CN116707706A - Channel capacity improving method and device based on multi-stage LDPC coding and probability shaping - Google Patents

Channel capacity improving method and device based on multi-stage LDPC coding and probability shaping Download PDF

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CN116707706A
CN116707706A CN202310530152.1A CN202310530152A CN116707706A CN 116707706 A CN116707706 A CN 116707706A CN 202310530152 A CN202310530152 A CN 202310530152A CN 116707706 A CN116707706 A CN 116707706A
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sequence
level
diversity
ldpc
channel capacity
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忻向军
田凤
李语田
张琦
高然
姚海鹏
王瑞春
田清华
王拥军
王富
李志沛
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Beijing University of Posts and Telecommunications
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Beijing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0056—Systems characterized by the type of code used
    • H04L1/0059—Convolutional codes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Error Detection And Correction (AREA)

Abstract

The invention provides a channel capacity improving method and device based on multi-level LDPC coding and probability shaping, comprising the following steps: acquiring an input signal sequence, converting the input signal sequence into a plurality of parallel input signal sequences in a serial-parallel manner, constructing check matrixes corresponding to each level of LDPC codes, determining each level of codes, and carrying out binary conversion on each level of codes to obtain a first input sequence; acquiring target probability distribution, output sequence length and target amplitude set, determining available diversity, acquiring a chaotic sequence, dividing each element in the chaotic sequence into a plurality of value intervals according to a value range, and constructing a chaotic index mapping relation between each value interval and the available diversity; dividing a first input sequence into a plurality of groups of subsequences, determining specific diversity corresponding to each group of subsequences, constructing a distribution matching probability shaping mapping relation corresponding to each available diversity, and determining a distribution matching probability shaping output sequence corresponding to the input signal sequence. The method reduces the code rate loss, improves the channel capacity and improves the error rate performance.

Description

Channel capacity improving method and device based on multi-stage LDPC coding and probability shaping
Technical Field
The invention relates to the technical field of communication, in particular to a channel capacity improving method and device based on multi-stage LDPC coding and probability shaping.
Background
In recent years, the rapid development of multimedia technology brings about the continuous increase of network data traffic, and single-mode optical fibers cannot meet the existing capacity requirements; space division multiplexing transmission systems are an attractive solution because of the spatial diversity they offer. Therefore, how to further improve the capacity and reliability of a communication system based on a space division multiplexing transmission system has been a focus of common attention in academia and industry. According to shannon theorem, reliable transmission approaching the channel capacity can be realized through error correction coding on the premise that the information transmission rate does not exceed the channel capacity; classical error correction coding schemes proposed in the current academy include Hamming codes, golay codes, reed-Solomon codes, LDPC codes, and the like, and convolutional LDPC codes and conventional Block Codes (BC) rely on check matrices, and compared with conventional block codes, convolutional LDPC codes have performance close to capacity limit under iterative belief propagation decoding due to irregular properties of check matrices.
In the prior art, although the convolutional LDPC coding has the performance close to the capacity limit, the convolutional LDPC coding has the defects of larger code rate loss, poor error rate performance and insignificant improvement of the channel capacity. Therefore, how to reduce the code rate loss, improve the channel capacity and improve the bit error rate performance is a technical problem to be solved.
Disclosure of Invention
In view of the foregoing, the present invention provides a method and apparatus for channel capacity boosting based on multi-level LDPC coding and probability shaping to solve one or more of the problems in the prior art.
According to one aspect of the present invention, the present invention discloses a channel capacity improving method based on multi-level LDPC coding and probability shaping, the method comprising:
acquiring an input signal sequence, converting the input signal sequence into a plurality of paths of parallel input signal sequences in a serial-parallel manner, constructing check matrixes corresponding to all levels of LDPC codes according to degree distribution, determining all levels of codes based on the plurality of paths of parallel input signal sequences and the plurality of check matrixes, and carrying out binary conversion on all levels of codes to obtain a first input sequence corresponding to a transmitting end;
acquiring a target probability distribution, an output sequence length and a target amplitude set, determining available diversity based on the target probability distribution, the output sequence length and the target amplitude set, acquiring a chaotic sequence, dividing each element in the chaotic sequence into a plurality of value intervals according to a value range, and constructing a chaotic index mapping relation between each value interval and the available diversity;
Dividing the first input sequence into a plurality of groups of subsequences, determining specific diversity corresponding to each group of subsequences based on the chaotic index mapping relation, constructing a distribution matching probability shaping mapping relation corresponding to each available diversity, and determining a distribution matching probability shaping output sequence corresponding to the input signal sequence based on each group of subsequences, the specific diversity corresponding to each group of subsequences and each distribution matching probability shaping mapping relation.
In some embodiments of the invention, the method further comprises:
and carrying out inverse mapping on the distribution matching probability shaping output sequence based on the distribution matching probability shaping mapping relation to obtain an inverse mapping sequence, carrying out multi-stage LDPC decoding on the inverse mapping sequence to obtain a multi-channel parallel output signal sequence, and carrying out parallel-serial conversion on the multi-channel parallel output signal sequence to obtain an output signal sequence.
In some embodiments of the invention, the method further comprises:
acquiring a training sequence, shaping an output sequence based on the training sequence and the distribution matching probability, and estimating a channel matrix based on the output signal sequence;
calculating a channel capacity based on the estimated channel matrix;
and updating the code rate and the degree distribution of each level of LDPC codes according to the channel capacity and the density evolution algorithm.
In some embodiments of the present invention, constructing a check matrix corresponding to each level of LDPC codes according to a degree distribution includes:
constructing a prototype matrix according to the degree distribution;
expanding the prototype matrix to obtain a check matrix corresponding to the first-stage LDPC code; wherein, each element in the check matrix corresponding to the first-stage LDPC code is a submatrix;
and constructing a check matrix corresponding to the second-stage LDPC code based on the check matrix corresponding to the first-stage LDPC code.
In some embodiments of the invention, the expression of the first input sequence is:
wherein V represents a first input sequence, L represents the number of LDPC coded stages, V l Representing the first level encoding.
In some embodiments of the invention, determining the available diversity based on the target probability distribution, the output sequence length, and the target amplitude set comprises:
determining a frequency set C of each amplitude occurrence in the target amplitude set type ；
Traversal lookup complianceDiversity sets of conditions, wherein->Represent C θ Complementary diversity of (a);
each diversity group obtained by traversing is used as the available diversity.
In some embodiments of the present invention, determining the specific diversity corresponding to each group of subsequences based on the chaotic index mapping relationship includes:
Calculating chaotic indexes of all groups of subsequences;
and determining the diversity corresponding to the chaotic indexes of each group of subsequences according to the chaotic index mapping relation.
In some embodiments of the present invention, updating the code rate and the degree distribution of each level of LDPC coding according to the channel capacity and density evolution algorithm includes:
calculating noise threshold of each level of coding under current code rate based on the estimated channel capacity and density evolution algorithm;
judging whether the calculated noise threshold of each level of LDPC codes is larger than a preset threshold;
and under the condition that the code rate and the degree distribution of each level of LDPC codes are larger than a preset threshold, adopting a particle swarm optimization algorithm to adjust the code rate and the degree distribution of each level of LDPC codes.
According to another aspect of the present invention, there is also disclosed a channel capacity boosting system based on multi-level LDPC coding and probability shaping, the system comprising a processor and a memory, the memory having stored therein computer instructions for executing the computer instructions stored in the memory, the system implementing the steps of the method as described in any of the embodiments above when the computer instructions are executed by the processor.
According to yet another aspect of the present invention, a computer-readable storage medium is also disclosed, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method according to any of the embodiments described above.
The embodiment of the invention discloses a channel capacity improving method and a device based on multi-level LDPC coding and probability shaping, which are characterized in that firstly, a check matrix corresponding to each level of LDPC coding is constructed according to degree distribution, namely, a multi-level LDPC coding scheme is introduced to code an input signal sequence to obtain a first input sequence corresponding to a transmitting end, then, specific diversity corresponding to each group of subsequences in the first input sequence is determined based on a constructed chaotic index mapping relation, and a distribution matching probability shaping output sequence corresponding to the input signal sequence is determined based on each constructed distribution matching probability shaping mapping relation. The method introduces a multilevel convolution LDPC coding scheme, so that the coding and decoding delay and the decoding complexity are reduced; by adopting the multi-diversity distribution matching scheme of the chaotic sequence index, the code rate loss is reduced, the channel capacity is improved, and the error rate performance is improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present application are not limited to the above-described specific ones, and that the above and other objects that can be achieved with the present application will be more clearly understood from the following detailed description.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and together with the description serve to explain the application. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. Corresponding parts in the drawings may be exaggerated, i.e. made larger relative to other parts in an exemplary device actually manufactured according to the present application, for convenience in showing and describing some parts of the present application. In the drawings:
fig. 1 is a flowchart illustrating a channel capacity boosting method based on multi-level LDPC coding and probability shaping according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a channel capacity boosting method based on multi-level LDPC coding and probability shaping according to another embodiment of the present application.
Fig. 3 is a schematic flow chart of multi-level LDPC encoding according to an embodiment of the present application.
Fig. 4 is a flowchart illustrating a process of constructing a check matrix for multi-level LDPC coding according to an embodiment of the present application.
Fig. 5 is a flow chart illustrating a multi-diversity distribution matching probability shaping mapping according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a chaotic index mapping relationship according to an embodiment of the present invention.
Fig. 7 is a flow chart illustrating inverse mapping of multi-diversity distribution matching probability shaping according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of a convolutional LDPC multistage decoding process according to an embodiment of the present invention.
Fig. 9 is a schematic diagram of a code rate and a degree distribution updating process based on density evolution according to an embodiment of the invention.
Fig. 10 is a schematic diagram of a communication system architecture based on multi-level LDPC coding and multi-diversity distribution matching according to an embodiment of the present invention.
Fig. 11 is a schematic diagram of simulation results of a communication system based on multi-level LDPC coding and multi-diversity distribution matching according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
It should be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present invention are shown in the drawings, while other details not greatly related to the present invention are omitted.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
In order to reduce code rate loss, improve channel capacity and improve bit error rate performance, the invention provides a channel capacity improving method and device based on multi-stage LDPC coding and probability shaping. The probability shaping technology is to reduce the average energy of the transmitted symbols, improve the channel capacity under the same transmission power and reduce the system error rate by remapping the probability distribution of the transmitted constellation points. Distribution Matching (DM) can be integrated in a coded modulation system as a class of probability shaping techniques, wherein the output sequence generated by multi-diversity distribution matching (MPDM) is an equal-length sequence with a target probability distribution, and the diversity of its mapping set increases the capacity of the selectable codebook, thereby reducing the code rate loss, and the sequence length required at a specific code rate loss is significantly lower than that of the conventional Constant Component Distribution Matching (CCDM).
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.
Fig. 1 is a flow chart of a channel capacity enhancing method based on multi-level LDPC coding and probability shaping according to an embodiment of the present invention, and referring to fig. 1, the channel capacity enhancing method at least includes steps S10 to S30.
Step S10: and acquiring an input signal sequence, carrying out serial-parallel conversion on the input signal sequence into a plurality of paths of parallel input signal sequences, constructing check matrixes corresponding to all levels of LDPC codes according to degree distribution, determining all levels of codes based on the plurality of paths of parallel input signal sequences and the plurality of check matrixes, and carrying out binary conversion on all levels of codes to obtain a first input sequence corresponding to a transmitting end.
In the step, the input signal sequence is an original binary information sequence input by an input end, namely, the step converts the original binary information sequence into multiple paths of signals in a serial-parallel manner, and then the multiple paths of signals are respectively encoded step by step and output through check matrixes corresponding to multi-level LDPC codes; the number of the LDPC codes is the same as the number of the paths of the parallel input signal sequences, and the number of the check matrices is also the same as the number of the paths of the parallel input signal sequences.
Fig. 3 is a schematic flow chart of multi-level LDPC coding according to an embodiment of the present invention, referring to fig. 3, a code rate, a code length, and a coding level are input first, so as to construct a prototype matrix, and a 0 th-level check matrix, a 1 st-level check matrix, and a 2 nd-level check matrix … are sequentially constructed based on the prototype matrix. In this embodiment, the input bit sequence represents an original one-way binary information sequence, and the number of stages of LDPC encoding in this embodiment is 3, and the input bit sequence is divided into three parallel input signal sequences such as information sequence 0, information sequence 1, and information sequence 2. It is understood that setting the number of LDPC coded stages to 3 in this embodiment is only one example, and in other embodiments, the number of LDPC coded stages may be set to two or more stages.
Further, constructing a check matrix corresponding to each level of LDPC codes according to the degree distribution, including: constructing a prototype matrix according to the degree distribution; expanding the prototype matrix to obtain a check matrix corresponding to the first-stage LDPC code; wherein, each element in the check matrix corresponding to the first-stage LDPC code is a submatrix; and constructing a check matrix corresponding to the second-stage LDPC code based on the check matrix corresponding to the first-stage LDPC code.
FIG. 4 is a flow chart of constructing a check matrix for multi-level LDPC encoding according to an embodiment of the present invention, as shown in FIG. 4, first according to a degree distribution (the degree distribution represents a prototype matrix B SC Neutron matrix B 0 ,B 1 ……,B u Number of non-zero elements per row or column) to construct prototype matrix B SC Wherein the method of constructing the prototype matrix is not limited; each submatrix in the prototype matrix is a bxc matrix. Further constructing a 0 th-level check matrix based on the prototype matrix expansion, specifically, when the expansion is constructed, the submatrix B in the prototype matrix 0 ,B 1 ……,B u The zero elements in the matrix are replaced by an all-zero matrix with the size of M multiplied by M, and the submatrix B is replaced by 0 ,B 1 ……,B u Randomly replacing non-zero elements in the matrix with a replacement matrix with the size of M multiplied by M; the permutation matrix is only one of each row element or each column elementWith a matrix of elements 1, e.g.Etc.; the prototype matrix is replaced by the above to obtain 0 th level check matrix +.> Each sub-matrix in (a) is respectively composed of prototype matrix B SC Is obtained by expanding the same-position submatrices in the matrix, and +.>The submatrices in (1) satisfy +.>When t max When=w, ->A matrix of size (W+μ) bM× WcM, and +.>The 0 th level coding rate of (c-b)/c; wherein b and c are the number of rows and columns of the submatrices in the prototype matrix, M is the number of rows or columns of the all-zero matrix or the permutation matrix, and μ is +. >The number of elements in each column.
After constructing the 0 th-level check matrix based on the prototype matrix, constructing the j (j is larger than or equal to 1) th-level check matrix based on the 0 th-level check matrix Submatrices in the matrix->Sub-matrix for corresponding position in j-1 th level check matrix>M before (1) j Row, m j The value of (2) is smaller than the number of rows of the submatrices in the j-1 th level check matrix, and +.>Matrix code rate is c-m j C; wherein, the value of i is 0,1, … mu. For example, assume +.>Sub-matrix-> Then when m 1 When=2, get->Submatrices in the check matrix->
After the check matrix of each level of LDPC codes is constructed, each path of parallel signal sequence is further coded, wherein the information sequence 0 is subjected to 0 th level LDPC coding, the signal sequence 1 is subjected to 1 st level LDPC coding, and the signal sequence 2 is subjected to 2 nd level LDPC coding.
Specifically, the first-level t-th legal codeword v l (t)∈{0,1} n Satisfy the following requirements Wherein the method comprises the steps ofN represents the length of a single codeword, a l (t) and s l (t) are all syndromes Accompanying->Wherein T represents a cycle period and μ is +.>The number of elements in each column, +.>Representing v l Transpose of (t).
For example, describing in detail the LDPC coding progression l=3, firstly, serial-to-parallel conversion is performed on an input bit sequence, and a binary sequence transmitted in a sequential and bitwise manner is converted into three parallel binary sequences transmitted simultaneously, so as to obtain an information sequence 0, an information sequence 1 and an information sequence 2, where the length of each path is one third of the original length. At level 0, the information sequence 0 is partitioned into sub-sequences u of length (c-b) M 0 (0),u 0 (1),u 0 (2)，…，u 0 (t),…u 0 (t max ) B and c are the number of rows and columns of the submatrices in the prototype matrix, respectively, and M is the number of rows or columns of the all-zero matrix or the permutation matrix. Because of the systematic code form, i.e. the encoded code word is composed of an input information sub-sequence u of length (c-b) M l (t) and redundancy information w l (t) is formed by concatenating, i.e. v l (t)＝[u l (t) w l (t)]，l∈[0,L]Based on the above formula, w is obtained l And (t) obtaining the first-stage code.
Further, the method comprises the steps of,can be expressed asIn this expression, l=0, and s can thus be obtained 0 (t) =0, i.e.)>Substitution into v 0 (0)＝[u 0 (0)w 0 (0)]Can solve w 0 (0) Further get v 0 (0). Similarly, v can be obtained based on the following formula 0 (1)、v 0 (2)、…、v 0 (t max )：
……
……
At this time, the first = 0-level encoded total output is: v 0 ＝[v 0 (0),v 0 (1),v 0 (2),…,v 0 (t),…,v 0 (t max )]。
And at level l=1, syndrome s 1 ＝[s 1 (0),s 1 (1),s 1 (2),...,s 1 (t),...,s 1 (t max )]At this time Representing v 0 Transpose of (t), similarly obtainable:
thereby further obtaining v 1 ＝[v 1 (0),v 1 (1),v 1 (2),…,v 1 (t),…v 1 (t max )]The method comprises the steps of carrying out a first treatment on the surface of the It can be appreciated that at level 1=2, the syndrome s is based on 2 V can also be obtained 2 。
After the LDPC codes of all levels are obtained based on the method, a first input sequence can be further obtained, and the expression of the first input sequence is as follows:
wherein V represents a first input sequence, L represents the total number of stages of LDPC encoding, V l Representing the first level encoding.
Step S20: acquiring a target probability distribution, an output sequence length and a target amplitude set, determining available diversity based on the target probability distribution, the output sequence length and the target amplitude set, acquiring a chaotic sequence, dividing each element in the chaotic sequence into a plurality of value intervals according to a value range, and constructing a chaotic index mapping relation between each value interval and the available diversity.
In the step, the available diversity is further determined, the chaotic sequence is obtained, and a chaotic index mapping relation between the chaotic sequence and the available diversity is established, so that the first input sequence obtained in the step S10 is subjected to distribution matching mapping through the chaotic sequence index, and probability shaping is further realized.
FIG. 5 is a flow chart of a multiple diversity distribution matching probability shaping map according to an embodiment of the invention, and referring to FIG. 5, in this step, a target probability distribution P is first determined A Outputting the sequence length N and the target amplitude set A, and according to the target probability distribution, the target amplitude set A and the diversity quantity N pairs Determining available diversity
Further, determining the available diversity based on the target probability distribution, the output sequence length, and the target amplitude set comprises: determining a frequency set C of each amplitude occurrence in the target amplitude set type The method comprises the steps of carrying out a first treatment on the surface of the Traversal lookup compliance Diversity sets of conditions, wherein->Represent C θ Complementary diversity of (a); each diversity group obtained by traversing is used as the available diversity.
Exemplary, the output sequence length n=10, the target amplitude profile P A ＝[0.4,0.3,0.2,0.1]Target amplitude set a= [1,2,3,4 ]]，P A ＝[0.4,0.3,0.2,0.1]The amplitude set a= [1,2,3,4 ] is represented in a sequence of length 10 ]The set of frequencies of occurrence of four amplitudes is C type ＝[4,3,2,1]I.e. amplitude a 1 The amplitude a occurs 4 times with =1 2 The amplitude a occurs 3 times with =2 3 The amplitude a occurs 2 times =3 4 =4 occurs 1 time. If one wants to achieve a target probability distribution P in the output signal A The frequency diversity that can be used includes two types, the first being C type Itself, the second meets the condition θ has a value of 1,2 … N pairs For diversity C θ Complementary diversity->The combination of the two satisfies P A For example C θ ＝[5,3,3,1]Andsatisfy->Namely C θ In a 1 =1 occurs 5 times, ++>In a 1 The number of occurrences of =1 is 3, a in both diversity 1 On average, 4 times, a 2 ,a 3 ,a 4 And the same is true. The final traversal satisfies-> Diversity logarithm N of (2) pairs =49, due to->Then 97 satisfactory diversity can be determined and the ranking number of available amplitudes N perms Number of all available diversity = 164214 ∈ ->
Step S30: dividing the first input sequence into a plurality of groups of subsequences, determining specific diversity corresponding to each group of subsequences based on the chaotic index mapping relation, constructing a distribution matching probability shaping mapping relation corresponding to each available diversity, and determining a distribution matching probability shaping output sequence corresponding to the input signal sequence based on each group of subsequences, the specific diversity corresponding to each group of subsequences and each distribution matching probability shaping mapping relation.
In the step, a distribution matching probability shaping mapping relation corresponding to each available diversity is constructed, and the first input sequence is further divided into a plurality of groups of subsequences, so that distribution matching probability shaping output corresponding to each subsequence is found out from the corresponding distribution matching probability shaping mapping relation.
Specifically, determining the specific diversity corresponding to each group of subsequences based on the chaotic index mapping relationship includes: calculating chaotic indexes of all groups of subsequences; and determining the diversity corresponding to the chaotic indexes of each group of subsequences according to the chaotic index mapping relation.
In one embodiment, the first input sequence is first grouped at k-bit intervals before mapping, k satisfying the conditionL represents the number of coding stages, N perms Representing the number of permutations of available amplitude, when l=3, then the first input sequence +.>t max Representing the total length of the first input sequence, Z represents a set of integers, i.e. each bit in the output sequence has 8 possibilities (integers in the range of 0 to 7 can be chosen), so that when a specific distribution matching probability shaping mapping is performed, the mapping is performed with 5 bits as a group of sub-sequences, and the sub-sequences after being grouped are denoted as g= [ g (0), g (1), g (2), …]Wherein g (i) = [ v (k.i), v (k.i+1), … v (k.i+4) ]I denotes the i-th group of sub-sequences, k denotes the number of bits of the sub-sequences, which may also be understood as the number of intervals used when grouping the first input sequence, v denotes the first input sequence.
Further, the chaos index corresponding to each group of subsequences is calculated based on the following formula:
wherein x is n The n-th item in the chaotic sequence is represented, the value range is (0, 1), and n represents the ordinal number of the item; u is a control parameter of the expression, and u may be in (0, 4]And selecting in a range. Exemplary, x can be set after 999 iterations 1000 、x 1001 Etc. are respectively taken as g (0), and the chaos index of g (1), namely the sub-sequence of g (i) corresponds to x 1000+i And (5) indexing.
FIG. 6 is a diagram ofAs shown in FIG. 6, in the chaotic index mapping relationship of an embodiment of the present invention, when the total diversity is 97, x is taken as n The value range of (2) is divided into 97 parts, and different diversity is respectively indexed so as to realize diversity selection. Illustratively, u=3.7 is chosen, with an initial value of x 0 ＝0.3，Iteration through chaotic sequence can obtain x 1000 Approximately equal to 0.0401, i.e.)>Therefore, based on the chaotic index mapping relationship, g (0) can be obtained to be corresponding to +.>Diversity. At this time, the target amplitude set A= [1,2,3,4 ] is further obtained]And->Construction->The distribution matching probability shaping mapping relation corresponding to the diversity is obtained by arranging and combining the elements in the amplitude set, and the number of the same elements in the output sequence meets the distribution probability corresponding to the use diversity. / >The distribution matching probability shaping mapping relation corresponding to diversity is shown in the following table:
in some embodiments of the present invention, the channel capacity boosting method based on multi-level LDPC coding and probability shaping further includes the steps of: and carrying out inverse mapping on the distribution matching probability shaping output sequence based on the distribution matching probability shaping mapping relation to obtain an inverse mapping sequence, carrying out multi-stage LDPC decoding on the inverse mapping sequence to obtain a multi-channel parallel output signal sequence, and carrying out parallel-serial conversion on the multi-channel parallel output signal sequence to obtain an output signal sequence.
Fig. 7 is a schematic flow chart of multi-diversity distribution matching probability shaping inverse mapping according to an embodiment of the present invention, as shown in fig. 7, when multi-diversity distribution matching probability shaping inverse mapping is performed, a distribution matching probability shaping mapping relation table identical to that of a transmitting end is first established at the receiving end, and an output end performs distribution matching inverse mapping on a received distribution matching probability shaping output sequence and outputs an inverse mapping sequence.
After the output obtains the inverse mapping sequence, the inverse mapping sequence is further subjected to multi-level LDPC decoding based on a convolution LDPC multi-level decoding unit, and the decoding method corresponds to a convolution LDPC multi-level encoding method by way of example and referring to FIG. 8. Specifically, a log-likelihood ratio (LLR) is first calculated based on an inverse mapping sequence v' received by the receiving end: Where r (j) represents the j-th signal received by the receiving end; then feeding LLR information of the 0 th level into a 0 th level sliding window decoder in batches, wherein the sliding window length is WMc symbols; and performing belief propagation iterative decoding in the sliding window decoder until the maximum iteration number is reached, outputting the first Mc symbols, and simultaneously moving the sliding window and the last Mc symbols. The information output by the 0 st stage decoding and the syndromes corresponding to the 1 st stage encoding are adopted before the 1 st stage decoding, and the log likelihood ratio information and the syndromes are sent to a 1 st stage sliding window decoder; level 2 decoding is the same. And finally, converting the decoding information outputted by the 0, 1 and 2-level decoding into parallel-serial conversion as system output. Wherein c is the number of rows and columns of the submatrices in the prototype matrix, M is the number of rows or columns of the all-zero matrix or the permutation matrix, and w=t max 。
FIG. 2 is a schematic diagram of another embodiment of the present inventionAs shown in FIG. 2, the method firstly carries out three-level LDPC coding on the original data input by an input end to obtain a first input sequence, then the first input sequence is subjected to multi-diversity distribution matching probability shaping mapping to obtain a distribution matching probability shaping output sequence, and the distribution matching probability shaping output sequence is modulated by 16QAM to obtain a modulation output S d Modulated output S d Further adding training sequence and space division multiplexing. The receiving end firstly performs space division multiplexing and demultiplexing, then performs 16QAM demodulation, and the demodulated information is subjected to multi-diversity distribution matching probability shaping inverse mapping to obtain a 2 mapping sequence, and the inverse mapping sequence finally obtains decoded received data based on multi-level LDPC decoding.
In an embodiment of the present invention, the channel capacity improving method based on multi-level LDPC coding and probability shaping further includes the steps of: acquiring a training sequence, shaping an output sequence based on the training sequence and the distribution matching probability, and estimating a channel matrix based on the output signal sequence; calculating a channel capacity based on the estimated channel matrix; and updating the code rate and the degree distribution of each level of LDPC codes according to the channel capacity and the density evolution algorithm.
In the step, the channel state is estimated based on the training sequence, the channel capacity is calculated, and the channel capacity is fed back to the transmitting end for adjusting the encoding rate of the transmitting end. Specifically, when estimating the channel state, let the training sequence of the transmitting end be S p The information sequence is denoted as S d The total transmission sequence is [ S ] p S d ]Wherein S is d A modulation output obtained by modulating the distribution matching probability shaping output sequence through 16 QAM; the sequence received by the receiving end and corresponding to the training sequence and the information sequence is Y p And Y d The total received sequence is [ Y ] p Y d ]The method comprises the steps of carrying out a first treatment on the surface of the The training sequence is in a known state at the transmitting end and the receiving end, and the channel matrix in the coherent time is set as G, and the Gaussian random variable is set as N, so that the relation is satisfied: [ Y ] p Y d ]＝G[S p S d ]+N. Illustratively, the channel matrix may be estimated based on an EM algorithm (expectation maximization algorithm), whichThe algorithm performs multiple iterations based on the desired computation to achieve the estimation of the channel matrix. First toRandomly initializing and carrying out expected calculation:
where S represents all the desirable constellation point sets, S [ n ]]And y [ n ]]Respectively represent the transmission signals S d And receive signal Y d N-th column of (b). And then based on expected pairsUpdating: /> S p H Representing a transmitting end training sequence S p Y represents the total received sequence, H represents the conjugate transpose, < >>Representing the estimated value of the channel matrix G in the first stage.
Further, a channel capacity C is calculated based on the estimated channel matrix,wherein lambda is i Represents G.G H R represents the rank of the channel matrix, +.>For signal to noise ratio, E 0 Representing the average signal energy transmitted per channel mode in each symbol period, L representing the mode average propagation loss, N 0 Represents the noise power spectral density G.G H Representing the product of the channel matrix and the conjugate transpose of the channel matrix.
In an embodiment, updating the code rate and the degree distribution of each level of LDPC codes according to the channel capacity and density evolution algorithm comprises: calculating noise threshold of each level of coding under current code rate based on the estimated channel capacity and density evolution algorithm; judging whether the calculated noise threshold of each level of LDPC codes is larger than a preset threshold; and under the condition that the code rate and the degree distribution of each level of LDPC codes are larger than a preset threshold, adopting a particle swarm optimization algorithm to adjust the code rate and the degree distribution of each level of LDPC codes.
In the above embodiment, the channel capacity improving method based on multi-stage LDPC coding and probability shaping first uses a multi-stage LDPC coding manner to transfer information between different coding layers, so as to improve the error rate performance of the receiving end; the chaos sequence is adopted to provide indexes for diversity selection of multi-diversity distribution matching, so that further reduction of code rate loss is realized; estimating the channel capacity by adding a training sequence, and optimizing the code rate of each level of encoder by adopting a density evolution algorithm; the method has low complexity, adopts a modularized design, and can be integrated into the existing communication system.
FIG. 9 is a schematic diagram of a code rate and density distribution update flow based on density evolution according to an embodiment of the present invention, wherein when updating the code rate and density distribution, as shown in FIG. 9, a noise threshold of each level of coding at the current code rate is calculated based on an estimated channel capacity and density evolution algorithm; and when the noise threshold difference is larger than a specified threshold, adopting a particle swarm optimization algorithm to adjust the code rate and the degree distribution of each level of codes until the noise threshold difference of each level is smaller than the noise threshold tolerance.
Illustratively, the set of degree distribution pairs is (d v ,d c ) The available code rate for regular LDPC codes isI.e. degree distribution and regular LDPC codeThe corresponding relation exists between the rates, and the channel capacity determines the upper limit of the sum of the coding rates of each level; by adopting a density evolution algorithm, the decoding noise threshold of each level of LDPC codes under given degree distribution can be calculated; by adopting a particle swarm optimization algorithm, the optimization of the degree distribution of each level of LDPC codes can be realized. Each level of coding should have an approximate noise threshold according to the multi-level coding rate design criteria. The specific flow of the algorithm is as follows:
step (1), initializing: determining noise threshold tolerance sigma th Space division multiplexing channel capacity, maximum number of iterations and optimized number of particles N p Particle motion parameters. The particle motion parameter comprises the initial particle motion velocity e ij Inertia factor w. From the above analysis, when the number of coding stages is l=3, the number of variables is 6 (total three stages, each stage corresponds to one degree distribution pair), thus randomly generating N p 6-dimensional particle vector X i ＝(x i1 ,x i2 ,x i3 ,x i4 ,x i5 ,x i6 )，0≤i≤N p . Wherein (x) i1 ,x i2 ) Is the degree distribution pair of the level 0 encoder, (x) i3 ,x i4 ) Is the level 1 encoder's degree distribution pair, (x) i5 ,x i6 ) Is the level 2 encoder's degree distribution pair. Each particle optimal position vector is initialized to P i ＝X i The method comprises the steps of carrying out a first treatment on the surface of the According to the density evolution algorithm, three-level noise thresholds sigma can be calculated i1 ,σ i2 ,σ i3 The difference of the noise threshold is calculated as sigma id ＝max{σ i1 ,σ i2 ,σ i3 }-min{σ i1 ,σ i2 ,σ i3 -a }; calculating the noise threshold difference value of all particles, finding out the particle position vector with the minimum threshold difference value in all particles, and marking the particle position vector as P g 。
Step (2), changing the particle position: x is x ij ＝x' ij +e ij 。
Wherein x is ij Representing the coordinates of the j-th dimension of the i-th particle after updating, x' ij Representing the coordinates of the j-th dimension of the ith particle before updating, e ij Representing the movement speed of the ith particle in the jth dimension.
Calculating the total code rate, i.eIf R is tot If the channel capacity is larger than the channel capacity, the step (4) is skipped to recalculate e ij 。
Step (3), calculating a particle threshold difference value: if the difference is smaller than the particle minimum difference recorded currently, updating the optimal position vector P i Otherwise P i Is unchanged. If the minimum threshold difference in the population of particles is less than the minimum difference in the population of particles prior to movement, P is updated g Otherwise P g Is unchanged.
Step (4), updating the particle movement speed: e, e ij ＝w·e′ ij +f 1 (p ij -x ij )+f 2 (p gj -x ij ). Acceleration factor f 1 ,f 2 Is [0,1]Random numbers uniformly distributed in the interval, w is an inertia factor, and p ij Is P i Coordinates of the j-th dimension, p gj Is P g Coordinates of the j-th dimension, x ij Representing the coordinates of the j-th dimension of the i-th particle, e i ' j For the movement speed of the jth dimension of the ith particle before updating, e ij The motion speed of the j dimension of the i-th particle after updating.
Step (5), termination condition: if the maximum number of iterations is reached or sigma is present id ＜σ th Output P g Otherwise, jumping to the step (2) and repeating the step (2) to the step (5).
Step (6), P is as follows g Splitting into corresponding degree distribution pairs of the three-level encoder and feeding back to the transmitting end encoder respectively.
In the channel capacity method based on multi-level LDPC coding and probability shaping disclosed by the invention, at a transmitting end, an original binary information sequence firstly generates a multi-system information sequence through convolution multi-level LDPC coding, and the system number is determined by the adopted coding level; the output information sequence is subjected to probability shaping remapping through a multi-diversity distribution matching unit, and the used diversity is indexed by a value of a corresponding position of the chaotic sequence; and then the signal packet generated by the multi-diversity distribution matching is subjected to 16QAM modulation and then is sent to a standard single-mode fiber channel for transmission. At the receiving end, firstly, 16QAM demodulation is carried out on the received signal, then the signal is sent to a multi-diversity distribution matching probability shaping demapping unit, the chaotic sequence used for index diversity adopts the same initial value and parameter as the transmitting end to select the same diversity, and then the signal is sent to a convolution LDPC multistage decoder for decoding. And meanwhile, estimating the channel capacity by adopting a training sequence, and optimizing the code rate of the encoder according to the channel capacity.
Correspondingly, the invention also discloses a channel capacity improving system based on multi-stage LDPC coding and probability shaping, which comprises a processor and a memory, wherein the memory is stored with computer instructions, the processor is used for executing the computer instructions stored in the memory, and the system realizes the steps of the method in any embodiment when the computer instructions are executed by the processor.
Specifically, the system comprises a convolution multi-stage LDPC coding unit, a multi-diversity distribution matching probability shaping mapping unit, a multi-diversity distribution matching probability shaping demapping unit, a convolution LDPC multi-stage decoding unit and a channel capacity calculating and code rate updating unit.
FIG. 10 is a schematic diagram of a communication system architecture based on multi-level LDPC coding and multi-diversity distribution matching according to an embodiment of the present invention, as shown in FIG. 10, in the system, a signal subjected to convolutional LDPC coding and distribution matching at a transmitting end is subjected to 16QAM modulation to form a signal; the output signal is converted in serial-parallel mode, converted in digital-analog mode in arbitrary waveform generator, amplified in linear mode in electric amplifier, mach-Zehnder modulator, variable optical attenuator to regulate the optical power of the transmitting end, and the multipath signal is coupled to the less-mode fiber channel, amplified in bidirectional amplifier and then reaching the receiving end. The receiving end signals are firstly demultiplexed, then the optical power of the receiving end is adjusted through a variable optical attenuator, then the optical signals are detected by using a photodiode, and the analog-to-digital conversion is finished by using a 50G Sa/s mixed signal oscilloscope; recovering the original information by adopting offline digital signal processing, wherein the method comprises multi-diversity distribution matching demapping and convolution LDPC multi-level decoding; the code rate updating module calculates the channel capacity according to each channel training sequence and updates the code rate, and feeds back to the transmitting end encoder through reverse transmission.
Taking the coding series l=3 as an example for simulation, the simulated bit error rate performance of the communication system based on convolutional multi-stage LDPC coding and multi-diversity distribution matching is shown in fig. 11. The decoding of each stage can utilize the posterior information provided by the previous stage and the prior information provided by the channel information, so that the error rate is gradually reduced along with the improvement of the stage number; in addition, the adoption of the multi-diversity distribution matcher reduces the average power of the transmitted signals, and further improves the system reliability and the channel capacity on the premise of not damaging the system code rate.
In addition, the application also discloses a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements the steps of the method according to any of the embodiments above.
According to the channel capacity improving method and device based on multi-level LDPC coding and probability shaping, disclosed by the application, each level of convolution LDPC coding utilizes the syndrome of the previous level of coding, and a decoding module at a receiving end decodes by means of channel priori information and posterior information of the previous level of decoding, so that information is transferred between different levels; by adopting the chaos sequence index target diversity in the multi-diversity distribution matching module, the quick selection of diversity and the probability shaping mapping are realized. Firstly, constructing a convolution LDPC check matrix step by step according to the degree distribution of the check matrix, then encoding from low level to high level according to the check matrix, improving the correlation between the levels, and increasing the code rate along with the increase of the level. And decoding at a receiving end, firstly, decoding the low level, then realizing the gradual decoding from low level to high level according to posterior information provided by the low level, finally realizing the gradual decrease of the error rate from the low level to the high level, and realizing the gradual increase of the code rate, thereby improving the overall performance. After coding, using multi-diversity distribution matching probability shaping mapping, using a chaotic sequence to index the used diversity, reducing code rate loss caused by adding an index tag in a traditional scheme, facilitating parallel processing and improving diversity searching speed; by combining coding and distribution matching, the average power of the transmission signal is reduced, the error rate performance is improved, and the channel capacity of the communication system is optimized. Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or a combination of both. The particular implementation is hardware or software dependent on the specific application of the solution and the design constraints. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
In this disclosure, features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for channel capacity boosting based on multi-level LDPC coding and probability shaping, the method comprising:
acquiring an input signal sequence, converting the input signal sequence into a plurality of paths of parallel input signal sequences in a serial-parallel manner, constructing check matrixes corresponding to all levels of LDPC codes according to degree distribution, determining all levels of codes based on the plurality of paths of parallel input signal sequences and the plurality of check matrixes, and carrying out binary conversion on all levels of codes to obtain a first input sequence corresponding to a transmitting end;
Acquiring a target probability distribution, an output sequence length and a target amplitude set, determining available diversity based on the target probability distribution, the output sequence length and the target amplitude set, acquiring a chaotic sequence, dividing each element in the chaotic sequence into a plurality of value intervals according to a value range, and constructing a chaotic index mapping relation between each value interval and the available diversity;
dividing the first input sequence into a plurality of groups of subsequences, determining specific diversity corresponding to each group of subsequences based on the chaotic index mapping relation, constructing a distribution matching probability shaping mapping relation corresponding to each available diversity, and determining a distribution matching probability shaping output sequence corresponding to the input signal sequence based on each group of subsequences, the specific diversity corresponding to each group of subsequences and each distribution matching probability shaping mapping relation.
2. The multi-level LDPC coding and probability shaping based channel capacity boosting method according to claim 1, further comprising:
and carrying out inverse mapping on the distribution matching probability shaping output sequence based on the distribution matching probability shaping mapping relation to obtain an inverse mapping sequence, carrying out multi-stage LDPC decoding on the inverse mapping sequence to obtain a multi-channel parallel output signal sequence, and carrying out parallel-serial conversion on the multi-channel parallel output signal sequence to obtain an output signal sequence.
3. The multi-level LDPC coding and probability shaping based channel capacity boosting method according to claim 2, further comprising:
acquiring a training sequence, shaping an output sequence based on the training sequence and the distribution matching probability, and estimating a channel matrix based on the output signal sequence;
calculating a channel capacity based on the estimated channel matrix;
and updating the code rate and the degree distribution of each level of LDPC codes according to the channel capacity and the density evolution algorithm.
4. The channel capacity boosting method based on multi-level LDPC coding and probability shaping according to claim 1, wherein constructing check matrices corresponding to each level of LDPC coding according to a degree distribution comprises:
constructing a prototype matrix according to the degree distribution;
expanding the prototype matrix to obtain a check matrix corresponding to the first-stage LDPC code; wherein, each element in the check matrix corresponding to the first-stage LDPC code is a submatrix;
and constructing a check matrix corresponding to the second-stage LDPC code based on the check matrix corresponding to the first-stage LDPC code.
5. The channel capacity boosting method based on multi-level LDPC coding and probability shaping according to claim 1, wherein the expression of the first input sequence is:
Where V represents a first input sequence, L represents the number of stages of LDPC encoding,v l representing the first level encoding.
6. The multi-level LDPC coding and probability shaping based channel capacity boosting method according to claim 1, wherein determining available diversity based on the target probability distribution, output sequence length, and target amplitude set comprises:
determining a frequency set C of each amplitude occurrence in the target amplitude set type ；
Traversal lookup complianceDiversity sets of conditions, wherein->Represent C θ Complementary diversity of (a);
each diversity group obtained by traversing is used as the available diversity.
7. The channel capacity boosting method based on multi-level LDPC coding and probability shaping according to claim 1, wherein determining specific diversity corresponding to each group of sub-sequences based on the chaotic index mapping relationship comprises:
calculating chaotic indexes of all groups of subsequences;
and determining the diversity corresponding to the chaotic indexes of each group of subsequences according to the chaotic index mapping relation.
8. A channel capacity boosting method based on multi-level LDPC coding and probability shaping as claimed in claim 3, wherein updating the code rate and the degree distribution of each level of LDPC coding according to the channel capacity and density evolution algorithm comprises:
Calculating noise threshold of each level of coding under current code rate based on the estimated channel capacity and density evolution algorithm;
judging whether the calculated noise threshold of each level of LDPC codes is larger than a preset threshold;
and under the condition that the code rate and the degree distribution of each level of LDPC codes are larger than a preset threshold, adopting a particle swarm optimization algorithm to adjust the code rate and the degree distribution of each level of LDPC codes.
9. A channel capacity boosting system based on multi-level LDPC coding and probability shaping, the system comprising a processor and a memory, wherein the memory has stored therein computer instructions for executing the computer instructions stored in the memory, the system implementing the steps of the method according to any one of claims 1 to 8 when the computer instructions are executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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