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

Abstract

The present invention discloses a coding-assisted blind frame synchronization method and system based on maximum value detection stop judgment, which belongs to the field of communication signal processing. The present invention obtains the frame synchronization normalization confidence used to estimate frame synchronization based on the normalized satisfaction probability of the check nodes of the LDPC factor graph model in the iterative decoding process, and uses the evolutionary graph convergence 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, thereby reducing unnecessary and useless iterative processes of blind frame synchronization and effectively improving system throughput. The present invention utilizes the structural characteristics of the coding itself to introduce coding gain through coding assistance, thereby reducing the performance loss of the communication system, and utilizes the single peak characteristic of the frame synchronization normalization confidence to find the maximum value to achieve frame synchronization of the continuous communication system, thereby achieving a fast and accurate frame synchronization process; and avoids the problem of reduced spectrum utilization caused by inserting pilot signals, thereby significantly improving the transmission rate of the communication system.

Description

Technical Field

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

[0002] Because channel coding and decoding technology can introduce coding gain and thus reduce the loss of communication system performance, it is often combined with frame synchronization, demodulation and other steps in the communication system receiver architecture to effectively improve the performance of the communication system. Code-assisted system architectures often iteratively update prior information based on external information to achieve maximum likelihood decoding, and use decoding iterative gain to improve the performance of processes such as frame synchronization and demodulation. Bit-Interleaved Coded Modulation-Iterative Decoding (BICM-ID), based on the combination of coding and modulation techniques, introduces channel coding and decoding gain into the demodulation process, effectively improving the demodulation threshold and system transmission efficiency.

[0003] Traditional communication receiver architectures typically use a method of inserting pilots in the time domain of transmitted data and combining it with binary hypothesis testing to achieve frame synchronization. The transmitter typically inserts a pilot with prior information at a fixed position in the data frame structure, and determines the starting position of the frame structure at the receiver by combining time domain correlation or matched filtering operations. Due to the random characteristics of channel noise and data bits, in order to prevent missed detection or erroneous estimation due to insufficient pilot information length, the pilots inserted into the frame structure typically have a large time domain share and signal energy, resulting in resource consumption and reduced transmission rate. Unlike the traditional method of adding pilot symbols to the sequence, code-assisted blind frame synchronization uses the characteristics of the coding structure itself to introduce coding gain. By eliminating the need to insert pilot symbols for detection, the transmission bandwidth efficiency is significantly improved. At the same time, coding gain is introduced into the frame synchronization process at the receiver to reduce performance loss. However, current blind frame synchronization algorithms have high computational complexity. The iterative detection of sufficient compensation positions for each fixed frame increases system throughput and complexity. Furthermore, the lack of real-time online stop judgment exponentially impacts system throughput with delays in iteration judgment. Consequently, receiver architectures based on blind frame synchronization suffer from long blind frame synchronization setup times and high system power consumption. Furthermore, current receiver architecture designs based on blind frame synchronization are mostly based on digital domain analysis of LLR information, lacking probabilistic domain algorithm analysis and corresponding circuit implementation suitable for iterative message passing algorithms. Therefore, the design of low-power, low-complexity, code-assisted blind frame synchronization methods and systems is urgently needed. Summary of the Invention

[0004] 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 continuous 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, and the current blind frame synchronization algorithm has large computational complexity and hardware complexity; (3) The current algorithm based on blind frame synchronization performs sufficient iterative detection on each fixed frame compensation position, without real-time online iteration stop judgment, and the iteration cycle affects the system throughput with multiple delays, thereby reducing the system throughput, and the hardware implementation is complex. The main purpose of the present invention is to provide a coding-assisted blind frame synchronization method and system based on maximum detection stop judgment, taking the LDPC decoding factor graph model as the research object, guiding the iterative stop judgment by the check node normalization satisfaction probability convergence analysis, and determining the frame synchronization normalization confidence by the check node normalization satisfaction probability of each frame compensation position of the continuously received data frame for the continuous communication system while ensuring that the coding gain is fully introduced, analyzing the relationship between the gain introduction point and the frame synchronization position according to the single peak characteristic of the frame synchronization normalization confidence, and realizing frame synchronization position estimation by determining the maximum value of the frame synchronization normalization confidence, thereby realizing frame synchronization and iterative decoding. The present invention has the following advantages: (1) the frame synchronization process does not require the insertion of pilot symbols at the transmitting end, thus avoiding the problem of decreased spectrum utilization and significantly improving the throughput and transmission rate of the system; (2) the coding structure characteristics of the coding itself are utilized to introduce coding gain through coding assistance to complete the frame synchronization process, thereby reducing the performance loss of the communication system and achieving a fast and accurate frame synchronization process; (3) the decoding factor graph is used to check the node normalization to meet the probability of determining the cost function of the frame synchronization position, and the single peak characteristic of the frame synchronization normalization confidence is used to find the maximum value to achieve the frame synchronization process of the continuous communication system, thereby estimating the frame synchronization position, achieving a fast and accurate frame synchronization process for the continuous communication system, and simultaneously completing the iterative process. The present invention uses the convergence of the evolutionary graph of the probability of satisfying the normalized decoding check nodes in the iterative decoding process as the condition for stopping the iteration to determine whether the iteration is stopped. If the condition for stopping the iteration is met, the iteration of the observation frame at the position is stopped, ensuring that the processing delay of the observation frame at the asynchronous position is reduced when the coding gain is fully introduced into the frame synchronization process, and reducing the unnecessary and useless iteration process of the blind frame synchronization, thereby effectively improving the system throughput.

[0005] The purpose of the present invention is achieved through the following technical solutions.

[0006] The present invention discloses a coding-assisted blind frame synchronization method based on maximum detection stop judgment. Based on the normalized check node satisfaction probability of the LDPC factor graph model during iterative decoding, a normalized frame synchronization confidence for estimating frame synchronization is obtained. The normalized frame synchronization confidence serves as the cost function for the frame synchronization position. In determining the cost function, 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, iteration of the observation frame at that position is stopped, reducing the processing delay of observation frames at non-synchronized positions, reducing unnecessary and useless blind frame synchronization iterations, and effectively improving system throughput. While ensuring sufficient coding gain is introduced, the normalized frame synchronization confidence is determined based on the normalized check node satisfaction probability at each frame compensation position. The relationship between the gain introduction point and the frame synchronization position is analyzed based on the single peak characteristic of the normalized frame synchronization confidence. The frame synchronization position is estimated by determining the maximum value of the normalized frame synchronization confidence, thereby achieving frame synchronization and iterative decoding. The present invention utilizes the structural characteristics of the coding itself to introduce coding gain through coding assistance, thereby reducing the performance loss of the communication system, and utilizes the single-peak characteristic of the normalized confidence level of frame synchronization to find the maximum value to achieve frame synchronization of the continuous communication system, thereby realizing a fast and accurate frame synchronization process; and avoids the problem of reduced spectrum utilization caused by inserting pilot signals, thereby significantly improving the throughput and transmission rate of the communication system.

[0007] The present invention discloses a coding-assisted blind frame synchronization method based on maximum value detection and stop judgment, comprising the following steps:

[0008] 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.

[0009] 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 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:

[0010]

[0011] 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:

[0012]

[0013] Node G ave Output the normalized satisfaction probability P of the check node in the kth iteration cycle (k) (G(k)|R(τ)) is:

[0014]

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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:

[0019] r(t)=s(t-τ0)+n(t)

[0020] 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.

[0021] 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:

[0022] P(τ|r(t)),τ∈[0,n c -1]

[0023] 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.

[0024] Step 3: According to the observation frame data R(τ:τ+N C -1) to initialize the variable node.

[0025] 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:

[0026]

[0027] 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.

[0028] Step 4: Update the global factor graph check nodes according to the variable node information and check constraint relationship.

[0029] 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:

[0030]

[0031] 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.

[0032] 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.

[0033] 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:

[0034]

[0035] 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

[0036] 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 of (G(k)|R(τ)) determines 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.

[0037] 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)):

[0038]

[0039] The stopping condition consists of the following two parts:

[0040] Stop iteration condition 1. Stop iteration when the iteration cycle reaches the maximum number of iterations;

[0041] 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.

[0042] 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 stops when (G(k)|R(τ)) reaches the decoding success threshold or meets the convergence threshold. This greatly reduces the computational complexity and iteration cycle of the asynchronous position observation frame while ensuring the full introduction of coding gain, and reduces invalid iterations. 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) The trend of (G(k)|R(τ)) still increases in the subsequent iteration process, which reduces the false alarm probability of asynchronous frames and improves the frame synchronization performance.

[0043] 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:

[0044]

[0045] 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.

[0046] Step 7: 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.

[0047] For the sequence R(τ0:τ0+N C -1) Update the approximate posterior probability and determine the output codeword. The approximate posterior probability of the codeword is:

[0048]

[0049] when When bit decision output B m =1, when When bit decision output B m =0, output decoding sequence

[0050] In step eight, the frame synchronization position is determined based on the normalized frame synchronization confidence obtained in step six. The relationship between the iterative gain introduction point and the frame synchronization position is analyzed using the single-peak characteristic of the normalized frame synchronization confidence. The frame synchronization position is estimated by determining the maximum value of the normalized frame synchronization confidence. By leveraging the inherent structural characteristics of the code to complete the frame synchronization process through coding-assisted introduction of coding gain, frame synchronization accuracy is improved, eliminating the need to insert pilot symbols at the transmitter, avoiding the problem of reduced spectrum utilization, and significantly improving the throughput and transmission rate of the communication system.

[0051] The sliding window observation frame R(τ:τ+N C -1) frame synchronization normalized confidence Π(τ) and hard decision codeword sequence Store, and set τ = τ + 1, jump to step 2, repeat steps 2 to 7, iteratively update the information sequence of the next sliding window to calculate Π (τ). After the frame synchronization normalized confidence and hard decision codeword sequence of all sliding windows are calculated and stored, the frame synchronization normalized confidence Π (τ) of all sliding window observation frames is compared to find the maximum value. Corresponding sliding window but For the correct frame synchronization position, is the correct frame sequence, namely:

[0052]

[0053] 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 between 0.5 and 0.9, 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 the characteristics of encoding structure itself, it constitutes a decodable type, and the check node normalization satisfies the probability P (k) In most cases, (G(k)|R(τ)) can reach the decoding success threshold T, which is significantly greater than the normalized probability of satisfaction of the check nodes at the remaining non-frame synchronization positions. In low signal-to-noise ratio conditions, the sequence at the correct frame synchronization position, even if there are non-decodable patterns, still better conforms to the check constraint. Therefore, based on distribution statistics, it can be seen that Π(τ) at the correct frame synchronization position is generally greater than П(τ) at the non-frame synchronization position. By finding the maximum Π(τ), the frame synchronization position can be determined.

[0054] Step 9: Output the decoding decision bit sequence for the correct frame synchronization position, completing the frame synchronization and decoding process. This combined synchronous processing of frame synchronization and channel decoding introduces coding gain compared to the traditional receiver frame synchronization and channel decoding sequential processing architecture, minimizing communication system performance loss.

[0055] When determining the frame synchronization position Then, the sequence R(τ0:τ0+N c -1) hard decision codeword sequence As the decoded output. At this time, based on the "frame synchronization & decoding" joint system model, the frame synchronization position is determined according to the maximum value distribution of Π(τ) The corresponding decoding process is completed through an iterative message passing algorithm, and a decoding codeword sequence is output, thereby realizing frame synchronization and decoding process.

[0056] The present invention also discloses a coding-assisted blind frame synchronization system based on maximum detection and stop judgment, which is used to implement the coding-assisted blind frame synchronization method based on maximum detection and stop judgment. The coding-assisted blind frame synchronization system based on maximum detection and stop judgment mainly consists of an observation frame sliding window module, a channel decoding module, a detection node module, a hard decision module, a buffer module, a confidence detection module, and a decoding output module.

[0057] 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.

[0058] 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.

[0059] 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.

[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] The cache module and confidence detection module are mainly used to realize the following functions: storing and comparing the normalized confidence of frame synchronization, using the single peak characteristic of the normalized confidence of frame synchronization to determine the correspondence between the iterative gain introduction point and the frame synchronization position, and realizing the detection of the frame synchronization position.

[0062] The decoding output module is mainly used to realize the following functions: reading and outputting the hard decision cache data of the synchronization position to realize decoding output.

[0063] Beneficial effects:

[0064] 1. The present invention discloses a coding-assisted blind frame synchronization method and system based on maximum detection stop judgment. While ensuring sufficient introduction of coding gain, the normalized check node of each frame compensation position is used to determine the probability of frame synchronization normalization confidence. The relationship between the gain introduction point and the frame synchronization position is analyzed based on the single peak characteristic of the frame synchronization normalization confidence. The frame synchronization position is estimated by determining the maximum value of the frame synchronization normalization confidence, thereby achieving frame synchronization and iterative decoding. The present invention does not require the insertion of pilot signals to achieve frame synchronization, thereby improving spectrum utilization and significantly enhancing the throughput and transmission rate of the communication system. The coding gain is introduced by utilizing the structural characteristics of the coding itself for frame synchronization, thereby reducing the performance loss of the communication system. For continuous communication systems, the maximum value is found based on the single peak characteristic of the frame synchronization normalization confidence of continuous data frames, thereby estimating the frame synchronization position, achieving a fast and accurate frame synchronization process, and simultaneously completing the iterative decoding process. Compared with the traditional frame synchronization and decoding sequential processing architecture, the present invention simultaneously completes frame synchronization and decoding with low complexity.

[0065] 2. The present invention discloses a coding-assisted blind frame synchronization method and system based on maximum value detection stop judgment. When determining the normalized confidence of frame synchronization by using the probability of normalization satisfaction of check nodes in the iterative process, the convergence of the evolutionary graph of the normalization satisfaction probability of decoding check nodes in the iterative process is used as the iterative stop judgment condition, and real-time online judgment is made on whether the iteration is stopped, thereby ensuring that the coding gain is fully introduced into the frame synchronization process, reducing the processing delay of the asynchronous position observation frame, reducing unnecessary and useless iterative processes of blind frame synchronization, and effectively improving the system throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of a coding-assisted blind frame synchronization method based on maximum value detection and stop judgment according to the present invention;

[0067] Figure 2 It is a decoding factor graph model with added detection nodes in the coding-assisted blind frame synchronization method based on maximum value detection and stop judgment according to an embodiment of the present invention;

[0068] Figure 3Schematic 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;

[0069] Figure 4 is a relationship diagram between the normalized confidence of frame synchronization and the frame synchronization position according to an embodiment of the present invention;

[0070] Figure 5 For the LDPC code type (480, 240), a simulation curve comparing the frame synchronization error rate (FSER) performance of the stop judgment blind frame synchronization method described in the embodiment of the invention with the pilot frame synchronization and maximum iteration number blind frame synchronization methods;

[0071] Figure 6 For the LDPC code type (480, 240), a simulation curve comparing the average number of iterations (ANI) performance of the blind frame synchronization method described in the embodiment of the invention and the maximum number of iterations blind frame synchronization method is provided;

[0072] Figure 7 This is a hardware architecture design of a coding-assisted blind frame synchronization system based on maximum value detection and stop judgment described in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] To make the above-mentioned objects, features and advantages of the present invention easier to understand, the following is a further detailed description thereof with reference to the accompanying drawings and specific embodiments. This embodiment is directed to the LDPC (480, 240) code type, a continuous frame coding sequence is a data frame, and a code-assisted blind frame synchronization method based on maximum detection and stop judgment is used to achieve frame synchronization and decoding. The system parameters in this embodiment are shown in the following table:

[0074] parameter Details Modulation method BPSK Bitrate 1 / 2 LDPC code type (480，240) Channel Model Gaussian white noise Maximum number of decoder iterations 50

[0075] like Figure 1 As shown, the present embodiment discloses a coding-assisted blind frame synchronization method based on maximum value detection and stop judgment, and the specific implementation steps are as follows:

[0076] 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.

[0077] 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:

[0078]

[0079] 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:

[0080]

[0081] Node G ave Output the normalized satisfaction probability P of the check node in the kth iteration cycle (k) (G(k)|R(τ)) is:

[0082]

[0083] 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.

[0084] 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.

[0085] 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, and the relationship between the sliding window and the observation frame structure is shown in the figure below. Figure 3 shown.

[0086] 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:

[0087] r(t)=s(t-τ0)+n(t)

[0088] 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.

[0089] 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:

[0090] P(τ|r(t)),τ∈[0,n c -1]

[0091] 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.

[0092] Step 3: According to the observation frame data R(τ:τ+N C -1) to initialize the variable node.

[0093] 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:

[0094]

[0095] 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.

[0096] Step 4: Update the global factor graph check nodes according to the variable node information and check constraint relationship.

[0097] 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:

[0098]

[0099] 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 Vm Other than check node C n The probability density function representation of all connected variable nodes modulo 2 sum to 0.

[0100] 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.

[0101] 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:

[0102]

[0103] 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

[0104] 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 of (G(k)|R(τ)) determines 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.

[0105] 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)):

[0106]

[0107] The stopping condition consists of the following two parts:

[0108] Stop iteration condition 1. Stop iteration when the iteration cycle reaches the maximum number of iterations;

[0109] 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.

[0110] 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 stops when (G(k)|R(τ)) reaches the decoding success threshold or meets the convergence threshold. This greatly reduces the computational complexity and iteration cycle of the asynchronous position observation frame while ensuring the full introduction of coding gain, and reduces invalid iterations. 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) The trend of (G(k)|R(τ)) still increases in the subsequent iteration process, which reduces the false alarm probability of asynchronous frames and improves the frame synchronization performance.

[0111] 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:

[0112]

[0113] 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.

[0114] Step 7: 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.

[0115] For the sequence R(τ0:τ0+N C -1) Update the approximate posterior probability and determine the output codeword. The approximate posterior probability of the codeword is:

[0116]

[0117] when When bit decision output B m =1, when When bit decision output B m =0, output decoding sequence

[0118] Step 8: Determine the frame synchronization position based on the frame synchronization normalization confidence obtained in step 6, and analyze the relationship between the iterative gain introduction point and the frame synchronization position using the single peak characteristic of the frame synchronization normalization confidence to determine the maximum value of the frame synchronization normalization confidence to achieve frame synchronization position estimation. The relationship between the frame synchronization normalization confidence and the frame synchronization position when Eb / N0 is 2dB is as follows: Figure 4 As shown in the figure, ITE represents the maximum number of iterations. The curves in the figure show the normalized confidence level of frame synchronization at each frame compensation position under different iteration settings. For a received sequence of length 480, the frame compensation positions are [-240, 240]. After iterative updates, the normalized confidence level of frame synchronization is output for each frame compensation position. By leveraging the inherent structural characteristics of the code to introduce coding gain through code assistance, the frame synchronization process is completed, improving frame synchronization accuracy. This eliminates the need to insert pilot symbols at the transmitter, avoids the problem of reduced spectrum utilization, and significantly improves the throughput and transmission rate of the communication system.

[0119] The sliding window observation frame R(τ:τ+N C -1) frame synchronization normalized confidence Π(τ) and hard decision codeword sequence Store, and set τ = τ + 1, jump to step 2, repeat steps 2 to 7, iteratively update the information sequence of the next sliding window to calculate Π (τ). After the frame synchronization normalized confidence and hard decision codeword sequence of all sliding windows are calculated and stored, the frame synchronization normalized confidence Π (τ) of all sliding window observation frames is compared to find the maximum value. Corresponding sliding window but For the correct frame synchronization position, is the correct frame sequence, namely:

[0120]

[0121] 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 between 0.5 and 0.9, 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 the characteristics of encoding structure itself, it constitutes a decodable type, and the check node normalization satisfies the probability P (k) In most cases, (G(k)|R(τ)) can reach the decoding success threshold T, which is significantly greater than the normalized probability of satisfaction of the check nodes at the remaining non-frame synchronization positions. In low signal-to-noise ratio conditions, the sequence at the correct frame synchronization position, even if there are non-decodable patterns, still better conforms to the check constraint. Therefore, based on distribution statistics, it can be seen that П(τ) at the correct frame synchronization position is generally greater than Π(τ) at the non-frame synchronization position. By finding the maximum Π(τ), the frame synchronization position can be determined.

[0122] Step 9: Output the decoding decision bit sequence for the correct frame synchronization position, completing the frame synchronization and decoding process. This combined synchronous processing of frame synchronization and channel decoding introduces coding gain compared to the traditional receiver frame synchronization and channel decoding sequential processing architecture, minimizing communication system performance loss.

[0123] When determining the frame synchronization position Then, the sequence R(τ0:τ0+N c -1) hard decision codeword sequence As the decoded output. At this time, based on the "frame synchronization & decoding" joint system model, the frame synchronization position is determined according to the maximum value distribution of Π(τ) The corresponding decoding process is completed through an iterative message passing algorithm, and a decoding codeword sequence is output, thereby realizing frame synchronization and decoding process.

[0124] The frame synchronization and decoding performance analysis of the code type of this embodiment is carried out. Considering that the correct output result is frame synchronization and correct decoding, the joint frame synchronization decoding error rate (JFSDER) is used as the performance indicator to measure the frame synchronization and decoding results. Different parameters are selected for the stopping condition of iteration to conduct comparative simulations, and the algorithm performance of stopping iteration when the maximum number of iterations is reached is used as the upper limit of JFSDER performance for comparison. The simulation results are shown in Figure 2. Figure 5As shown. Since this method introduces coding gain, its performance has a "waterfall zone", and the frame synchronization performance is greatly improved at high signal-to-noise ratios. It not only solves the problem of reduced transmission rate caused by the use of pilot signals, but also reduces system performance loss and ensures frame synchronization performance. Adding a stop judgment will cause performance regression to a certain extent. When the optimal parameters are selected, its performance is close to the performance upper limit. The average number of iterations (Average Number Of Iteration, ANI) performance simulation analysis is performed on the code type of this embodiment. The ANI performance is analyzed with or without a stop judgment and with different stop judgment parameter settings, and the results are shown as follows. Figure 6 As shown in the figure, the addition of stop judgment significantly reduces the average number of iterations. Considering that the average number of iterations affects system throughput exponentially with latency, 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. For suitable parameters, maximum value detection and stop judgment can significantly improve system throughput without affecting frame synchronization and decoding performance, realizing a low-power communication system.

[0125] This embodiment also discloses a coding-assisted blind frame synchronization system based on maximum detection and stop judgment, which is used to implement the coding-assisted blind frame synchronization method based on maximum detection and stop judgment. The coding-assisted blind frame synchronization system based on maximum detection and stop judgment is designed and analyzed based on the factor graph model in step 1 and mainly consists of an observation frame sliding window module, a channel decoding module, a detection node module, a hard decision module, a buffer module, a confidence detection module, and a decoding output module.

[0126] The observation frame sliding window module involves step 2, which determines the observation frame structure on the received sequence based on the frame structure sliding window, providing input for the channel decoding module. The channel decoding module involves steps 3, 4, and 5, introducing iteration gain through channel decoding to improve frame synchronization performance and simultaneously complete the channel decoding process. The detection node module involves step 6, which adds a detection node module to the traditional channel decoding module structure. Based on the probability convergence analysis of the check node normalization, the decoding iteration status is determined and the iteration process is stopped. This reduces system dynamic power consumption and improves system throughput by reducing unnecessary iteration cycles. The hard decision module involves step 7, which implements approximate posterior probability calculation and hard decision of codewords. The cache module and confidence detection module involve step 8, which stores and compares the normalized confidence of frame synchronization. The single-peak characteristic of the normalized confidence of frame synchronization is used to determine the correspondence between the iteration gain introduction point and the frame synchronization position, thereby detecting the frame synchronization position. The decoding output module involves step 9, which reads and outputs the hard decision cache data of the synchronization position to achieve decoding output.

[0127] 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 maximum value detection and stop judgment, characterized in that: 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) the 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 of (G(k)|R(τ)) determines 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 7: 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. Step 8: Determine the frame synchronization position based on the frame synchronization normalization confidence obtained in step 6, analyze the relationship between the iterative gain introduction point and the frame synchronization position using the single peak characteristic of the frame synchronization normalization confidence, and determine the maximum value of the frame synchronization normalization confidence to achieve frame synchronization position estimation; since the coding gain is introduced by coding assistance using the coding structure characteristics to complete the frame synchronization process, the frame synchronization accuracy is improved, and 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; Step nine: Output the decoding decision bit sequence of the correct frame synchronization position to complete the frame synchronization and decoding process; combine frame synchronization and channel decoding for synchronous processing, introduce coding gain compared with the traditional receiver frame synchronization and channel decoding sequential processing architecture, and reduce the loss of communication system performance.

2. The coding-assisted blind frame synchronization method based on maximum value detection and stop judgment according to claim 1, wherein: 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 maximum value 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 maximum value 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 maximum value detection and stop judgment according to claim 4, characterized in that: The implementation method of step 4 is: In the known observation frame data 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 to 0.

6. The coding-assisted blind frame synchronization method based on maximum value 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 maximum value 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 stopping 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) 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 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 stops when (G(k)|R(τ)) reaches the decoding success threshold or meets the convergence threshold. This greatly reduces the computational complexity and iteration cycle of the asynchronous position observation frame while ensuring the full introduction of coding gain, and reduces invalid iterations. 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) The trend of (G(k)|R(τ)) still increases in subsequent iterations, which reduces the false alarm probability of asynchronous frames and improves the frame synchronization performance; 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: Where k0 is the stopping iteration period, Π(τ) represents the observation frame data R(τ:τ+N C -1) The final normalized satisfaction probability of the check node after iterative update; The implementation method of step seven is: For the sequence R(τ0:τ0+N C -1) Update the approximate posterior probability and determine the output codeword. 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 .

8. The coding-assisted blind frame synchronization method based on maximum value detection and stop judgment according to claim 7, characterized in that: The implementation method of step eight is: The sliding window observation frame data R(τ:τ+N C -1) frame synchronization normalized confidence Π(τ) and hard decision codeword sequence Store, and set τ = τ + 1, jump to step 2, repeat steps 2 to 7, iteratively update the information sequence of the next sliding window to calculate Π(τ); when the frame synchronization normalized confidence and hard decision codeword sequence of all sliding windows are calculated and stored, the frame synchronization normalized confidence Π(τ) of all sliding window observation frames is compared to find the maximum value Corresponding sliding window but For the correct frame synchronization position, is the correct frame sequence, namely: 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 between 0.5 and 0.9, 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 the characteristics of encoding structure itself, it constitutes a decodable type, and the check node normalization satisfies the probability P (k) In most cases, (G(k)|R(τ)) can reach the decoding success threshold T, which is much larger than the normalized satisfaction probability of the check nodes at the other non-frame synchronization positions. In the case of low signal-to-noise ratio, the sequence of the correct frame synchronization position still better conforms to the check constraint even if there is a non-decodable type. Therefore, according to distribution statistics, the Π(τ) of the correct frame synchronization position is greater than the Π(τ) of the non-frame synchronization position in most cases. The frame synchronization position can be determined by finding the maximum value of Π(τ).

9. The coding-assisted blind frame synchronization method based on maximum value detection and stop judgment according to claim 8, characterized in that: The implementation method of step nine is: When determining the frame synchronization position Then, the sequence R(τ0:τ0+N c -1) hard decision codeword sequence As the decoded output; at this time, based on the "frame synchronization & decoding" joint system model, the frame synchronization position is determined according to the Π(τ) maximum value distribution The corresponding decoding process is completed through an iterative message passing algorithm, and a decoding codeword sequence is output, thereby realizing frame synchronization and decoding process.

10. A coding-assisted blind frame synchronization system based on maximum value detection and stop judgment, configured to implement a coding-assisted blind frame synchronization method based on maximum value detection and stop judgment as claimed in claim 1, 2, 3, 4, 5, 6, 7, 8 or 9, characterized in that: It mainly consists of observation frame sliding window module, channel decoding module, detection node module, hard decision module, cache module, confidence detection module and decoding output 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 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; The cache module and confidence detection module are mainly used to implement the following functions: store and compare the normalized confidence of frame synchronization, use the single peak characteristic of the normalized confidence of frame synchronization to determine the correspondence between the iterative gain introduction point and the frame synchronization position, and realize the detection of the frame synchronization position; The decoding output module is mainly used to realize the following functions: reading and outputting the hard decision cache data of the synchronization position to realize decoding output.

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