Coding-assisted blind frame synchronization method and system based on threshold detection stop judgment

Abstract

The present invention discloses a coding-assisted blind frame synchronization method and system based on threshold detection stop judgment, which belongs to the field of communication signal processing. The present invention determines the frame synchronization normalization confidence for frame synchronization position estimation based on the check node normalization satisfaction probability of the iterative decoding LDPC factor graph model. In the process of determining the frame synchronization normalization confidence, the evolutionary graph convergence of the check node normalization satisfaction probability in the iteration process is used as the iteration stop judgment condition, and whether the iteration is stopped is judged in real time online, thereby reducing unnecessary and useless iterations of blind frame synchronization and effectively improving system throughput. The relationship between the iterative gain introduction point and the frame synchronization position is analyzed based on the frame synchronization and non-frame synchronization position frame synchronization normalization confidence, and the frame synchronization position is determined based on whether the frame synchronization normalization confidence exceeds the threshold, thereby realizing efficient and stable blind frame synchronization and decoding suitable for burst communication systems, effectively avoiding false alarms, shortening the frame synchronization establishment time, and improving frame synchronization accuracy.

Description

Technical Field

[0001] The present invention relates to a coding-assisted blind frame synchronization method and system based on threshold detection stop judgment, and in particular to an LDPC coding-assisted blind frame synchronization method and system based on threshold detection stop judgment applicable to a burst communication system, belonging to the field of communication signal processing. Background Art

[0002] In the context of modern warfare and the development of cyber information security countermeasures, the requirements for the reliability, security, and confidentiality of communication systems are increasing. Burst communication systems typically use randomized transmission windows and shortened signal duration to effectively improve the communication system's anti-interference and anti-interception capabilities. Due to their random, bursty, and transient characteristics, burst communication systems have been widely studied in the military communications field.

[0003] Traditional frame synchronization architectures typically insert a pilot signal with a priori information at a fixed position in the data frame structure. The receiving end then uses time-domain correlation or matched filtering to determine the frame's starting position. To prevent missed detection or misestimation due to insufficient pilot signal length, the pilot signal inserted into the frame structure typically has a large time domain footprint and signal energy, resulting in resource consumption and reduced transmission rates.

[0004] For burst communication systems, the frame synchronization subsystem, as one of the key links, must meet the following requirements: a high probability of correct frame synchronization, continuous and stable capture of frame synchronization codes, and the shortest possible frame synchronization establishment time. Code-assisted blind frame synchronization uses the characteristics of the coding structure to introduce coding gain, which can effectively reduce the performance loss of the communication system. It also uses a threshold that conforms to the signal judgment characteristics to detect blind frame synchronization, which can effectively avoid false alarms and achieve a fast and accurate frame synchronization establishment process. However, current code-assisted blind frame synchronization does not have real-time online stop judgment, and the judgment of iterations affects the system throughput by multiple delays. As a result, the receiver architecture design based on blind frame synchronization has problems such as long blind frame synchronization establishment time and high system power consumption. There is a lack of efficient and stable blind frame synchronization algorithm design and corresponding circuit implementation suitable for burst communication systems. Therefore, it is urgent to develop a low-power, low-complexity code-assisted blind frame synchronization method and system design that can perform real-time online stop judgment and is suitable for burst communication systems. Summary of the Invention

[0005] In order to solve the following technical defects in the existing technology: (1) Traditional frame synchronization inserts a pilot with prior information in the time domain of the data frame structure, resulting in resource consumption and reduced transmission rate; (2) For burst communication systems, the current receiver lacks the algorithm and architecture design that introduces coding gain into the frame synchronization technology, resulting in loss of communication system performance; (3) The current algorithm based on blind frame synchronization needs to detect all frame compensation positions of the received sequence, and the frame synchronization processing delay is relatively large; (4) The current algorithm based on blind frame synchronization performs sufficient iterative detection on each fixed frame compensation position, and there is no real-time online iteration stop judgment. The traditional maximum number of iterations to stop judgment limits the system throughput and energy consumption performance, and the iteration cycle affects the system throughput by multiple delays. The system throughput is reduced, and offline judgment of iterative stop is prone to cause problems such as limited system throughput and easy change in scope of application. The main purpose of the present invention is to provide a coding-assisted blind frame synchronization method and system based on threshold detection stop judgment. Under the condition of ensuring that the coding gain is fully introduced, the frame synchronization normalization confidence is determined by the probability of satisfying the normalization of the check nodes of the LDPC factor graph model in the iterative decoding process, and the relationship between the iterative gain introduction point and the frame synchronization position is analyzed by the frame synchronization normalization confidence. A threshold is set for the threshold decision tolerance characteristics of the frame synchronization normalization confidence of the frame synchronization and non-frame synchronization positions, and a binary hypothesis test is performed. According to the threshold detection result of the binary hypothesis test, the frame synchronization position estimation is realized, and then frame synchronization and iterative decoding are realized. The present invention has the following advantages: (1) there is no need to insert pilot symbols at the transmitting end, and the coding gain is introduced by coding assistance using the inherent structural characteristics of the coding to complete the frame synchronization process, thereby solving the problem of reduced spectrum utilization and significantly improving the throughput and transmission rate of the communication system; (2) the coding gain is introduced by coding assistance using the inherent structural characteristics of the coding to reduce the performance loss of the communication system and simultaneously complete the iterative decoding process, thereby completing frame synchronization and decoding at the same time with low complexity compared with the traditional frame synchronization and decoding sequence processing architecture; (3) for burst communication systems, blind frame synchronization is detected using a threshold threshold that conforms to the normalized confidence decision tolerance characteristic of frame synchronization, which can effectively avoid false alarms, improve frame synchronization accuracy, realize a fast and accurate frame synchronization establishment process, and complete the iterative decoding process; (4) compared with the full-frame structure detection method, the threshold detection replaces the process of detecting all frame compensation positions with the over-threshold detection process, thereby reducing the detection of unnecessary frame compensation positions, reducing the frame synchronization processing delay, and reducing hardware resource occupancy and design complexity.In addition, in the process of using the normalized satisfaction probability of the check nodes of the LDPC factor graph model in the iterative decoding process as the cost function, the present invention uses the convergence of the evolutionary graph of the normalized satisfaction probability of the decoding check nodes in the iterative process as the iteration stop judgment condition, and judges whether the iteration is stopped in real time online. If the iteration stop judgment condition is met, the iterative process of the position observation frame is stopped, reducing unnecessary and useless iterative processes of blind frame synchronization, greatly reducing the average number of iterations, and thus reducing the processing delay of asynchronous position observation frames, thereby improving the frame synchronization and decoder system throughput and performance.

[0006] The purpose of the present invention is achieved through the following technical solutions.

[0007] The present invention discloses a coding-assisted blind frame synchronization method based on threshold detection and stop judgment. Based on the normalized check node satisfaction probability of the LDPC factor graph model during iterative decoding, a normalized frame synchronization confidence is obtained for estimating frame synchronization. This normalized frame synchronization confidence serves as the cost function for the frame synchronization position. During the cost function determination process, the convergence of the evolutionary graph of the normalized check node satisfaction probability during the iteration process is used as the iteration stop judgment condition. A real-time online judgment is made as to whether the iteration should stop. If the iteration stop judgment condition is met, the iteration process for the observation frame at that position is stopped. This reduces the processing delay of observation frames at non-synchronized positions, eliminates unnecessary and useless blind frame synchronization iterations, and effectively improves system throughput. The normalized frame synchronization confidence of the frame synchronization and non-frame synchronization positions is used to analyze the relationship between the iteration gain introduction point and the frame synchronization position. A threshold is set based on the threshold decision tolerance characteristic for binary hypothesis testing. The frame synchronization position is determined by detecting whether the normalized frame synchronization confidence exceeds the threshold. In other words, the frame synchronization position is estimated based on the detection results of the binary hypothesis test, thereby achieving frame synchronization and iterative decoding. The present invention does not require the insertion of pilot symbols at the transmitting end, thus avoiding the problem of decreased spectrum utilization; utilizes the inherent structural characteristics of coding to introduce coding gain through coding assistance to complete the frame synchronization process, thereby reducing the performance loss of the communication system; compared with the traditional frame synchronization and decoding sequential processing architecture, the present invention simultaneously completes frame synchronization and decoding with low complexity; reduces the detection of unnecessary frame compensation positions through threshold detection, reduces the frame synchronization processing delay, and reduces hardware resource occupation and design complexity; at the same time, the setting of the threshold further reduces the false alarm probability of frame synchronization, especially significantly reducing the false alarm probability in burst communication systems.

[0008] The present invention discloses a coding-assisted blind frame synchronization method based on threshold detection stop judgment, comprising the following steps:

[0009] Step 1: Based on the LDPC decoding factor graph model, add check nodes to meet the probability detection node set GNs and connect them to the check nodes in a unidirectional manner; add check nodes to normalize the probability statistics node G ave, output the normalized satisfaction probability of the check node, and obtain the normalized confidence of frame synchronization for estimating frame synchronization. The normalized confidence of frame synchronization is the cost function of the frame synchronization position.

[0010] Establish the LDPC decoding factor graph model G = (VNs∪CNs,Ξ), where VNs represents the variable node set and the size is the row dimension N of the check matrix C ; CNs represents the check node set, the size of which is the column dimension N of the check matrix C -N b , where N C is the codeword sequence length, N b is the length of the information sequence; Ξ represents the edge set connecting the variable node and the check node. At the check node CNs=(C1, C2, ..., C k ) based on the addition of check nodes to meet the probability detection node GNs = (G1, G2, ..., G k ), output the check node to meet the probability Indicates that under the conditions of codeword sequence R and check matrix H, node G is detected after k iterations n Output check node C n The probability of meeting the verification constraint, that is, the verification node C n The probability that the sum of the symbolic information products modulo 2 of all variable nodes of the constraint is 0:

[0011]

[0012] Among them, C n Represents the check node receiving probability information, V m Represents the variable node that transmits probability information, g n Indicates the detection node symbol index, the value is 0 or 1, V m' ∈N(C n ) represents V m' Belongs to the check node C n The set of all connected variable nodes, {V m} represents the set of all variable nodes, the superscript k represents the decoding iteration cycle, I c (C n ) represents the check node C n Corresponding validation constraints Established, that is:

[0013]

[0014] Node G ave Output the normalized satisfaction probability P of the check node in the kth iteration cycle (k) (G(k)|R(τ)) is:

[0015]

[0016] Where ρ is the normalization factor, so that P (k) The value of (G(k)|R(τ)) ranges from 0 to 1. The summation symbol indicates that the satisfaction probabilities of all check nodes are summed, and the average value is finally obtained to represent the constraint satisfaction probability of all check nodes.

[0017] For the complete data frame received by the receiver, a complete codeword length of data in the received sequence is taken as a frame with different delays as the input of the factor graph model, that is, N in the sliding window Γ(τ). C Length information sequence R(τ:τ+N C -1), where τ is the frame synchronization transmission delay, defining the starting position of the sliding window. The factor graph output is bit Hard decision information representing the approximate a posteriori probability information of the codeword.

[0018] Step 2: Determine the frame synchronization sliding window position Γ(τ), move the sliding window Γ(τ) to obtain the observation frame data R(τ: τ+N C -1), the information sequence R(τ:τ+N) in the sliding window C -1) is the input of the factor graph model.

[0019] A frame synchronization system model is established. Considering that the previous-stage timing synchronization and carrier synchronization have completed the accurate estimation of the timing error and carrier phase, the frame synchronization process is simplified to the estimation problem of the frame compensation amount τ0. r(t) represents the received signal input into the frame synchronization system after the AWNG signal, s(t) represents the transmitting end signal, n(t) is the noise superimposed at the symbol level, which is a complex Gaussian white noise with a mean of 0 and a unilateral power spectral density of N0, and τ0 represents the transmission delay. The frame synchronization system model is obtained as follows:

[0020] r(t)=s(t-τ0)+n(t)

[0021] Where s(t) is the symbol sent at time t, which is 0 or 1. Based on this system model, a frame synchronization sliding window Γ(τ) is set, and the sliding window length is selected as N C , that is, the sliding window always contains data of a complete coding sequence length.

[0022] By moving the frame synchronization sliding window in units of t on the received sequence, the frame structure of the relative position of the sliding window Γ(τ) is obtained, and the observation frame data R(τ:τ+N C -1). The correct frame synchronization probability corresponding to position τ is:

[0023] P(τ|r(t)),τ∈[0,n c -1]

[0024] τ represents the possible position of correct frame synchronization when the receiving sufficient statistics is r(t), n c is the length of the frame. By maximizing the above posterior probability by sliding the frame synchronization sliding window on r(t), the possible position of the correct frame synchronization when receiving r(t) is obtained.

[0025] Step 3: According to the observation frame data R(τ:τ+N C -1) to initialize the variable node.

[0026] The observation frame R(τ:τ+N C -1) Input the channel prior probability information into the factor graph model and initialize the variable nodes. According to the Gaussian white noise channel probability distribution, the initialization information of the factor graph variable nodes when the frame structure t = τ is:

[0027]

[0028] Among them, x i Indicates the symbolic index of the variable node, which takes a value of 0 or 1. Represents the sequence R(τ:τ+N) when the sliding window is Γ(τ) C -1) channel prior probability information, where φ(t) represents the amplitude of the received sequence, l∈[0,N C ) represents the sequence index in the frame synchronization sliding window, σ 2 is the channel noise variance.

[0029] Step 4: Update the global factor graph check nodes according to the variable node information and check constraint relationship.

[0030] In the known observation frame sequence R(τ:τ+N C -1) and the check constraint relationship H, the check node transmits the update probability information set to the variable node. That is, the check node passes the symbol x to the variable node i The probability of being 0 or 1:

[0031]

[0032] Among them, V m' ∈N(C n )\V m Indicates V m' Belongs to C n connected, excluding V m The set of all variable nodes, ~{V m} means except V m The set of all variable nodes except , the superscript k represents the decoding iteration cycle. Check node information Represents V m Other than check node C n The probability density function representation of all connected variable nodes modulo 2 sum to 0.

[0033] Step 5: According to R(τ:τ+N C -1)’s channel prior probability information and check node information update the global factor graph variable nodes.

[0034] The variable node transmits the updated probability information set to the check node That is, under the condition of known channel receiving sequence R and check matrix H, the variable node transmits symbol x to the check node i The probability of being 0 or 1, according to the verification constraint relationship and verification node information, the variable node update information set is:

[0035]

[0036] Among them, C n' ∈N(V m )\C n Indicates C n' Belongs to V m Connected, excluding C n The set of all check nodes, λ mn is the normalization parameter, so that the relationship satisfies

[0037] Step 6: In the process of determining the cost function, the verification node normalization satisfies the probability convergence as the iteration stop judgment condition, that is, the node G ave The normalized check node output satisfies the probability P (k) The convergence and convergence value of (G(k)|R(τ)) determine the iteration state. When the number of iterations reaches the maximum number of iterations, or P (k) When (G(k)|R(τ)) meets the conditions for stopping iteration, the iteration process is stopped and the process is skipped to step 7 to reduce the processing delay of the asynchronous position observation frame, reduce the unnecessary and useless iteration process of blind frame synchronization, and effectively improve the system throughput; otherwise, the process is skipped to step 4 and repeated from step 4 to step 6 until the iterative process is terminated when the conditions for stopping step 6 are met, and the P value of the current iteration cycle is output. (k) (G(k)|R(τ)) is used as the frame synchronization normalized confidence Π(τ) and step seven is performed.

[0038] Normalization of graph model check nodes satisfies probability statistics node G ave The output of the check node is the normalized satisfaction probability P (k) (G(k)|R(τ)):

[0039]

[0040] The stopping condition consists of the following two parts:

[0041] Stop iteration condition 1. Stop iteration when the iteration cycle reaches the maximum number of iterations;

[0042] Stop iterative condition 2, take {T, β, N} as the threshold parameters, where T represents the decoding success threshold, β represents the change range threshold, and N represents the continuous cycle threshold for judging convergence. (k) The iteration stops when (G(k)|R(τ)) reaches the decoding success threshold T; when P (k) When (G(k)|R(τ)) meets the convergence judgment, that is, within N consecutive iteration cycles, P (k) (G(k)|R(τ)) and the previous period P (k-1) The iteration is stopped when the difference between (G(k-1)|R(τ)) is within the change range threshold β.

[0043] Compared with the traditional decoding iteration, which determines the stopping condition by judging whether the check equation is established and whether the maximum number of iterations is reached, this step determines the stopping condition by (k) Convergence analysis of (G(k)|R(τ)) (k) The iterative update process is stopped when (G(k)|R(τ)) reaches the decoding success threshold or meets the convergence threshold. The iterative stop judgment greatly reduces the computational complexity and iteration cycle of the asynchronous position observation frame, reduces the processing delay of the asynchronous position observation frame, reduces the unnecessary and useless iteration process of the blind frame synchronization, and effectively improves the system throughput. At the same time, through P (k) (G(k)|R(τ)) represents the state where the codeword satisfies the check constraint, which is beneficial for stopping P in time when an undecipherable frame is detected. (k) The changing trend of (G(k)|R(τ)) prevents the P of untranslatable frames (k) (G(k)|R(τ)) still has an increasing trend in the subsequent iteration process, which reduces the false alarm probability of asynchronous frames and thus improves the frame synchronization performance.

[0044] When P (k) When (G(k)|R(τ)) meets the conditions for stopping iteration, the frame synchronization normalized confidence Π(τ) can be obtained as:

[0045]

[0046] Where k0 is the stopping iteration period, Π(τ) represents the observation frame R(τ:τ+N C -1) The final normalized satisfaction probability of the check node after iterative update.

[0047] Step 7, judging the frame synchronization position according to the frame synchronization normalization confidence obtained in step 6, setting a threshold value to perform binary hypothesis test detection based on the threshold judgment tolerance characteristic of the frame synchronization normalization confidence of the frame synchronization and non-frame synchronization position, determining the frame synchronization position by detecting whether the threshold value is exceeded, that is, estimating the frame synchronization position according to the threshold detection result of the binary hypothesis test, and then achieving frame synchronization. The present invention reduces the detection of unnecessary frame compensation positions by detecting the threshold value, reduces the frame synchronization processing delay, and reduces the hardware resource occupancy and design complexity. At the same time, the setting of the threshold value further reduces the false alarm probability of frame synchronization, especially significantly reducing the false alarm probability in burst communication systems. In addition, since the coding gain is introduced by coding-assisted coding using the coding structure characteristics itself to complete the frame synchronization process, there is no need to insert pilot symbols at the transmitting end, avoiding the problem of decreased spectrum utilization, and significantly improving the throughput and transmission rate of the communication system.

[0048] Compare and judge the sliding window observation frame R(τ:τ+N C -1) frame synchronization normalized confidence Π(τ) and threshold parameter θ r When Π(τ)>θ r When the current frame compensation position exceeding the threshold is output as the frame synchronization position Stop the frame synchronization iteration process and proceed to step eight; otherwise, continue the frame synchronization iteration process and repeat steps two to seven until the frame synchronization normalized confidence meets the threshold requirement.

[0049] From the changing trend of the normalized satisfaction probability of different code type check nodes in the iterative process, we can know that the non-frame synchronization sequence R(τ:τ+N C -1) The sequence of input factor graph is undecodable, and its check node normalization satisfies the probability P (k) (G(k)|R(τ)) converges or oscillates continuously in any interval less than 1, and finally converges to a value range less than the decoding success threshold. For the sequence R(τ0:τ0+N C -1), considering that the sequence of input factor graphs has its own structural characteristics, the frame synchronization normalized confidence Π(τ) is greater than the given threshold parameter θ in most cases r , and the threshold parameter θ r The check node normalization meets the probability threshold tolerance.

[0050] At the same time, considering the data frames with different coding lengths selected for the received sequence with different delays, the process includes selecting a coding sequence with a frame structure that is exactly a complete frame with correct frame synchronization delay. The frame synchronization normalized confidence of the sequence meets the above situation, and then the position of the frame compensation can be compensated according to the threshold value. The frame synchronization position is determined, i.e., frame synchronization compensation is performed based on the threshold detection result of the binary hypothesis test, thereby achieving frame synchronization. The present invention reduces the detection of unnecessary frame compensation positions by detecting over-threshold values, thereby reducing frame synchronization processing delay, hardware resource usage, and design complexity. By utilizing the inherent structural characteristics of the coding to introduce coding gain through coding assistance to complete the frame synchronization process, there is no need to insert pilot symbols at the transmitting end, thus avoiding the problem of reduced spectrum utilization and significantly improving the throughput and transmission rate of the communication system.

[0051] Step eight, calculate the approximate posterior probability of the codeword and make a hard decision, output the decoding decision result of the frame synchronization position, and obtain the bit sequence after the frame synchronization and decoding process, that is, realize blind frame synchronization and decoding.

[0052] When determining the frame synchronization position After that, the approximate posterior probability of the codeword is calculated and hard decision is made to obtain the sequence R(τ0:τ0+N C -1) decoding bit sequence The approximate posterior probability of the codeword is:

[0053]

[0054] when When bit decision output B m =1, when When bit decision output B m =0, output decoding sequence At this time, based on the "frame synchronization & decoding" joint system model, according to the threshold θ r Determine the frame synchronization position The corresponding decoding process is completed through an iterative message passing algorithm, and a decoding codeword sequence is output, thereby achieving frame synchronization and decoding.

[0055] The present invention also discloses a coding-assisted blind frame synchronization system based on threshold detection and stop judgment, which is used to implement the coding-assisted blind frame synchronization method based on threshold detection and stop judgment. The coding-assisted blind frame synchronization system based on threshold detection and stop judgment mainly consists of an observation frame sliding window module, a channel decoding module, a detection node module, a confidence detection module, and a hard decision module.

[0056] The observation frame sliding window module is mainly used to implement the following functions: determine the observation frame structure on the received sequence according to the frame structure sliding window, and provide input for the channel decoding module.

[0057] The channel decoding module is mainly used to achieve the following functions: improve frame synchronization performance by introducing iterative gain through channel decoding, and complete the channel decoding process at the same time.

[0058] A detection node module is added to the traditional channel decoding module structure. Its main function is to construct a cost function for estimating the frame synchronization position and input this cost function into the confidence detection module. In the process of iterative decoding, the normalized check node satisfaction probability output by the LDPC factor graph model is used as the cost function. The convergence of the normalized check node satisfaction probability is used as the iteration stopping judgment condition. By using the convergence of the evolutionary graph of the normalized decoding check node satisfaction probability during the iteration as the iteration stopping judgment condition, the real-time online judgment of whether the iteration should be stopped is made. If the iteration stopping judgment condition is met, the iteration process of the position observation frame is stopped, reducing the processing delay of the asynchronous position observation frame, eliminating unnecessary and useless iterations of blind frame synchronization, and effectively improving system throughput.

[0059] The confidence detection module is mainly used to realize the following functions: judging the frame synchronization position according to the frame synchronization normalized confidence obtained by the detection node module, analyzing the relationship between the iterative gain introduction point and the frame synchronization position with the single peak characteristic of the frame synchronization normalized confidence, setting a threshold for the threshold decision tolerance characteristic of the frame synchronization normalized confidence of the frame synchronization and non-frame synchronization positions to perform binary hypothesis test detection, determining the estimate of the frame compensation position by detecting the threshold, that is, performing frame synchronization compensation according to the threshold detection result of the binary hypothesis test, and then realizing frame synchronization. The present invention reduces the detection of unnecessary frame compensation positions by detecting the threshold, reduces the frame synchronization processing delay, and reduces the hardware resource occupancy and design complexity. Since the frame synchronization process is completed by introducing coding gain through coding assistance using the coding's own structural characteristics, there is no need to insert pilot symbols at the transmitting end, avoiding the problem of reduced spectrum utilization, and significantly improving the throughput and transmission rate of the communication system.

[0060] The hard decision module is mainly used to implement the following functions: through hard decision, the channel decoding soft information is judged as 0 or 1 bits, and the codeword sequence after frame synchronization and channel decoding is output.

[0061] Beneficial effects:

[0062] 1. The present invention discloses a code-assisted blind frame synchronization method and system based on threshold detection stop judgment. The cost function is the normalized probability of satisfying the check nodes of the LDPC factor graph model during iterative decoding. The cost function of the frame synchronization position is the normalized confidence of the frame synchronization. A threshold is set based on the threshold decision tolerance characteristics of the normalized confidence of the frame synchronization and non-frame synchronization positions to perform a binary hypothesis test. By detecting whether the threshold is exceeded, the frame synchronization position is estimated based on the threshold detection result of the binary hypothesis test, thereby achieving frame synchronization and iterative decoding. By detecting whether the threshold is exceeded, the present invention reduces the detection of unnecessary frame compensation positions, reduces frame synchronization processing delay, and reduces hardware resource utilization and design complexity. At the same time, the setting of the threshold further reduces the false alarm probability of frame synchronization, especially significantly reducing the false alarm probability in burst communication systems. Because the frame synchronization process is completed by code-assisted coding gain using the inherent structural characteristics of the code, there is no need to insert pilot symbols at the transmitting end, avoiding the problem of reduced spectrum utilization and significantly improving the throughput and transmission rate of the communication system.

[0063] 2. The present invention discloses a coding-assisted blind frame synchronization method and system based on threshold detection stop judgment. In a process using the normalized probability of check node satisfaction of the LDPC factor graph model as the cost function, the method uses the convergence of the evolutionary graph of the normalized probability of decoding check nodes during the iteration process as the iteration stop judgment condition. The method determines whether to stop the iteration in real time online. If the condition is met, the iteration of the observation frame is stopped. This real-time online stop judgment can analyze frame synchronization and decoding status in real time, reducing the processing delay of observation frames in asynchronous positions and unnecessary and useless blind frame synchronization iterations, effectively improving system throughput. The method is applicable to the entire signal-to-noise ratio range and different channel parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flow chart of a coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to the present invention;

[0065] Figure 2 It is a decoding factor graph model with added detection nodes in the coding-assisted blind frame synchronization method based on threshold detection stop judgment according to an embodiment of the present invention;

[0066] Figure 3 Schematic diagram of the relationship between the sliding window and the observation frame structure of the frame synchronization system model according to an embodiment of the present invention;

[0067] Figure 4For the LDPC code type (480, 240), a simulation curve comparing the performance of the blind frame synchronization method described in this embodiment of the invention with pilot frame synchronization and blind frame synchronization with a maximum number of iterations stopping judgment is presented.

[0068] Figure 5 This is a simulation curve comparing the average number of iterations (ANI) performance of the blind frame synchronization method described in this embodiment of the invention for the LDPC code type (480, 240);

[0069] Figure 6 This is a hardware system block diagram of a coding-assisted blind frame synchronization system based on threshold detection and stop judgment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] To facilitate understanding of the aforementioned objectives, features, and advantages of the present invention, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments. This embodiment utilizes an LDPC (480, 240) code format, wherein a continuous frame of coded data is used as a data frame. Code-assisted blind frame synchronization based on threshold detection and stop judgment is used to implement frame synchronization position estimation and LDPC decoding. The system parameters for this embodiment are shown in the following table:

[0071] parameter Details Modulation method BPSK Bitrate 1 / 2 LDPC code type (480，240) Channel Model Gaussian white noise Continuous frames 1 Maximum number of decoder iterations 50

[0072] like Figure 1 As shown, the present embodiment discloses a coding-assisted blind frame synchronization method based on threshold detection stop judgment, and the specific implementation steps are as follows:

[0073] Step 1, such as Figure 2 As shown, based on the LDPC decoding factor graph model, the check nodes are added to meet the probability detection node set GNs and are unidirectionally connected to the check nodes; the check nodes are added to normalize the probability statistics node G ave , output the normalized satisfaction probability of the check node, and obtain the normalized confidence of frame synchronization for estimating frame synchronization. The normalized confidence of frame synchronization is the cost function of the frame synchronization position.

[0074] Establish the LDPC decoding factor graph model G = (VNs∪CNs,Ξ), where VNs represents the variable node set and the size is the row dimension N of the check matrix C ; CNs represents the check node set, the size of which is the column dimension N of the check matrix C -N b , where N C is the codeword sequence length, N bis the length of the information sequence; Ξ represents the edge set connecting the variable node and the check node. At the check node CNs=(C1, C2, ..., C k ) based on the addition of check nodes to meet the probability detection node GNs = (G1, G2, ..., G k ), output the check node to meet the probability Indicates that under the conditions of codeword sequence R and check matrix H, node G is detected after k iterations n Output check node C n The probability of meeting the check constraint, that is, the check node C n The probability that the sum of the symbolic information products modulo 2 of all variable nodes of the constraint is 0:

[0075]

[0076] Among them, C n Represents the check node receiving probability information, V m Represents the variable node that transmits probability information, g n Indicates the detection node symbol index, the value is 0 or 1, V m' ∈N(C n ) represents V m' Belongs to the check node C n The set of all connected variable nodes, {V m} represents the set of all variable nodes, the superscript k represents the decoding iteration cycle, I c (C n ) represents the check node C n Corresponding validation constraints Established, that is:

[0077]

[0078] Node G ave Output the normalized satisfaction probability P of the check node in the kth iteration cycle (k) (G(k)|R(τ)) is:

[0079]

[0080] Where ρ is the normalization factor, so that P (k) The value of (G(k)|R(τ)) ranges from 0 to 1. The summation symbol indicates that the satisfaction probabilities of all check nodes are summed, and the average value is finally obtained to represent the constraint satisfaction probability of all check nodes.

[0081] For the complete data frame received by the receiver, a complete codeword length of data in the received sequence is taken as a frame with different delays as the input of the factor graph model, that is, N in the sliding window Γ(τ). CLength information sequence R(τ:τ+N C -1), where τ is the frame synchronization transmission delay, defining the starting position of the sliding window. The factor graph output is bit Hard decision information representing the approximate a posteriori probability information of the codeword.

[0082] Step 2: Determine the frame synchronization sliding window position Γ(τ), move the sliding window Γ(τ) to obtain the observation frame data R(τ: τ+N C -1), the information sequence R(τ:τ+N) in the sliding window C -1) is the input of the factor graph model, the sliding process of the sliding window on the received data frame and the corresponding observation frame data structure are as follows Figure 3 shown.

[0083] A frame synchronization system model is established. Considering that the previous-stage timing synchronization and carrier synchronization have completed the accurate estimation of the timing error and carrier phase, the frame synchronization process is simplified to the estimation problem of the frame compensation amount τ0. r(t) represents the received signal input into the frame synchronization system after the AWNG signal, s(t) represents the transmitting end signal, n(t) is the noise superimposed at the symbol level, which is a complex Gaussian white noise with a mean of 0 and a unilateral power spectral density of N0, and τ0 represents the transmission delay. The frame synchronization system model is obtained as follows:

[0084] r(t)=s(t-τ0)+n(t)

[0085] Where s(t) is the symbol sent at time t, which is 0 or 1. Based on this system model, a frame synchronization sliding window Γ(τ) is set, and the sliding window length is selected as N C , that is, the sliding window always contains data of a complete coding sequence length.

[0086] By moving the frame synchronization sliding window in units of t on the received sequence, the frame structure of the relative position of the sliding window Γ(τ) is obtained, and the observation frame data R(τ:τ+N C -1). The correct frame synchronization probability corresponding to position τ is:

[0087] P(τ|r(t)),τ∈[0,n c -1]

[0088] τ represents the possible position of correct frame synchronization when the receiving sufficient statistics is r(t), n c is the length of the frame. By maximizing the above posterior probability by sliding the frame synchronization sliding window on r(t), the possible position of the correct frame synchronization when receiving r(t) is obtained.

[0089] Step 3: According to the observation frame data R(τ:τ+N C-1) to initialize the variable node.

[0090] The observation frame R(τ:τ+N C -1) Input the channel prior probability information into the factor graph model and initialize the variable nodes. According to the Gaussian white noise channel probability distribution, the initialization information of the factor graph variable nodes when the frame structure t = τ is:

[0091]

[0092] Among them, x i Indicates the symbolic index of the variable node, which takes a value of 0 or 1. Represents the sequence R(τ:τ+N) when the sliding window is Γ(τ) C -1) channel prior probability information, where φ(t) represents the amplitude of the received sequence, l∈[0,N C ) represents the sequence index in the frame synchronization sliding window, σ 2 is the channel noise variance.

[0093] Step 4: Update the global factor graph check nodes according to the variable node information and check constraint relationship.

[0094] In the known observation frame sequence R(τ:τ+N C -1) and the check constraint relationship H, the check node transmits the update probability information set to the variable node. That is, the check node passes the symbol x to the variable node i The probability of being 0 or 1:

[0095]

[0096] Among them, V m' ∈N(C n )\V m Indicates V m' Belongs to C n connected, excluding V m The set of all variable nodes, ~{V m} means except V m The set of all variable nodes except , the superscript k represents the decoding iteration cycle. Check node information Represents V m Other than check node C n The probability density function representation of all connected variable nodes modulo 2 sum to 0.

[0097] Step 5: According to R(τ:τ+N C -1)’s channel prior probability information and check node information update the global factor graph variable nodes.

[0098] The variable node transmits the updated probability information set to the check node That is, under the condition of known channel receiving sequence R and check matrix H, the variable node transmits symbol x to the check node i The probability of being 0 or 1, according to the verification constraint relationship and verification node information, the variable node update information set is:

[0099]

[0100] Among them, C n' ∈N(V m )\C n Indicates C n' Belongs to V m Connected, excluding C n The set of all check nodes, where λ mn is the normalization parameter, so that the relationship satisfies

[0101] Step 6: In the process of determining the cost function, the verification node normalization satisfies the probability convergence as the iteration stop judgment condition, that is, the node G ave The normalized check node output satisfies the probability P (k) The convergence and convergence value of (G(k)|R(τ)) determine the iteration state. When the number of iterations reaches the maximum number of iterations, or P (k) When (G(k)|R(τ)) meets the conditions for stopping iteration, the iteration process is stopped and the process is skipped to step 7 to reduce the processing delay of the asynchronous position observation frame, reduce the unnecessary and useless iteration process of blind frame synchronization, and effectively improve the system throughput; otherwise, the process is skipped to step 4 and repeated from step 4 to step 6 until the iterative process is terminated when the conditions for stopping step 6 are met, and the P value of the current iteration cycle is output. (k) (G(k)|R(τ)) is used as the frame synchronization normalized confidence Π(τ) and step seven is performed.

[0102] Normalization of graph model check nodes satisfies probability statistics node G ave The output of the check node is the normalized satisfaction probability P (k) (G(k)|R(τ)):

[0103]

[0104] The stopping condition consists of the following two parts:

[0105] Stop iteration condition 1. Stop iteration when the iteration cycle reaches the maximum number of iterations;

[0106] Stop iterative condition 2, take {T, β, N} as the threshold parameters, where T represents the decoding success threshold, β represents the change range threshold, and N represents the continuous cycle threshold for judging convergence. (k) When (G(k)|R(τ)) reaches the decoding success threshold T, the iteration stops; when P (k) When (G(k)|R(τ)) meets the convergence judgment, that is, within N consecutive iteration cycles, P (k) (G(k)|R(τ)) and the previous period P (k-1) The iteration is stopped when the difference between (G(k-1)|R(τ)) is within the change range threshold β.

[0107] Compared with the traditional decoding iteration, which determines the stopping condition by judging whether the check equation is established and whether the maximum number of iterations is reached, this step determines the stopping condition by (k) Convergence analysis of (G(k)|R(τ)) (k) When (G(k)|R(τ)) reaches the decoding success threshold or meets the convergence threshold, the iterative update process is stopped, and real-time online stop judgment is realized. The real-time online iterative stop judgment greatly reduces the computational complexity and iteration cycle of the asynchronous position observation frame, reduces the processing delay of the asynchronous position observation frame, reduces the unnecessary and useless iteration process of the blind frame synchronization, effectively improves the system throughput, and is applicable to the full signal-to-noise ratio range. At the same time, through P (k) (G(k)|R(τ)) represents the state where the codeword satisfies the check constraint, which is beneficial for stopping P in time when an undecipherable frame is detected. (k) The changing trend of (G(k)|R(τ)) prevents the P of untranslatable frames (k) (G(k)|R(τ)) still has an increasing trend in the subsequent iteration process, which reduces the false alarm probability of asynchronous frames and thus improves the frame synchronization performance.

[0108] When P (k) When (G(k)|R(τ)) meets the conditions for stopping iteration, the frame synchronization normalized confidence Π(τ) can be obtained as:

[0109]

[0110] Where k0 is the stopping iteration period, Π(τ) represents the observation frame R(τ:τ+N C -1) The final normalized satisfaction probability of the check node after iterative update.

[0111] Step seven, judging the frame synchronization position according to the frame synchronization normalization confidence obtained in step six, analyzing the relationship between the iterative gain introduction point and the frame synchronization position with the single peak characteristic of the frame synchronization normalization confidence, setting a threshold for the threshold decision tolerance characteristic of the frame synchronization normalization confidence of the frame synchronization and non-frame synchronization positions to perform binary hypothesis test detection, and performing frame synchronization position estimation by detecting whether the threshold is exceeded, that is, according to the threshold detection result of the binary hypothesis test. The present invention reduces the detection of unnecessary frame compensation positions by detecting the threshold, reduces the frame synchronization processing delay, and reduces the hardware resource occupancy and design complexity. Since the coding gain is introduced by coding assistance using the coding structure characteristics itself to complete the frame synchronization process, there is no need to insert pilot symbols at the transmitting end, avoiding the problem of reduced spectrum utilization, and significantly improving the throughput and transmission rate of the communication system.

[0112] Compare and judge the sliding window observation frame R(τ:τ+N C -1) frame synchronization normalized confidence Π(τ) and threshold parameter θ r When Π(τ)>θ r When the current frame compensation position that exceeds the threshold is output as the frame synchronization position Stop the frame synchronization iteration process and proceed to step eight; otherwise, continue the frame synchronization iteration process and repeat steps two to seven until the frame synchronization normalized confidence meets the threshold requirement.

[0113] From the changing trend of the normalized satisfaction probability of different code type check nodes in the iterative process, we can know that the non-frame synchronization sequence R(τ:τ+N C -1) The sequence of input factor graph is undecodable, and its check node normalization satisfies the probability P (k) (G(k)|R(τ)) converges or oscillates continuously in any interval less than 1, and finally converges to a value range less than the decoding success threshold. For the sequence R(τ0:τ0+N C -1), considering that the sequence of input factor graphs has its own structural characteristics, the frame synchronization normalized confidence Π(τ) is greater than the given threshold parameter θ in most cases r , and the threshold parameter θ r The check node normalization meets the probability threshold tolerance.

[0114] At the same time, considering the data frames with different coding lengths selected for the received sequence with different delays, the process includes selecting a coding sequence with a frame structure that is exactly a complete frame with correct frame synchronization delay. The frame synchronization normalized confidence of the sequence meets the above situation, and then the position of the frame compensation can be compensated according to the threshold value. The frame synchronization position is determined, i.e., frame synchronization compensation is performed based on the threshold detection result of the binary hypothesis test, thereby achieving frame synchronization. The present invention reduces the detection of unnecessary frame compensation positions by detecting over-threshold values, thereby reducing frame synchronization processing delay, hardware resource usage, and design complexity. By utilizing the inherent structural characteristics of the coding to introduce coding gain through coding assistance to complete the frame synchronization process, there is no need to insert pilot symbols at the transmitting end, thus avoiding the problem of reduced spectrum utilization and significantly improving the throughput and transmission rate of the communication system.

[0115] Step eight, calculate the approximate posterior probability of the codeword and make a hard decision, output the decoding decision result of the frame synchronization position, and obtain the bit sequence after the frame synchronization and decoding process, that is, realize blind frame synchronization and decoding.

[0116] When determining the frame synchronization position After that, the approximate posterior probability of the codeword is calculated and hard decision is made to obtain the sequence R(τ0:τ0+N C -1) decoding bit sequence The approximate posterior probability of the codeword is:

[0117]

[0118] when When bit decision output B m =1, when When bit decision output B m =0, output decoding sequence At this time, based on the "frame synchronization & decoding" joint system model, according to the threshold θ r Determine the frame synchronization position The corresponding decoding process is completed through an iterative message passing algorithm, and a decoding codeword sequence is output, thereby achieving frame synchronization and decoding.

[0119] The performance of frame synchronization error rate (FSER) and average number of iterations (ANI) is simulated and analyzed for the code type of this embodiment. Different parameters are selected for stop judgment to conduct comparative simulation, and the FSER of the maximum number of iterations stop judgment algorithm is used as the performance upper limit. The pilot-based frame synchronization algorithm is used as the comparison algorithm. The simulation results are as follows: Figure 4As shown. Among them, the frame synchronization method based on pilot-related judgment refers to the frame synchronization method mentioned by J.Massey in the journal "Optimum frame synchronization" published in IEEE Transactions on Communications in 1972. It can be clearly seen from the simulation curve that the introduction of coding gain makes the blind frame synchronization performance of this embodiment close to and better than the pilot-based method at high signal-to-noise ratio, effectively reducing the system performance loss and improving the transmission rate. There is no significant degradation in the FSER performance of the stop judgment with appropriate parameters. The ANI performance of the stop judgment with different parameter settings and the traditional maximum number of iterations is analyzed, and the results are shown as follows. Figure 5 As shown, compared to traditional iterative stop judgment, threshold detection significantly reduces the ANI. Considering that the average number of iterations affects system throughput exponentially with latency, stopping at the maximum number of iterations limits system throughput and energy performance. However, this stop judgment algorithm uses real-time online judgment to significantly reduce the average number of iterations, thereby reducing decoding processing latency and improving decoder system throughput and performance. Therefore, selecting appropriate stop judgment parameters can significantly reduce system processing latency without affecting frame synchronization and decoding performance, thereby improving system throughput and reducing system energy consumption.

[0120] The present invention also discloses a coding-assisted blind frame synchronization system based on threshold detection stop judgment, such as Figure 6 As shown, the coding-assisted blind frame synchronization method based on threshold detection and stop judgment is implemented. The coding-assisted blind frame synchronization system based on threshold detection and stop judgment is designed and analyzed according to the factor graph model in step 1. It mainly consists of an observation frame sliding window module, a channel decoding module, a detection node module, a confidence detection module, and a hard decision module.

[0121] The observation frame sliding window module involves step two, which determines the observation frame structure on the received sequence according to the frame structure sliding window, and provides input for the channel decoding module. The channel decoding module involves steps three, four and five, which introduces iterative gain through channel decoding to improve the frame synchronization performance and complete the channel decoding process at the same time. The detection node module involves step six, which adds a detection node module on the basis of the traditional channel decoding module structure, constructs a cost function for estimating the frame synchronization position, and inputs the cost function to the confidence detection module. In the process of using the normalized satisfaction probability of the check node output by the LDPC factor graph model in the iterative decoding process as the cost function, the convergence of the evolutionary graph of the normalized satisfaction probability of the decoding check node in the iterative process is used as the iteration stop judgment condition, and the real-time online judgment is made on whether the iteration is stopped. If the iteration stop judgment condition is met, the iterative process of the observation frame at that position is stopped, thereby reducing the processing delay of the asynchronous position observation frame, reducing the unnecessary and useless iterative process of blind frame synchronization, and effectively improving the system throughput. The confidence detection module involves step seven, and according to the frame synchronization normalized confidence obtained by the detection node module, a threshold is set for the threshold decision tolerance characteristics of the frame synchronization normalized confidence of the frame synchronization and non-frame synchronization positions to perform binary hypothesis test detection, and the frame synchronization position is determined by detecting whether it exceeds the threshold, that is, the frame synchronization position is estimated according to the threshold detection result of the binary hypothesis test, thereby achieving frame synchronization. The present invention reduces the detection of unnecessary frame compensation positions by detecting the threshold, reduces the frame synchronization processing delay, and reduces hardware resource occupancy and design complexity. Since the coding gain is introduced by coding assistance using the structural characteristics of the coding itself to complete the frame synchronization process, there is no need to insert pilot symbols at the transmitting end, avoiding the problem of reduced spectrum utilization, and significantly improving the throughput and transmission rate of the communication system. The hard decision module involves step eight, and the channel decoding soft information is judged as 0 or 1 bits through hard decision, and the codeword sequence after frame synchronization and channel decoding is output.

[0122] The above specific description further illustrates the purpose, technical solutions and beneficial effects of the invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A coding-assisted blind frame synchronization method based on threshold detection and stop judgment, characterized by: The following steps are included: Step 1: Based on the LDPC decoding factor graph model, add check nodes to satisfy the probability detection node set GNs and connect them unidirectionally. Add check nodes to normalize and satisfy the probability statistics node G ave , output the normalized satisfaction probability of the check node, and obtain the normalized confidence of frame synchronization for estimating frame synchronization, wherein the normalized confidence of frame synchronization is the cost function of the frame synchronization position; Step 2: Determine the position of the frame synchronization sliding window Γ(τ), move the sliding window Γ(τ) to obtain the observation frame data R(τ: τ+N C -1), the observation frame data R(τ:τ+N) in the sliding window C -1) is the input of the factor graph model; Step 3: According to the observation frame data R(τ:τ+N C -1)’s amplitude initialization variable node; Step 4: Update the global factor graph check nodes according to the variable node information and check constraint relationship; Step 5: According to the observation frame data R(τ:τ+N C -1) channel prior probability information and check node information update the global factor graph variable nodes; Step 6: In the process of determining the cost function, the verification node normalization satisfies the probability convergence as the iteration stop judgment condition, that is, the node G ave The normalized check node output satisfies the probability P (k) The convergence and convergence value of (G(k)|R(τ)) determine the iteration state. When the number of iterations reaches the maximum number of iterations, or P (k) When (G(k)|R(τ)) meets the conditions for stopping iteration, the iteration process is stopped and the process is skipped to step 7 to reduce the processing delay of the asynchronous position observation frame, reduce the unnecessary and useless iteration process of blind frame synchronization, and effectively improve the system throughput; otherwise, the process is skipped to step 4 and repeated from step 4 to step 6 until the iterative process is terminated when the conditions for stopping step 6 are met, and the P value of the current iteration cycle is output. (k) (G(k)|R(τ)) is used as the frame synchronization normalized confidence Π(τ) and step 7 is performed; Step seven, judging the frame synchronization position according to the normalized confidence of frame synchronization obtained in step six, setting a threshold value for binary hypothesis test based on the threshold decision tolerance characteristic of the normalized confidence of frame synchronization between frame synchronization and non-frame synchronization position, and determining the frame synchronization position by detecting whether the threshold value is exceeded, that is, estimating the frame synchronization position according to the threshold detection result of the binary hypothesis test, thereby achieving frame synchronization; by detecting whether the threshold value is exceeded, the detection of unnecessary frame compensation positions is reduced, the frame synchronization processing delay is reduced, and the hardware resource occupancy and design complexity are reduced. At the same time, the setting of the threshold value further reduces the false alarm probability of frame synchronization; in addition, since the coding gain is introduced by coding assistance using the structural characteristics of the coding itself to complete the frame synchronization process, there is no need to insert pilot symbols at the transmitting end, thereby avoiding the problem of reduced spectrum utilization and significantly improving the throughput and transmission rate of the communication system; Step eight, calculate the approximate posterior probability of the codeword and make a hard decision, output the decoding decision result of the frame synchronization position, and obtain the bit sequence after the frame synchronization and decoding process, that is, realize blind frame synchronization and decoding.

2. The coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to claim 1, characterized in that: The implementation method of step one is: Establish the LDPC decoding factor graph model G = (VNs∪CNs,Ξ), where VNs represents the variable node set and the size is the row dimension N of the check matrix C ; CNs represents the check node set, the size of which is the column dimension N of the check matrix C -N b , where N C is the codeword sequence length, N b is the length of the information sequence; Ξ represents the edge set connecting the variable node and the check node; at the check node CNs=(C1, C2, ..., C k ) based on the addition of check nodes to meet the probability detection node GNs = (G1, G2, ..., G k ), output the check node to meet the probability Indicates that under the conditions of codeword sequence R and check matrix H, node G is detected after k iterations n Output check node C n The probability of meeting the verification constraint, that is, the verification node C n The probability that the sum of the symbolic information products modulo 2 of all variable nodes of the constraint is 0: Among them, C n Represents the check node receiving probability information, V m Represents the variable node that transmits probability information, g n Indicates the detection node symbol index, the value is 0 or 1, V m' ∈N(C n ) represents V m' Belongs to the check node C n The set of all connected variable nodes, {V m } represents the set of all variable nodes, the superscript k represents the decoding iteration cycle, I c (C n ) represents the check node C n Corresponding validation constraints Established, that is: Node G ave Output the normalized satisfaction probability P of the check node in the kth iteration cycle (k) (G(k)|R(τ)) is: Where ρ is the normalization factor, so that P (k) The value of (G(k)|R(τ)) ranges from 0 to 1. The summation symbol indicates that the satisfaction probability of all check nodes is summed up, and the average value is finally obtained to represent the constraint satisfaction probability of all check nodes. For the complete data frame received by the receiver, a complete codeword length of data in the received sequence is taken as a frame with different delays as the input of the factor graph model, that is, N in the sliding window Γ(τ). C Length observation frame data R(τ:τ+N C -1), where τ is the frame synchronization transmission delay, defining the starting position of the sliding window; the factor graph output is bit Hard decision information representing the approximate a posteriori probability information of the codeword.

3. The coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to claim 2, characterized in that: The implementation method of step 2 is: A frame synchronization system model is established. Considering that the previous-stage timing synchronization and carrier synchronization have completed the accurate estimation of the timing error and carrier phase, the frame synchronization process is simplified to the estimation problem of the frame compensation amount τ0. r(t) represents the received signal input into the frame synchronization system after the AWNG signal, s(t) represents the transmitting end signal, n(t) is the noise superimposed at the symbol level, which is a complex Gaussian white noise with a mean of 0 and a unilateral power spectral density of N0, and τ0 represents the transmission delay. The frame synchronization system model is obtained as follows: r(t)=s(t-τ0)+n(t) Where s(t) is the symbol sent at time t, which is 0 or 1. Based on this system model, a frame synchronization sliding window Γ(τ) is set, and the sliding window length is selected as N. C , that is, the sliding window always contains data of a complete coding sequence length; By moving the frame synchronization sliding window in units of t on the received sequence, the frame structure of the relative position of the sliding window Γ(τ) is obtained, and the observation frame data R(τ:τ+N C -1); the probability of correct frame synchronization corresponding to position τ is: P(τ|r(t)),τ∈[0,n c -1] τ represents the possible position of correct frame synchronization when the receiving sufficient statistics is r(t), n c is the length of the frame; by maximizing the above posterior probability by sliding the frame synchronization sliding window on r(t), the possible position of the correct frame synchronization when receiving r(t) is obtained 4. The coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to claim 3, wherein: The implementation method of step three is: The observation frame data R(τ:τ+N C -1) Input the channel prior probability information into the factor graph model and initialize the variable nodes. According to the Gaussian white noise channel probability distribution, the initialization information of the factor graph variable nodes when the frame structure t = τ is: Among them, x i Indicates the symbolic index of the variable node, with a value of 0 or 1; Represents the observation frame data R(τ:τ+N) when the sliding window is Γ(τ) C -1) channel prior probability information, where φ(t) represents the amplitude of the received sequence, l∈[0,N C ) represents the sequence index in the frame synchronization sliding window, σ 2 is the channel noise variance.

5. The coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to claim 4, characterized in that: The implementation method of step 4 is: In the known observation frame sequence R(τ:τ+N C -1) and the check constraint relationship H, the check node transmits the update probability information set to the variable node. That is, the check node passes the symbol x to the variable node i The probability of being 0 or 1: Among them, V m' ∈N(C n )\V m Indicates V m' Belongs to C n connected, excluding V m The set of all variable nodes, ~{V m } means except V m The set of all variable nodes except the one with superscript k indicating the decoding iteration cycle; the check node information Represents V m Other than check node C n The probability density function representation of all connected variable nodes modulo 2 sum is 0.

6. The coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to claim 5, characterized in that: The implementation method of step five is: The variable node transmits the updated probability information set to the check node That is, under the condition of known channel receiving sequence R and check matrix H, the variable node transmits symbol x to the check node i The probability of being 0 or 1, according to the verification constraint relationship and verification node information, the variable node update information set is: Among them, C n' ∈N(V m )\C n Indicates C n' Belongs to V m Connected, excluding C n The set of all check nodes, λ mn is the normalization parameter, so that the relationship satisfies 7. The coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to claim 6, characterized in that: The implementation method of step six is: Normalization of graph model check nodes satisfies probability statistics node G ave The output of the check node is the normalized satisfaction probability P (k) (G(k)|R(τ)): The stop iteration condition consists of the following two parts composition: Stop iteration condition 1. Stop iteration when the iteration cycle reaches the maximum number of iterations; Stop iterative condition 2, take {T, β, N} as the threshold parameters, where T represents the decoding success threshold, β represents the change range threshold, and N represents the continuous cycle threshold for judging convergence; when P (k) The iteration stops when (G(k)|R(τ)) reaches the decoding success threshold T; when P (k) When (G(k)|R(τ)) meets the convergence judgment, that is, within N consecutive iteration cycles, P (k) (G(k)|R(τ)) and the previous period P (k-1) The iteration stops when the difference between (G(k-1)|R(τ)) is within the change range threshold β; Compared with the traditional decoding iteration, which determines the stopping condition by judging whether the check equation is established and whether the maximum number of iterations is reached, this step determines the stopping condition by (k) Convergence analysis of (G(k)|R(τ)) (k) The iterative update process is stopped when (G(k)|R(τ)) reaches the decoding success threshold or meets the convergence threshold; the computational complexity and iteration cycle of the asynchronous position observation frame are greatly reduced through iterative stop judgment, the processing delay of the asynchronous position observation frame is reduced, and the unnecessary and useless iteration process of blind frame synchronization is reduced, thereby effectively improving the system throughput; at the same time, through P (k) (G(k)|R(τ)) represents the state where the codeword satisfies the check constraint, which is beneficial for stopping P in time when an undecipherable frame is detected. (k) The changing trend of (G(k)|R(τ)) prevents the P of untranslatable frames (k) (G(k)|R(τ)) still tends to increase in subsequent iterations, reducing the false alarm probability of asynchronous frames and thus improving frame synchronization performance; When P (k) When (G(k)|R(τ)) meets the conditions for stopping iteration, the frame synchronization normalized confidence Π(τ) is obtained as: Where k0 is the stopping iteration period, Π(τ) represents the observation frame R(τ:τ+N C -1) The final normalized satisfaction probability of the check node after iterative update.

8. The coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to claim 7, characterized in that: The implementation method of step seven is: Compare and judge the sliding window observation frame data R(τ:τ+N C -1) frame synchronization normalized confidence Π(τ) and threshold parameter θ r ; When Π(τ)>θ r When the current frame compensation position that exceeds the threshold is output as the frame synchronization position Stop the frame synchronization iteration process and proceed to step 8; otherwise, continue the frame synchronization iteration process and repeat steps 2 to 7 until the frame synchronization normalized confidence meets the threshold requirement; From the changing trend of the normalized satisfaction probability of different code type check nodes in the iterative process, we can know that the observation frame data R(τ:τ+N C -1) The sequence of input factor graph is undecodable, and its check node normalization satisfies the probability P (k) (G(k)|R(τ)) converges or oscillates continuously in any interval less than 1, and finally converges to a value range less than the decoding success threshold; and for the sequence R(τ0:τ0+N C -1), considering that the sequence of input factor graphs has its own structural characteristics, the frame synchronization normalized confidence Π(τ) is greater than the given threshold parameter θ in most cases r , and the threshold parameter θ r The check node normalization meets the probability threshold tolerance; At the same time, considering the data frames with different coding lengths selected for the received sequence with different delays, the process includes selecting a coding sequence with a frame structure that is exactly a complete frame with correct frame synchronization delay. The frame synchronization normalized confidence of the sequence meets the above situation, and then the position of the frame compensation can be compensated according to the threshold value. Determine the frame synchronization position, that is, perform frame synchronization compensation according to the threshold detection result of the binary hypothesis test, and then achieve frame synchronization; reduce the detection of unnecessary frame compensation positions by detecting over-threshold values, reduce frame synchronization processing delay, and reduce hardware resource occupancy and design complexity; since the frame synchronization process is completed by introducing coding gain through coding assistance using the coding's own structural characteristics, there is no need to insert pilot symbols at the transmitting end, avoiding the problem of reduced spectrum utilization, and significantly improving the throughput and transmission rate of the communication system.

9. The coding-assisted blind frame synchronization method based on threshold detection and stop judgment according to claim 8, characterized in that: The implementation method of step eight is: When determining the frame synchronization position After that, the approximate posterior probability of the codeword is calculated and hard decision is made to obtain the sequence R(τ0:τ0+N C -1) decoding bit sequence The approximate posterior probability of the codeword is: when When bit decision output B m =1, when When bit decision output B m =0, output decoding sequence At this time, based on the "frame synchronization & decoding" joint system model, according to the threshold value θ r Determine the frame synchronization position The corresponding decoding process is completed through an iterative message passing algorithm, and a decoding codeword sequence is output, thereby achieving frame synchronization and decoding.

10. A coding-assisted blind frame synchronization system based on threshold detection and stop judgment, configured to implement a coding-assisted blind frame synchronization method based on threshold detection and stop judgment as claimed in claim 1, 2, 3, 4, 5, 6, 7, 8 or 9, characterized in that: It is mainly composed of observation frame sliding window module, channel decoding module, detection node module, confidence detection module and hard decision module; The observation frame sliding window module is mainly used to implement the following functions: determine the observation frame structure on the received sequence according to the frame structure sliding window, and provide input for the channel decoding module; The channel decoding module is mainly used to achieve the following functions: improve frame synchronization performance by introducing iterative gain through channel decoding, and complete the channel decoding process at the same time; A detection node module is added to the traditional channel decoding module structure. The detection node module is mainly used to construct a cost function for estimating the frame synchronization position and input the cost function into the confidence detection module. In the process of using the normalized satisfaction probability of the check nodes output by the LDPC factor graph model during iterative decoding as the cost function, the convergence of the normalized satisfaction probability of the check nodes is used as the iteration stop judgment condition. By using the convergence of the evolutionary graph of the normalized satisfaction probability of the decoding check nodes during the iteration as the iteration stop judgment condition, it is judged in real time whether the iteration is to be stopped. If the iteration stop judgment condition is met, the iteration process of the position observation frame is stopped, reducing the processing delay of the asynchronous position observation frame, reducing unnecessary and useless iteration processes of blind frame synchronization, and effectively improving the system throughput; The confidence detection module is mainly used to implement the following functions: judging the frame synchronization position according to the frame synchronization normalized confidence obtained by the detection node module, analyzing the relationship between the iterative gain introduction point and the frame synchronization position with the single peak characteristic of the frame synchronization normalized confidence, setting a threshold for the threshold decision tolerance characteristic of the frame synchronization normalized confidence of the frame synchronization and non-frame synchronization positions to perform binary hypothesis test detection, determining the estimate of the frame compensation position by detecting the threshold, that is, performing frame synchronization compensation according to the threshold detection result of the binary hypothesis test, thereby achieving frame synchronization; reducing the detection of unnecessary frame compensation positions by detecting the threshold, reducing the frame synchronization processing delay, and reducing the hardware resource occupancy and design complexity; since the coding gain is introduced by coding assistance using the coding structure characteristics itself to complete the frame synchronization process, there is no need to insert pilot symbols at the transmitting end, avoiding the problem of reduced spectrum utilization, and significantly improving the throughput and transmission rate of the communication system; The hard decision module is mainly used to implement the following functions: through hard decision, the channel decoding soft information is judged as 0 or 1 bits, and the codeword sequence after frame synchronization and channel decoding is output.

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