CN115189803B

CN115189803B - Multi-user signal detection method, device, equipment and storage medium

Info

Publication number: CN115189803B
Application number: CN202110358445.7A
Authority: CN
Inventors: 李元杰; 董超; 索士强; 牛凯; 白伟
Original assignee: Datang Mobile Communications Equipment Co Ltd
Current assignee: Datang Mobile Communications Equipment Co Ltd
Priority date: 2021-04-01
Filing date: 2021-04-01
Publication date: 2024-03-08
Anticipated expiration: 2041-04-01
Also published as: CN115189803A

Abstract

The application discloses a multi-user signal detection method, a multi-user signal detection device, multi-user signal detection equipment and a storage medium, and relates to the technical field of communication. The specific implementation scheme is as follows: the number of active users in a channel is acquired, multi-level prior distribution information or multi-level information is determined according to the number of users, a receiving symbol is acquired, and the log likelihood ratio of binary domain superposition code words of all users on the receiving symbol is determined according to the multi-level prior distribution information or the multi-level information. Therefore, the recovery success rate of the receiving end to the multi-user superposition information is improved, and the access error probability of the whole macro access cascade code system is minimized.

Description

Multi-user signal detection method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting a multi-user signal.

Background

As mobile communication progresses, a plurality of organizations begin to study new wireless communication systems. At present, the macro access is a new multiple access scheme, it can be understood that the macro access can directly perform random access coding on user information and directly send out the user information, and all terminals use the same encoder, so that the macro access can realize that a large number of terminals access the network simultaneously.

In the related art, the macro access is realized by adopting a cascade code design mode, and specifically, the macro access is completed by adopting a cascade mode of multiple access channel coding and error channel reliability coding. However, the above manner detects the multi-user signal based on the binary domain equivalent decision, so that the performance loss of the multi-user signal detection is relatively large, and the final multi-user signal detection accuracy is relatively low.

Disclosure of Invention

The application provides a multi-user signal detection method, a multi-user signal detection device, multi-user signal detection equipment and a storage medium.

According to an aspect of the present application, there is provided a multi-user signal detection method, the method comprising:

acquiring the number of active users in a channel;

determining multilevel prior distribution information or multilevel information according to the number of users;

and acquiring a receiving symbol, and determining the log-likelihood ratio of the binary domain superposition code words of each user on the receiving symbol according to the multi-level prior distribution information or the multi-level information.

Optionally, the determining the log-likelihood ratio of the binary domain superposition codeword for each user on the received symbol according to the multi-level prior distribution information includes:

acquiring noise information in a channel, and acquiring a first comparison table comprising the multi-level prior distribution information;

Calculating according to a preset first algorithm or a second algorithm, a preset first parameter, the noise information and the first comparison table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users;

and acquiring the log-likelihood ratio of the binary domain superposition codeword according to the first log-likelihood ratio component and the second log-likelihood ratio component.

Optionally, the calculating is performed according to a preset first algorithm, a preset first parameter, the noise information and the first comparison table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users, comprising:

presetting the first parameter, which comprises: l (L) ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0; wherein l ₀ Is thatSerial number of multilevel symbol, l ₁ Is->Serial number of multilevel symbol,/, of (a)>Superimposing codewords for binary domains corresponding to received symbols, LLR ₀ Is->Log-likelihood ratio component, LLR ₁ Is->Log likelihood ratio components of (a);

querying a first comparison table to obtainCalculating the corresponding conditional probability->And->Wherein (1)>First generated for multi-user signal superposition ₀ Multiple multi-level symbols>First generated for multi-user signal superposition ₁ Multiple multi-level symbols, y being the received symbol, delta ² The first comparison table is a mapping table containing multi-level prior distribution information;

querying the first comparison table to obtain corresponding prior probabilityAnd->Wherein (1)>Is the first ₁ A priori probabilities of multiple multilevel symbols, +.>Is the first ₀ The prior probabilities of the multiple multi-level symbols;

updating the LLR according to the first algorithm ₀ And the LLR is described as ₁ And then l ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ The values of the first log likelihood ratio component and the second log likelihood ratio component are larger than the number of users, otherwise, the conditional probability and the prior probability are updated and calculated; wherein the first algorithm is

The obtaining the log-likelihood ratio of the binary domain superposition codeword according to the first log-likelihood ratio component and the second log-likelihood ratio component includes:

optionally, the calculating is performed according to a preset second algorithm, a preset first parameter, the noise information and the first comparison table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users, comprising:

querying a first comparison table to obtainCalculating the corresponding conditional probability->Andwherein (1)>First generated for multi-user signal superposition ₀ Multiple multi-level symbols>First generated for multi-user signal superposition ₁ Multiple multi-level symbols, y being the received symbol, delta ² Is the noise variance of the channel;

querying the first comparison table to obtain corresponding prior probabilityAnd->And calculate its logarithmic form +.>And (3) withWherein (1)>Is the first ₁ A priori probabilities of multiple multilevel symbols, +.>Is the first ₀ The prior probabilities of the multiple multi-level symbols are shown in the first comparison table, and the first comparison table is a mapping table containing multi-level prior distribution information;

respectively comparing the existing LLRs ₀ ，LLR ₁ The relation with the LLR values calculated by the second algorithm is that larger values are respectively assigned to LLRs ₀ ，LLR ₁ Then l is taken up ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ The values of the first log likelihood ratio component and the second log likelihood ratio component are larger than the number of users, otherwise, the conditional probability and the prior probability are updated and calculated; wherein the second algorithm is

Optionally, the determining, according to the multi-level information, a log-likelihood ratio of a binary domain superposition codeword of each user on the received symbol includes:

acquiring noise information in a channel and acquiring a second comparison table comprising the multi-level information;

calculating according to a preset third algorithm, a preset second parameter and the second comparison table, and obtaining a detection value of the binary domain superposition code word when the value of l is larger than the number of users;

and acquiring the log-likelihood ratio of the binary domain superposition codeword according to the detection value and the noise information.

Optionally, the calculating according to the third preset algorithm, the second preset parameter and the second comparison table, to obtain the detection value of the binary field superposition codeword when the value of l is greater than the number of users, includes:

presetting the second parameter, including: l=0 and,wherein l is the serial number of the multilevel symbol, < >>Superimposing codewords for the binary field corresponding to the received symbol, < >>The current minimum normalized Euclidean distance;

querying a second comparison table to obtain mu _l Calculating conditional probabilityWhere y is the received symbol, delta ² Is the noise variance of the channel, mu _l A first multi-level symbol generated for multi-user signal superposition;

Updating according to the third algorithmInquiring the second comparison table to obtain corresponding +.>l=l+1, if L is larger than or equal to L, obtaining the detection value of the binary domain superposition codeword when the value of L is larger than the number of users, otherwise updating and calculating the conditional probability sum +.>Wherein the third algorithm is if ∈>Then->If->Then->

The obtaining the log-likelihood ratio of the binary domain superposition codeword according to the detection value and the noise information includes:

wherein (1)>Is->Delta ² Is the noise variance in the channel.

Optionally, the multi-user signal detection method further includes:

acquiring noise measurement values of a plurality of noise signals;

calculating according to the noise measured value to obtain a noise variance;

and determining the calculation mode of the log-likelihood ratio of the binary domain superposition code words of each user on the received symbol according to the multi-level prior distribution information or the multi-level information according to the comparison result of the noise variance and the preset threshold variance.

According to another aspect of the present application, there is provided a network side device, including a memory, a transceiver, and a processor:

a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:

Acquiring the number of active users in a channel;

Presetting the first parameter, which comprises: l (L) ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0; wherein l ₀ Is thatSerial number of multilevel symbol, l ₁ Is->Is a multi-level symbol sequence of (2)Number (1)/(2)>Superimposing codewords for binary domains corresponding to received symbols, LLR ₀ Is->Log-likelihood ratio component, LLR ₁ Is->Log likelihood ratio components of (a);

querying a first comparison table to obtainCalculating the corresponding conditional probability->And->Wherein (1)>First generated for multi-user signal superposition ₀ Multiple multi-level symbols>First generated for multi-user signal superposition ₁ Multiple multi-level symbols, y being the received symbol, delta ² Is the noise variance of the channel;

updating according to the third algorithmInquiring the second comparison table to obtain corresponding +.>l=l+1, if L is greater than or equal to L, obtaining a binary field stack when the value of L is greater than the number of usersAdding the detection value of the code word, otherwise updating and calculating the conditional probability sum +.>Wherein the third algorithm is if ∈>Then->If->Then->

wherein (1)>Is->Delta ² Is the noise variance in the channel.

Optionally, the network side device further includes:

acquiring noise measurement values of a plurality of noise signals;

calculating according to the noise measured value to obtain a noise variance;

According to another aspect of the present application, there is provided a multi-user signal detection apparatus, the apparatus comprising:

the acquisition module is used for acquiring the number of active users in the channel;

the determining module is used for determining multilevel prior distribution information or multilevel information according to the number of the users;

and the acquisition determining module is used for acquiring a received symbol and determining the log-likelihood ratio of the binary domain superposition code words of all users on the received symbol according to the multi-level prior distribution information or the multi-level information.

According to another aspect of the present application, there is provided a processor-readable storage medium storing a computer program for causing the processor to execute the method for multi-user signal detection as described above.

According to another aspect of the present application, there is provided a computer program product which, when executed by an instruction processor in the computer program product, performs the method for multi-user signal detection as described above.

The application has the following technical effects: the number of activated users in the channel is obtained, multi-level prior distribution information or multi-level information is determined according to the number of the users, a receiving symbol is obtained, the log-likelihood ratio of binary domain superposition code words of all the users on the receiving symbol is determined according to the multi-level prior distribution information or the multi-level information, so that the obtained log-likelihood ratio is input into a channel decoder for processing to detect multi-user signals, the information loss generated by directly detecting and judging the multi-level is minimized, the correct detection of the multi-user information is ensured to the greatest extent, the prior distribution information of the multi-level symbol is fully utilized, the division of the multi-level judgment domain is more reasonable, and the error probability of multi-user signal detection is minimized.

It should be understood that the description of this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.

Drawings

The drawings are for better understanding of the present solution and do not constitute a limitation of the present application. Wherein:

FIG. 1 is a schematic diagram of a cascaded code macro access system architecture;

FIG. 2 is a flow chart of a tandem-code macro access system according to an embodiment of the present disclosure;

fig. 3 is a schematic diagram of a slotted ALOHA mode provided in an embodiment of the present application;

fig. 4 is a flow chart of a multi-user signal detection method according to an embodiment of the present application;

FIG. 5 is a diagram illustrating a multi-user signal detection method according to an embodiment of the present application;

FIG. 6 is a flowchart of another multi-user signal detection method according to an embodiment of the present disclosure;

FIG. 7 is a diagram illustrating another exemplary method for detecting a multi-user signal according to an embodiment of the present application;

FIG. 8 is a schematic diagram of performance contrast analysis of different symbol decision algorithms provided in accordance with embodiments of the present application;

fig. 9 is a schematic structural diagram of a network side device according to an embodiment of the present application;

Fig. 10 is a schematic structural diagram of a multi-user signal detection device according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of another multi-user signal detection apparatus according to an embodiment of the present application.

Detailed Description

The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

That is, in the embodiment of the present application, the term "and/or" describes the association relationship of the association objects, which means that three relationships may exist, for example, a and/or B may be represented: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

Currently, with the gradual completion of the standardization of the 5G technology, the academy and industry begin to look for future 6G technology, wherein an important 6G prospect is the continuous evolution from massive machine type communication (emtc, massive Machine Type Communication) which is one of the important scenes of 5G to ultra-massive machine type communication (umMTC, ultra-massive Machine Type Communication), and the industry demands the evolution from the internet of everything (Internet of Everything, ioE) to the ultra-intelligent internet of everything (Hyper Intelligent Internet of Everything, HIIoE).

The macro access technology is oriented to a non-cooperative scene, and the optimization target is average access error rate of each user. The non-cooperative technology does not need unified scheduling of a cooperative center, and users randomly generate collision in a channel. The active terminals independently transmit information regardless of whether other terminals are occupying channels, and thus, when the user simultaneously transmits information, collisions occur on the channels for transmitting information. Meanwhile, the superposition relation of the user information on the channel is also random, and the access behavior of the terminal is more flexible. It will be appreciated that the non-cooperative features are mainly: (1) non-uniform access scheduling; (2) no pilot; (3) no pre-allocated resources (4) the user active state is unknown. The non-cooperative feature requires that all users use exactly the same set of transmission protocols, occupying resources at all random. Therefore, the method is suitable for application scenes of a large number of users, high overload factors and low data rate.

In the related art, the method mainly adopts a cascade code design mode to realize the macro access, and specifically adopts a cascade mode of multiple access channel coding and error channel reliability coding to finish the macro access. Based on the above description, based on the structural features of the non-cooperative mode, the manner for macro access needs to solve two problems: 1) User random collision and information recovery; 2) Channel noise effects. The cascade code system adopts a two-stage coding structure to respectively treat the two problems.

Specifically, the concatenated code structure is a system structure based on a non-cooperative mode, and a non-cooperative mechanism is adopted for transmitting user code words, so that superposition collision exists on the same resource, and a BAC (Binary Adder Channel, binary addition) channel is formed. Therefore, to cope with collision superposition on a channel, the user information payload first enters a multiple access encoder (first level encoder) at the transmitting end, and after multiple access encoding, the receiver at the receiving end can recover the respective messages of the respective users participating in collision superposition. The second-stage encoder is a channel reliability encoder and is used for resisting noise in a channel and reducing the error probability of a user data packet in the noisy channel transmission process. The receiving end, the first step sends the received code word into the channel decoder to finish the operation Forward (CoF) stage; and the second step is to reversely recover the respective codewords of the multiple users which are overlapped together through a multiple access decoder, namely a BAC stage.

As one possible implementation, w as shown in fig. 1 and 2 ₁ And w is equal to ₂ The information sent by the user 1 and the user 2 are respectively output multiple access code components alpha after being coded by the multiple access coder ₁ And alpha is ₂ The components are respectively sent to a channel reliability encoder to obtain codeword components c with cascade coding completed ₁ And c ₂ . Where α and c are both binary bit sequences, and modulation is required for transmission in the actual channel. After modulation, a symbol sequence x is obtained ₁ And x ₂ While the simultaneously transmitted symbol sequences are directly superimposed in the channel (user timing aligned), pass through a fading channel (channel h is omitted in fig. 1 and 2) and superimpose additive noise n in the channel. The channel output received at the receiving end is y, which is mapped to the input of the channel reliability decoder after multi-user signal detectionI.e. the multi-user information sequence in the superimposed state. Decoding the code word regarded as the channel reliability code, and obtaining a multi-user coding sequence in a superposition state at the output end of a decoder after error correction is completed>Then send it into the multiple access decoder to obtain the information detection result of user 1 and user 2>And->

Thus, it can be seen that the above system design has several features: (1) Although different users perform independent coding, the encoder configuration of the users participating in transmission is identical, and the distributed multi-user coding system is adopted at the originating end; (2) The non-cooperativity of the system enables the transmission of the user information of the transmitting end to be carried out randomly in the time domain, so that the problem of multi-user collision can be generated when the user information is transmitted on a channel, namely, a plurality of user information is transmitted on the channel at the same time, and at the receiving end, the information of the collision superposition of the plurality of users is processed in a centralized way by a unified receiver, and the superposed signal is recovered; (3) From the perspective of collision, because the collision is generated on the channel, the superposition of signals is completed on the channel, and the superposition is at the symbol level, so that the receiving end receives the multi-level symbol, and the receiving end firstly detects the multi-user signal in the superposition state and then sends the multi-user signal to the channel reliability decoder when decoding and demodulating, thereby starting the two-stage decoding process of the cascade code.

In the embodiment of the application, a time slot ALOHA mechanism adopted in the macro access forms a random collision channel for multi-user superposition. As shown in FIG. 3, the vertical axis is K _a Individual users (i.e., active user numbers). The n-long data frames on the horizontal axis are divided intoPersonal->Long sub-frame blocks (i.e. the length of the information block sent by the user per slot is +.>) Each subframe block is an orthogonal resource block, and V is the number of allocated slots. Each user picks a random subframe to send +.>Long codewords. Due to the randomness of the user's transmitted information, it may happen that the same time slot is occupied by multiple users simultaneously.

In fig. 3, taking the 2 nd slot as an example, user m ₁ And m is equal to ₂ Respectively transmitted symbol sequences x ₁ And x ₂ A superimposed collision occurs to generate the multi-level signal u. Further finding the concatenated coded codeword bit c ₁ And c ₂ Parity check result formed by binary domain collisionThere is a certain mapping rule f between multi-level symbols u formed by overlapping with BPSK (Binary Phase Shift Keying ) symbols, and each level distribution on the symbol domain is a priori unequal probability. Since symbol superposition is performed symbol by symbol, only a single symbol superposition collision channel model needs to be of interest. One element in the vector is represented by a positive-body variable symbol corresponding to the bold italic vector symbol (i.e., a single symbol, e.g., u replaces one symbol in the multi-level symbol vector u).

Specifically, in a symbol collision channel, the codeword of the user is not a direct modulo-two addition relationship, but a symbol superposition relationship:wherein y represents a single received symbol, x, assuming the fading channel h is equalized _l Representing the superimposed BPSK symbols transmitted by the first user, participating in the slot at the symbol position, u being the generated multilevel symbol,is an AWGN (Additive White Gaussian Noise),additive white gaussian noise).

Let user l send two-level coded bits c _l The result of the codeword superposition of each user at that location should be:

after BPSK modulation, the user code word generates a transmission symbol x _l E { +1, -1}, satisfy x _l ＝2c _l -1. From the channel model, the parity relation formed by binary domain collision between bits can be obtainedMultilevel symbol formed by overlapping BPSK symbol>There is a certain mapping rule between: />

For symbol detection, a binary domain equivalent decision algorithm is used. Due to the existence of noise in the channel, a corresponding symbol detection algorithm is designed according to the distribution characteristic of the received symbols y, and the conversion of the parity check relation formed by the collision of the received multilevel symbols y and the binary domain is completed according to the mapping rule. Namely:

for the binary domain equivalent decision algorithm, the equivalent transformation g (y) is adopted, and the received symbol y is mapped back to the modulus Yu Oujian of [0, 2) to obtain an equivalent symbol:

Wherein,is a binary field bit so that the modulo two result remains unchanged while the AWGN noise n is also being suppressedMapped to the modulo space of [0, 2). The decision device then needs to be in this interval according to +.>Distribution characteristics pair->And the decision domain division is carried out on the value of the (a). Regardless of the a priori distribution of the multilevel symbols, according to a minimum euclidean distance criterion (i.e., ML criterion), the decision domain is generated by the following rule:

thereby, the mapping from the symbol domain to the binary domain is completed,the channel reliability coding code word which is overlapped in the binary domain equivalent channel can be directly sent to the next level decoder to finish decoding. It should be noted that +.>In operation, it has been assumed that the receiving end knows the number of users L that are now superimposed on the channel.

The multi-user signal detection mode firstly carries out equivalent mapping on multi-user signals back to a modulo two domain, then carries out binary judgment according to the mapping equivalent relation between a binary domain and a symbol domain, and outputs a bit sequence. Because the cascade code system is realized by sequential cascade, the performance loss exists, for example, the performance loss exists in an equivalent mapping link in the method, the superposition result of the multi-user signal under the influence of Gaussian noise is merged into a binary signal through equivalent mapping for processing, and compared with the performance loss of a direct processing scheme of a multi-level judgment mode; for example, the method does not consider the characteristic of prior distribution of multi-user signals, obviously, the prior distribution of mapping from multiple levels to binary domains is not equal probability, in the multi-user signal detection algorithm, the prior distribution of symbols will influence the correct division of a decision domain, namely, the rule that a received signal is mapped back to a bit information domain, while the decision domain division method of the binary domain equivalent decision algorithm assumes equal prior probability of user information and does not consider the characteristic of unequal prior probability of symbols superimposed in a channel, therefore, the binary domain equivalent decision algorithm is not the algorithm with the lowest error rate for multi-user signal detection, for example, because a channel reliability decoder adopts a soft information input and soft information output (called soft in soft out for short) algorithm to perform iterative decoding, the initial input information of the iteration is the soft information output from a multi-user information detection module, but because the binary domain equivalent decision algorithm adopted by a classical scheme outputs a hard decision result, the performance of the soft in soft out algorithm with high performance is greatly discounted.

Aiming at the problems, the application provides a multi-user signal detection method, which comprises the steps of obtaining the number of active users in a channel, determining multi-level prior distribution information or multi-level information according to the number of users, obtaining a receiving symbol, and determining the log-likelihood ratio of binary domain superposition codewords of each user on the receiving symbol according to the multi-level prior distribution information or the multi-level information, so that the obtained log-likelihood ratio is input into a channel decoder for processing to detect multi-user signals, the information loss generated by directly detecting and judging the multi-level is minimum, the correct detection of the multi-user information is ensured to the greatest extent, the prior distribution information of the multi-level symbol is fully utilized, and the division of the multi-level judgment domain is more reasonable, thereby minimizing the error probability of multi-user signal detection.

The multi-user signal detection method, apparatus, device and storage medium of the present embodiment are described below with reference to the accompanying drawings.

Fig. 4 is a flowchart of a multi-user signal detection method according to an embodiment of the present application.

As shown in fig. 4, the multi-user signal detection method includes:

step 101, the number of active users in the channel is obtained.

Step 102, multi-level prior distribution information or multi-level information is determined according to the number of users.

Step 103, obtaining the received symbol, and determining the log-likelihood ratio of the binary domain superposition code words of each user on the received symbol according to the multi-level prior distribution information or the multi-level information.

It should be noted that, the present application is mainly directed to the improvement of the symbol hard decision making and the decision result converting to the log likelihood ratio module described in fig. 2, and no improvement of other modules is involved, for example, multiple access decoding and reliability decoding can all use the existing decoding algorithms such as low density check codes.

In the embodiment of the application, the multi-level symbol judgment is firstly carried out by directly adopting a multi-level judgment method. That is, according to the characteristics of the multi-user signal, multi-level symbols with unequal probability prior distribution are superimposed on the same time slot, and the multi-level symbols have a direct mapping relationship with multi-user information superimposed in a binary bit domain. Therefore, the information loss generated by directly carrying out detection decision on the multi-level symbol is minimum, thereby ensuring the correct detection of multi-user information to the greatest extent.

There are various ways to obtain the number of active users in the channel, and as an example, a received signal in the channel is obtained, a pilot sequence signal is extracted from the received signal, and the number of active users is determined according to the pilot sequence signal, that is, the number of active users can be obtained by pilot sequence correlation detection, so as to obtain the number of superimposed users. In the foregoing manner, the influence of the fading channel is not considered, because the receiving end can perform channel estimation through the pilot sequence and then perform channel equalization.

Wherein a received symbol refers to a received multi-level symbol.

Further, there are various ways of obtaining the number of active users in the channel, generating multi-level prior distribution information or multi-level information according to the number of users, obtaining the received symbol, and determining the log-likelihood ratio of the binary domain superposition codeword of each user on the received symbol according to the multi-level prior distribution information or multi-level information, for example, as follows.

In a first example, noise information in a channel is obtained, a first comparison table including multi-level prior distribution information is obtained, and calculation is performed according to a preset first algorithm or a preset second algorithm, a preset first parameter, the noise information and the first comparison table to obtain l ₀ ，l ₁ The values of the two-dimensional domain superposition code words are larger than the first log-likelihood ratio component and the second log-likelihood ratio component when the number of users is larger than the number of users, and the log-likelihood ratios of the two-dimensional domain superposition code words are obtained according to the first log-likelihood ratio component and the second log-likelihood ratio component.

In a second example, noise information in a channel is acquired, a second comparison table including the multi-level information is acquired, calculation is performed according to a preset third algorithm, a preset second parameter and the second comparison table, a detection value of a binary domain superposition codeword when a value of l is larger than the number of users is acquired, and a log likelihood ratio of the binary domain superposition codeword is acquired according to the detection value and the noise information.

In summary, in the embodiment of the present application, the number of active users in a channel is acquired, multi-level prior distribution information or multi-level information is determined according to the number of users, a received symbol is acquired, and the log-likelihood ratio of the binary domain superposition codeword of each user on the received symbol is determined according to the multi-level prior distribution information or the multi-level information. Therefore, the recovery success rate of the receiving end to the multi-user superposition information is improved, and the access error probability of the whole macro access cascade code system is minimized.

Based on the description of the above embodiment, there are various ways to obtain the received symbol and obtain the log-likelihood ratio of the binary domain superposition codeword according to the multi-level a priori distribution information, and the above first example is described in detail below with reference to fig. 5.

Fig. 5 is a flowchart of another multi-user signal detection method according to an embodiment of the present application.

As shown in fig. 5, the multi-user signal detection method includes:

in step 201, noise information in a channel is obtained, and a first look-up table comprising multi-level a priori distribution information is obtained.

In the embodiment of the present application, a first lookup table including multi-level prior distribution information is generated according to the number of users, where the first lookup table generated when the number of users is even or odd is different, for example as follows.

According to the first exampleAnd->Mapping rules and the number of superimposed users L on the channel, selecting and +.>The corresponding probability center, namely the generated first comparison table comprising the multi-level prior distribution information is shown in the table 1, and the number L of users is an odd number of first comparison tables:

table 1 first comparative table 1

According to the first exampleAnd->Mapping rules and the number of superimposed users L on the channel, selecting and +.>The corresponding probability center, namely the generated first comparison table comprising the multi-level prior distribution information is shown in the table 2, and the number L of users is an even number of first comparison tables:

table 2 first comparative table 2

Wherein mu _l Representing multiple usersThe superposition level takes a value, i represents the number of users with the codeword taking 0 in the users, and P _l Representing the prior probability.

Therefore, the prior distribution information of the multi-level symbols can be fully utilized, wherein the probability of compliance test of the multi-level symbols generated by superposition in a channel is 1/2, and the number of tests is binomial distribution of the number L of superposition users. That is, the receiving end only knows the number of users superimposed on the time slot, and can accurately calculate the level value and the prior probability of each symbol in the multi-level symbol set, so that the division of the symbol decision domain can be more reasonable by utilizing the prior distribution information in the process of multi-user signal detection, and the error probability of multi-user signal detection is minimized.

Step 202, calculating according to a preset first algorithm or a second algorithm, a preset first parameter, noise information and a first comparison table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components are each larger than the number of users.

Step 203, obtaining the log-likelihood ratio of the binary domain superposition codeword according to the first log-likelihood ratio component and the second log-likelihood ratio component.

In the implementation of the present application, different algorithms may be selected differently for calculation, and as a possible implementation manner, the calculation is performed according to a preset first algorithm, a preset first parameter, noise information and a first comparison table to obtain l ₀ ，l ₁ The values of the two-dimensional domain superposition code words are larger than the first log-likelihood ratio component and the second log-likelihood ratio component when the number of users is larger than the number of users, and the log-likelihood ratios of the two-dimensional domain superposition code words are obtained according to the first log-likelihood ratio component and the second log-likelihood ratio component. More specifically:

presetting a first parameter, which comprises the following steps: l (L) ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0; wherein l ₀ Is thatSerial number of multilevel symbol, l ₁ Is->Serial number of multilevel symbol,/, of (a)>Superimposing codewords for binary domains corresponding to received symbols, LLR ₀ Is thatLog-likelihood ratio component, LLR ₁ Is->Log likelihood ratio components of (a); inquiring the first comparison table to obtain- >Calculating the corresponding conditional probability->And->Wherein (1)>First generated for multi-user signal superposition ₀ Multiple multi-level symbols>First generated for multi-user signal superposition ₁ Multiple multi-level symbols, y being the received symbol, delta ² The first comparison table is a mapping table containing multi-level prior distribution information for noise variance in the channel; querying a first comparison table to obtain corresponding prior probability +.>And->Wherein (1)>Is the first ₁ A priori probabilities of multiple multilevel symbols, +.>Is the first ₀ The prior probabilities of the multiple multi-level symbols, the LLR is updated according to a first algorithm ₀ Sum LLR (LLR) ₁ And then l ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ The values of the number of the first log-likelihood ratio components and the second log-likelihood ratio components are obtained if the number of the first log-likelihood ratio components is larger than the number of the users, otherwise, the conditional probability and the prior probability are updated and calculated; wherein the first algorithm is +.>Obtaining the log-likelihood ratio of the binary domain superposition codeword according to the first log-likelihood ratio component and the second log-likelihood ratio component, comprising:

in the implementation of the present application, as another possible implementation manner, the calculation is performed according to a preset second algorithm, a preset first parameter, noise information and a first comparison table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components are each larger than the number of users. More specifically:

Presetting a first parameter, which comprises the following steps: l (L) ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0; wherein l ₀ Is thatSerial number of multilevel symbol, l ₁ Is->Serial number of multilevel symbol,/, of (a)>Binary for receiving symbol correspondenceDomain superposition codeword, LLR ₀ Is thatLog-likelihood ratio component, LLR ₁ Is->Log likelihood ratio components of (a); inquiring the first comparison table to obtain->Calculating the corresponding conditional probability->And->Wherein (1)>First generated for multi-user signal superposition ₀ Multiple multi-level symbols>First generated for multi-user signal superposition ₁ Multiple multi-level symbols, y being the received symbol, delta ² For the noise variance of the channel, the first comparison table is queried to obtain the corresponding prior probability ++>And->And calculate its logarithmic form +.>And (3) withWherein (1)>Is the first ₁ A priori probabilities of multiple multilevel symbols, +.>Is the first ₀ The first comparison table is a mapping table containing multi-level prior distribution information, and compares the prior LLRs respectively ₀ ，LLR ₁ The relation with the LLR value calculated by the second algorithm is that larger values are respectively assigned to LLR ₀ ，LLR ₁ Then l is taken up ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ The values of the number of the first log-likelihood ratio components and the second log-likelihood ratio components are obtained if the number of the first log-likelihood ratio components is larger than the number of the users, otherwise, the conditional probability and the prior probability are updated and calculated; wherein the second algorithm is +. >Obtaining the log-likelihood ratio of the binary domain superposition codeword according to the first log-likelihood ratio component and the second log-likelihood ratio component, comprising: />

In summary, in the embodiment of the present application, by generating the first comparison table including the multi-level prior distribution information, and calculating based on the first comparison table to obtain the log-likelihood ratio, so that the multi-user signal detection is performed by inputting the obtained log-likelihood ratio into the channel decoder for processing, so as to minimize the information loss generated by directly performing detection decision on multiple levels, thereby maximally ensuring the correct detection of the multi-user information, and fully utilizing the prior distribution information of the multi-level symbols, the division of the multi-level decision domain can be more reasonable, so that the error probability of multi-user signal detection is minimized.

As an example, as shown in fig. 6, the number of users L superimposed on the channel is input, the received symbol y, the noise variance y, and the LLR algorithm selects the symbol s (s takes a value of 0 or 1); generating a comparison table comprising multi-level prior distribution information according to the number of users and the symbolsThe number s selects LLS algorithm, e.g. s takes a value of 0, initializes parameters, l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0, calculate the corresponding conditional probabilityAnd- >Lookup tables 1 and 2 obtain a priori probabilities +.>And->Updating the code words to LLR values of 0 and 1, < >>And then l ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ If the number of users is larger than the number of users, acquiring a first log likelihood ratio component and a second log likelihood ratio component, otherwise updating and calculating conditional probability and prior probability, and finally calculating +.>For another example, s takes a value of 1, initializes a parameter, l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0, calculating the corresponding conditional probability +.>And->Lookup tables 1 and 2 obtain a priori probabilities +.>And->And calculate its logarithmic form +.>And->Comparing existing LLRs ₀ ，LLR ₁ And->And->The calculated relationship of LLR values assigns larger values to LLRs ₀ ，LLR ₁ Then l is taken up ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ If the number of users is larger than the number of users, acquiring a first log likelihood ratio component and a second log likelihood ratio component, updating and calculating conditional probability and prior probability, and finally calculating +.>

From this, it follows thatThe multi-user signal detection can be directly sent to a channel reliability decoder to finish decoding, so that the multi-level symbol judgment method based on Bayesian detection fully considers +.>The influence of the prior distribution of (2) on the decision domain division is adjusted from the perspective of the Bayesian detection criterion according to the distribution of likelihood functions. That is, the symbol trip points of the LLRs are decision domain boundaries divided by multi-level symbol decisions based on bayesian detection. Therefore, the prior distribution information of the multi-level symbol is fully utilized, so that the multi-level decision domain can be divided more reasonably, and the error probability of multi-user signal detection is minimized.

Based on the above description of the embodiments, the present application further moves out a multi-user signal detection method for hard information, which is described below with reference to fig. 7.

Fig. 7 is a flowchart of another multi-user signal detection method according to an embodiment of the present application.

As shown in fig. 7, the multi-user signal detection method may include the steps of:

step 301, obtaining noise information in a channel, and obtaining a second lookup table including multi-level information.

In this embodiment of the present application, the second lookup table is generated according to the number of users, where the second lookup table generated when the number of users is even or odd is different, for example as follows.

According to the first example And->Mapping rule and number L of superimposed users on channel to obtain different decision level value mu _l The generated second lookup table including the multi-level prior distribution information is shown in table 3, and the number of users L is an odd number of second lookup tables:

TABLE 3 second comparative Table 3

According to the first exampleAnd->Mapping rule and number L of superimposed users on channel to obtain different decision level value mu _l The generated second lookup table including the multi-level prior distribution information is shown in table 4, and the number of users L is an even number of second lookup tables:

table 4 second comparison table 4

Wherein mu _l Representing the value of multi-user superposition level, l represents the number of users with the codeword of 0, P _l Representing the prior probability.

Step 302, calculating according to a preset third algorithm, a preset second parameter and a second comparison table, and obtaining a detection value of the binary field superposition codeword when the value of l is greater than the number of users.

Step 303, obtaining the log-likelihood ratio of the binary domain superposition codeword according to the detection value and the noise information.

In the embodiment of the application, the hard information detection performs multi-level symbol judgment to obtain LLR, and the LLR comprises three aspects of multi-level judgment, multi-level information utilization and hard information output.

In this embodiment of the present application, as one possible implementation manner, presetting the second parameter includes: l=0 and,wherein l is the serial number of the multilevel symbol, < >>Superimposing codewords for the binary field corresponding to the received symbol, < >>The current minimum normalized Euclidean distance; querying a second comparison table to obtain mu _l Calculating conditional probability->Where y is the received symbol, delta ² Mu, the variance of noise in the channel _l A first multi-level symbol generated for multi-user signal superposition; updating ∈according to the third algorithm>Inquiring the second comparison table to obtain the corresponding +.>l=l+1, if L is greater than or equal to L, obtaining the detection value of the binary domain superposition codeword when the value of L is greater than the number of users, otherwise updating the calculation condition probability sum +.>Wherein the third algorithm is ifThen->If->Then->The obtaining the log-likelihood ratio of the binary domain superposition codeword according to the detection value and the noise information comprises the following steps: />Wherein,/>is->Delta ² Is the noise variance in the channel.

From this, it follows thatAnd then the signal can be directly sent into a channel reliability decoder to finish decoding, so that the signal is input into the channel decoder for processing based on the acquired log likelihood ratio to detect multi-user signals. The decision domain is directly divided by multi-level symbol decision based on the distribution of P { y }, which solves +. >The problem of a priori unequal probabilities when L is odd.

In summary, in the embodiment of the present application, by generating the second comparison table including the multi-level prior distribution information, and calculating based on the second comparison table to obtain the log-likelihood ratio, so that the multi-user signal detection is performed by inputting the obtained log-likelihood ratio into the channel decoder for processing, so as to minimize the information loss generated by directly performing detection decision on multiple levels, thereby maximally ensuring the correct detection of the multi-user information, and fully utilizing the prior distribution information of the multi-level symbols, the division of the multi-level decision domain can be more reasonable, so that the error probability of multi-user signal detection is minimized.

As an example, as shown in fig. 8, the number of users L superimposed on the channel, the received symbol y, the noise variance y, a comparison table including multi-level a priori distribution information is input, the parameters are initialized, l=0,calculating conditional probability->Assign a smaller value to +.>Inquiring the second comparison table to obtain the corresponding +.>l=l+1, if L is greater than or equal to L, obtaining the detection value of the binary domain superposition codeword, otherwise updating the calculation condition probability and +.>According to the detection value and the noise variance, obtaining the log likelihood ratio of the binary domain superposition code word as +. >And output.

Therefore, the division of the multi-level decision domain can be made more reasonable, thereby minimizing the error probability of multi-user signal detection.

Based on the description of the foregoing embodiments, as a possible implementation manner of the present application, the present application further proposes a method for detecting soft and hard information in a mixed manner, that is, obtaining noise measurement values of a plurality of noise signals, calculating according to the noise measurement values, obtaining noise variance, and determining a calculation manner of log likelihood ratio of binary domain superposition codewords of each user on the received symbol according to multi-level prior distribution information or multi-level information according to a comparison result of the noise variance and a preset threshold variance.

Specifically, the soft and hard information mixed detection algorithm performs multi-level symbol judgment to obtain LLR, and the LLR comprises five updating aspects of multi-level judgment, priori distribution information utilization, soft information output, hard information output and information output selection.

As an example, the number of users activated on the channel L, the received symbol y, the noise variance σ ² Threshold varianceLLR algorithm selects symbol s _h (s _h Value 0 or 1), and determining the noise variance sigma ² And threshold variance->If the size relation of (a)Soft information detection of multi-user signals: according to- >And->Mapping rules and number of superimposed users on channel Lselect and +.>Corresponding mu _l As shown in table 1 when L is an odd number, and as shown in table 2 when L is an odd number. Selecting LLS algorithm according to symbol s, such as s takes value of 0, initializing parameter, l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0, calculate the corresponding conditional probabilityAnd->Lookup tables 1 and 2 obtain a priori probabilities +.>And->LLR values updating codeword to 0 and 1 +.>And then l ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ The values of the numbers are all larger than the number of users, the first is obtainedThe log-likelihood ratio component and the second log-likelihood ratio component, otherwise updating the calculated conditional probability and the prior probability, and finally calculating +.>For another example, s takes a value of 1, initializes a parameter, l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0, calculating the corresponding conditional probability +.>And->Lookup tables 1 and 2 obtain a priori probabilities +.>And->And calculate its logarithmic form +.>And->Comparing existing LLRs ₀ ，LLR ₁ And (3) withAnd->Calculating the relationship of LLR values, assigning larger values to LLR ₀ ，LLR ₁ Then l is taken up ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ If the values of the number of the users are larger than the number of the users, acquiring a first log-likelihood ratio component and the second log-likelihood ratio component, updating and calculating conditional probability and prior probability, and finally calculating ++>

From this, it follows thatThe multi-user signal detection can be directly sent to a channel reliability decoder to finish decoding, so that the multi-level symbol judgment method based on Bayesian detection fully considers +. >The influence of the prior distribution of (2) on the decision domain division is adjusted from the perspective of the Bayesian detection criterion according to the distribution of likelihood functions. That is, the symbol trip points of the LLRs are decision domain boundaries divided by multi-level symbol decisions based on bayesian detection. Therefore, the prior distribution information of the multi-level symbol is fully utilized, so that the multi-level decision domain can be divided more reasonably, and the error probability of multi-user signal detection is minimized.

If it isSoft information detection of multi-user signals: according to->And->Mapping rules and number of superimposed users on channel Lselect and +.>Corresponding mu _l As shown in table 3 when L is odd, as shown in table 4 when L is odd, initializing parameters, l=0, +.>Calculating conditional probability->Assign a smaller value to +.>Inquiring the second comparison table to obtain the corresponding +.>l=l+1, if L is greater than or equal to L, obtaining the detection value of the binary domain superposition codeword when the value of L is greater than the number of users, otherwise updating the calculation condition probability sum +.>According to the detection value and the noise variance, obtaining the log likelihood ratio of the binary domain superposition code word as +.>And output.

In order to further verify that the multi-user signal detection method can be applied to a multi-user random collision channel to improve the symbol detection performance, the performance comparison between the existing detection scheme in the macro access concatenated code system and the soft information detection scheme of the application is given below.

Specifically, the data frame length: 3600bit, system code rate:active user number: k (K) _a ＝50、K _a =100, maximum number of collisions allowed: t=10, ldpc code rate: />BCH code rate: />Number of bits per frame transmitted per user: m=12. The simulation results are shown in FIG. 9, and from the overall performance, the soft information detection scheme is compared with the existing detection schemeAt->The energy efficiency of the system is improved by 1.8dB under the code rate, and the code rate is +.>The code rate is improved by 1.7dB

Therefore, the method improves the recovery success rate of the receiving end to the multi-user superposition information through the design method of multi-level judgment, combining the prior information and the soft information output, so that the access error probability of the whole macro access cascade code system is minimized, the soft information detection algorithm is properly simplified through the multi-level judgment and the mode of converting the hard information into soft symbol output, the overall performance of the method is still improved to a certain extent under the condition of high signal-to-noise ratio compared with the traditional scheme, and finally, the two calculation modes can be flexibly switched through setting the threshold value of the receiving signal-to-noise ratio, so that the flexibility of multi-user signal detection is further improved.

In order to achieve the above embodiments, the present application further provides a network side device.

Fig. 9 is a schematic structural diagram of a network side device according to an embodiment of the present application.

The network device according to the embodiments of the present application may be configured to exchange received air frames with internet protocol (Internet Protocol, IP) packets, as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device may also coordinate attribute management for the air interface. For example, the network device according to the embodiments of the present application may be a network device (Base Transceiver Station, BTS) in a global system for mobile communications (Global System for Mobile communications, GSM) or code division multiple access (Code Division Multiple Access, CDMA), a network device (NodeB) in a wideband code division multiple access (Wide-band Code Division Multiple Access, WCDMA), an evolved network device (evolutional Node B, eNB or e-NodeB) in a long term evolution (long term evolution, LTE) system, a 5G base station (gNB) in a 5G network architecture (next generation system), a home evolved base station (Home evolved Node B, heNB), relay node (relay node), home base station (femto), pico base station (pico), and the like. In some network structures, the base station may include a Centralized Unit (CU) node and a Distributed Unit (DU) node, which may also be geographically separated.

It should be noted that the technical solution provided in the embodiments of the present application may be applicable to various systems, especially a 5G system. For example, applicable systems may be global system for mobile communications (Global System of Mobile communication, GSM for short), code division multiple access (Code Division Multiple Access, CDMA for short), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA for short), general packet Radio service (General Packet Radio Service, GPRS for short), long term evolution (Long Term Evolution, LTE) system, LTE frequency division duplex (Frequency Division Duplex, FDD for short), LTE time division duplex (Time Division Duplex, TDD for short), long term evolution-advanced (Long Term Evolution Advanced, LTE-a) system, universal mobile system (Universal Mobile Telecommunication System, UMTS for short), worldwide interoperability for microwave access (Worldwide interoperability for Microwave Access, wiMAX) system, new air interface (New Radio for short NR) system, and the like. Terminal devices and base stations are included in these various systems. The system may further include a core network part, such as an evolved packet system (Evloved Packet System, abbreviated EPS), a 5G system (5 GS), and the like.

As shown in fig. 10, the network side device may include: transceiver 1000, processor 1010, memory 1020, wherein:

a transceiver 1000 for receiving and transmitting data under the control of a processor 1010.

Wherein in fig. 10, a bus architecture may comprise any number of interconnected buses and bridges, and in particular one or more processors represented by processor 1010 and various circuits of memory represented by memory 1020, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. Transceiver 1000 may be a number of elements, including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc. The processor 1010 is responsible for managing the bus architecture and general processing, and the memory 1020 may store data used by the processor 1010 in performing operations.

The processor 1010 may be a central processing unit (Central Processing Unit, CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or a complex programmable logic device (Complex Programmable Logic Device, CPLD), or a multi-core architecture.

The processor 1010 executes the following operations by calling a computer program stored in the memory:

acquiring the number of active users in a channel;

calculating according to a preset first algorithm or a second algorithm, a preset first parameter, the noise information and the first comparison table to obtain l ₀ ，l ₁ The values of (a) are all greater than the number of usersA first log-likelihood ratio component and a second log-likelihood ratio component;

Alternatively, as another embodiment, the calculating is performed according to a preset first algorithm, a preset first parameter, the noise information, and the first comparison table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users, comprising:

alternatively, as another embodiment, the calculating is performed according to a preset second algorithm, a preset first parameter, the noise information, and the first comparison table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users, comprising:

querying the first comparison table to obtain corresponding prior probabilityAnd->And calculate its logarithmic form +.>And (3) withWherein (1)>Is the first ₁ A priori probabilities of multiple multilevel symbols, +. >Is the first ₀ The prior probabilities of the multiple multi-level symbols are shown in the first comparison table, and the first comparison table is a mapping table containing multi-level prior distribution information;

respectively comparing the existing LLRs ₀ ，LLR ₁ The relation with the LLR values calculated by the second algorithm is that larger values are respectively assigned to LLRs ₀ ，LLR ₁ Then l is taken up ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ All have large valuesAcquiring the first log-likelihood ratio component and the second log-likelihood ratio component according to the number of users, otherwise updating and calculating the conditional probability and the prior probability; wherein the second algorithm is

optionally, as another embodiment, the determining, according to the multi-level information, a log-likelihood ratio of a binary domain superposition codeword of each user on the received symbol includes:

Optionally, as another embodiment, the calculating according to the preset third algorithm, the preset second parameter and the second lookup table, to obtain the detection value of the binary domain superposition codeword when the value of l is greater than the number of users includes:

wherein (1)>Is->Delta ² Is the noise variance in the channel.

Optionally, as another embodiment, the multi-user signal detection method further includes: acquiring noise measurement values of a plurality of noise signals; calculating according to the noise measured value to obtain a noise variance; and determining the calculation mode of the log-likelihood ratio of the binary domain superposition code words of each user on the received symbol according to the multi-level prior distribution information or the multi-level information according to the comparison result of the noise variance and the preset threshold variance.

It should be noted that, the network side device provided in the embodiment of the present application can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the embodiment are not described in detail herein.

In order to implement the above embodiment, the present application further proposes a multi-user signal detection apparatus.

Fig. 11 is a schematic structural diagram of a multi-user signal detection device according to an embodiment of the present application.

As shown in fig. 11, the multi-user signal detection apparatus may include: an acquisition module 1110, a determination module 1120, and an acquisition determination module 1130.

Wherein, the acquiring module 1110 is configured to acquire the number of active users in the channel.

A determining module 1120, configured to determine multi-level a priori distribution information or multi-level information according to the number of users.

And the acquisition determining module 1130 is configured to acquire a received symbol, and determine a log-likelihood ratio of a binary domain superposition codeword of each user on the received symbol according to the multi-level prior distribution information or the multi-level information.

Therefore, the number of activated users in the channel is acquired, multi-level prior distribution information or multi-level information is determined according to the number of the users, a receiving symbol is acquired, the log-likelihood ratio of binary domain superposition code words of all the users on the receiving symbol is determined according to the multi-level prior distribution information or the multi-level information, so that the acquired log-likelihood ratio is input into a channel decoder for processing to detect multi-user signals, the information loss generated by directly detecting and judging the multi-level is minimized, the correct detection of the multi-user information is ensured to the greatest extent, the prior distribution information of the multi-level symbol is fully utilized, the division of the multi-level judgment domain is more reasonable, and the error probability of multi-user signal detection is minimized.

It should be noted that, the multi-user signal detection device provided in this embodiment of the present application can implement all the method steps implemented in the method embodiments of fig. 1 to 9, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiments in this embodiment are omitted herein.

It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice. In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network side device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM for short), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

It should be noted that, the above device provided in this embodiment of the present application can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in this embodiment are omitted.

In another aspect, embodiments of the present application further provide a processor-readable storage medium storing a computer program for causing a processor to perform the method illustrated in the embodiment of fig. 5 of the present application.

Among other things, the above-described processor-readable storage medium may be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memories (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical memories (e.g., CD, DVD, BD, HVD, etc.), semiconductor memories (e.g., ROM, EPROM, EEPROM, nonvolatile memories (NAND FLASH), solid State Disks (SSDs)), etc.

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, magnetic disk storage, 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-executable instructions. These computer-executable 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 processor-executable instructions may also be stored in a processor-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 processor-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 processor-executable 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.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims

1. A method for multi-user signal detection, the method comprising:

acquiring the number of active users in a channel;

2. The method of multi-user signal detection according to claim 1, wherein said determining the log-likelihood ratio of the binary domain superposition codeword for each user on the received symbol based on the multi-level a priori distribution information comprises:

calculating according to a preset first algorithm or a second algorithm, a preset first parameter, the noise information and the first comparison table to obtain l ₀ ，l ₁ A first log-likelihood ratio component and a second log-likelihood ratio component when the values of (a) are larger than the number of users, wherein l ₀ Is thatSerial number of multilevel symbol, l ₁ Is->Serial number of multilevel symbol,/, of (a)>Superimposing the code words for the binary fields corresponding to the received symbols;

3. The method of claim 2, wherein the calculating is performed according to a preset first algorithm, a preset first parameter, the noise information, and the first comparison table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users, comprising:

Presetting the first parameter, which comprises: l (L) ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0; wherein LLR is ₀ Is thatLog-likelihood ratio component, LLR ₁ Is->Log likelihood ratio components of (a);

Wherein (1)>Is->Is a detection value of (a).

4. The method of claim 2, wherein the calculating is performed according to a predetermined second algorithm, a predetermined first parameter, the noise information, and the first lookup table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users, comprising:

querying the first comparison table to obtain corresponding prior probabilityAnd->And calculate its logarithmic form +.>And->Wherein (1)>Is the first ₁ A priori probabilities of multiple multilevel symbols, +.>Is the first ₀ The prior probabilities of the multiple multi-level symbols are shown in the first comparison table, and the first comparison table is a mapping table containing multi-level prior distribution information;

wherein (1)>Is->Is a detection value of (a).

5. The method for detecting a multi-user signal according to claim 1, wherein said determining the log-likelihood ratio of the binary domain superposition codeword for each user on the received symbol based on the multi-level information comprises:

calculating according to a preset third algorithm, a preset second parameter and the second comparison table, and obtaining a detection value of a binary field superposition codeword when the value of l is larger than the number of users, wherein l is the serial number of the multi-level symbol;

6. The method for detecting multi-user signals according to claim 5, wherein said calculating according to a third predetermined algorithm, a second predetermined parameter and the second comparison table to obtain the detected value of the binary domain superposition codeword when the value of l is greater than the number of users comprises:

Presetting the second parameter, including: l=0 and, wherein (1)>Superimposing codewords for the binary field corresponding to the received symbol, < >>The current minimum normalized Euclidean distance;

querying a second comparison table to obtain mu _l Calculating conditional probabilityWhere y is the received symbol, delta ² Is the noise variance of the channel, mu _l Is multipurposeA first multi-level symbol generated by superposition of user signals;

updating according to the third algorithmInquiring the second comparison table to obtain corresponding +.>If L is more than or equal to L, wherein L is the number of users, acquiring the detection value of the binary domain superposition code word when the value of L is greater than the number of users, otherwise updating and calculating the conditional probability sum ++>Wherein the third algorithm is if ∈>Then->If->Then

wherein (1)>Is->Delta ² Is the noise variance in the channel.

7. The multi-user signal detection method according to any one of claims 1 to 6, further comprising:

acquiring noise measurement values of a plurality of noise signals;

calculating according to the noise measured value to obtain a noise variance;

8. A network side device, comprising a memory, a transceiver, and a processor:

acquiring the number of active users in a channel;

9. The network side device of claim 8 wherein said determining log-likelihood ratios of binary domain superposition codewords for each user on said received symbol based on said multi-level a priori distribution information comprises:

10. The network side device of claim 9 wherein the computing is performed according to a preset first algorithm, a preset first parameter, the noise information, and the first lookup table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users, comprising:

querying the first comparison table to obtain corresponding prior probability And->Wherein (1)>Is the first ₁ A priori probabilities of multiple multilevel symbols, +.>Is the first ₀ The prior probabilities of the multiple multi-level symbols;

wherein (1)>Is->Is a detection value of (a).

11. The network side device of claim 9 wherein the computing is performed according to a predetermined second algorithm, a predetermined first parameter, the noise information, and the first lookup table to obtain l ₀ ，l ₁ The first and second log-likelihood ratio components having values greater than the number of users, comprising:

querying a first comparison table to obtainCalculating the corresponding conditional probability- >And->Wherein (1)>First generated for multi-user signal superposition ₀ Multiple multi-level symbols>First generated for multi-user signal superposition ₁ Multiple multi-level symbols, y being the received symbol, delta ² Is the noise variance of the channel;

querying the first comparison table to obtain corresponding prior probabilityAnd->And calculates the logarithmic form lnP thereof _l1 And->Wherein (1)>Is the first ₁ A priori probabilities of multiple multilevel symbols, +.>Is the first ₀ The prior probabilities of the multiple multi-level symbols are shown in the first comparison table, and the first comparison table is a mapping table containing multi-level prior distribution information;

wherein (1)>Is->Is a detection value of (a).

12. The network side device of claim 8 wherein said determining the log likelihood ratio of the binary domain superposition codeword for each user on the received symbol based on the multi-level information comprises:

13. The network side device of claim 12, wherein the calculating according to the third preset algorithm, the second preset parameter and the second comparison table to obtain the detection value of the binary domain superposition codeword when the value of l is greater than the number of users includes:

presetting the second parameter, including: l=0 and,wherein (1)>Superimposing codewords for the binary field corresponding to the received symbol, < >>The current minimum normalized Euclidean distance;

updating according to the third algorithmInquiring the second comparison table to obtain corresponding +. >If L is more than or equal to L, wherein L is the number of users, acquiring the detection value of the binary domain superposition code word when the value of L is greater than the number of users, otherwise updating and calculating the conditional probability sum ++>Wherein the third algorithm is if ∈>Then->If->Then

wherein (1)>Is->Delta ² Is the noise variance in the channel.

14. The network-side device according to any one of claims 8-13, further comprising:

acquiring noise measurement values of a plurality of noise signals;

calculating according to the noise measured value to obtain a noise variance;

15. A multi-user signal detection apparatus, the apparatus comprising:

16. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program for causing the processor to perform the method of any one of claims 1-7.