CN106877980A - Mixing Sparse Code multiple access method - Google Patents
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Abstract
The present invention provides a kind of mixing Sparse Code multiple access method.The method includes：Data to multiple client layers are respectively channel encoded, and obtain multiple bit streams, and multiple bit streams are divided into the first bit stream and the second bit stream；First bit stream is mapped to the first planisphere, the second bit stream is mapped to the second planisphere；Constellation point and the constellation point of the second planisphere respectively to the first planisphere carries out mapping treatment by mapping matrix, obtains corresponding first code word of the first bit stream, corresponding second code word of the second bit stream；Nonzero element to the nonzero element in the first code word and the second code word carries out arrangement treatment by permutation matrix, obtains stacked system of each client layer on running time-frequency resource；According to stacked system of each client layer on running time-frequency resource, carry out data transmission.The present invention improves first convergence reliability for being detected signal in the quality and each MPA receivers iterative process of MPA receiver initial informations.
Description
Technical Field
The invention relates to a wireless communication technology, in particular to a hybrid sparse code multiple access method.
Background
The method has the advantages of low time delay, high reliability, low power consumption and mass access, and is a main technical characteristic of a future 5G wireless transmission network. In wireless communication systems, multiple access is a primary technology. Generally, most communication systems employ orthogonal multiple access techniques. The orthogonal multiple access technique still cannot meet the requirements of the 5G system. Non-orthogonal multiple access has become a research hotspot in recent years. Sparse Code Multiple Access (SCMA) is a non-orthogonal Multiple Access technique based on a multi-dimensional codebook, and its structure is similar to Low Density Signaling (LDS). The LDS is different from a conventional orthogonal spreading sequence, and is a sparse spreading sequence. In SCMA systems, the first input data stream is mapped onto a multidimensional constellation. The mapping matrix then maps the multidimensional constellation points onto the SCMA codewords. One codebook of the SCMA consists of a certain number of codewords. And the transmission data layer of the SCMA has corresponding codebooks. To improve the spectral efficiency, two or more data layers will be superimposed on the same time-frequency resource. The size and length of the constellation of all superimposed data layers are the same.
The SCMA receiver adopts a joint decoding algorithm of Successive Interference Cancellation (SIC) and Message Passing Algorithms (MPA), the initial information of the MPA module comes from the output of the SIC module, but the quality of the initial information of the MPA module is easily affected by multipath and noise. Although SCMA increases the value of the difference in energy of the data layers at each resource node, the difference in energy between data layers may be unexpected. This reduces the reliability of convergence of the first detected data layer during each iteration of the MPA module. In summary, the performance of the SCMA receiver is expected to be further improved under multipath fading channel conditions.
Disclosure of Invention
The invention provides a hybrid sparse code multiple access method, which aims to solve the problems that initial information of an MPA module in an SCMA receiver is easily influenced by multipath and noise, the convergence reliability of a first detected data layer in each MPA module iteration process is not ideal and the like.
The invention provides a mixed sparse code multiple access method, which comprises the following steps:
respectively carrying out channel coding on data of a plurality of user layers to obtain a plurality of bit streams, and equally dividing the bit streams into a first bit stream and a second bit stream;
mapping the first bit stream to a first constellation diagram and mapping the second bit stream to a second constellation diagram, wherein the first constellation diagram is N1Dimension constellation diagram, the second constellation diagram is N2Dimension constellation diagram;
mapping the constellation points of the first constellation diagram and the constellation points of the second constellation diagram through a mapping matrix to obtain a first code word corresponding to the first bit stream and a second code word corresponding to the second bit stream; the first constellation map corresponds to a first codebook, the second constellation map corresponds to a second codebook, the first codeword is a codeword in the first codebook, and the second codeword is a codeword in the second codebook;
arranging non-zero elements in the first code word and non-zero elements in the second code word through an arrangement matrix to obtain a superposition mode of each user layer on time-frequency resources; the permutation matrix enables the dimension Euclidean distance between user layers superposed on the resource nodes to be increased;
and carrying out data transmission according to the superposition mode of each user layer on the time frequency resource.
Optionally, the mapping matrix is composed of non-zero rows and all-zero rows, the first codeword and the second codeword each include a non-zero element and a zero element, the non-zero element of the first codeword corresponds to a constellation point of the first constellation, and the non-zero element of the second codeword corresponds to a constellation point of the second constellation.
Optionally, the time-frequency resource includes K resource nodes, where K is N1+N2；
The number of the non-zero elements of the user layer superposed on each resource node is the same and meets the requirement
Wherein d isfA number of non-zero elements representing a user layer on each of the resource nodes;
the total number of the user layers is J-2 x df。
Optionally, the non-zero elements superimposed on each of the resource nodes correspond to non-zero elements of the first codeword; or
The non-zero elements superposed on each resource node correspond to the non-zero elements of the second code word; or
A portion of the non-zero elements superimposed on each of the resource nodes corresponds to non-zero elements of the first codeword, and another portion corresponds to non-zero elements of the second codeword.
Optionally, each non-zero element of the first codeword corresponds to a first dimension on the first constellation, and each non-zero element of the second codeword corresponds to a second dimension on the second constellation; the first dimension is any dimension on the first constellation diagram, and the second dimension is any dimension on the second constellation diagram.
Optionally, before the arranging the non-zero elements in the first codeword and the non-zero elements in the second codeword by using the arrangement matrix to obtain the superposition mode of each user layer on the time-frequency resource, the method further includes:
acquiring all allocation modes of the user layer allocated on each resource node;
calculating the sum of the dimension Euclidean distances between user layers corresponding to the resource nodes aiming at each distribution mode;
determining a minimum dimension Euclidean distance and a corresponding target resource node in the dimension Euclidean distance sums;
acquiring target dimension Euclidean distance sums corresponding to all distribution modes corresponding to the target resource node;
selecting a maximum dimension Euclidean distance sum from all the target dimension Euclidean distance sums;
and determining the arrangement matrix according to the maximum dimension Euclidean distance and the corresponding distribution mode.
Optionally, the calculating a sum of euclidean distances between user layers corresponding to the resource nodes includes:
obtaining Euclidean distance of dimensionalities of any two user layers on each resource node in a target constellation diagram, wherein the target constellation diagram is a first constellation diagram or a second constellation diagram corresponding to each user layer in each resource node under the distribution formula;
and summing the obtained Euclidean distances to obtain the dimensionality Euclidean distance sum.
Optionally, before determining the permutation matrix according to the maximum dimension euclidean distance and the corresponding distribution mode, the method further includes:
determining that the number of the maximum dimension Euclidean distance sums is larger than 1, and obtaining the maximum dimension Euclidean distance and a corresponding target distribution mode;
calculating the variance of the Euclidean distance sum of corresponding dimensions on all the resource points under each target distribution mode;
determining the maximum dimension Euclidean distance sum corresponding to the maximum variance;
the determining the permutation matrix according to the maximum dimension Euclidean distance and the corresponding distribution mode comprises:
and determining the arrangement matrix according to the maximum dimension Euclidean distance corresponding to the maximum variance and the corresponding target distribution mode.
The mixed sparse code multiple access method obtains the first bit stream and the second bit stream after the equalization by carrying out channel coding on a plurality of user layers, maps the first bit stream and the second bit stream into a first constellation diagram and a second constellation diagram with two different dimensionalities, and respectively maps the constellation points of the first constellation diagram and the constellation points of the second constellation diagram by mapping matrixes to obtain a first code word corresponding to the first bit stream and a second code word corresponding to the second bit stream, thereby being beneficial to increasing the Euclidean distance between the transmission signal dimensionalities of each user superposed on each resource point. And then, arranging the non-zero elements in the first code word and the non-zero elements in the second code word through the arrangement matrix to obtain a superposition mode of each user layer on each resource node of the time-frequency resource. The invention further increases the distance between the transmission signal dimensions of each user superposed on each resource point by increasing the Euclidean distance sum between the transmission signal dimensions of the user layers of each resource node and improving the difference between the Euclidean distance sum of the transmission signal dimensions of the user layers of all the resource nodes, thereby improving the quality of the initial information of the MPA receiver and also improving the convergence reliability of the first detected user transmission signal in each iteration process of the MPA receiver.
Drawings
FIG. 1 is a first flowchart of a hybrid sparse code multiple access method of the present invention;
FIG. 2 is a factor graph of the hybrid sparse code multiple access method of the present invention;
FIG. 3 is a schematic diagram of the transmission process of the hybrid sparse code multiple access method of the present invention;
FIG. 4 is a schematic diagram of two codebooks in the hybrid sparse code multiple access method of the present invention;
FIG. 5 is a flow chart of a hybrid sparse code multiple access method of the present invention;
fig. 6 is a flow chart three of the hybrid sparse code multiple access method of the present invention.
Detailed Description
Fig. 1 is a first flowchart of a hybrid sparse code multiple access method of the present invention, and as shown in fig. 1, the method of the present embodiment includes:
step 101, performing channel coding on data of a plurality of user layers respectively to obtain a plurality of bit streams, and equally dividing the plurality of bit streams into a first bit stream and a second bit stream.
Specifically, in this embodiment, a plurality of bit streams obtained after channel coding is performed on a plurality of user layers in the MSCMA are divided into two parts, namely, a first bit stream and a second bit stream.
Step 102, mapping the first bit stream to a first constellation diagram, and mapping the second bit stream to a second constellation diagram, where the first constellation diagram is N1Dimension constellation diagram, the second constellation diagram is N2Dimension constellation diagram.
In particular, a first bit stream is mapped to N1Mapping the second bit stream to N at constellation points of a dimensional constellation2And (5) maintaining the constellation points of the constellation diagram.
103, respectively mapping the constellation points of the first constellation diagram and the constellation points of the second constellation diagram through a mapping matrix to obtain a first code word corresponding to the first bit stream and a second code word corresponding to the second bit stream; the first constellation diagram corresponds to a first codebook, the second constellation diagram corresponds to a second codebook, the first code word is a code word in the first codebook, and the second code word is a code word in the second codebook.
Specifically, the constellation points of the first constellation and the constellation points of the second constellation are mapped by the mapping matrix. The first constellation and the second constellation correspond to two different codebooks, namely a first codebook and a second codebook. The constellation point of the first constellation corresponds to a first codeword of the first codebook, and the constellation point of the second constellation corresponds to a second codeword of the second codebook. According to step 102, the first bit stream is mapped to constellation points of a first constellation and the second bit stream is mapped to constellation points of a second constellation. It can be seen that the first bit stream corresponds to the first codeword and the second bit stream corresponds to the second codeword.
Further, in the MSCMA of this embodiment, the mapping matrix is a sparse matrix. Optionally, the mapping matrix consists of non-zero rows and all-zero rows. Thus, the first codeword and the second codeword each contain a non-zero element and a zero element. The non-zero elements of the first codeword correspond to constellation points of the first constellation diagram, and the non-zero elements of the second codeword correspond to constellation points of the second constellation diagram.
Alternatively, the mapping matrix of the MSCMA needs to satisfy the following characteristics:
1) the time frequency resource comprises K resource nodes, wherein K is N1+N2；
2) The number of non-zero elements of the user layer superposed on each resource node is the same and meets the requirement
Wherein d isfA number of non-zero elements representing a user layer on each resource node;
3) the total number of user layers is J-2 × df。
Specifically, the number of occupied time-frequency resource nodes is determined through the dimensions of the first constellation diagram and the second constellation diagram. And the number of rows and columns in the mapping matrix is determined by the number of resource nodes and the dimensions of two constellation diagrams, and after a first code word corresponding to a first bit stream of a user layer and a second code word corresponding to a second bit stream of the user layer are mapped, the mapping process that non-zero elements of the first code word correspond to constellation points of the first constellation diagram and non-zero elements of the second code word correspond to constellation points of the second constellation diagram can be realized through the mapping matrix.
In a specific embodiment, the mapping process of step 101-103 may be described by an MSCMA having J user layers, which includes the following specific contents:
first, log of user layer j after user layer j is channel coded2(Mj) Bit mapping to MjPoint NjWei constellation Cj；
Second, the binary mapping matrix V of the user layer jjWill NjWei constellation CjIs mapped to K-dimensional code words. And all K-dimensional codewords of user layer j constitute a codebook of user layer j.
Wherein the binary mapping matrix VjContaining K-NjAll zero rows, and remove VjAfter all zero lines of (V)jCan be expressed as an identity matrixThat is, the K-dimensional codeword of user layer j has NjA non-zero element and K-NjAnd zero elements.
Finally, the MSCMA contains two different types of codebooks, which are respectively formed by M1Point N1Wei constellation C1And M2Point N2Wei constellation C2Mapping to generate Nj∈{N1,N2},Mj∈{M1,M2}，Cj∈{C1,C2}，
Fig. 2 is a factor graph of the hybrid sparse code multiple access method of the present invention, wherein circles in fig. 2 represent user layers, and boxes represent resource nodes. For example, in an MSCMA system with 6 transport user layers and 5 resource nodes, as shown in the figure2 when N is1＝2,N2When df is 3, the factor graph matrix F corresponding to the MSCMA is as follows:
the transmission structure of the hybrid sparse code multiple access method can be represented by a factor graph matrix.
Fig. 3 is a schematic diagram of a transmission process of the hybrid sparse code multiple access method of the present invention, and fig. 4 is a schematic diagram of two codebooks of the hybrid sparse code multiple access method of the present invention, as shown in fig. 3 and fig. 4, in an MSCMA transmission structure 6000 including 6 user layers, including codebooks 6100, 6200, 6300, 6400, 6600. Codebooks 6100, 6200, 6300, 6400, 6500, 6600 correspond to layer 1, layer 2, layer 3, layer 4, layer5, layer6, respectively. As shown in fig. 4, the MSCMA contains two different types of codebooks, where codebooks 6100, 6200, 6300 belong to one type of codebook and codebooks 6400, 6500, 6600 belong to another type of codebook. Wherein, the codebook 6100 includes codewords 6101, 6102, 6103, … …, 6164, binary bit stream '000000' is mapped to the codeword 6101, binary bit stream '000001' is mapped to the codewords 6102, … …, and binary bit stream '111111' is mapped to the codeword 6164. Codebook 6200 contains codewords 6201, 6202, 6203, … …, 6264, where binary bit stream '000000' is mapped to codeword 6201, binary bit stream '000001' is mapped to codewords 6202, … …, and binary bit stream '111111' is mapped to codeword 6264. The codebook 6300 contains codewords 6301, 6302, 6303, … …, 6364, and the binary bit stream '000000' is mapped to codeword 6301, the binary bit stream '000001' is mapped to codewords 6302, … …, and the binary bit stream '111111' is mapped to codeword 6364. Codebook 6400 contains codewords 6401, 6402, 6403, … …, 6416, binary bit stream '0000' is mapped to codeword 6401, binary bit stream '0001' is mapped to codewords 6402, … …, and binary bit stream '1111' is mapped to codeword 6416. Codebook 6500 contains codewords 6501, 6502, 6503, … …, 6516, binary bit stream '0000' is mapped to codeword 6501, binary bit stream '0001' is mapped to codewords 6502, … …, and binary bit stream '1111' is mapped to codeword 6516. Codebook 6600 contains codewords 6601, 6602, 6603, … …, 6616, with binary bit stream '0000' mapped to codeword 6601, binary bit stream '0001' mapped to codewords 6602, … …, and binary bit stream '1111' mapped to codeword 6616. Assuming that the transmission bit streams corresponding to layer 1, layer 2, and layer 3 are '000000', layer 4, layer5, and layer6 are '0000', the codewords 6101, 6201, 6301, 6401, 6501, and 6601 are superimposed on the same time-frequency resource to form a transmission structure 6000.
In this way, the multiple user layers obtain a first bit stream and a second bit stream through channel coding, the first bit stream is mapped onto the first constellation diagram and the second bit stream is mapped onto the second constellation diagram, and then the constellation points of the first constellation diagram and the constellation points of the second constellation diagram are mapped through the mapping matrix, so that a first code word in a first codebook corresponding to the first bit stream and a second code word in a second codebook corresponding to the second bit stream are realized.
104, arranging non-zero elements in the first code word and non-zero elements in the second code word through an arrangement matrix to obtain a superposition mode of each user layer on time-frequency resources; wherein the matrix is arranged such that the dimension euclidean distance between user layers superimposed on respective resource nodes is increased.
Specifically, there are many allocation ways for each user to be stacked on each resource node, and there are also many corresponding ways for the dimensionality of the constellation points of each user layer on the constellation diagram on one resource node, and one stacking way can be selected through the arrangement matrix, so that the sum of the euclidean distances of the dimensionalities between the user layers stacked on each resource node is maximized on the one hand, and the difference between the sum of the euclidean distances of the dimensionalities between the user layers on each resource node is maximized on the other hand.
And 105, transmitting data according to the superposition mode of each user layer on the time frequency resource.
Specifically, data in each user layer is transmitted in a finally determined superposition manner. Conventional SCMA codebook designs primarily exploit SIC characteristics to separate the transmission signals of users that are superimposed together. The traditional SCMA adopts SIC-MPA receiver, the initial information of MPA comes from the soft information output by SIC, but the soft information output by SIC is easily affected by multipath fading and noise. In addition, the conventional SCMA codebook arrangement rule improves the difference in energy between the dimensions of the transmission signals of the users superimposed on the respective resource nodes, but the difference in energy between the transmission signals of the respective users may not reach an expected value. This reduces the reliability of convergence of the transmitted signal of the first detected user during each iteration of the MPA receiver.
For a single MPA receiver, the initial information of the MPA receiver is related to the distance between the dimensions of the transmission signals of the user layers superimposed on the resource points. In the MSCMA of this embodiment, two types of codebooks are used, and the two types of codebooks correspond to constellations with different dimensions, respectively, which is helpful for increasing the euclidean distance between the transmission signal dimensions of each user layer superimposed on each resource point. In addition, the transmission mode of the permutation matrix not only increases the dimension Euclidean distance sum between the transmission signals of the user layers on each resource node, but also further improves the difference between the dimension Euclidean distance sum of the transmission signals of the user layers of all the resource nodes, thereby further increasing the Euclidean distance between the transmission signal dimensions of each user layer superposed on each resource point. Therefore, the quality of the initial information of the MPA receiver is improved, and the convergence reliability of the first detected user transmission signal in each iteration process of the MPA receiver is also improved. Finally, the Bit Error Rate (BER) performance of the MSCMA is better than that of the SCMA.
In the hybrid sparse code multiple access method of the embodiment, the first bit stream and the second bit stream which are equally divided are obtained by performing channel coding on a plurality of user layers, the first bit stream and the second bit stream are mapped into the first constellation diagram and the second constellation diagram with two different dimensions, and the constellation points of the first constellation diagram and the constellation points of the second constellation diagram are respectively mapped through the mapping matrix to obtain the first code word corresponding to the first bit stream and the second code word corresponding to the second bit stream, which is beneficial to increasing the Euclidean distance between the transmission signal dimensions of each user superimposed on each resource point. And then, arranging the non-zero elements in the first code word and the non-zero elements in the second code word through the arrangement matrix to obtain a superposition mode of each user layer on each resource node of the time-frequency resource. In this embodiment, by increasing the euclidean distance between the transmission signal dimensions of the user layers of each resource node and improving the difference between the euclidean distance between the transmission signal dimensions of the user layers of all resource nodes, the euclidean distance between the transmission signal dimensions of each user superimposed on each resource point is further increased, so as to improve the quality of the initial information of the MPA receiver and improve the reliability of convergence of the user transmission signal detected first in each iteration of the MPA receiver.
How to obtain the arrangement matrix described in the above embodiment is described below with reference to fig. 5. Fig. 5 is a flow chart of a hybrid sparse code multiple access method of the present invention. As shown in fig. 5, the method includes:
step 201, obtaining all allocation modes of the user layer allocated on each resource node.
Specifically, in the MSCMA of this embodiment, the nonzero element superimposed on each resource point may correspond to a nonzero element of the first codeword, or may correspond to a nonzero element of the second codeword. Optionally, the non-zero elements superimposed on each resource node correspond to non-zero elements of the first codeword; or the non-zero elements superposed on each resource node correspond to the non-zero elements of the second code word; or one part of the non-zero elements superposed on each resource node corresponds to the non-zero elements of the first code word, and the other part corresponds to the non-zero elements of the second code word. Thus, there are many allocation ways for the user layers to be superimposed on the resource nodes.
Step 202, calculating the sum of the dimension Euclidean distances between the user layers corresponding to the resource nodes aiming at each allocation mode.
Specifically, since there are many allocation manners of the user layer on each resource node, after all the allocation manners are obtained, the sum of the euclidean distances between the user layers corresponding to the resource nodes needs to be calculated for each allocation manner.
Optionally, the euclidean distance of the dimensionality of any two user layers on each resource node in a target constellation diagram is obtained, and the target constellation diagram is a first constellation diagram or a second constellation diagram corresponding to each user layer in each resource node under the distribution formula; and summing the obtained plurality of dimension Euclidean distances to obtain a dimension Euclidean distance sum.
Specifically, an allocation mode is randomly selected, the Euclidean distances between the constellation point dimensions mapped to the corresponding target constellation diagram by every two user layers of all the user layers superposed on one resource node are calculated, and then the Euclidean distances between all the dimensions are added to obtain the dimension Euclidean distance sum. And for the rest distribution modes, adopting the same calculation process, calculating the dimensionality Euclidean distances between every two user layers superposed on the rest resource nodes, and summing the dimensionality Euclidean distances to obtain the dimensionality Euclidean distance sum. Thus, the dimension Euclidean distance sum of all the user layers superposed on each resource node under all the allocation modes is obtained.
And 203, determining the minimum dimension Euclidean distance and the corresponding target resource node in the sum of the dimension Euclidean distances.
Specifically, an allocation mode is randomly selected, the smallest dimension Euclidean distance sum is selected by comparing the dimension Euclidean distances sum corresponding to each resource node, and then the corresponding target resource node is selected.
And 204, acquiring target dimension Euclidean distance sums corresponding to all distribution modes corresponding to the target resource node.
And step 205, selecting the maximum dimension Euclidean distance sum in all the target dimension Euclidean distance sums.
Specifically, according to the process of step 203, target dimension euclidean distance sums corresponding to all allocation manners corresponding to the target resource node may be obtained, and a maximum dimension euclidean distance sum is selected from the target dimension euclidean distance sums.
And step 206, determining a permutation matrix according to the maximum dimension Euclidean distance and the corresponding distribution mode.
Specifically, the maximum dimension euclidean distance and the corresponding allocation mode are used as the mode of finally arranging and superposing the user layers of each resource node, which non-zero elements of the user layers are superposed on which resource node is determined, the dimension of the constellation point on the corresponding constellation diagram corresponding to each user layer is also determined, and then the arrangement matrix is determined.
Further, there may be many maximum dimension euclidean distances and corresponding allocation manners selected, and optionally, each non-zero element of the first codeword corresponds to a first dimension on the first constellation diagram, and each non-zero element of the second codeword corresponds to a second dimension on the second constellation diagram; the first dimension is any dimension on the first constellation diagram, and the second dimension is any dimension on the second constellation diagram.
Specifically, if the dimension of the first constellation is 2, the non-zero element of the first codeword may correspond to a first dimension of a constellation point of the first constellation, and may also correspond to a second dimension of the constellation point of the first constellation, and if the dimension of the second constellation is 3, the non-zero element of the second codeword may correspond to a first dimension of a constellation point of the second constellation, and may also correspond to a second dimension of the constellation point of the second constellation, and may also correspond to a third dimension of the constellation point of the second constellation. In this way, the non-zero elements of the first codeword may correspond to different dimensions of the constellation points of the first constellation diagram, and the non-zero elements of the second codeword may correspond to different dimensions of the constellation points of the second constellation diagram, so that the first codeword may have a variety of allocation manners corresponding to the constellation points of the first constellation diagram, and similarly, the second codeword may have a variety of manners corresponding to the constellation points of the second constellation diagram.
For the embodiment shown in fig. 5, when there are a plurality of dispensing manners, one dispensing manner may be selected from the plurality of dispensing manners. One possible implementation of selecting one of the plurality of allocation manners is given below in conjunction with fig. 6.
Fig. 6 is a flow chart of a hybrid sparse code multiple access method of the present invention, as shown in fig. 6, the method includes:
step 301, determining that the number of the maximum dimension Euclidean distance sums is larger than 1, and obtaining the maximum dimension Euclidean distance sum and a corresponding target distribution mode.
And 302, calculating the variance of the Euclidean distance sum of corresponding dimensions on all resource points under each target distribution mode.
And step 303, determining the maximum dimension Euclidean distance sum corresponding to the maximum variance.
Step 304, determining a permutation matrix according to the maximum dimension Euclidean distance and the corresponding distribution mode, wherein the permutation matrix comprises: and determining the arrangement matrix according to the maximum dimension Euclidean distance corresponding to the maximum variance and the corresponding target distribution mode.
Specifically, the number of target allocation modes is determined by the number of the maximum dimension euclidean distance sums. In all target allocation modes, the maximum variance is selected by calculating the variance of the Euclidean distance sums of the corresponding dimensions of all resource nodes. Because the maximum variance corresponds to the maximum dimension Euclidean distance sum, and the maximum dimension Euclidean distance sum corresponds to the target distribution mode, the corresponding target distribution mode can be determined according to the maximum variance, and finally the arrangement matrix is determined.
In a specific embodiment, for better description of the permutation matrix, it is assumed that the operation on the multidimensional constellation of the user layer j at this time is only the permutation matrix pijThen the codeword for user layer j can be represented as xj＝qj＝Vjπjz。
Wherein, z (z)1,z2,...,zN),N＝max(N1,N2) Represented as a multidimensional constellation C1Or C2One constellation point of, zn∈{nC1,nC2}，Representing a multidimensional constellation C1Is measured in the n-th dimension of (c),representing a multidimensional constellation C2The nth dimension of (a).
The received signal may be represented asWherein p (z) ═ p1(z),p2(z),...,pK(z))T，pk(z)＝dk1z+dk2z2+...+dkNzNJ represents the number of superimposed users, and K represents the number of resource nodes occupied by the users, which is an interference polynomial of a resource node K. Since the number of non-zero elements of the user layer superimposed on each resource node is the same, the number of non-zero elements of the user layer is the sameSuppose N1＝2,N2＝3,dfWith 4, the interference polynomial of resource node 1 is p1(z)＝2z+2z2(i.e., resource node 1 has superimposed thereon the non-zero elements of four users, where the non-zero elements of two users are from dimension 1 of the multidimensional constellation and the remaining are from dimension 2 of the multidimensional constellation). That is, p (z) and permutation matrix setIs in a one-to-one correspondence. For a single MPA receiver, the initial information of the MPA receiver is mainly related to the euclidean distance between transport user layers, the criterion of the permutation matrix is to increase the euclidean distance between transport user layers as much as possible, and the specific permutation moment is described as follows:
first, assume df＝m，Then the dimension euclidean distance sum n (p) between the code words superimposed on resource node k is definedk(z)) can be expressed as:
wherein,is the real part of the nth dimension of the user layer j superimposed on the resource node k and is the imaginary part of the nth dimension of the user layer j superimposed on the resource node k.
Secondly, a certain number of array matrix sets pi are selected by using the formula (4)*＝{∏1*,∏2*,., their corresponding n (p (z) ═ n (p)1(z)),...,n(pK(z)) } the smallest n (p) of the n (p) sk(z) maximum.
Finally, the final array matrix pi is selected by the formula (5)l*Its corresponding n (p (z) ═ n (p)1(z)),...,n(pK(z)) } the variance is largest.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A hybrid sparse code multiple access method, comprising:
respectively carrying out channel coding on data of a plurality of user layers to obtain a plurality of bit streams, and equally dividing the bit streams into a first bit stream and a second bit stream;
mapping the first bit stream to a first constellation diagram and mapping the second bit stream to a second constellation diagram, wherein the first constellation diagram is N1Dimension constellation diagram, the second constellation diagram is N2Dimension constellation diagram;
mapping the constellation points of the first constellation diagram and the constellation points of the second constellation diagram through a mapping matrix to obtain a first code word corresponding to the first bit stream and a second code word corresponding to the second bit stream; the first constellation map corresponds to a first codebook, the second constellation map corresponds to a second codebook, the first codeword is a codeword in the first codebook, and the second codeword is a codeword in the second codebook;
arranging non-zero elements in the first code word and non-zero elements in the second code word through an arrangement matrix to obtain a superposition mode of each user layer on time-frequency resources; the permutation matrix enables the dimension Euclidean distance between user layers superposed on the resource nodes to be increased;
and carrying out data transmission according to the superposition mode of each user layer on the time frequency resource.
2. The method of claim 1, wherein the mapping matrix is composed of non-zero rows and all-zero rows, wherein the first codeword and the second codeword each include non-zero elements and zero elements, wherein the non-zero elements of the first codeword correspond to constellation points of the first constellation, and wherein the non-zero elements of the second codeword correspond to constellation points of the second constellation.
3. The method of claim 2, wherein the time-frequency resource comprises K resource nodes, and wherein K is N1+N2；
The number of the non-zero elements of the user layer superposed on each resource node is the same and meets the requirement
Wherein d isfA number of non-zero elements representing a user layer on each of the resource nodes;
the total number of the user layers is J-2 x df。
4. The method of claim 3, wherein the non-zero elements superimposed on each of the resource nodes correspond to non-zero elements of a first codeword; or
The non-zero elements superposed on each resource node correspond to the non-zero elements of the second code word; or
A portion of the non-zero elements superimposed on each of the resource nodes corresponds to non-zero elements of the first codeword, and another portion corresponds to non-zero elements of the second codeword.
5. The method of claim 4, wherein each non-zero element of the first codeword corresponds to a first dimension on the first constellation and each non-zero element of the second codeword corresponds to a second dimension on the second constellation; the first dimension is any dimension on the first constellation diagram, and the second dimension is any dimension on the second constellation diagram.
6. The method according to claim 5, wherein before the arranging the non-zero elements of the first codeword and the non-zero elements of the second codeword by the arrangement matrix to obtain the superposition mode of each of the user layers on the time-frequency resource, the method further comprises:
acquiring all allocation modes of the user layer allocated on each resource node;
calculating the sum of the dimension Euclidean distances between user layers corresponding to the resource nodes aiming at each distribution mode;
determining a minimum dimension Euclidean distance and a corresponding target resource node in the dimension Euclidean distance sums;
acquiring target dimension Euclidean distance sums corresponding to all distribution modes corresponding to the target resource node;
selecting a maximum dimension Euclidean distance sum from all the target dimension Euclidean distance sums;
and determining the arrangement matrix according to the maximum dimension Euclidean distance and the corresponding distribution mode.
7. The method of claim 6, wherein calculating the sum of Euclidean distances in dimension between user layers corresponding to the resource nodes comprises:
obtaining Euclidean distance of dimensionalities of any two user layers on each resource node in a target constellation diagram, wherein the target constellation diagram is a first constellation diagram or a second constellation diagram corresponding to each user layer in each resource node under the distribution formula;
and summing the obtained Euclidean distances to obtain the dimensionality Euclidean distance sum.
8. The method according to claim 6, wherein before determining the permutation matrix according to the maximum dimension euclidean distance and the corresponding allocation manner, the method further comprises:
determining that the number of the maximum dimension Euclidean distance sums is larger than 1, and obtaining the maximum dimension Euclidean distance and a corresponding target distribution mode;
calculating the variance of the Euclidean distance sum of corresponding dimensions on all the resource points under each target distribution mode;
determining the maximum dimension Euclidean distance sum corresponding to the maximum variance;
the determining the permutation matrix according to the maximum dimension Euclidean distance and the corresponding distribution mode comprises:
and determining the arrangement matrix according to the maximum dimension Euclidean distance corresponding to the maximum variance and the corresponding target distribution mode.
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