CN107864029A - A kind of method for reducing Multiuser Detection complexity - Google Patents
A kind of method for reducing Multiuser Detection complexity Download PDFInfo
- Publication number
- CN107864029A CN107864029A CN201711102464.3A CN201711102464A CN107864029A CN 107864029 A CN107864029 A CN 107864029A CN 201711102464 A CN201711102464 A CN 201711102464A CN 107864029 A CN107864029 A CN 107864029A
- Authority
- CN
- China
- Prior art keywords
- user
- updating
- resource
- scps
- gaussian
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
- H04L1/0045—Arrangements at the receiver end
- H04L1/0047—Decoding adapted to other signal detection operation
- H04L1/0048—Decoding adapted to other signal detection operation in conjunction with detection of multiuser or interfering signals, e.g. iteration between CDMA or MIMO detector and FEC decoder
-
- 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
- H04L1/0045—Arrangements at the receiver end
- H04L1/0047—Decoding adapted to other signal detection operation
- H04L1/005—Iterative decoding, including iteration between signal detection and decoding operation
-
- 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
- H04L1/0045—Arrangements at the receiver end
- H04L1/0052—Realisations of complexity reduction techniques, e.g. pipelining or use of look-up tables
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0446—Resources in time domain, e.g. slots or frames
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
Abstract
The invention belongs to wireless communication technology field, particularly relates to a kind of method for reducing Multiuser Detection complexity.The present invention, with sending the distance between signal less than the SCPs in Gauss threshold areas, is given up the SCPs outside Gauss threshold areas, can so realize and MPA algorithm complexes are greatly lowered, and ensure that BER performances are not lost substantially using reception signal.SCMA system decoding complexities can be greatly lowered in the present invention, and by selecting suitable Gauss threshold value, decoding performance does not almost lose；Due to reception signal constellation point and send the Euclidean distance Gaussian distributed between SCPs, the present invention can make full use of gaussian distribution characteristic to adaptively determine threshold value, by choosing different threshold values, it can be compromised between detection complexity and decoding performance, while can also meet the communication scenes of 5G different quality requirements.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a method for reducing complexity of multi-user detection, in particular to a method for reducing complexity of Message Passing Algorithm (MPA) in an SCMA (Message publishing Algorithm) system based on a Gaussian threshold.
Background
Driven by the rapid development of mobile internet services, a large number of terminal device accesses and extreme user experiences in the future pose a huge challenge to mobile communication. Therefore, in order to meet the demand of future communication, a 5G communication system is in force, wherein a wireless air interface technology is a hot issue of current 5G research. In the face of these challenges in 5G, the conventional orthogonal multiple access technology has not been able to meet its requirements, so some non-orthogonal multiple access technologies have been proposed. The Sparse Code Multiple Access (SCMA) technology is a non-orthogonal Multiple Access scheme for sharing frequency resources by Multiple users, and is used as a further development of the low-density signal Code division Multiple Access technology to solve the problem of system overload of mass connection. However, to become a very competitive air interface technology in 5G, the SCMA system still needs to solve the following problems: codebook optimization design, efficient decoding algorithm, bit error performance, channel estimation and distribution, no scheduling, active user number detection and the like. In SCMA systems, efficient multi-user detection is an important component of the success of SCMA systems. The maximum posterior probability algorithm is used as an optimal multi-user detection scheme of the SCMA system, and the complexity is too high to be easily realized. The MPA is used as suboptimal multi-user detection, the MAP performance can be effectively approached by utilizing the character of code word sparsity, and the decoding complexity is greatly reduced. However, as the number of users and the size of the codebook increase, the decoding complexity of the MPA algorithm increases exponentially, so that it is still worth studying to reduce the complexity of the MPA algorithm.
Disclosure of Invention
The invention aims to solve the problems and provide an MPA algorithm based on a Gaussian threshold to reduce the complexity of multi-user detection of an uplink SCMA system. According to the Gaussian threshold value, the invention can make compromise between decoding complexity and Bit Error Rate (BER) performance. Therefore, the invention can adaptively meet the communication quality requirements under different 5G scenes.
The technical scheme of the invention is as follows:
a method for reducing multi-user detection complexity is used for message transmission in an uplink SCMA system, the uplink SCMA communication system model is preset as J users share K time-frequency resources, the number of constellation points adopted by each user is M, and overload factors of the users are defined as:
the method is characterized by comprising the following steps:
s1, setting SCMA system parameters including user number J and time-frequency resource K, and setting maximum iteration number as t max ；
S2, initializing the message transmission probability value as follows:
wherein, the first and the second end of the pipe are connected with each other,refers to the user node u in the factor graph j To the resource node r k Wherein t is the number of iterations, x j ＝(x 1,j ,x 2,j …,x K,j ) T ∈C K Representing a codeword transmitted by a jth user;
s3, setting a Gaussian threshold value delta;
s4, entering message iteration updating, judging whether t exceeds the maximum iteration times set in the step S1, and if t is less than or equal to t max Then the process goes to step S5, if t>t max If yes, ending message iteration updating and entering step S7;
s5, updating the messages of the resource nodes and the user nodes, specifically:
s51, dividing the SCPs of each resource block according to the size of the Gaussian threshold, and selecting the SCPs in the Gaussian threshold area to participate in the updating of resource nodes;
s52, updating the information of the resource nodes, and aiming at all the resource nodes r k And (3) calculating:
where σ is the standard deviation of the Gaussian distribution, y k Is the signal received by the base station on the kth time-frequency resource,is and | Φ * (k) The corresponding combination of superimposed codewords,represents the set of candidate constellation points on the k-th resource block, h is the channel,refers to the user node u in the factor graph p To the resource node r k The message update process of (1);
s53, updating the message of the user node, and updating all the user nodes u j And (3) calculating:
s6, updating the iteration times t = t +1, and returning to the step S4;
s7, completing multi-user detection by using the updated probability distribution, and outputting soft decision to the user as follows:
the basis of the invention is that due to the existence of Gaussian noise, a certain Euclidean distance exists between a received signal Constellation point and a transmitted superposition code word Constellation point (SCPs), and the Euclidean distance obeys Gaussian distribution, so that the distance between a received signal and a transmitted signal in a Constellation domain can be determined by the characteristics of the Gaussian distribution. In the original MPA algorithm, all SCPs participate in the message passing process, and this scheme causes a great deal of computational redundancy. The invention only utilizes the SCPs in the region of the Gaussian threshold that the distance between the received signal and the transmitted signal is lower than the SCPs in the region of the Gaussian threshold, and discards the SCPs outside the region of the Gaussian threshold, thereby greatly reducing the complexity of the MPA algorithm and ensuring that the BER performance is basically not lost.
The beneficial effects of the invention are: the decoding complexity of the SCMA system can be greatly reduced, and the decoding performance is hardly lost by selecting a proper Gaussian threshold value; because the Euclidean distance between the received signal constellation points and the SCPs obeys Gaussian distribution, the invention can make full use of the characteristics of the Gaussian distribution to adaptively determine the threshold value, and can compromise between the detection complexity and the decoding performance by selecting different threshold values, and can also meet the communication scenes with different 5G quality requirements.
Drawings
FIG. 1 is a block diagram of an uplink SCMA system;
FIG. 2 is an indicator matrix and factor graph for SCMA;
FIG. 3 shows a constellation diagram of a transmitted superposition codeword and a received signal;
FIG. 4 is a SCMA decoding flow chart.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings and examples.
Considering an uplink SCMA communication system, J users share K time-frequency resources, the number of constellation points adopted by each user is M, and overload factors of the users are defined as follows:
the method represents the capability of an SCMA system for accessing users, and in general, lambda is greater than 1, which means that more users can be accessed by the SCMA system at the same time under the condition of the same number of resource blocks, thereby meeting the requirement of massive connection in one of three 5G application scenarios.
As shown in fig. 1, a data stream at a transmitting end is subjected to SCMA coding, a binary bit stream is mapped into a multi-dimensional complex field codeword, the codeword is transmitted through a channel after being mapped by physical resources, and codewords selected by different users are non-orthogonally superimposed on the same time-frequency resource. At the receiving end, after physical resource de-mapping, MPA is adopted to carry out multi-user detection, and the original bit stream is recovered. The SCMA encoding process is a process of selecting a codeword from a pre-designed codebook from an input bit sequence. The bit sequences and the code words are in one-to-one correspondence, and codebook mapping is defined as:wherein x ∈ C K And | χ | = M. The SCMA signal is a superposition of codewords from J different user codebooks, with J × log total 2 M bits of information, all user signals received by the base station are represented as:
in the formula x j ＝(x 1,j ,x 2,j …,x K,j ) T ∈C K Indicating the code word sent by the jth user, h j ＝(h 1,j ,h 2,j …,h K,j ) T ∈C K Representing the channel of the jth user, n is Gaussian white noise and obeys the distribution n-CN (0, sigma) 2 I)。
The signal received by the base station is the superposition of all user signals, and the signal received at the kth time-frequency resource may be represented as:
the overall architecture of the SCMA system may be as shown in fig. 2 with an indication matrix F = (F) 1 ,F 2 ,…,F J ) And a factor graph, where F j ＝(f 1,j ,f 2,j ,…,f K,j )∈B K An indicator vector for user j. If f is k,j And =1, it indicates that the user j occupies the kth time-frequency resource. The factor graph is another expression form of the SCMA code, and the factor graph of the SCMA system shown in FIG. 2 has the following relation with the indication matrix: the User Node (UN) is connected to the Resource Node (RN) and only if f k,j =1. In the indication matrix F, it is,represents the time-frequency resource occupied by user j and | ζ j |＝d c ，Represents the user node with which resource node k is associated and | ξ k |＝d r 。
The SCMA technology combines high-dimensional modulation and spread spectrum of input bit streams to be directly mapped into multi-dimensional sparse code words in a codebook, so that the SCMA system can decode bit information sent by each user by using a multi-user detection algorithm according to code word sparse characteristics. Meanwhile, an SCMA communication system provides a suboptimal MPA multi-user detection algorithm by using the sparsity of code words, wherein the MPA algorithm is the message transmission between variable nodes and resource nodes and then is iterated continuously.Refers to a resource node r in the factor graph k To user node u j The message update procedure of (1) is,refers to the user node u in the factor graph j To the resource node r k Wherein t is the number of iterations. The original MPA algorithm requires computation at each resource nodeThe constellation points of the code words are superimposed, and in the constellation domain, the distance between the constellation point of the received signal and the transmitted SCPsThe distance determines the confidence with which the user sends the codeword. The greater the distance between the constellation point of the received signal and the transmitted SCPs, the less likely the code word combination transmitted by the user will be detected, i.e. the lower the reliability of the SCPs. Therefore, the invention only selects SCPs with higher credibility to participate in the process of message iteration, thereby greatly reducing the calculation redundancy and enabling the decoding algorithm to be more efficient.
The confidence in how SCPs are distinguished will be described next.Represents a set of candidate constellation points on the k-th resource block, fromIt can be seen that the term n is due to gaussian noise k The received signal constellation point and any constellation point which sends the superposition code word can not be overlapped, and a certain Euclidean distance exists between the received signal constellation point and any constellation point which sends the superposition code word, so that the signal constellation point and the constellation point can be obtained
WhereinIs the euclidean distance between the ith candidate constellation point and the received signal on the kth resource block.
Suppose phi k ζ is the correct superposition codeword constellation point sent, and is equivalent toThen the following expression can be obtained
For n without Gaussian noise k If | l =0, the constellation point of the received signal will be compared with one of the constellation pointsAlternative SCPs overlap. In an actual communication system, gaussian noise always exists, and then constellation points of received signals are located between alternative SCPs, it is difficult to distinguish the constellation points of the transmitted superposition codewords through the received signals, as shown in fig. 3. The SCP is more trustworthy if it is closer to the constellation point of the received signal. The original MPA requires the use of all SCPs for multi-user detection, but in practice it is true forThe larger candidate constellation point has lower confidence and does not need to participate in the process of message iterative update. As can be seen from the foregoing analysis,and n k The distribution characteristics of (2) are consistent and all obey Gaussian distribution. The invention therefore proposes to determine the gaussian threshold value Δ from the standard deviation σ of the gaussian distribution, while the observation center point is the constellation point of the received signal. Gaussian distribution there is a 3 σ principle, and when Δ =2 σ, there is a 95.4% probability that the correct constellation point transmitted lies within the circular area, as shown in fig. 3. SCPs in the circular area have high reliability and also participate in the MPA algorithm to update messages. On the contrary, SCPs outside the circular region have low reliability and have little influence on decoding accuracy, and SCPs having low reliability are discarded in order to avoid a large amount of calculation redundancy. The value of the Gaussian threshold determines the complexity of multi-user detection of the SCMA system, so the method can adaptively reduce the complexity according to the value of the Gaussian threshold.
MPA is a message updating and propagating algorithm, is a practical technology for solving a probabilistic inference problem by using a factor graph model, and is suitable for iterative operation in a low-density factor graph. The computational complexity of the original MPA is mainly focused on the operation at resource nodes, and each time-frequency resource block hasLikelihood to calculateAnd each possibilityCorresponding to the constellation points of the superimposed codeword in the constellation domain. The invention correspondingly reduces the SCPs to be calculated by reducing the number of SCPs participating in the operationThereby enabling a reduction in the computational complexity of the MPA detector. L Φ * (k) If | is the number of SCPs in the k-th resource block lower than the gaussian threshold, then the complexity expressions of the original MPA and GT-MPA multi-user detection algorithms are as follows:
based on the constructed model and the definition, the invention provides an MPA algorithm based on the Gaussian threshold to reduce the multi-user detection complexity of an uplink SCMA system, and the decoding process of the SCMA system by utilizing a GT-MPA algorithm is shown in figure 4.
Examples
In this embodiment, a Matlab simulation platform is used for the experiment.
The purpose of the embodiment is realized by the following steps:
s1, setting SCMA system parameters. The system parameters in this example are as follows: the number of users J =6, the number of time-frequency resources K =4, the simulation channel is AWGN, the overload factor is lambda =150%, and the simulation times is 10 5 Maximum number of iterations t max The codebook used is shown in the following table:
s2, initializing a message transmission probability value,t＝1，j∈{1,…,J}，k∈ζ j 。
and S3, determining a Gaussian threshold value delta according to the current communication quality requirement.
S4, judging whether t exceeds the preset maximum iteration times or not, and if t is less than or equal to t max Then, the following message updating is carried out; if t>t max Then the message iterative update is terminated.
S5, updating the messages of the resource node and the user node as follows:
and S51, dividing the SCPs of each resource block according to the size of the Gaussian threshold, and selecting the SCPs in the Gaussian threshold area to participate in the updating of the resource nodes.
And S52, updating the message of the resource node. For all resource nodes r k Computing
WhereinIs and | Φ * (k) And | corresponding superposition code word combination.
And S53, updating the message of the user node. For all user nodes u j Calculating out
And S6, updating the iteration times t = t +1, returning to the step S2, and continuously executing the MPA algorithm.
S7, completing multi-user detection by using the updated probability distribution, and outputting soft decision to the user
The method of the invention is adopted to carry out simulation test. The BER performance of the original MPA and GT-MPA is compared first and the computational complexity of the original MPA and GT-MPA is compared second. With the continuous decrease of the value of the Gaussian threshold, the GT-MPA has a larger and larger ratio of reducing the computational complexity. In summary, the invention uses the gaussian threshold technique for the SCMA system to perform multi-user detection for the first time, the GT-MPA can greatly reduce the computational complexity, and the proper gaussian threshold value is selected to ensure that the BER performance is not lost basically.
Those skilled in the art will appreciate that the examples described herein are set forth by way of example in a SCMA system 4 x 6 codebook (J =6,k = 4) to aid the reader in understanding the principles of the invention, and that the method may be extended to applications in any SCMA codebook format, and that the scope of the invention is not limited to such specific statements and implementations; those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (1)
1. A method for reducing multi-user detection complexity is used for message transmission in an uplink SCMA system, the uplink SCMA communication system model is preset as J users share K time-frequency resources, the number of constellation points adopted by each user is M, and overload factors of the users are defined as:
the method is characterized by comprising the following steps:
s1, setting SCMA system parameters including user number J and time-frequency resource K, and setting maximum iteration number as t max ；
S2, initializing the message transmission probability value as follows:
wherein the content of the first and second substances,refers to the user node u in the factor graph j To the resource node r k Wherein t is the number of iterations, x j ＝(x 1,j ,x 2,j …,x K,j ) T ∈C K Representing the codeword transmitted by the jth user;
s3, setting a Gaussian threshold value delta;
s4, entering message iteration updating, judging whether t exceeds the maximum iteration times set in the step S1, and if t is less than or equal to t max Then the process goes to step S5, if t>t max If yes, ending message iteration updating and entering step S7;
s5, updating the messages of the resource nodes and the user nodes, specifically:
s51, dividing the SCPs of each resource block according to the size of the Gaussian threshold, and selecting the SCPs in the Gaussian threshold area to participate in the updating of the resource nodes;
s52, updating the information of the resource nodes, and performing r updating on all the resource nodes k And (3) calculating:
where σ is the standard deviation of the Gaussian distribution, y k Is the signal received by the base station on the kth time-frequency resource,is and | Φ * (k) L the corresponding combination of the superposition codewords,representing the set of candidate constellation points on the k-th resource block, h being the channel,refers to the user node u in the factor graph p To the resource node r k The message update process of (1);
s53, updating the message of the user node, and updating all the user nodes u j And (3) calculating:
s6, updating the iteration times t = t +1, and returning to the step S4;
s7, completing multi-user detection by using the updated probability distribution, and outputting soft decision to the user as follows:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711102464.3A CN107864029A (en) | 2017-11-10 | 2017-11-10 | A kind of method for reducing Multiuser Detection complexity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711102464.3A CN107864029A (en) | 2017-11-10 | 2017-11-10 | A kind of method for reducing Multiuser Detection complexity |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107864029A true CN107864029A (en) | 2018-03-30 |
Family
ID=61700164
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711102464.3A Pending CN107864029A (en) | 2017-11-10 | 2017-11-10 | A kind of method for reducing Multiuser Detection complexity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107864029A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108521317A (en) * | 2018-04-09 | 2018-09-11 | 哈尔滨工业大学 | A kind of SCMA method of uplink transmission based on asynchronous detection |
| CN109586848A (en) * | 2018-12-07 | 2019-04-05 | 南京邮电大学 | A kind of message-passing decoding algorithm in SCMA system |
| CN109831281A (en) * | 2019-03-21 | 2019-05-31 | 西安电子科技大学 | A kind of low complex degree Sparse Code multiple access system multi-user test method and device |
| CN109889283A (en) * | 2019-01-25 | 2019-06-14 | 武汉虹信通信技术有限责任公司 | A kind of SCMA ascending communication system multi-user test method and device |
| CN110113136A (en) * | 2019-05-16 | 2019-08-09 | 南京邮电大学 | Low complex degree decoding algorithm in a kind of SCMA system |
| CN110381003A (en) * | 2019-07-25 | 2019-10-25 | 电子科技大学 | The multiuser signal detection method inhibited for peak-to-average force ratio in SCMA-OFDM system |
| CN113852443A (en) * | 2021-06-17 | 2021-12-28 | 天翼智慧家庭科技有限公司 | Low-complexity multi-user detection method in SCMA (sparse code multiple Access) system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107276725A (en) * | 2017-07-31 | 2017-10-20 | 北京交通大学 | Multi-user test method in SCMA systems |
-
2017
- 2017-11-10 CN CN201711102464.3A patent/CN107864029A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107276725A (en) * | 2017-07-31 | 2017-10-20 | 北京交通大学 | Multi-user test method in SCMA systems |
Non-Patent Citations (2)
| Title |
|---|
| LIN YANG: ""Low Complexity MPA Detector Based on Sphere Decoding for SCMA"", 《IEEE COMMUNICATIONS LETTERS》 * |
| 吉明明: ""基于变量节点串行策略的SCMA低复杂度译码研究"", 《电信科学》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108521317A (en) * | 2018-04-09 | 2018-09-11 | 哈尔滨工业大学 | A kind of SCMA method of uplink transmission based on asynchronous detection |
| CN108521317B (en) * | 2018-04-09 | 2020-10-30 | 哈尔滨工业大学 | SCMA uplink transmission method based on asynchronous detection |
| CN109586848A (en) * | 2018-12-07 | 2019-04-05 | 南京邮电大学 | A kind of message-passing decoding algorithm in SCMA system |
| CN109586848B (en) * | 2018-12-07 | 2021-05-18 | 南京邮电大学 | Message transmission decoding method in SCMA system |
| CN109889283A (en) * | 2019-01-25 | 2019-06-14 | 武汉虹信通信技术有限责任公司 | A kind of SCMA ascending communication system multi-user test method and device |
| CN109889283B (en) * | 2019-01-25 | 2021-10-15 | 武汉虹信科技发展有限责任公司 | Multi-user detection method and device for SCMA uplink communication system |
| CN109831281A (en) * | 2019-03-21 | 2019-05-31 | 西安电子科技大学 | A kind of low complex degree Sparse Code multiple access system multi-user test method and device |
| CN110113136A (en) * | 2019-05-16 | 2019-08-09 | 南京邮电大学 | Low complex degree decoding algorithm in a kind of SCMA system |
| CN110381003A (en) * | 2019-07-25 | 2019-10-25 | 电子科技大学 | The multiuser signal detection method inhibited for peak-to-average force ratio in SCMA-OFDM system |
| CN110381003B (en) * | 2019-07-25 | 2021-08-17 | 电子科技大学 | Multi-user signal detection method aiming at peak-to-average ratio suppression in SCMA-OFDM system |
| CN113852443A (en) * | 2021-06-17 | 2021-12-28 | 天翼智慧家庭科技有限公司 | Low-complexity multi-user detection method in SCMA (sparse code multiple Access) system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107864029A (en) | A kind of method for reducing Multiuser Detection complexity | |
| US9831895B2 (en) | System and method for a message passing algorithm | |
| CN105721106B (en) | SCMA ascending communication system multi-user test method based on serial strategy | |
| KR101681617B1 (en) | System and method for low density spreading modulation detection | |
| CN110445581B (en) | Method for reducing channel decoding error rate based on convolutional neural network | |
| KR20170102173A (en) | Data transfer method and device | |
| CN107231158B (en) | Polarization code iterative receiver, system and polarization code iterative decoding method | |
| WO2018137663A1 (en) | Encoding method, decoding method, encoding device, and decoding device | |
| KR20110135597A (en) | Apparatus and method using matrix network coding | |
| CN107508657A (en) | A kind of SCMA multi-user test methods based on weight factor message transmission | |
| CN109831281B (en) | Multi-user detection method and device for low-complexity sparse code multiple access system | |
| CN110601796B (en) | Downlink multi-user joint channel coding transmitting and receiving method and system | |
| Cyriac et al. | Polar code encoder and decoder implementation | |
| CN113438746B (en) | Large-scale random access method based on energy modulation | |
| Miao et al. | A low complexity multiuser detection scheme with dynamic factor graph for uplink SCMA systems | |
| CN108199805A (en) | A kind of method for reducing Sparse Code multi-address system decoding complexity | |
| WO2018201983A1 (en) | Polar code channel encoding method, device and communications system | |
| CN107181567B (en) | Low-complexity MPA algorithm based on threshold | |
| KR20190117186A (en) | Learning-based channel code decoding method and apparatus | |
| Azmi et al. | Multichannel cooperative spectrum sensing that integrates channel decoding with fusion-based decision | |
| Meng et al. | A universal receiver for uplink noma systems | |
| Ji et al. | Low Complexity Decoding Algorithm for Uplink SCMA Based on Aerial Spherical Decoding. | |
| Chen et al. | Memory AMP for Generalized MIMO: Coding Principle and Information-Theoretic Optimality | |
| CN106911431B (en) | Improved partial edge information transmission method applied to demodulation process of sparse code multiple access system | |
| CN107483151B (en) | serial multi-user dynamic iteration method based on SCMA system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180330 |
|
| WD01 | Invention patent application deemed withdrawn after publication |