CN120165999A - SCMA transmission method and device based on compressed sensing measurement matrix - Google Patents
SCMA transmission method and device based on compressed sensing measurement matrix Download PDFInfo
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Abstract
本申请公开了一种基于压缩感知测量矩阵的SCMA传输方法及装置，方法包括：发送端获取待发送的多用户数据和用户码本；根据用户码本构建压缩感知测量矩阵；基于压缩感知测量矩阵，对多用户数据进行稀疏SCMA编码得到稀疏SCMA编码后的数据；对稀疏SCMA编码后的数据添加导频、逆傅里叶变换、添加循环前缀以及并串变换得到复数信号；采用数字上变频技术将复数信号转换为实数信号；将实数信号通过信道进行传输。接收端基于压缩感知测量矩阵，利用平滑范数稀疏恢复算法对逆处理的第一数据进行稀疏SCMA解码得到多用户数据。本申请显著提高了卫星物联网中大规模设备接入的通信性能。
The present application discloses an SCMA transmission method and device based on a compressed sensing measurement matrix, the method comprising: the transmitting end obtains multi-user data and a user codebook to be transmitted; constructs a compressed sensing measurement matrix according to the user codebook; based on the compressed sensing measurement matrix, sparse SCMA encoding is performed on the multi-user data to obtain sparse SCMA encoded data; a pilot is added to the sparse SCMA encoded data, an inverse Fourier transform is performed, a cyclic prefix is added, and a parallel-to-serial transform is performed to obtain a complex signal; a digital up-conversion technique is used to convert the complex signal into a real signal; and the real signal is transmitted through a channel. Based on the compressed sensing measurement matrix, the receiving end uses a smoothed norm sparse recovery algorithm to perform sparse SCMA decoding on the inverse processed first data to obtain multi-user data. The present application significantly improves the communication performance of large-scale device access in satellite Internet of Things.
Description
Technical Field
The application belongs to the technical field of optical communication, and particularly relates to a SCMA transmission method and device based on a compressed sensing measurement matrix.
Background
Existing cellular networks and traditional wireless communication networks are capable of providing reliable connectivity services for most cities and densely populated areas, but in remote areas such as forests, oceans and deserts, effective coverage is difficult to achieve with these technologies due to geographical environmental constraints. The satellite Internet of things is not limited by ground facilities, can provide stable data transmission service for the areas, supports works such as resource exploration and disaster early warning, and shows unique advantages.
The satellite network is used as the supplement and expansion of the ground communication network, so that the problem of insufficient coverage of the Internet of things in remote areas can be effectively solved, and even the global environment monitoring and disaster early warning functions are supported. However, the situation of the satellite internet of things has a significant difference from the traditional communication network, and the traditional communication technology can be applied to the satellite internet of things to a certain extent, but cannot be perfectly adapted to the specific environment of the satellite internet of things. Therefore, it is imperative to make a compatible communication technology design aiming at the unique communication condition of the satellite internet of things.
The number of the user equipment accessed by the satellite Internet of things is huge, and the satellite uses the solar cell panel to supply power, so that the available energy and calculation capacity are very limited. The non-orthogonal multiple access (NOMA) technology enables a plurality of devices to share the same resource block, improves the spectrum efficiency of the system and the throughput of edge users, and can meet the access requirement of a large number of devices. In the NOMA technology, the code domain NOMA, that is, the sparse code division multiple access (SCMA) technology has been studied to prove that the system has stronger system robustness and is more suitable for a scene of large-scale interconnection. In the general SCMA technique, a Message Passing Algorithm (MPA) is employed at the receiving end for signal recovery, which can provide optimal detection by traversing codeword combinations. However, when the number of users increases or the codebook becomes large, the performance of the codebook is seriously degraded, and the performance of the conventional MPA is determined by the number of iterations, so that the amount of required computing resources is large.
Disclosure of Invention
In view of at least one of the above technical problems, the application provides an SCMA transmission method and device based on a compressed sensing measurement matrix, which is designed based on an SCMA codebook and a multi-user sparse recovery algorithm, and aims to remarkably reduce the computational resource requirement of receiving end signal recovery and improve the decoding efficiency on the premise of ensuring the performance of a communication system.
According to the application, the compressed sensing equivalent aliasing processing is carried out on the transmission data at the transmitting end by utilizing the sparse attribute of the signal in the satellite Internet of things, and the smooth norm (SL 0) sparse recovery algorithm is used for replacing the general multi-user detection algorithm at the receiving end, so that the effect of reducing the calculation complexity is achieved. And constructing a compressed sensing measurement matrix from the inherent property of the SCMA user codebook, so as to realize high-efficiency decoding energy efficiency under the light weight condition. The decoding process in the SCMA technology is optimized through a proper measurement matrix and a sparse recovery algorithm, so that lower error rate and power consumption are realized, and the method is particularly suitable for the communication requirements of the satellite Internet of things under large-scale equipment access and limited computing resources.
The application adopts the technical scheme that:
In a first aspect, the present application provides an SCMA transmission method based on a compressed sensing measurement matrix, applied to a transmitting end, including:
acquiring multi-user data to be transmitted and a user codebook;
Constructing a compressed sensing measurement matrix according to a user codebook;
based on the compressed sensing measurement matrix, performing sparse SCMA coding on the multi-user data to obtain sparse SCMA coded data;
adding pilot frequency, inverse Fourier transform, cyclic prefix and parallel-serial transform to the data after sparse SCMA coding to obtain complex signals;
Converting the complex signal into a real signal by adopting a digital up-conversion technology;
the real signal is transmitted over a channel.
Further, based on the compressed sensing measurement matrix, performing sparse SCMA coding on the multi-user data to obtain sparse SCMA coded data, including:
converting the multi-user data into code words through a sparse code table;
Splicing codewords of the same time slot to obtain sparse data;
multiplying the compressed sensing measurement matrix with the sparse data, and then performing real-virtual recombination to obtain the data after sparse SCMA coding.
In a second aspect, the present application provides an SCMA transmission method based on a compressed sensing measurement matrix, which is applied to a receiving end, and includes:
acquiring a compressed sensing measurement matrix and a real signal transmitted by a channel;
converting the real signal into a complex signal by adopting a down-conversion technology;
Performing serial-parallel conversion on the complex signals, removing cyclic prefix, performing Fourier conversion and removing pilot frequency to obtain first data;
and based on the compressed sensing measurement matrix, performing sparse SCMA decoding on the first data by using a smooth norm sparse recovery algorithm to obtain multi-user data.
Further, based on the compressed sensing measurement matrix, performing sparse SCMA decoding on the first data by using a smooth norm sparse recovery algorithm to obtain multi-user data, including:
Performing real-virtual grouping on the first data, and solving to obtain sparse data by using a compressed sensing measurement matrix in a mode of iteratively approaching a smooth norm;
Grouping the sparse data to obtain code words of the same time slot of multiple users;
And converting the code words of each user into corresponding user data through a sparse code table.
In a third aspect, the present application provides an SCMA transmission device based on a compressed sensing measurement matrix, including a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the method according to the first or second aspect.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of the first or second aspect.
In a fifth aspect, the present application provides a computer device comprising a memory storing a computer program and a processor implementing the method of the first or second aspect when the processor executes the computer program.
The SCMA transmission method and device based on the compressed sensing measurement matrix have the advantages that the communication performance of large-scale equipment access in the satellite Internet of things is remarkably improved by combining the compressed sensing technology and the SCMA transmission method. The system fully utilizes the natural sparsity of the user information in the satellite Internet of things, and realizes low-complexity signal recovery at the receiving end. Compared with the traditional multi-user detection algorithm, the compressed sensing-assisted sparse recovery method not only remarkably reduces the calculation complexity, but also optimizes the signal decoding process and improves the transmission performance of the system on the premise of keeping a lower error rate. The communication system of the scheme can be operated efficiently in the environment of limited computing resources and power supply, and has good energy-saving characteristic. By reducing the resource consumption required by calculation and signal processing, the method ensures the efficient performance and low power consumption performance of the satellite Internet of things system in the face of huge number of access devices. The technical scheme of the application not only effectively solves the contradiction between large-scale equipment access and limited resource constraint in the satellite Internet of things, but also optimizes spectrum utilization and power consumption management, meets the requirements of a modern satellite communication system on efficient, low-cost and large-scale interconnection, and has wide application prospect.
Drawings
Fig. 1 is a flow chart of an SCMA transmission method based on a compressed sensing measurement matrix according to an embodiment of the present application;
FIG. 2 is a diagram of conventional SCMA coding;
FIG. 3 is a schematic diagram of sparse SCMA coding of multi-user data using a constructed compressed sensing measurement matrix according to one embodiment of the present application, wherein "null" indicates that the user is in a standby state in a current time slot, and does not transmit data, and the user data is null in the current time slot;
FIG. 4 is a diagram illustrating a system error rate performance curve for decoding by MPA algorithm, log-MPA algorithm and SL0 algorithm in a single user active situation according to an embodiment of the present application;
FIG. 5 is a diagram illustrating the performance curves of the system error rates decoded by MPA algorithm, log-MPA algorithm and SL0 algorithm in the case of active dual users according to one embodiment of the present application;
FIG. 6 is a schematic diagram of decoding time using MPA algorithm, log-MPA algorithm and SL0 algorithm in single user active situation according to one embodiment of the present application;
Fig. 7 is a schematic diagram of decoding time using MPA, log-MPA, and SL0 algorithms in a case where the dual users are active according to an embodiment of the present application.
Detailed Description
The application is further described below with reference to the drawings and examples. The following examples are only for more clearly illustrating the technical aspects of the present application, and are not intended to limit the scope of the present application.
In the description of the present application, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number is understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present application, the descriptions of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The term "and/or" is merely an association relation describing the association object, and means that three kinds of relations may exist, for example, a and/or B, and that three kinds of cases where a exists alone, a and B exist together, and B exists alone. The character "/", generally indicates that the front and rear associated objects are an or relationship.
The embodiment provides an SCMA transmission method based on a compressed sensing measurement matrix, as shown in fig. 1, at a transmitting end, multi-user data is firstly subjected to sparse SCMA coding by the compressed sensing measurement matrix constructed by a user codebook, and then pilot frequency and inverse fourier transform are added to convert a frequency domain signal into a time domain signal. The time domain signal adds a cyclic prefix, which helps to reduce interference between subcarriers, and then converts the parallel data stream into a serial format through parallel-to-serial conversion, so that the complex signal is converted into a real signal through mixing by adopting a digital up-conversion technology in consideration of the fact that the complex signal cannot be transmitted in an intensity modulation/direct detection system, so that the complex signal can be transmitted in a transmission channel. The processed signal meets the technical requirements of the system and ensures the transmission efficiency and quality of the signal. After receiving the real number signal transmitted by the channel, the receiving end reversely processes the data of the real number signal according to the flow of the transmitting end, and then the original multi-user data can be solved.
Embodiment 1. This embodiment provides a SCMA transmission method based on a compressed sensing measurement matrix, which is applied to a transmitting end, as shown in fig. 1, and includes:
s1, acquiring multi-user data to be transmitted and a user codebook;
S2, constructing a compressed sensing measurement matrix A according to a user codebook;
In some embodiments, in step S2, the method for constructing the compressed sensing measurement matrix a includes:
in the SCMA system, there are J users, K orthogonal subcarriers, each user codebook size is kxk, and the user codebook is expressed as: wherein M is the codeword length selected by each user, L is the number of layers of the user codebook, and each user maps M symbols onto K subcarriers;
The ith column and the (K-i+1) th column of the user codebook are reciprocal codewords, and the real part and the imaginary part of the user codebook are spliced in the column direction to obtain a matrix Each user codebook takes M/2 linearly uncorrelated columns of the matrix, and a compressed sensing measurement matrix is constructed。
The user codebooks have the property of an antisymmetric structure, namely that reciprocal codewords exist between the codebooks, and each codeword has a linear correlation vector with a coefficient of-1. Thus, the compressed sensing measurement matrix can be constructed from the codebook inherent properties. Taking the SCMA system with 150% overload rate as an example, it is assumed that there are 6 users, 4 orthogonal subcarriers, each user codebook size 4×4. Then 1/4 and 2/3 columns of the user codebook are reciprocal codewords, and two columns of non-reciprocal codewords in all user codebooks are taken, so that a 4×12 linear irrelevant matrix B can be formed. Since each element in the linear independent matrix is in a complex form, the real part and the imaginary part of each element can be separated, and the imaginary part is spliced under the real part to obtain:
Wherein, 、、、Respectively represent the real parts of the elements of the 1 st row, the 1 st column, the 1 st row, the 12 th column, the 4 th row, the 1 st column and the 4 th row and the 12 th column in the linear irrelevant matrix,、、、The imaginary parts of the 1 st row, 1 st column, 1 st row, 12 th column, 4 th row, 1 st column, 4 th row, and 12 th column elements in the linear independent matrix are respectively represented.
This gives a compressed sensing measurement matrix a with good linear uncorrelated properties, which is 8 x 12 in size.
S3, based on the compressed sensing measurement matrix A, performing sparse SCMA coding on the multi-user data to obtain sparse SCMA coded data;
For the case of 6 users and 4 orthogonal subcarriers, the conventional SCMA coding scheme is shown in fig. 2. Each user has its own code table with 4 code words per code table. In a specific SCMA encoding process, binary data is first mapped into a codebook. User 1 inputs (0, 1) then selects codeword 2 of codebook 1, user 2 inputs (1, 1) then selects codeword 4 of codebook 2, and so on. The codewords for all users are then directly added to form the encoded result. There are only 2 non-empty carriers per codebook, so there will be signal aliasing for multiple users on the same carrier. Through different codebooks, signals of users can be distinguished in a frequency domain, so that multiple users share the same frequency resource.
The traditional SCMA coding realizes resource overload through a codebook and a multi-user detection algorithm, but in the satellite Internet of things scene, not all users are active in the same time slot. Therefore, the sparse property of the transmitted information can be fully utilized, and the compressed sensing method is adopted for sparse SCMA coding. The sparse SCMA coding mode proposed by the application is shown in figure 3. The different colors in fig. 2 and 3 represent different codewords and data mapped by different users according to respective code tables.
In some embodiments, step S3, based on the compressed sensing measurement matrix a, performs sparse SCMA encoding on the multi-user data to obtain sparse SCMA encoded data, as shown in fig. 3, includes:
converting the multi-user data into code words through a sparse code table;
Splicing codewords of the same time slot to obtain sparse data;
multiplying the compressed sensing measurement matrix A with sparse data, and then performing real-virtual recombination to obtain data (multi-carrier data) after sparse SCMA coding.
Thus, the sparse SCMA coding is completed by using the compressed sensing measurement matrix constructed by the user codebook. At the receiving end, a sparse recovery algorithm is used for replacing the traditional multi-user detection algorithm, so that the advantage of remarkably reducing the calculation complexity is replaced by smaller error rate performance loss.
S4, adding pilot frequency, inverse Fourier transform, cyclic prefix and parallel-serial transform to the data after sparse SCMA coding to obtain a complex signal;
The sparse SCMA coded data is subjected to pilot frequency addition, so that a receiving end is helped to perform channel estimation and time-frequency synchronization to eliminate the influence of fading and interference of a wireless channel on signal transmission. The receiving end can estimate the channel characteristics by extracting these known pilot signals, calculating the ratio of the received signal to the pilot, and then using these estimates to equalize and correct the signal of the data portion.
After sparse SCMA coding and pilot addition, the signal is represented in the frequency domain. By inverse fourier transformation, these frequency domain signals are converted into time domain signals for transmission over a wireless channel. Before transmission, a cyclic prefix is first added to each time domain symbol to combat inter-symbol interference due to multipath effects. And then, carrying out parallel-to-serial conversion on the parallel signals added with the cyclic prefix, converting the parallel signals into a serial format, forming a continuous time domain signal stream, and transmitting the continuous time domain signal stream through a wireless channel. After receiving the signal, the receiving end firstly restores the serial signal to a parallel format through serial-parallel conversion, then removes the cyclic prefix, and then carries out Fourier conversion to restore the time domain signal to a frequency domain signal for channel estimation and data decoding.
S5, converting the complex signal into a real signal by adopting a digital up-conversion technology;
In an intensity modulation/direct detection system, since a signal can only carry real values and cannot directly transmit complex signals, digital up-conversion technology is required to convert the complex signals into transmittable real signals. The up-converted signal becomes real, and the light intensity can be directly modulated and transmitted through a channel. The receiving end can perform down-conversion after acquiring the real signal, and restore the real signal into a complex signal, so that the subsequent decoding processing is facilitated.
S6, transmitting the real signal through a channel.
Embodiment 2. This embodiment provides a SCMA transmission method based on a compressed sensing measurement matrix, which is applied to a receiving end, as shown in fig. 1, and includes:
acquiring a compressed sensing measurement matrix A and a real signal transmitted by a channel;
converting the real signal into a complex signal by adopting a down-conversion technology;
Performing serial-parallel conversion on the complex signals, removing cyclic prefix, performing Fourier conversion and removing pilot frequency to obtain first data;
based on the compressed sensing measurement matrix A, performing sparse SCMA decoding on the first data by using a smooth norm (SL 0) sparse recovery algorithm to obtain multi-user data.
In some embodiments, based on the compressed sensing measurement matrix a, performing sparse SCMA decoding on the first data by using a smooth norm (SL 0) sparse recovery algorithm to obtain multi-user data, including:
performing real-virtual grouping on the first data, and solving to obtain sparse data by using a compressed sensing measurement matrix A in a mode of iteratively approaching a smooth norm;
Grouping the sparse data to obtain code words of the same time slot of multiple users;
And converting the code words of each user into corresponding user data through a sparse code table.
The existing low-complexity signal detection algorithm of the receiving end, such as Log-message passing algorithm (Log-MPA) and the like, can reduce the calculated amount to a certain extent, but does not fully utilize the characteristic that signals in the satellite Internet of things have sparse properties, so that if the characteristic that only a few devices are in an active state in the same time window under the satellite Internet of things environment can be combined, the error rate performance of a system can be improved to the greatest extent under the condition of fully reducing the algorithm complexity.
Compressed Sensing (CS) technology is a method to find sparse solutions for underdetermined linear systems that can reconstruct signals from a small number of measurements at far lower sampling rates than traditional nyquist. In the satellite Internet of things, the natural sparsity of the user activities can help to solve the problem of multi-user detection, and the measurement matrix is used for compressing signals at the transmitting end, so that the problem of multi-user detection can be replaced by the problem of sparse signal recovery with lower complexity at the receiving end, thereby saving computing resources and reducing algorithm complexity.
Matlab simulation verification:
(1) Bit error rate performance verification of sparse SCMA decoding-SL 0 algorithm
In order to verify the bit error rate performance of the proposed algorithm, simulation verification is performed on single-user active and double-user active conditions in the system respectively. The system decodes by adopting MPA algorithm, log-MPA algorithm and SL0 algorithm of the application, the single user active error rate performance curve of the system is shown in figure 4, and the dual user active error rate performance curve is shown in figure 5. Fig. 4 and fig. 5 show that the error rate performance of the smooth norm (SL 0) sparse recovery algorithm provided by the present application is better than that of the conventional MPA algorithm and Log-MPA algorithm under the single-user active and dual-user active conditions.
(2) Computation complexity verification of sparse SCMA decoding-SL 0 algorithm
In order to verify the computational complexity of the proposed algorithm, decoding time tests were performed on the MPA algorithm, the Log-MPA algorithm and the SL0 algorithm of the application, respectively. The decoding time required for the MPA algorithm, the Log-MPA algorithm and the SL0 algorithm in the single user case is shown in FIG. 6, and the decoding time required for the MPA algorithm, the Log-MPA algorithm and the SL0 algorithm in the dual user case is shown in FIG. 7, with the signal to noise ratio (SNR) fixed to 15. It can be seen from fig. 6 and 7 that the smooth norm (SL 0) sparse recovery algorithm provided by the present application has significantly less decoding time than the MPA algorithm and the Log-MPA algorithm in both single user active and dual user active situations.
Embodiment 3 based on embodiment 1, the embodiment provides an SCMA transmission device based on a compressed sensing measurement matrix, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
The processor is configured to operate according to the instructions to perform the method according to embodiment 1 or embodiment 2.
Embodiment 4 based on embodiment 1, the present embodiment provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of embodiment 1 or embodiment 2.
Embodiment 5 based on embodiment 1, this embodiment provides a computer device comprising a memory storing a computer program and a processor implementing the method of embodiment 1 or embodiment 2 when executing the computer program.
Embodiment 6 based on embodiment 1, this embodiment provides a computer program product comprising a computer program which, when executed by a processor, implements the method described in embodiment 1 or embodiment 2.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.
Claims (10)
1. An SCMA transmission method based on a compressed sensing measurement matrix, which is applied to a transmitting end, is characterized by comprising the following steps:
acquiring multi-user data to be transmitted and a user codebook;
Constructing a compressed sensing measurement matrix according to a user codebook;
based on the compressed sensing measurement matrix, performing sparse SCMA coding on the multi-user data to obtain sparse SCMA coded data;
adding pilot frequency, inverse Fourier transform, cyclic prefix and parallel-serial transform to the data after sparse SCMA coding to obtain complex signals;
Converting the complex signal into a real signal by adopting a digital up-conversion technology;
the real signal is transmitted over a channel.
2. The method of claim 1, wherein sparse SCMA encoding the multi-user data based on the compressed sensing measurement matrix to obtain sparse SCMA encoded data comprises:
converting the multi-user data into code words through a sparse code table;
Splicing codewords of the same time slot to obtain sparse data;
multiplying the compressed sensing measurement matrix with the sparse data, and then performing real-virtual recombination to obtain the data after sparse SCMA coding.
3. The method according to claim 1 or 2, wherein the method for constructing the compressed sensing measurement matrix comprises:
in the SCMA system, there are J users, K orthogonal subcarriers, each user codebook size is kxk, and the user codebook is expressed as: wherein M is the codeword length selected by each user, L is the number of layers of the user codebook, and each user maps M symbols onto K subcarriers;
The ith column and the (K-i+1) th column of the user codebook are reciprocal codewords, and the real part and the imaginary part of the user codebook are spliced in the column direction to obtain a matrix Each user codebook takes M/2 linearly uncorrelated columns of the matrix, and a compressed sensing measurement matrix is constructed。
4. An SCMA transmission method based on a compressed sensing measurement matrix, applied to a receiving end, is characterized by comprising the following steps:
acquiring a compressed sensing measurement matrix and a real signal transmitted by a channel;
converting the real signal into a complex signal by adopting a down-conversion technology;
Performing serial-parallel conversion on the complex signals, removing cyclic prefix, performing Fourier conversion and removing pilot frequency to obtain first data;
and based on the compressed sensing measurement matrix, performing sparse SCMA decoding on the first data by using a smooth norm sparse recovery algorithm to obtain multi-user data.
5. The method of claim 4, wherein sparse SCMA decoding the first data using a smooth norm sparse recovery algorithm based on the compressed sensing measurement matrix to obtain multi-user data comprises:
Performing real-virtual grouping on the first data, and solving to obtain sparse data by using a compressed sensing measurement matrix in a mode of iteratively approaching a smooth norm;
Grouping the sparse data to obtain code words of the same time slot of multiple users;
And converting the code words of each user into corresponding user data through a sparse code table.
6. An SCMA transmission device based on a compressed sensing measurement matrix is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
The processor being operative according to the instructions to perform the method of any one of claims 1 to 3.
7. An SCMA transmission device based on a compressed sensing measurement matrix is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
The processor is operative according to the instructions to perform the method according to any one of claims 4 to 5.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any one of claims 1 to 3.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 4 to 5.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 3 or the method of any one of claims 4 to 5 when executing the computer program.
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