序列的第二因子图；The factor graph building module 101 is used to establish the first factor graph of the local pseudo-random sequence corresponding to the symbol 0 and the local pseudo-random sequence corresponding to the symbol 1 according to the original polynomial of the local pseudo-random sequence and the set of constraint equations S The second factor graph of ;

第一运算模块102，用于根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值和第一因子图，计算码元为0时发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}在满足本地伪随机序列约束方程组S的条件下各码片出现1的概率P₀(x_i＝1|Y,S)和出现0的概率P₀(x_i＝0|Y,S)，其中1≤i≤n，n＞1；根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值和第二因子图，计算码元为1时发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}在满足本地伪随机序列约束方程组S的条件下各码片出现1的概率P₁(x_i＝1|Y,S)和出现0的概率P₁(x_i＝0|Y,S)；The first operation module 102 is used to calculate according to the value of the chip sequence Y={y₁ , y₂ ,...,y_i ,...,y_n } in the received spreading sequence symbol and the first factor diagram When the symbol is 0, the chip sequence X={x₁ ,x₂ ,…,_xi ,…,x_n } sent by the sending end satisfies the local pseudo-random sequence constraint equations S Probability P₀ (_xi =1|Y,S) and probability of 0 occurrence P₀ (_xi =0|Y,S), where 1≤i≤n, n>1; according to the received spreading sequence code The value of the chip sequence Y={y₁ ,y₂ ,...,y_i ,...,y_n } in the element and the second factor graph, calculate the chip sequence X sent by the sending end when the symbol is 1={x₁ ,x₂ ,…,_xi ,…,x_n }The probability of occurrence of 1 in each chip P₁ (_xi =1|Y,S) and occurrence of 0 Probability P₁ (_xi = 0|Y,S);

确定模块103，用于根据第一因子图输出的P₀(x_i＝1|Y,S)和P₀(x_i＝0|Y,S)，确定发送端发送的码片序列中的第i个码片与本地伪随机序列的第i个码片数值相等的概率P₀(z_i1)和P₀(z_i2)；根据第二因子图输出的P₁(x_i＝1|Y,S)和P₁(x_i＝0|Y,S)，确定发送端发送的码片序列中的第i个码片与本地伪随机序列的第i个码片数值相等的概率P₁(z_i1)和P₁(z_i2)，A determining module 103, configured to determine the first chip sequence in the chip sequence sent by the sending end according to P₀ (_xi =1|Y,S) and P₀ (_xi =0|Y,S) output by the first factor graph The probability P₀ (z_i1 ) and P₀ (z_i2 ) that i chips are equal to the value of the i-th chip of the local pseudo-random sequence; P₁ (_xi =1|Y, S) and P₁ (_xi = 0|Y, S), determine the probability that the i-th chip in the chip sequence sent by the sender is equal to the value of the i-th chip in the local pseudo-random sequence P₁ (z_i1 ) and P₁ (z_i2 ),

其中P_x(z_i1)为码元为x时发送端发送的第i个码片与本地伪随机序列i个码片皆为0的概率，P_x(z_i2)为码元为x时发送端发送的第i个码片与本地伪随机序列i个码片皆为1的概率，x＝0或1；Among them, P_x (z_i1 ) is the probability that the i-th chip sent by the sender when the symbol is x and the i-chip of the local pseudo-random sequence are all 0, and P_x (z_i2 ) is the probability that the i-th chip sent by the sender is 0 when the symbol is x. The probability that the ith chip sent by the end and the i chip of the local pseudo-random sequence are all 1, x=0 or 1;

图结构建立模块104，用于根据本地伪随机序列码片长度建立码元为0对应的第一图结构和码元为1对应的第二图结构；The graph structure establishment module 104 is used to establish the first graph structure corresponding to 0 and the second graph structure corresponding to 1 according to the local pseudo-random sequence chip length;

第二运算模块105，用于将根据第一因子图确定的P₀(z_i1)和P₀(z_i2)作为第一图结构第一层计算节点的输入，根据所述第一图结构，对P₀(D)进行运算，计算扩频前码元为0的概率P(D＝0)其中p₀(z_i)为P₀(z_i1)或P₀(z_i2)；将根据第二因子图确定的P₁(z_i1)和P₁(z_i2)作为第二图结构第一层计算节点的输入，根据所述第二图结构，对P₁(D)进行运算，计算扩频前码元为1的概率P(D＝1)，其中p₁(z_i)为P₁(z_i1)或P₁(z_i2)；The second operation module 105 is configured to use P₀ (z_i1 ) and P₀ (z_i2 ) determined according to the first factor graph as the input of the first layer calculation node of the first graph structure, and according to the first graph structure, Perform operations on P₀ (D), and calculate the probability P(D=0) that the symbol before spreading is 0 where p₀ (z_i ) is P₀ (z_i1 ) or P₀ (z_i2 ); P₁ (z_i1 ) and P₁ (z_i2 ) determined according to the second factor graph are used as the first layer of the second graph structure Calculate the input of the node, according to the second graph structure, perform operations on P₁ (D), and calculate the probability P(D=1) that the symbol before spreading is 1, wherein p₁ (z_i ) is P₁ (z_i1 ) or P₁ (z_i2 );

码元概率判定模块106，用于当p(D＝0)≥p(D＝1)时，判定扩频前码元为0的概率为p(D＝0)，码元为1的概率为1-p(D＝0)；当p(D＝0)<p(D＝1)时，判定扩频前码元为1的概率为p(D＝1)，码元为0的概率为1-p(D＝1)。Symbol probability determination module 106, for when p(D=0)≥p(D=1), the probability that the code element before the judgment spreading is 0 is p(D=0), and the probability that the code element is 1 is 1-p(D=0); when p(D=0)<p(D=1), it is determined that the probability that the symbol before spreading is 1 is p(D=1), and the probability that the symbol is 0 is 1-p (D=1).

优选地，所述第一运算模块具体包括：Preferably, the first computing module specifically includes:

第一子计算模块，用于根据接收到的扩频序列码元内的码片序列Y＝{y₁,y₂,…,y_i,…,y_n}的数值，计算发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}中各码片为0的概率P₀(x_i＝0|y_i)和发送端发送的码片序列X＝{x₁,x₂,…,x_i,…,x_n}中各码片为1的概率P₀(x_i＝1|y_i)；The first sub-calculation module is used to calculate the code sent by the sending end according to the value of the chip sequence Y={y₁ , y₂ ,...,y_i ,...,y_n } in the received spreading sequence symbol Chip sequence X={x₁ ,x₂ ,...,_xi ,...,x_n } the probability P₀ (_xi =0|y_i ) of each chip being 0 and the chip sequence X sent by the sender= Probability P₀ that each chip in {x₁ , x₂ ,...,_xi ,...,x_n } is 1 (_xi =1|y_i );

第二子计算模块，用于将P₀(x_i＝1|y_i)作为第一因子图中与变量y_i对应的变量节点的概率1输入，将P₀(x_i＝0|y_i)作为第一因子图中与变量y_i对应的变量节点的概率0输入，对第二因子图进行迭代计算，得到P₀(x_i＝1|Y,S)和P₀(x_i＝0|Y,S)；The second sub-calculation module is used to input P₀ (_xi = 1|y_i ) as the probability 1 of the variable node corresponding to the variable y_i in the first factor graph, and input P₀ (_xi = 0|y_i ) is input as the probability 0 of the variable node corresponding to the variable y_i in the first factor graph, and iteratively calculates the second factor graph to obtain P₀ (_xi =1|Y,S) and P₀ (_xi =0 |Y,S);

第三子计算模块，用于将P₁(x_i＝1|y_i)作为第二因子图中与变量y_i对应的变量节点的概率0输入，将P₁(x_i＝0|y_i)作为第二因子图中与变量y_i对应的变量节点的概率1输入，对第二因子图进行迭代计算，得到P₁(x_i＝1|Y,S)和P₁(x_i＝0|Y,S)。The third sub-calculation module is used to input P₁ (_xi = 1|y_i ) as the probability 0 of the variable node corresponding to the variable y_i in the second factor graph, and input P₁ (_xi = 0|y_i ) is input as the probability 1 of the variable node corresponding to the variable y_i in the second factor graph, and iteratively calculates the second factor graph to obtain P₁ (_xi =1|Y,S) and P₁ (_xi =0 |Y, S).

优选地，所述确定模块具体包括：Preferably, the determination module specifically includes:

第一子确定模块，用于当本地伪随机序列第i个码片为0时，选取所述第一因子图第i个变量节点输出的P₀(x_i＝0|Y,S)作为P₀(z_i1)，当本地伪随机序列第i个码片为1时，选取所述第一因子图第i个变量节点输出的P₀(x_i＝1|Y,S)作为P₀(z_i2)；The first sub-determining module is used to select P₀ (_xi =0|Y,S) output by the i-th variable node of the first factor graph when the i-th chip of the local pseudo-random sequence is 0 as P₀ (z_i1 ), when the i-th chip of the local pseudo-random sequence is 1, select P₀ (_xi =1|Y,S) output by the i-th variable node of the first factor graph as P₀ ( z_i2 );

第二子确定模块，用于当本地伪随机序列第i个码片为1时，选取所述第二因子图第i个变量节点输出的P₁(x_i＝0|Y,S)作为P₁(z_i1)，当本地伪随机序列第i个码片为1时，选取所述第二因子图第i个变量节点输出的P₁(x_i＝1|Y,S)作为P₁(z_i2)。The second sub-determination module is used to select P₁ (_xi =0|Y, S) output by the i-th variable node of the second factor graph when the i-th chip of the local pseudo-random sequence is 1 as P₁ (z_i1 ), when the i-th chip of the local pseudo-random sequence is 1, select P₁ (_xi =1|Y,S) output by the i-th variable node of the second factor graph as P₁ ( z_i2 ).

优选地，所述第一图结构和所述第二图结构皆为相同的树结构：Preferably, both the first graph structure and the second graph structure are the same tree structure:

当本地伪随机序列的长度n＝2^k时，树结构的第一层有2^k-1个2输入或节点，第j层有2^k-j个2输入或节点，第j层的第i个节点的两对输入分别与第j-1层的与两个节点的输出连接，其中，1≤i≤2^k-j，1＜j≤k，k＞1；When the length of the local pseudo-random sequence is n=2^k , the first layer of the tree structure has 2^k-1 2-inputs or nodes, the j-th layer has 2^kj 2-inputs or nodes, and the i-th node of the j-th layer The two pairs of inputs are respectively compared with the j-1th layer's and The output connection of two nodes, where, 1≤i≤2^kj , 1<j≤k, k>1;

当本地伪随机序列的长度n＝2^k+b时，树结构的第一层有2^k-1个或节点，其中前b个节点为3输入或节点，其余为2输入或节点；第j层有2^k-j个2输入或节点，第j层的第i个节点的两对输入分别与第j-1层的与两个节点的输出端连接，其中，1≤b≤2^k-1；When the length of the local pseudo-random sequence n=^2k +b, the first layer of the tree structure has^2k-1 or nodes, wherein the first b nodes are 3 input or nodes, and the rest are 2 input or nodes; the jth The layer has 2^kj 2-inputs or nodes, the i-th node of the j-th layer The two pairs of inputs are respectively compared with the j-1th layer's and The output terminals of the two nodes are connected, where 1≤b≤2^k-1 ;

当本地伪随机序列的长度n＝2^k+2^k-1+a时，树结构的第一层有2^k-1+2^k-2个或节点，其中前b个为3输入或节点，其余为2输入或节点；第二层有2^k-2个3输入或节点；第m层有2^k-m个2输入或节点；树结构的第二层中，第n个节点的三对输入分别与第一层的第3n-2、3n-1和3n个节点的输出连接；第m层的第q个节点的两对输入分别与第m-1层的与两个节点的输出端连接，其中1≤a＜2^k-1，2<m≤k，1≤n≤2^k-2，1≤q≤2^k-m。When the length of the local pseudo-random sequence n=^2k +^2k-1 +a, the first layer of the tree structure has^2k-1 +^2k-2 or nodes, of which the first b are 3 input or nodes, The rest are 2 inputs or nodes; the second layer has 2^k-2 3 inputs or nodes; the mth layer has 2^km 2 inputs or nodes; in the second layer of the tree structure, the three pairs of inputs of the nth node are respectively Connect to the output of the 3n-2, 3n-1, and 3nth nodes of the first layer; the qth node of the mth layer The two pairs of inputs are respectively related to the m-1th layer and The output terminals of the two nodes are connected, where 1≤a<2^k-1 , 2<m≤k, 1≤n≤2^k-2 , 1≤q≤2^km .

由上述技术方案可知，本发明提出的一种扩频序列解扩方法，能实现基于因子图模型的码捕获和码跟踪，具有能纠正接收码片错误的能力，并且能直接输出每组扩频码对应码元的概率信息。较传统解扩计算方法，本发明提出的解扩计算方法具有的纠正接收码片错误的能力能提高解扩的成功率，并且能直接输出后续译码步骤需要的码元概率信息，能减少后续将解扩结果转化为码元概率信息的步骤。It can be seen from the above-mentioned technical scheme that a kind of spreading sequence despreading method proposed by the present invention can realize code acquisition and code tracking based on factor graph model, has the ability to correct received chip errors, and can directly output each group of spread spectrum The code corresponds to the probability information of the symbol. Compared with the traditional despreading calculation method, the despreading calculation method proposed by the present invention has the ability to correct received chip errors, can improve the success rate of despreading, and can directly output the symbol probability information required by the subsequent decoding steps, which can reduce the subsequent A step of converting the despreading result into symbol probability information.

在本发明中，术语“第一”、“第二”“第三”仅用于描述目的，而不能理解为指示或暗示相对重要性。术语“多个”指两个或两个以上，除非另有明确的限定。In the present invention, the terms "first", "second" and "third" are used for descriptive purposes only, and should not be understood as indicating or implying relative importance. The term "plurality" means two or more, unless otherwise clearly defined.

以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (8)

1. a frequency expansion sequence despreading method, is characterized in that, comprising:

Step S1, according to the primitive polynomial of local pseudo random sequence and Constrained equations S, set up the factor Ⅱ figure that factor I figure that code element is the local pseudo random sequence of 0 correspondence and code element are the local pseudo random sequence of 1 correspondence;

Chip sequence Y={y in the frequency expansion sequence code element that step S2, basis receive₁, y₂..., y_i..., y_nnumerical value and factor I figure, calculate the code element chip sequence X={x that transmitting terminal sends when being 0₁, x₂..., x_i..., x_nthere is the probability P of 1 in each chip under the condition of satisfied local pseudo random sequence Constrained equations S₀(x_i=1|Y, S) and occur 0 probability P₀(x_i=0|Y, S), wherein 1≤i≤n, n > 1;

According to the chip sequence Y={y in the frequency expansion sequence code element received₁, y₂..., y_i..., y_nnumerical value and factor Ⅱ figure, calculate the code element chip sequence X={x that transmitting terminal sends when being 1₁, x₂..., x_i..., x_nthere is the probability P of 1 in each chip under the condition of satisfied local pseudo random sequence Constrained equations S₁(x_i=1|Y, S) and occur 0 probability P₁(x_i=0|Y, S);

Step S3, the P exported according to factor I figure₀(x_i=1|Y, S) and P₀(x_i=0|Y, S), determine the probability P that i-th chip in the chip sequence that transmitting terminal sends is equal with i-th chip value of local pseudo random sequence₀(z_i1) and P₀(z_i2);

According to the P that factor Ⅱ figure exports₁(x_i=1|Y, S) and P₁(x_i=0|Y, S), determine the probability P that i-th chip in the chip sequence that transmitting terminal sends is equal with i-th chip value of local pseudo random sequence₁(z_i1) and P₁(z_i2),

Wherein P_x(z_i1) for when code element is x transmitting terminal send i-th chip and a local pseudo random sequence i chip be all 0 probability, P_x(z_i2) for when code element is x transmitting terminal send i-th chip and a local pseudo random sequence i chip be all 1 probability, x=0 or 1;

Step S4, to set up the second graph structure that the first graph structure that code element is 0 correspondence and code element are 1 correspondence according to local pseudo random sequence chip lengths;

Step S5, the P will determined according to factor I figure₀(z_i1) and P₀(z_i2) as the input of the first graph structure ground floor computing node, according to described first graph structure, to P₀(D) carry out computing, calculate code element before spread spectrum be the probability P (D=0) of 0 whereinp₀(z_i) be P₀(z_i1) or P₀(z_i2);

By the P determined according to factor Ⅱ figure₁(z_i1) and P₁(z_i2) as the input of the second graph structure ground floor computing node, according to described second graph structure, to P₁(D) carry out computing, before calculating spread spectrum, code element is the probability P (D=1) of 1, whereinp₁(z_i) be P₁(z_i1) or P₁(z_i2);

Step S6, as p (D=0) >=p (D=1), judge code element before spread spectrum as 0 probability as p (D=0), code element be 1 probability be 1-p (D=0); As p (D=0) <p (D=1), judge code element before spread spectrum as 1 probability as p (D=1), code element be 0 probability be 1-p (D=1).

2. frequency expansion sequence despreading method according to claim 1, is characterized in that, described step S2 specifically comprises:

Chip sequence Y={y in the frequency expansion sequence code element that step S21, basis receive₁, y₂..., y_i..., y_nnumerical value, calculate transmitting terminal send chip sequence X={x₁, x₂..., x_i..., x_nin each chip be the probability P of 0₀(x_i=0|y_i) and transmitting terminal send chip sequence X={x₁, x₂..., x_i..., x_nin each chip be the probability P of 1₀(x_i=1|y_i);

Step S22, by P₀(x_i=1|y_i) as in factor I figure with variable y_ithe probability 1 of corresponding variable node inputs, by P₀(x_i=0|y_i) as in factor I figure with variable y_ithe probability 0 of corresponding variable node inputs, and carries out iterative computation, obtain P to factor Ⅱ figure₀(x_i=1|Y, S) and P₀(x_i=0|Y, S);

By P₁(x_i=1|y_i) as in factor Ⅱ figure with variable y_ithe probability 0 of corresponding variable node inputs, by P₁(x_i=0|y_i) as in factor Ⅱ figure with variable y_ithe probability 1 of corresponding variable node inputs, and carries out iterative computation, obtain P to factor Ⅱ figure₁(x_i=1|Y, S) and P₁(x_i=0|Y, S).

3. frequency expansion sequence despreading method according to claim 1, is characterized in that, described step S3 specifically comprises:

When local pseudo random sequence i-th chip is 0, choose the P that described factor I figure i-th variable node exports₀(x_i=0|Y, S) as P₀(z_i1), when local pseudo random sequence i-th chip is 1, choose the P that described factor I figure i-th variable node exports₀(x_i=1|Y, S) as P₀(z_i2);

When local pseudo random sequence i-th chip is 1, choose the P that described factor Ⅱ figure i-th variable node exports₁(x_i=0|Y, S) as P₁(z_i1), when local pseudo random sequence i-th chip is 1, choose the P that described factor Ⅱ figure i-th variable node exports₁(x_i=1|Y, S) as P₁(z_i2).

4. frequency expansion sequence despreading method according to claim 1, is characterized in that, described first graph structure and described second graph structure are all identical tree structure:

As the length n=2 of local pseudo random sequence^ktime, the ground floor of tree structure has 2^k-1individual 2 input or nodes, jth layer has 2^k-jindividual 2 input or nodes, i-th node of jth layertwo couples input respectively with jth-1 layerwiththe output of two nodes connects, wherein, and 1≤i≤2^k-j, 1 < j≤k, k > 1;

As the length n=2 of local pseudo random sequence^kduring+b, the ground floor of tree structure has 2^k-1individual or node, wherein, b node is 3 inputs or node, and all the other are 2 inputs or node; Jth layer has 2^k-jindividual 2 input or nodes, i-th node of jth layertwo couples input respectively with jth-1 layerwiththe output of two nodes connects, wherein, and 1≤b≤2^k-1;

As the length n=2 of local pseudo random sequence^k+ 2^k-1during+a, the ground floor of tree structure has 2^k-1+ 2^k-2individual or node, wherein, b is 3 inputs or node, and all the other are 2 inputs or node; The second layer has 2^k-2individual 3 input or nodes; M layer has 2^k-mindividual 2 input or nodes; In the second layer of tree structure, three couples input of the n-th node is connected with the output of 3n-2,3n-1 and 3n node of ground floor respectively; Q node of m layertwo couples input respectively with m-1 layerwiththe output of two nodes connects, wherein 1≤a < 2^k-1, 2<m≤k, 1≤n≤2^k-2, 1≤q≤2^k-m.

5. a frequency expansion sequence despreading system, is characterized in that, comprising:

Factor graph sets up module, for according to the primitive polynomial of local pseudo random sequence and Constrained equations S, sets up the factor Ⅱ figure that factor I figure that code element is the local pseudo random sequence of 0 correspondence and code element are the local pseudo random sequence of 1 correspondence;

First computing module, for the chip sequence Y={y in the frequency expansion sequence code element that basis receives₁, y₂..., y_i..., y_nnumerical value and factor I figure, calculate the code element chip sequence X={x that transmitting terminal sends when being 0₁, x₂..., x_i..., x_nthere is the probability P of 1 in each chip under the condition of satisfied local pseudo random sequence Constrained equations S₀(x_i=1|Y, S) and occur 0 probability P₀(x_i=0|Y, S), wherein 1≤i≤n, n > 1; According to the chip sequence Y={y in the frequency expansion sequence code element received₁, y₂..., y_i..., y_nnumerical value and factor Ⅱ figure, calculate the code element chip sequence X={x that transmitting terminal sends when being 1₁, x₂..., x_i..., x_nthere is the probability P of 1 in each chip under the condition of satisfied local pseudo random sequence Constrained equations S₁(x_i=1|Y, S) and occur 0 probability P₁(x_i=0|Y, S);

Determination module, for the P exported according to factor I figure₀(x_i=1|Y, S) and P₀(x_i=0|Y, S), determine the probability P that i-th chip in the chip sequence that transmitting terminal sends is equal with i-th chip value of local pseudo random sequence₀(z_i1) and P₀(z_i2); According to the P that factor Ⅱ figure exports₁(x_i=1|Y, S) and P₁(x_i=0|Y, S), determine the probability P that i-th chip in the chip sequence that transmitting terminal sends is equal with i-th chip value of local pseudo random sequence₁(z_i1) and P₁(z_i2),

Wherein P_x(z_i1) for when code element is x transmitting terminal send i-th chip and a local pseudo random sequence i chip be all 0 probability, P_x(z_i2) for when code element is x transmitting terminal send i-th chip and a local pseudo random sequence i chip be all 1 probability, x=0 or 1;

Graph structure sets up module, for setting up the second graph structure that the first graph structure that code element is 0 correspondence and code element are 1 correspondence according to local pseudo random sequence chip lengths;

Second computing module, for the P will determined according to factor I figure₀(z_i1) and P₀(z_i2) as the input of the first graph structure ground floor computing node, according to described first graph structure, to P₀(D) carry out computing, calculate code element before spread spectrum be the probability P (D=0) of 0 whereinp₀(z_i) be P₀(z_i1) or P₀(z_i2); By the P determined according to factor Ⅱ figure₁(z_i1) and P₁(z_i2) as the input of the second graph structure ground floor computing node, according to described second graph structure, to P₁(D) carry out computing, before calculating spread spectrum, code element is the probability P (D=1) of 1, whereinp₁(z_i) be P₁(z_i1) or P₁(z_i2);

Symbol probability determination module, for as p (D=0) >=p (D=1), judge code element before spread spectrum as 0 probability as p (D=0), code element be 1 probability be 1-p (D=0); As p (D=0) <p (D=1), judge code element before spread spectrum as 1 probability as p (D=1), code element be 0 probability be 1-p (D=1).

6. frequency expansion sequence despreading system according to claim 5, is characterized in that, described first computing module specifically comprises:

First sub-computing module, for the chip sequence Y={y in the frequency expansion sequence code element that basis receives₁, y₂..., y_i..., y_nnumerical value, calculate transmitting terminal send chip sequence X={x₁, x₂..., x_i..., x_nin each chip be the probability P of 0₀(x_i=0|y_i) and transmitting terminal send chip sequence X={x₁, x₂..., x_i..., x_nin each chip be the probability P of 1₀(x_i=1|y_i);

Second sub-computing module, for by P₀(x_i=1|y_i) as in factor I figure with variable y_ithe probability 1 of corresponding variable node inputs, by P₀(x_i=0|y_i) as in factor I figure with variable y_ithe probability 0 of corresponding variable node inputs, and carries out iterative computation, obtain P to factor Ⅱ figure₀(x_i=1|Y, S) and P₀(x_i=0|Y, S);

3rd sub-computing module, for by P₁(x_i=1|y_i) as in factor Ⅱ figure with variable y_ithe probability 0 of corresponding variable node inputs, by P₁(x_i=0|y_i) as in factor Ⅱ figure with variable y_ithe probability 1 of corresponding variable node inputs, and carries out iterative computation, obtain P to factor Ⅱ figure₁(x_i=1|Y, S) and P₁(x_i=0|Y, S).

7. frequency expansion sequence despreading system according to claim 5, is characterized in that, described determination module specifically comprises:

First sub-determination module, for when local pseudo random sequence i-th chip is 0, chooses the P that described factor I figure i-th variable node exports₀(x_i=0|Y, S) as P₀(z_i1), when local pseudo random sequence i-th chip is 1, choose the P that described factor I figure i-th variable node exports₀(x_i=1|Y, S) as P₀(z_i2);

Second sub-determination module, for when local pseudo random sequence i-th chip is 1, chooses the P that described factor Ⅱ figure i-th variable node exports₁(x_i=0|Y, S) as P₁(z_i1), when local pseudo random sequence i-th chip is 1, choose the P that described factor Ⅱ figure i-th variable node exports₁(x_i=1|Y, S) as P₁(z_i2).

8. frequency expansion sequence despreading system according to claim 5, is characterized in that, described first graph structure and described second graph structure are all identical tree structure:

As the length n=2 of local pseudo random sequence^ktime, the ground floor of tree structure has 2^k-1individual 2 input or nodes, jth layer has 2^k-jindividual 2 input or nodes, i-th node of jth layertwo couples input respectively with jth-1 layerwiththe output of two nodes connects, wherein, and 1≤i≤2^k-j, 1 < j≤k, k > 1;

As the length n=2 of local pseudo random sequence^kduring+b, the ground floor of tree structure has 2^k-1individual or node, wherein, b node is 3 inputs or node, and all the other are 2 inputs or node; Jth layer has 2^k-jindividual 2 input or nodes, i-th node of jth layertwo couples input respectively with jth-1 layerwiththe output of two nodes connects, wherein, and 1≤b≤2^k-1;

As the length n=2 of local pseudo random sequence^k+ 2^k-1during+a, the ground floor of tree structure has 2^k-1+ 2^k-2individual or node, wherein, b is 3 inputs or node, and all the other are 2 inputs or node; The second layer has 2^k-2individual 3 input or nodes; M layer has 2^k-mindividual 2 input or nodes; In the second layer of tree structure, three couples input of the n-th node is connected with the output of 3n-2,3n-1 and 3n node of ground floor respectively; Q node of m layertwo couples input respectively with m-1 layerwiththe output of two nodes connects, wherein 1≤a < 2^k-1, 2<m≤k, 1≤n≤2^k-2, 1≤q≤2^k-m.