CN115632921A - Encoding auxiliary blind frame synchronization method and system based on threshold detection - Google Patents
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Abstract
The invention discloses a coding auxiliary blind frame synchronization method and a coding auxiliary blind frame synchronization system based on threshold detection, and belongs to the field of communication signal processing. The invention determines the frame synchronization normalization confidence coefficient by using the check node normalization satisfaction probability of the LDPC factor graph model, analyzes the relationship between the iterative gain introduction point and the frame synchronization position by using the frame synchronization normalization confidence coefficient, sets a threshold value aiming at the threshold value judgment tolerance characteristic of the frame synchronization normalization confidence coefficient of the frame synchronization position and the non-frame synchronization position to carry out binary hypothesis test, detects a useful data frame by judging whether the threshold value is exceeded or not and determines the frame synchronization position, thereby realizing the high-efficiency stable blind frame synchronization and decoding suitable for a burst communication system. The invention introduces coding gain with the assistance of coding, improves the frame synchronization performance, obviously improves the throughput and the transmission rate of a communication system, reduces the detection of unnecessary frame compensation positions by threshold detection, reduces the frame synchronization processing time delay and the false alarm rate, realizes quick and accurate frame synchronization and decoding, and reduces the resource occupation and the design complexity.
Description
Technical Field
The invention relates to a coding auxiliary blind frame synchronization method and system based on threshold detection, in particular to an LDPC coding auxiliary blind frame synchronization method and system based on threshold detection, which are suitable for a burst communication system and belong to the field of communication signal processing.
Background
In the context of the development of modern war and network information security countermeasure technologies, the requirements for reliability, security and confidentiality of communication systems are increasing. The burst communication system generally selects a sending window randomly and shortens the signal duration to effectively improve the anti-interference and anti-interception capabilities of the communication system, and the burst communication system is widely applied to the field of military communication due to the characteristics of burst, short duration and randomness.
Conventional frame synchronization architectures typically do this by inserting pilots with a priori information at fixed locations in the data frame structure and determining the start of the frame structure at the receiving end in conjunction with a time domain correlation or matched filtering operation. In order to prevent missed detection or erroneous estimation caused by insufficient pilot information length, the pilots inserted by the frame structure usually have large time domain ratio and signal energy, which results in resource consumption and reduction of transmission rate.
In the case of a burst communication system, the frame synchronization subsystem needs to have, as one of the important elements: the correct frame synchronization probability is extremely high, useful data frames are captured continuously and stably, and the frame synchronization establishment time is as short as possible. The coding auxiliary blind frame synchronization introduces coding gain by using the characteristics of the self structure of the code, so that the loss of the performance of a communication system can be effectively reduced, and the blind frame synchronization is detected by using the threshold which accords with the signal judgment characteristics, so that the false alarm phenomenon can be effectively avoided, and the quick and accurate frame synchronization establishment process is realized. However, the current receiver architecture design based on blind frame synchronization lacks an efficient and stable blind frame synchronization algorithm design and a corresponding circuit implementation suitable for a burst communication system, and therefore, development of a low-power-consumption low-complexity coding auxiliary blind frame synchronization method and system suitable for the burst communication system is urgently needed.
Disclosure of Invention
In order to solve the following technical defects existing in the prior art: the conventional frame synchronization inserts pilot frequency with prior information in a data frame structure time domain, which causes resource consumption and reduction of transmission rate; secondly, the current communication receiver lacks an algorithm and an architecture design which introduce coding gain into a frame synchronization technology, so that the performance loss of a communication system is caused; thirdly, aiming at a burst communication system, data frames need to be stably captured and the frame synchronization establishing time needs to be shortened as much as possible, the current algorithm based on blind frame synchronization needs to detect all frame compensation positions of a receiving sequence, and the frame synchronization processing time delay is large; the invention mainly aims to provide a coding auxiliary blind frame synchronization method and a coding auxiliary blind frame synchronization system based on threshold detection. The invention has the following advantages: the method comprises the following steps that (I) a pilot frequency symbol is not required to be inserted into a transmitting end, the self structural characteristics of codes are utilized, and coding gain is introduced through the assistance of the codes to complete a frame synchronization process, so that the problem of reduction of the frequency spectrum utilization rate is avoided, the throughput and the transmission rate of a communication system are obviously improved, and the frame synchronization performance is improved by introducing the coding gain; secondly, aiming at a burst communication system, determining a frame synchronization normalization confidence coefficient according to the fact that the normalization of check nodes of a decoding factor graph meets the probability, detecting blind frame synchronization by using a threshold which meets the judgment tolerance characteristic of the frame synchronization normalization confidence coefficient, so that a false alarm phenomenon can be effectively avoided, the frame synchronization accuracy is improved, the setting and judgment of the threshold are favorable for stably capturing data frames, and the frame synchronization and iterative decoding process is completed; and (III) compared with the full-frame structure detection method, the threshold detection process is used for replacing the process of detecting all frame compensation positions, so that the detection of unnecessary frame compensation positions is reduced, the frame synchronization processing time delay is reduced, the rapid and accurate frame synchronization establishment process is facilitated, and the hardware resource occupation and the design complexity are reduced.
The purpose of the invention is realized by the following technical scheme.
The invention discloses a coding auxiliary blind frame synchronization method and a coding auxiliary blind frame synchronization system based on threshold detection, wherein a frame synchronization normalization confidence coefficient used for estimating frame synchronization is determined according to the probability satisfied by the normalization of check nodes of an LDPC factor graph model in an iterative decoding process, the relationship between an iteration gain introduction point and a frame synchronization position is analyzed according to the frame synchronization normalization confidence coefficient, a threshold is set according to the threshold judgment tolerance characteristic of the frame synchronization normalization confidence coefficient of the frame synchronization position and the non-frame synchronization position for binary hypothesis test, the frame synchronization position is determined by detecting whether the frame synchronization normalization confidence coefficient exceeds the threshold, namely, the frame synchronization position estimation is performed according to the binary hypothesis test result of the threshold detection, and further, the frame synchronization and the iterative decoding are realized. The invention reduces the detection of unnecessary frame compensation positions through threshold detection, reduces the frame synchronization processing time delay, reduces the hardware resource occupation and the design complexity, and simultaneously, the setting of the threshold further reduces the false alarm probability of the frame synchronization, and particularly obviously reduces the false alarm probability in a burst communication system. Because the frame synchronization process is completed by introducing coding gain through the aid of coding by utilizing the structural characteristics of the coding, a pilot frequency symbol does not need to be inserted at a transmitting end, the problem of reduction of the frequency spectrum utilization rate is avoided, the throughput and the transmission rate of a communication system are obviously improved, and the frame synchronization performance is improved by introducing the coding gain.
The invention discloses a coding auxiliary blind frame synchronization method based on threshold detection, which comprises the following steps:
adding check nodes to meet the probability detection node set GNs on the basis of an LDPC decoding factor graph model, and connecting the check nodes in a one-way mode; node G for normalization and satisfaction of probability statistics by adding check nodes ave Outputting normalized satisfied probability of check node to obtain normalized confidence of frame synchronization for estimating frame synchronization, wherein the normalized confidence of frame synchronization is the frame synchronization positionA cost function.
Establishing an LDPC decoding factor graph model G = (VNs U CNs, xi), wherein VNs represents a variable node set and has the size of a check matrix row dimension N = (N) ((N)) C (ii) a CNs represents a check node set with the dimension of a check matrix column N C -N b In which N is C Is the length of the codeword sequence, N b Is the information sequence length; xi represents the set of edges connecting the variable nodes and the check nodes. At check node CNs = (C) 1 ,C 2 ,...,C k ) On the basis, a check node is added to meet the requirement of a probability detection node GNs = (G) 1 ,G 2 ,...,G k ) Output check node satisfaction probability
Indicating that under the condition of known code word sequence R and check matrix H, node G is detected after k iterations n Output check node C n Probability of compliance with check constraint, i.e. check node C n The symbolic information of all variable nodes of the constraint is multiplied by the probability that the product modulo two sum is 0:
wherein, C n Check node, V, representing reception probability information m Variable nodes representing transfer probability information, g n Indicating the symbol index of the detected node, and taking the value of 0 or 1,V m' ∈N(C n ) Represents V m' Belong to and check node C n Set of all variable nodes connected, { V m Denotes all variable node sets, the superscript k denotes the decoding iteration period, I c (C n ) Represents check node C n Corresponding check constraint
The following holds, namely:
node G ave Outputting the k iteration cycle check node normalization satisfaction probability P (k) (G (k) | R (τ)) is:
where ρ is a normalization factor such that P (k) (G (k) | R (τ)) ranges from 0 to 1. The summation symbol shows that the satisfied probabilities of all check nodes are summed, and finally, an average value is obtained to represent the constraint satisfied probability condition of all check nodes.
For a complete data frame received by a receiver, taking data of a complete codeword length in a received sequence as a frame at different time delays as input to a factorial graph model, i.e., N within a sliding window Γ (τ) C Length information sequence R (tau:tau + N) C -1) where τ is the frame sync propagation delay, defining the sliding window start position. Factor graph output as bit B = (B) 1 ,B 2 ,....B Nb ) Hard decision information representing approximate a posteriori probability information of the codeword.
Step two, determining the frame synchronization sliding window position gamma (tau), moving the sliding window gamma (tau) to obtain observation frame data R (tau: tau + N) C -1), an information sequence R (τ: tau + N C -1) is a factor graph model input.
Establishing a frame synchronization system model, considering that the preceding stage timing synchronization and the carrier synchronization have finished the accurate estimation of the timing error and the carrier phase, simplifying the frame synchronization process into a frame compensation amount tau 0 The estimation problem of (2) is that r (t) represents the received signal of the frame synchronization system after passing through the AWNG signal, s (t) represents the signal at the transmitting end, N (t) is the noise superposed at the symbol level, the mean value is 0, and the unilateral power spectral density is N 0 Complex white Gaussian noise, τ 0 The transmission delay is expressed, and the obtained frame synchronization system model is as follows:
r(t)＝s(t-τ 0 )+n(t)
where s (t) is the symbol transmitted at time t and is 0 or 1. Setting frame synchronization sliding on the basis of the system modelWindow gamma (tau), with a sliding window length N selected C I.e. the sliding window always contains data for the length of a complete code sequence.
By shifting a frame synchronization sliding window in a reception sequence by t, a frame structure of the relative position of a sliding window Γ (τ) is obtained, and observation frame data R (τ: τ + N) of the relative frame structure in the window is obtained C -1). The probability of correct frame synchronization corresponding to the position τ is:
P(τ|r(t)),τ∈[0,n c -1]
τ denotes the possible location of the correct frame synchronization with a sufficient statistic of reception r (t), n c Is the length of the frame. Maximizing the above a posteriori probability by sliding the frame sync sliding window over r (t) to obtain the likely position of correct frame sync at the receiver r (t)
Step three, according to the observation frame data R (tau: tau + N) C -1) initializing variable nodes.
Synchronizing the observation frame R (tau, tau + N) in the sliding window C -1) inputting channel prior probability information into a factor graph model, and initializing variable nodes. According to Gaussian white noise channel probability distribution, the initialization information of the variable nodes of the factor graph when the frame structure t = tau is as follows:
wherein x is i And the symbol index of the variable node is represented, and the value is 0 or 1.
The sequence R (tau, tau + N) when the sliding window is gamma (tau) C -1) channel prior probability information, where φ (t) represents the received sequence magnitude, l ∈ [0,N) C ) Representing the sequence index, σ, within a frame-synchronous sliding window 2 Is the channel noise variance.
And step four, updating the check nodes of the global factor graph according to the variable node information and the check constraint relation.
In a known observation frame sequence R (tau:tau + N) C -1) under the condition of prior information R and check constraint relation H, obtaining a set of probability information for transmitting update from check node to variable node
I.e. the check nodes pass the symbol x to the variable nodes i Probability of 0 or 1:
wherein, V m' ∈N(C n )\V m Represents V m' Belong to and C n Connected, not including V m All variable node sets of (1) ({ V) m Mean in addition to V m And all variable node sets except the variable node sets, and the superscript k represents a decoding iteration period. Checking node information
Represents except V m Outer and check node C n And the modulo-two sum of all the connected variable nodes is 0.
Step five, according to R (tau, tau + N) c -updating global factor graph variable nodes with the channel prior probability information and check node information of 1). And after the fifth step is finished, stopping the iteration process when the maximum iteration times is reached, and skipping to the sixth step, otherwise skipping to the fourth step, and repeating the fourth step to the fifth step until the maximum iteration times is reached.
Variable node transfers update probability information set to check node
That is, under the condition of knowing the channel receiving sequence R and the check matrix H, the variable node transfers the symbol x to the check node i And the probability is 0 or 1, and according to the check constraint relation and the check node information, the variable node updating information is integrated as follows:
wherein, C n' ∈N(V m )\C n Is represented by C n' Belong to and V m Connected, not including C n All check node sets of λ mn To normalize the parameters such that the relation is satisfied
And step six, judging the frame synchronization position according to the frame synchronization normalization confidence coefficient pi (tau), analyzing the relation between the iterative gain introduction point and the frame synchronization position according to the frame synchronization normalization confidence coefficient, setting a threshold value aiming at the threshold value judgment tolerance characteristic of the frame synchronization normalization confidence coefficient of the frame synchronization position and the non-frame synchronization position to detect the binary hypothesis, and estimating the frame synchronization position according to the binary hypothesis detection result of the threshold value detection by detecting whether the threshold value is exceeded or not so as to realize the frame synchronization. The invention reduces the detection of unnecessary frame compensation positions by detecting the threshold, reduces the frame synchronization processing time delay, reduces the hardware resource occupation and the design complexity, and further reduces the false alarm probability of frame synchronization by setting the threshold, especially obviously reduces the false alarm probability in a burst communication system. In addition, the frame synchronization process is completed by introducing coding gain through the aid of coding by utilizing the structural characteristics of the coding, a pilot frequency symbol does not need to be inserted into a transmitting end, the problem of reduction of the frequency spectrum utilization rate is avoided, and the throughput and the transmission rate of a communication system are remarkably improved.
Calculating the node G at the time of the last iteration cycle ave Output check node normalization satisfaction probability
Wherein k is 0 To stop iterationPeriod, i.e. maximum number of iterations; Π (tau) represents the observation frame R (tau: tau + N) within the sliding window gamma (tau) c -1) iteratively updated frame sync normalized confidence.
Comparative decision sliding window observation frame R (tau:tau + N) C -1) normalized confidence level Π (τ) of frame synchronization with a threshold parameter θ r . When pi (tau) > theta r Then, the frame compensation position of the current threshold value is output as the frame synchronization position
Stopping the frame synchronization iterative process, performing the step seven, otherwise, continuing the frame synchronization iterative process, and repeating the steps two to six until the frame synchronization normalization confidence coefficient meets the threshold parameter theta r And (4) requiring.
The variable trend of the probability in the iterative process is satisfied by the normalization of check nodes of different code patterns, and the non-frame synchronization sequence R (tau: tau + N) C -1) the sequence of the input factor graph is of the undecodable type with the check nodes normalized to satisfy the probability P (k) (G (k) | R (tau)) converges or continuously oscillates in any interval smaller than 1, and finally converges in a numerical range smaller than a decoding success threshold value, and the obtained frame synchronization normalization confidence coefficient pi (tau) is smaller than a threshold parameter theta r . And for the sequence R (tau) of the correct frame synchronization position 0 ：τ 0 +N C -1) considering the existence of the code self-structure characteristic in the sequence of the input factor graph, the normalized confidence level pi (tau) of the frame synchronization at this moment is mostly greater than a given threshold parameter theta r And a threshold parameter theta r The coincident check node normalization satisfies a probability threshold tolerance.
Meanwhile, the data frames with different coding lengths are selected for a receiving sequence of the burst communication system in consideration of different time delays, the process comprises the step that the selected frame structure is just a complete coding sequence with correct frame synchronization time delay, the frame synchronization normalization confidence coefficient of the sequence meets the condition, the frame synchronization code can be captured according to an over-threshold value, and the correct frame synchronization position is determined according to the frame compensation position corresponding to the over-threshold value
Namely, frame synchronization position estimation is carried out according to the threshold detection binary hypothesis test result, and further frame synchronization is realized.
And step seven, calculating the approximate posterior probability of the code words and hard judging, outputting the decoding judgment result of the frame synchronization position, and obtaining the bit sequence after the frame synchronization and decoding process, namely realizing blind frame synchronization and decoding.
When determining the frame synchronization position
Then, calculating the code word approximate posterior probability and hard-deciding to obtain the sequence R (tau) 0 ：τ 0 +N c -1) of a decoded bit sequence
The approximate posterior probability of a codeword is:
when in use
Time, bit decision output B m =1, when
Time, bit decision output B m =0, output decoding sequence
At this time, based on the LDPC decoding factor graph model and the frame synchronization position estimation, the threshold theta is crossed according to pi (tau) r Determining frame synchronization position
And completes the corresponding decoding process by the iterative message transfer algorithm, and outputs the decoded codeword sequence, thereby realizing the frame synchronization and decoding process.
The invention also discloses a coding auxiliary blind frame synchronization system based on threshold detection, which is used for realizing the coding auxiliary blind frame synchronization method based on threshold detection. The coding auxiliary blind frame synchronization system based on threshold detection mainly comprises an observation frame sliding window module, a channel decoding module, a confidence detection module and a hard decision module.
The observation frame sliding window module is mainly used for realizing the following functions: and determining an observation frame structure on the receiving sequence according to the frame structure sliding window, and providing input for a channel decoding module.
The channel decoding module is mainly used for realizing the following functions: iterative gain is introduced through channel decoding to improve the frame synchronization performance, and the channel decoding process is completed at the same time.
The confidence detection module is mainly used for realizing the following functions: the method comprises the steps of obtaining a frame synchronization normalization confidence coefficient through a check node normalization satisfaction probability, analyzing the relation between an iteration gain introduction point and a frame synchronization position by using the frame synchronization normalization confidence coefficient, setting a threshold value aiming at the threshold value judgment tolerance characteristic of the frame synchronization normalization confidence coefficient of a frame synchronization position and a non-frame synchronization position to carry out binary hypothesis test, capturing a burst communication channel useful data frame and determining the frame synchronization position by detecting whether the threshold value is exceeded or not, namely realizing frame synchronization according to the binary hypothesis test result of the threshold value detection. The invention reduces the detection of unnecessary frame compensation positions by detecting the over-threshold, reduces the frame synchronization processing time delay, reduces the hardware resource occupation and the design complexity, and reduces the frame synchronization false alarm phenomenon in the burst communication system.
The hard decision module is mainly used for realizing the following functions: and judging the channel decoding soft information into 0 and 1 bits through hard judgment, and outputting a code word sequence after frame synchronization and channel decoding.
Has the advantages that:
1. the invention discloses a coding auxiliary blind frame synchronization method and a coding auxiliary blind frame synchronization system based on threshold detection, wherein channel coding and decoding gains are introduced into frame synchronization by using the structural characteristics of codes per se, the corresponding relation between an iterative gain introduction point and a frame synchronization position is analyzed to realize blind frame synchronization, pilot symbols do not need to be inserted into a transmitting end, the reduction of the frequency spectrum utilization rate is avoided, and the throughput and the transmission rate of the system are obviously improved.
2. The invention discloses a coding auxiliary blind frame synchronization method and a coding auxiliary blind frame synchronization system based on threshold detection, which take the normalization satisfaction probability of a check node of a decoding factor graph as a cost function of a frame synchronization position, carry out joint synchronization processing on a frame synchronization module and a channel decoding module, introduce coding gain compared with a traditional receiver frame synchronization and channel decoding inertia sequence processing framework, reduce the performance loss of a communication system and improve the correct frame synchronization probability.
3. The invention discloses a coding auxiliary blind frame synchronization method and a coding auxiliary blind frame synchronization system based on threshold detection, which are used for detecting blind frame synchronization by using a threshold according with judgment characteristics aiming at a burst communication system, effectively avoiding false alarm phenomenon, continuously and stably capturing data frames and realizing a quick and accurate frame synchronization process.
4. The invention discloses a coding auxiliary blind frame synchronization method and a coding auxiliary blind frame synchronization system based on threshold detection, which replace the process of detecting all frame compensation positions with the process of over-threshold detection, do not need to detect all frame compensation positions, reduce the detection of unnecessary frame compensation positions, reduce the frame synchronization processing time delay, shorten the frame synchronization establishing time as much as possible, and reduce the hardware resource occupation and the design complexity.
Drawings
Fig. 1 is a schematic flow chart of a coding-assisted blind frame synchronization method based on threshold detection according to the present invention;
fig. 2 is a decoding factor graph model of an added detection node of a coding assisted blind frame synchronization method based on threshold detection according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a relationship between a sliding window of a frame synchronization system model and an observation frame structure according to an embodiment of the present invention;
fig. 4 is a simulation curve comparing Frame Synchronization Error probability (FSER) performance of the blind Frame Synchronization method and the pilot Frame Synchronization according to the embodiment of the present invention for the LDPC code pattern (480,240);
fig. 5 is a simulation curve comparing error rate performance of the blind frame synchronization threshold determination method according to the embodiment of the present invention;
fig. 6 is a block diagram of a hardware system of a coding assisted blind frame synchronization system based on threshold detection according to an embodiment of the present invention.
Detailed Description
To make the above objects, features and advantages of the present invention more comprehensible, the present embodiment, which is described in further detail below with reference to the accompanying drawings and the detailed description, is directed to an LDPC (480, 240) code pattern, in which a continuous frame coding sequence is a data frame, and frame synchronization position estimation and LDPC decoding are implemented by coding-assisted blind frame synchronization based on threshold detection. The system parameters in this example are shown in the following table:
| parameter(s) | Details of |
| Modulation system | |
| Code rate | |
| 1/2 | |
| LDPC code pattern | (480，240) |
| Channel model | White gaussian noise |
| Maximum number of iterations of decoder | 50 |
As shown in fig. 1, the coding assisted blind frame synchronization method based on threshold detection disclosed in this embodiment includes the following specific steps:
step one, as shown in fig. 2, on the basis of an LDPC decoding factor graph model, adding check nodes to satisfy a probability detection node set GNs, and unidirectionally connecting the check nodes; node G for adding check node to normalize and satisfy probability statistics ave And outputting the normalized satisfaction probability of the check node to obtain a frame synchronization normalized confidence coefficient for estimating frame synchronization, wherein the frame synchronization normalized confidence coefficient is a cost function of a frame synchronization position.
Establishing an LDPC decoding factor graph model G = (VNs U CNs, xi), wherein VNs represents a variable node set and has the size of a check matrix row dimension N = (N) ((N)) C (ii) a CNs represents a check node set with the dimension of a check matrix column N C -N b In which N is C Is the length of the codeword sequence, N b Is the information sequence length; xi represents the set of edges connecting the variable nodes and the check nodes. At check node CNs = (C) 1 ,C 2 ,...,C k ) On the basis, a check node is added to meet the requirement of a probability detection node GNs = (G) 1 ,G 2 ,...,G k ) Output check node satisfaction probability
Indicating that under the condition of a code word sequence R and a check matrix H, a node G is detected after k iterations n Output check node C n Probability of compliance with check constraint, i.e. check node C n The symbolic information of all variable nodes of the constraint is multiplied by the probability that the product modulo two sum is 0:
wherein, C n Check node, V, representing reception probability information m Variable nodes representing transfer probability information, g n Indicating the symbol index of the detected node, and taking the value of 0 or 1,V m' ∈N(C n ) Represents V m' Belong to and check node C n Set of all variable nodes connected, { V m Denotes all variable node sets, the superscript k denotes the decoding iteration period, I c (C n ) Represents check node C n Corresponding check constraint
The following is true, namely:
node G ave Outputting the k iteration cycle check node normalization satisfaction probability P (k) (G (k) | R (τ)) is:
where ρ is a normalization factor such that P (k) (G (k) | R (τ)) ranges from 0 to 1. The summation symbol shows that the satisfied probabilities of all check nodes are summed, and finally, an average value is obtained to represent the constraint satisfied probability condition of all check nodes.
For a complete data frame received by a receiver, taking data of a complete codeword length in a received sequence as a frame at different time delays as input to a factorial graph model, i.e., N within a sliding window Γ (τ) C Length information sequence R (tau:tau + N) C -1), where τ is the frame synchronization transmission delay, defining the sliding window starting position. Factor graph output as bit B = (B) 1 ,B 2 ,....B Nb ) Hard decision information representing approximate a posteriori probability information of the codeword.
Step two, determining the frame synchronization sliding window position gamma (tau), moving the sliding window gamma (tau) to obtain observation frame data R (tau: tau + N) C -1), the information sequence R (τ: tau + N C -1) as a factor graph model input, the sliding process of the moving sliding window on the received data frame and the corresponding observed frame data structure are as shown in fig. 3.
Establishing a frame synchronization system model, considering that the preceding stage timing synchronization and the carrier synchronization have finished the accurate estimation of the timing error and the carrier phase, simplifying the frame synchronization process into a frame compensation amount tau 0 The estimation problem of (1) is that r (t) represents a received signal of an input frame synchronization system after passing through an AWNG signal, s (t) represents a transmitting end signal, N (t) is noise superposed at a symbol level, the mean value is 0, and the unilateral power spectral density is N 0 Of complex white Gaussian noise, [ tau ] 0 Representing the transmission delay, and obtaining a frame synchronization system model as follows:
r(t)＝s(t-τ 0 )+n(t)
where s (t) is the symbol transmitted at time t and is 0 or 1. Setting a frame synchronization sliding window gamma (tau) on the basis of the system model, and selecting the sliding window with the length N C I.e. the sliding window always contains data for the length of a complete code sequence.
By shifting a frame synchronization sliding window in a reception sequence by t, a frame structure of the relative position of a sliding window Γ (τ) is obtained, and observation frame data R (τ: τ + N) of the relative frame structure in the window is obtained C -1). The probability of correct frame synchronization corresponding to the position τ is:
P(τ|r(t)),τ∈[0,n c -1]
τ denotes the possible position of the correct frame synchronization, n, in the case of a reception sufficient statistic r (t) c Is the length of the frame. Maximizing the above a posteriori probability by sliding the frame sync sliding window over r (t) to obtain the likely position of correct frame sync at the receiver r (t)
Step three, according to the observation frame data R (tau, tau + N) C -1) initializing variable nodes.
Synchronizing the observation frame R (tau, tau + N) in the sliding window C -1) inputting channel prior probability information into a factor graph model, and initializing variable nodes. According to Gaussian white noise channel probability distribution, the initialization information of the variable nodes of the factor graph when the frame structure t = tau is as follows:
wherein x is i Representing variable nodesAnd symbol index, which takes the value of 0 or 1.
The sequence R (tau, tau + N) when the sliding window is denoted by gamma (tau) C -1) channel prior probability information, where φ (t) represents the received sequence magnitude, l ∈ [0,N) C ) Indicating the sequence index, σ, within a frame-synchronous sliding window 2 Is the channel noise variance.
And step four, updating the check nodes of the global factor graph according to the variable node information and the check constraint relation.
In a known observation frame sequence R (tau:tau + N) C -1) under the condition of prior information R and check constraint relation H, obtaining a set of probability information for transmitting update from check node to variable node
I.e. the check nodes pass the symbol x to the variable nodes i Probability of 0 or 1:
wherein, V m' ∈N(C n )\V m Represents V m' Belong to and C n Connected, not including V m All variable node sets of (1) ({ V) m Mean in addition to V m And (4) all variable node sets except the variable node sets, and the superscript k represents a decoding iteration period. Checking node information
Represents except V m Outer and check node C n And the modulo-two sum of all the connected variable nodes is 0.
Step five, according to R (tau, tau + N) c -updating global factor graph variable nodes with the channel prior probability information and check node information of 1). After the fifth step is finished, stopping the iteration process when the maximum iteration times is reached, skipping to the sixth step, otherwise skipping to the fourth step, and repeating the fourth step to the fifth step until the maximum iteration times is reachedThe iteration is stopped for several times.
Variable node transfers update probability information set to check node
That is, under the condition of knowing the channel receiving sequence R and the check matrix H, the variable node transfers the symbol x to the check node i And the probability is 0 or 1, and according to the check constraint relation and the check node information, the variable node update information set is as follows:
wherein, C n' ∈N(V m )\C n Is represented by C n' Belong to and V m Connected, not including C n All check node sets of λ mn To normalize the parameters such that the relation is satisfied
And step six, judging the frame synchronization position according to the frame synchronization normalization confidence coefficient pi (tau), analyzing the relation between the iterative gain introduction point and the frame synchronization position according to the frame synchronization normalization confidence coefficient, setting a threshold value aiming at the threshold value judgment tolerance characteristic of the frame synchronization normalization confidence coefficient of the frame synchronization position and the non-frame synchronization position to carry out binary hypothesis test, and determining the frame synchronization position by detecting whether the threshold value is exceeded or not so as to further realize the frame synchronization. The invention reduces the detection of unnecessary frame compensation positions by detecting the threshold, reduces the frame synchronization processing time delay, reduces the hardware resource occupation and the design complexity, and further reduces the false alarm probability of frame synchronization by setting the threshold. In addition, the frame synchronization process is completed by introducing coding gain through the aid of coding by utilizing the structural characteristics of the coding, a pilot frequency symbol does not need to be inserted into a transmitting end, the problem of reduction of the frequency spectrum utilization rate is avoided, and the throughput and the transmission rate of a communication system are remarkably improved.
Calculating the node G at the time of the last iteration cycle ave Output check node normalization satisfaction probability
Wherein k is 0 Stopping the iteration cycle, namely the maximum iteration times; II (tau) represents observation frame R (tau: tau + N) in sliding window gamma (tau) c -1) the check node normalization satisfaction probability of the last cycle after iterative update.
Comparative decision sliding window observation frame R (tau:tau + N) C -1) frame-synchronous normalized confidence level Π (τ) with a threshold parameter θ r . When pi (tau) > theta r Then, the frame compensation position of the current threshold value is output as the frame synchronization position
Stopping the frame synchronization iterative process, performing the seventh step, otherwise, continuing the frame synchronization iterative process, and repeating the second step to the sixth step until the frame synchronization normalized confidence coefficient meets the threshold parameter theta r And (4) requiring.
The non-frame synchronization sequence R (tau: tau + N) is obtained by normalizing check nodes of different code patterns to meet the change trend of the probability in the iterative process C -1) the sequence of the input factor graph is an indecipherable version whose check nodes are normalized to satisfy the probability P (k) (G (k) | R (tau)) converges or continuously oscillates in any interval smaller than 1, and finally converges in a numerical range smaller than a decoding success threshold value, and the obtained frame synchronization normalization confidence coefficient pi (tau) is smaller than a threshold parameter theta r . And for the sequence R (tau) of the correct frame synchronization position 0 ：τ 0 +N C -1) considering the existence of the code self-structure characteristic in the sequence of the input factor graph, the normalized confidence level pi (tau) of the frame synchronization at this moment is mostly greater than a given threshold parameter theta r And a threshold parameter theta r The coincident check node normalization satisfies a probability threshold tolerance.
At the same time, the receiving sequence of the burst communication system is selected with different time delaysThe process of coding a data frame with a length comprises that a selected frame structure is just a complete coding sequence of correct frame synchronization time delay, the frame synchronization normalization confidence coefficient of the sequence meets the condition, the capture of the frame synchronization code can be further completed according to an over-threshold value, and the correct frame synchronization position is determined according to the frame compensation position corresponding to the over-threshold value
Namely, frame synchronization position estimation is carried out according to the threshold detection binary hypothesis test result, and further frame synchronization is realized.
And step seven, calculating the approximate posterior probability of the code words and hard judging, outputting the decoding judgment result of the frame synchronization position, and obtaining the bit sequence after the frame synchronization and decoding process, namely realizing blind frame synchronization and decoding.
When determining the frame synchronization position
Then, calculating the code word approximate posterior probability and hard-deciding to obtain the sequence R (tau) 0 ：τ 0 +N c -1) decoded bit sequence
The approximate posterior probability of a codeword is:
when in use
Time, bit decision output B m =1, when
Time, bit decision output B m =0, output decoding sequence
At this time, based on "frame synchronization&Decoding "federationSystem model, according to pi (tau) over threshold theta r Determining frame synchronization position
And completes the corresponding decoding process by the iterative message transfer algorithm, and outputs the decoded codeword sequence, thereby realizing the frame synchronization and decoding process.
Frame Synchronization Error Rate (FSER) and bit Error Rate performance simulation analysis are performed for the pattern of the embodiment. A frame synchronization method based on pilot frequency correlation decision is selected as a comparison algorithm, referring to a frame synchronization method mentioned in IEEE Transactions on Communications published by IEEE Transactions, and "optimal frame synchronization" in 1972, the blind frame synchronization method described in this embodiment is compared with FSER simulation of a pilot frequency hard decision, soft decision and optimal pilot frequency method, and the result is shown in fig. 4. It is obvious from the simulation curve that with the improvement of the signal-to-noise ratio, the frame synchronization performance is influenced by the LDPC coding gain, the frame synchronization correct probability is gradually improved, and the method is superior to the partial pilot frequency frame synchronization method. The introduction of coding gain makes the blind frame synchronization performance of the embodiment approach and be superior to a pilot-based method at high signal-to-noise ratio, effectively reduces system performance loss, improves frame synchronization probability, and can further improve transmission rate without using a pilot method. The performance of the error rate under different parameter settings is analyzed, and the result is shown in fig. 5, which shows that the excellent decoding performance of the LDPC is maintained, and that several dB of coding gain exists at a medium-high signal-to-noise ratio.
The invention also discloses a coding auxiliary blind frame synchronization system based on threshold detection, which is used for realizing the coding auxiliary blind frame synchronization method based on threshold detection, as shown in fig. 6. The coding auxiliary blind frame synchronization system based on threshold detection is designed and analyzed according to the step-factor graph model and mainly comprises an observation frame sliding window module, a channel decoding module, a confidence detection module and a hard decision module.
And the observation frame sliding window module relates to a second step, determines an observation frame structure on the receiving sequence according to the frame structure sliding window and provides input for the channel decoding module. And the channel decoding module relates to the third step, the fourth step and the fifth step, introduces iterative gain through channel decoding to improve the frame synchronization performance and simultaneously completes the channel decoding process. The confidence detection module relates to a sixth step, a detection node structure is added on the basis of a traditional channel decoding structure, the normalization confidence of the frame synchronization is obtained by checking the node normalization satisfaction probability, a threshold is set for the threshold judgment tolerance characteristic of the frame synchronization and non-frame synchronization position frame synchronization normalization confidence for detection of binary hypothesis test, and estimation of the frame synchronization position is determined by detecting whether the threshold is exceeded or not, namely, frame synchronization is performed according to the threshold detection result of the binary hypothesis test. And a hard decision module relates to a seventh step, and decides the channel decoding soft information into 0 bit and 1 bit through hard decision and outputs a decoding sequence after frame synchronization and channel decoding.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A coding auxiliary blind frame synchronization method based on threshold detection is characterized in that: comprises the following steps of (a) carrying out,
adding check nodes on the basis of an LDPC (low density parity check) decoding factor graph model to meet a probability detection node set GNs (global navigation system) and connecting the check nodes in a one-way mode; node G for adding check node to normalize and satisfy probability statistics ave Outputting normalized satisfaction probability of the check nodes to obtain a frame synchronization normalized confidence coefficient for estimating frame synchronization, wherein the frame synchronization normalized confidence coefficient is a cost function of a frame synchronization position;
step two, determining the frame synchronization sliding window position gamma (tau), and moving the sliding window gamma (tau) to obtain observation frame data
Sliding in-window information sequence
Inputting a factor graph model;
step three, according to the observation frame data
Initializing variable nodes by the amplitude value of (1);
updating check nodes of the global factor graph according to the variable node information and the check constraint relation;
step five, according to
Updating global factor graph variable nodes by the channel prior probability information and the check node information; after the fifth step is finished, stopping the iteration process when the maximum iteration times is reached, and skipping to the sixth step, otherwise skipping to the fourth step, and repeating the fourth step to the fifth step until the maximum iteration times is reached, and stopping the iteration;
judging a frame synchronization position according to a frame synchronization normalization confidence coefficient pi (tau), analyzing the relation between an iteration gain introduction point and the frame synchronization position according to the frame synchronization normalization confidence coefficient, setting a threshold value aiming at the threshold value judgment tolerance characteristic of the frame synchronization normalization confidence coefficient of the frame synchronization position and the non-frame synchronization position to detect a binary hypothesis test, and estimating the frame synchronization position according to the binary hypothesis test result of the threshold value detection by detecting whether the threshold value is exceeded or not so as to realize frame synchronization; the invention reduces the detection of unnecessary frame compensation positions by detecting the threshold, reduces the frame synchronization processing time delay, reduces the hardware resource occupation and the design complexity, and simultaneously further reduces the false alarm probability of frame synchronization by setting the threshold, particularly obviously reduces the false alarm probability in a burst communication system; in addition, because the frame synchronization process is completed by introducing coding gain through the aid of coding by utilizing the structural characteristics of the codes, pilot symbols do not need to be inserted into a transmitting end, the problem of reduction of the frequency spectrum utilization rate is avoided, and the throughput and the transmission rate of a communication system are obviously improved;
and step seven, calculating the approximate posterior probability of the code words and hard judging, outputting the decoding judgment result of the frame synchronization position, and obtaining the bit sequence after the frame synchronization and decoding process, namely realizing blind frame synchronization and decoding.
2. A method for coding assisted blind frame synchronization based on threshold detection as claimed in claim 1, characterized in that: the first implementation method comprises the following steps of,
establishing an LDPC decoding factor graph model G = (VNs U CNs, xi), wherein VNs represents a variable node set and has the size of a check matrix row dimension N = (N) ((N)) C (ii) a CNs represents a check node set with the dimension of a check matrix column N C -N b In which N is C Is the length of the codeword sequence, N b Is the information sequence length; xi represents a set of edges connecting the variable nodes and the check nodes; at check node CNs = (C) 1 ,C 2 ,...,C k ) On the basis, a check node is added to meet the requirement of a probability detection node GNs = (G) 1 ,G 2 ,...,G k ) Output check node satisfaction probability
Indicating that under the condition of a code word sequence R and a check matrix H, a node G is detected after k iterations n Output check node C n Probability of compliance with check constraint, i.e. check node C n The symbolic information of all variable nodes of the constraint is multiplied by the probability that the product modulo two sum is 0:
wherein, C n Check node, V, representing reception probability information m Variable nodes representing transfer probability information, g n Indicating the symbol index of the detected node, and taking the value of 0 or 1,V m' ∈N(C n ) Represents V m' Belong to and check node C n Set of all variable nodes connected, { V m Denotes thatAll variable node sets, superscript k denotes the decoding iteration period, I c (C n ) Represents check node C n Corresponding check constraint
The following holds, namely:
node G ave Outputting the k iteration cycle check node normalization satisfaction probability P (k) (G (k) | R (τ)) is:
where ρ is a normalization factor such that P (k) (G (k) | R (τ)) ranges from 0 to 1; the summation symbol shows that the satisfaction probabilities of all the check nodes are summed, and finally the average value is obtained to represent the constraint satisfaction probability condition of all the check nodes;
for a complete data frame received by a receiver, data of a complete codeword length in a received sequence is taken as a frame at different time delays as input of a factor graph model, namely N within a sliding window Γ (τ) C Length information sequence
Wherein τ is the frame synchronization transmission delay, defining the starting position of the sliding window; factor graph output as bits
Hard decision information representing approximate a posteriori probability information for the codeword.
3. A method for coding assisted blind frame synchronization based on threshold detection as claimed in claim 2, characterized in that: the second step is realized by the method that,
establishing a frame synchronization system model, considering that the preceding stage timing synchronization and the carrier synchronization have finished the accurate estimation of the timing error and the carrier phase, simplifying the frame synchronization process into a frame compensation amount tau 0 The estimation problem of (1) is that r (t) represents a received signal of an input frame synchronization system after passing through an AWNG signal, s (t) represents a transmitting end signal, N (t) is noise superposed at a symbol level, the mean value is 0, and the unilateral power spectral density is N 0 Complex white Gaussian noise, τ 0 Representing the transmission delay, and obtaining a frame synchronization system model as follows:
r(t)＝s(t-τ 0 )+n(t)
wherein s (t) is a symbol transmitted at the t moment and is 0 or 1; setting a frame synchronization sliding window gamma (tau) on the basis of the system model, and selecting the sliding window with the length N C That is, the sliding window always contains data of a complete code sequence length;
by shifting a frame synchronization sliding window in a reception sequence by t, a frame structure of the relative position of a sliding window Γ (τ) is obtained, and the observation frame data of the relative frame structure within the window
The probability of correct frame synchronization corresponding to the position τ is:
P(τ|r(t)),τ∈[0,n c -1]
τ denotes the possible position of the correct frame synchronization, n, in the case of a reception sufficient statistic r (t) c Is the length of the frame; maximizing the above a posteriori probability by sliding the frame sync sliding window over r (t) to obtain the likely position of correct frame sync at the receiver r (t)
4. A method for coding assisted blind frame synchronization based on threshold detection as claimed in claim 3, characterized in that: the third step is to realize the method as follows,
synchronizing frames to observation frames within a sliding window
Inputting channel prior probability information into a factor graph model, and initializing variable nodes; according to Gaussian white noise channel probability distribution, factor graph variable node initialization information when a frame structure t = tau is as follows:
wherein x is i Representing a variable node symbol index, and taking the value of 0 or 1;
representing the sliding window as a sequence of gamma (tau)
Wherein φ (t) represents the received sequence magnitude, and l ∈ [0,N ] C ) Representing the sequence index, σ, within a frame-synchronous sliding window 2 Is the channel noise variance.
5. The method of claim 4, wherein the method comprises: the implementation method of the fourth step is that,
in a known sequence of observation frames
Under the condition of the prior information R and the check constraint relation H, the obtained check node transmits an updated probability information set to the variable node
I.e. the check nodes passing the symbol x to the variable nodes i Probability of 0 or 1:
wherein, V m' ∈N(C n )\V m Represents V m' Belong to and C n Connected, not including V m All variable node sets of (1) ({ V) m Mean in addition to V m Except all variable node sets, the superscript k represents the decoding iteration period; checking node information
Represents except V m Outer and check node C n And the modulo-two sum of all the connected variable nodes is 0.
6. A method for coding assisted blind frame synchronization based on threshold detection as claimed in claim 5, characterized in that: the fifth step is to realize that the method is that,
variable node transfers update probability information set to check node
That is, under the condition of knowing the channel receiving sequence R and the check matrix H, the variable node transfers the symbol x to the check node i And the probability is 0 or 1, and according to the check constraint relation and the check node information, the variable node updating information is integrated as follows:
wherein, C n' ∈N(V m )\C n Is represented by C n' Belong to and V m Connected, not including C n All check node sets of λ mn To normalize the parameters such that the relation is satisfied
7. The method of claim 6, wherein the method comprises: the sixth realization method comprises the following steps of,
calculating the node G at the time of the last iteration cycle ave Output check node normalization satisfaction probability
Wherein k is 0 Stopping the iteration cycle, namely the maximum iteration times; II (tau) represents the observation frame in the sliding window gamma (tau)
The confidence coefficient of the frame synchronization after iterative updating is normalized;
comparison decision sliding window observation frame
Frame synchronization normalization confidence pi (τ) and threshold parameter θ r . When pi (tau) > theta r Then, the frame compensation position of the current threshold value is output as the frame synchronization position
Stopping the frame synchronization iterative process, performing the step seven, otherwise, continuing the frame synchronization iterative process, and repeating the steps two to six until the frame synchronization normalization confidence coefficient meets the threshold parameter theta r Requiring;
the variable trend of the probability in the iterative process is known by the normalization of check nodes of different code patterns
The input factor graph has sequence of non-translatable type, and its check node normalization satisfies probability P (k) (G (k) | R (tau)) converges or oscillates continuously in any interval smaller than 1, and finally converges to a value range smaller than the decoding success threshold valueThe obtained frame synchronization normalization confidence coefficient pi (tau) is less than the threshold parameter theta r (ii) a And for the sequence of correct frame synchronization positions
Considering the existence of the self-structure characteristic of the code in the sequence of the input factor graph, the normalization confidence coefficient pi (tau) of the frame synchronization at the moment is mostly larger than a given threshold parameter theta r And a threshold parameter theta r The normalization of the check nodes is met to meet the tolerance of the probability threshold;
meanwhile, the data frames with different coding lengths are selected for a receiving sequence of the burst communication system in consideration of different time delays, the process comprises the step that the selected frame structure is just a complete coding sequence with correct frame synchronization time delay, the frame synchronization normalization confidence coefficient of the sequence meets the condition, the frame synchronization code can be captured according to an over-threshold value, and the correct frame synchronization position is determined according to the frame compensation position corresponding to the over-threshold value
Namely, frame synchronization position estimation is carried out according to the threshold detection binary hypothesis test result, and further frame synchronization is realized.
8. The method of claim 7, wherein the method comprises: the seventh implementation method comprises the following steps of,
when determining the frame synchronization position
Then, calculating the code word approximate posterior probability and hard-deciding to obtain the sequence R (tau) 0 ：τ 0 +N c -1) of a decoded bit sequence
The approximate posterior probability of a codeword is:
when in use
Time, bit decision output B m =1, when
Time, bit decision output B m =0, output decoding sequence
At this time, based on "frame synchronization&Decoding the' joint system model by passing a threshold θ according to pi (τ) r Determining frame synchronization position
And completes the corresponding decoding process by the iterative message transfer algorithm, and outputs the decoded codeword sequence, thereby realizing the frame synchronization and decoding process.
9. Code assisted blind frame synchronization system based on threshold detection, for implementing a code assisted blind frame synchronization method based on threshold detection as claimed in claim 1, 2, 3, 4, 5, 6, 7 or 8, characterized in that: the device mainly comprises an observation frame sliding window module, a channel decoding module, a confidence coefficient detection module and a hard decision module;
the observation frame sliding window module is mainly used for realizing the following functions: determining an observation frame structure on a receiving sequence according to a frame structure sliding window, and providing input for a channel decoding module;
the channel decoding module is mainly used for realizing the following functions: iterative gain is introduced through channel decoding to improve the frame synchronization performance, and the channel decoding process is completed at the same time;
the confidence detection module is mainly used for realizing the following functions: obtaining a frame synchronization normalization confidence coefficient by normalizing the satisfaction probability of the check node, analyzing the relationship between an iteration gain introduction point and a frame synchronization position by using the frame synchronization normalization confidence coefficient, setting a threshold value aiming at the threshold value judgment tolerance characteristic of the frame synchronization normalization confidence coefficient of a frame synchronization position and a non-frame synchronization position to carry out binary hypothesis test, determining the frame synchronization position by detecting whether the threshold value is exceeded or not, namely realizing the frame synchronization according to the binary hypothesis test result of the threshold value detection; the invention reduces the detection of unnecessary frame compensation positions by detecting the over-threshold, reduces the frame synchronization processing time delay, reduces the hardware resource occupation and the design complexity, and reduces the frame synchronization false alarm phenomenon in the burst communication system;
the hard decision module is mainly used for realizing the following functions: and judging the channel decoding soft information into 0 and 1 bits through hard judgment, and outputting a code word sequence after frame synchronization and channel decoding.
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