CN115189803A

CN115189803A - Multi-user signal detection method, device, equipment and storage medium

Info

Publication number: CN115189803A
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: 2022-10-14
Anticipated expiration: 2041-04-01
Also published as: CN115189803B

Abstract

The application discloses a multi-user signal detection method, a multi-user signal detection device, a 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 method comprises the steps of obtaining the number of activated users in a channel, determining multilevel prior distribution information or multilevel information according to the number of the users, obtaining received symbols, and determining the log-likelihood ratio of binary domain superposition codewords of each user on the received symbols according to the multilevel prior distribution information or the multilevel information. Therefore, the success rate of the receiving end for recovering the multi-user superposition information is improved, and the access error probability of the whole macro-address access cascade code system is minimized.

Description

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

Technical Field

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

Background

As mobile communications have evolved, several organizations have begun to research new wireless communication systems. At present, macro access is a novel multiple access scheme, and it can be understood that macro access can directly perform random access coding on user information and directly send out the user information, and all terminals use the same coder, so that macro access can realize that a large number of terminals access a network simultaneously.

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

Disclosure of Invention

The application provides a multi-user signal detection method, a multi-user signal detection device, a 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 including:

acquiring the number of active users in a channel;

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

and obtaining a 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 multilevel prior distribution information or the multilevel information.

Optionally, the determining, according to the multilevel prior distribution 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 first look-up table comprising the multilevel 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 A first log likelihood ratio component and a second log likelihood ratio component when both of the values of (a) and (b) are greater than the number of users;

and obtaining the log-likelihood ratio of the binary domain superposition code words according to the first log-likelihood ratio component and the second log-likelihood ratio component.

Optionally, the method further comprises the step of calculating the noise information according to a preset first algorithm, a preset first parameter, and the noise informationThe first comparison table is calculated to obtain l ₀ ，l ₁ The first and second log likelihood ratio components when both of the values of (a) and (b) are greater than the number of users, comprising:

presetting the first parameter, including: l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0; wherein l ₀ Is composed of

Number of multilevel symbols,/, of ₁ Is composed of

The number of the multi-level symbols of (a),

for binary field superposition code word corresponding to received symbol, LLR ₀ Is composed of

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of;

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

generated for superposition of multiuser signals ₀ A number of multi-level symbols of the symbol,

generated for superposition of multiuser signals ₁ Multiple multilevel symbols, y being the received symbols, δ ² The first mapping table is a mapping table containing multi-level prior distribution information, wherein the first mapping table is the noise variance of a channel;

inquiring the first comparison table to obtain corresponding prior probability

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ A prior probability of a number of multilevel symbols;

updating the LLR according to the first algorithm ₀ And the LLR ₁ And then will l ₀ ，l ₁ Respectively adding 2, judging ₀ ，l ₁ If the values of the first and second log likelihood ratio components are greater than the user number, the first and second log likelihood ratio components are obtained, otherwise, the conditional probability and the prior probability are updated and calculated; wherein the first algorithm is

The obtaining a log-likelihood ratio of a 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 When the values of (a) are both greater than the number of users, a first log likelihood ratio component and a second log likelihood ratio component, comprising:

presetting the first parameter, including: l. the ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0; wherein l ₀ Is composed of

Number of multilevel symbols,/, of ₁ Is composed of

The sequence number of the multi-level symbol of (c),

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of;

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

generated for superposition of multiuser signals ₀ A plurality of multi-level symbols, each having a different sign,

generated for superposition of multiuser signals ₁ Multiple multilevel symbols, y being the received symbols, δ ² Is the noise variance of the channel;

inquiring the first comparison table to obtain corresponding prior probability

And

and calculating its logarithmic form

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ The first mapping table is a mapping table containing multi-level prior distribution information;

compare existing LLRs separately ₀ ，LLR ₁ A relation with the second algorithm to calculate LLR values, larger values being respectively given to the LLRs ₀ ，LLR ₁ Then l is added ₀ ，l ₁ Respectively adding 2, judging ₀ ，l ₁ If the values of the first and second log likelihood ratio components are greater than the user number, the first and second log likelihood ratio components are obtained, otherwise, the conditional probability and the prior probability are updated and calculated; wherein the second algorithm is

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

acquiring noise information in a channel and acquiring a second look-up table comprising the multilevel information;

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

and obtaining the log-likelihood ratio of the binary domain superposition code word according to the detection value and the noise information.

Optionally, the 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 greater than the number of the users includes:

presetting the second parameter, including: l =0, and the ratio of the total of the components,

wherein l is the number of the multilevel symbol,

to receive the binary field superposition codeword for the symbol,

is the current minimum normalized euclidean distance;

querying the second lookup table for μ _l Calculating conditional probability

Where y is the received symbol, δ ² Is the noise variance, mu, of the channel _l Is multipurposeThe first multilevel symbol generated by the superposition of the user signals;

updating according to the third algorithm

Inquiring the second comparison table to obtain the corresponding

L = L +1, if L is larger than or equal to L, the detection value of the binary domain superposition code word is obtained when the value of L is larger than the number of the users, otherwise, the conditional probability sum is calculated in an updating way

Wherein the third algorithm is if

Then

If it is

Then

The obtaining the log-likelihood ratio of the binary domain superposition code words according to the detection value and the noise information comprises the following steps:

wherein the content of the first and second substances,

is composed of

Detected value of δ ² Is the noise variance in the channel.

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

obtaining noise measurements of a plurality of noise signals;

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

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

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

and acquiring a 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 multilevel prior distribution information or the multilevel information.

Optionally, the first algorithm is preset according to a preset first algorithmCalculating parameters, the noise information and the first comparison table to obtain l ₀ ，l ₁ The first and second log likelihood ratio components when both of the values of (a) and (b) are greater than the number of users, comprising:

Number of multilevel symbols, l ₁ Is composed of

The number of the multi-level symbols of (a),

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of;

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

generated for superposition of multiuser signals ₁ Multiple multilevel symbols, y being the received symbols, δ ² The first mapping table is a mapping table containing multi-level prior distribution information and is the noise variance of a channel;

inquiring the first comparison table to obtain corresponding prior probability

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ A prior probability of a number of multilevel symbols;

optionally, the processing is performed according to a preset second algorithm, a preset first parameter, the noise information and the first comparison tableCalculating to obtain l ₀ ，l ₁ The first and second log likelihood ratio components when both of the values of (a) and (b) are greater than the number of users, comprising:

Number of multilevel symbols, l ₁ Is composed of

The number of the multi-level symbols of (a),

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of;

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

inquiring the first comparison table to obtain corresponding prior probability

And

and calculating its logarithmic form

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ The prior probability of a plurality of multilevel symbols, wherein the first mapping table is a mapping table containing multilevel prior distribution information;

compare existing LLRs separately ₀ ，LLR ₁ A relation with the second algorithm to calculate LLR values, larger values being respectively given to the LLRs ₀ ，LLR ₁ Then l is added ₀ ，l ₁ Respectively adding 2, judging ₀ ，l ₁ If the values of the first log likelihood ratio component and the second log likelihood ratio component are both larger than the user number, acquiring the first log likelihood ratio component and the second log likelihood ratio component, and if not, updating and calculating the conditional probability and the prior probability; wherein the second 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:

and obtaining the log-likelihood ratio of the binary domain superposition code words according to the detection value and the noise information.

wherein, l is the serial number of the multilevel symbol,

to receive the binary field superposition codeword for the symbol,

is the current minimum normalized euclidean distance;

querying the second lookup table for μ _l Calculating conditional probability

Where y is the received symbol, δ ² Being a channelVariance of noise, μ _l Generating a first multilevel symbol for the multi-user signal superposition;

updating according to the third algorithm

Inquiring the second comparison table to obtain the corresponding

Wherein the third algorithm is if

Then

If it is

Then

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

wherein the content of the first and second substances,

is composed of

Detected value of (d), δ ² Is the noise variance in the channel.

Optionally, the network side device further includes:

obtaining noise measurements of a plurality of noise signals;

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

the acquisition module is used for acquiring the number of the 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 obtaining and determining module is used for 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 multilevel prior distribution information or the multilevel information.

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

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

The application has the following technical effects: the method comprises the steps of obtaining the number of activated users in a channel, determining multilevel prior distribution information or multilevel information according to the number of the users, obtaining a received symbol, determining the log-likelihood ratio of binary domain superposition code words of each user on the received symbol according to the multilevel prior distribution information or the multilevel information, inputting the log-likelihood ratio to a channel decoder for processing and carrying out multi-user signal detection based on the obtained log-likelihood ratio, and realizing the minimum information loss generated by directly carrying out detection judgment on the multilevel, thereby ensuring the correct detection of the multi-user information to the maximum extent, fully utilizing the prior distribution information of the multilevel symbol, being capable of more reasonably dividing a multilevel judgment domain, and further minimizing the error probability of multi-user signal detection.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

FIG. 1 is a block diagram of a concatenated code macro access system architecture;

FIG. 2 is a flowchart illustrating a concatenated code macro access system according to an embodiment of the present disclosure;

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

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

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

fig. 6 is a schematic flowchart of another multiuser signal detection method according to an embodiment of the application;

fig. 7 is a diagram of another example of a multi-user signal detection method provided in an embodiment of the present application;

FIG. 8 is a schematic diagram of a performance comparison analysis of different symbol decision algorithms provided in accordance with an embodiment 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 apparatus 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 technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

That is, in the embodiment of the present application, the term "and/or" describes an association relationship of associated objects, and means that there may be three relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

Currently, with the gradual completion of 5G technology standardization, the academic and industrial circles begin to look forward on future 6G technologies, wherein an important 6G vision is the continuous evolution from massive Machine Type Communication (mtc), which is one of the important scenes of 5G, to ultra-massive Machine Type Communication (umMTC), and the industrial circles require the evolution from the Internet of Everything (IoE) to the Internet of things (Hyper-Intelligent Internet of evolution, HIIoE).

The macro-address access technology is oriented to a non-cooperative scene, and the optimization target is the average access error rate of each user. The non-cooperative technology does not need the unified scheduling of a cooperative center, and the user randomly generates collision in a channel. The active terminals send information independently without considering whether other terminals occupy channels, so that when users send information simultaneously, collision can be generated on the channels for transmitting information. Meanwhile, the superposition relationship 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) there is no uniform access scheduling; (2) no pilot; and (3) no pre-allocated resources are available (4) the user activity state is unknown. The non-cooperative feature requires all users to use the same set of transmission protocol, and occupy resources completely at random. Therefore, the uncooperative method is suitable for application scenarios with a large number of users, high overload factors and low data rates.

In the related art, the macro address access is mainly realized by adopting a cascading code design mode, and specifically, the macro address access is completed by adopting a mode of cascading multiple access channel coding and error channel reliability coding. Based on the above description, based on the structural features of the non-cooperative mode, the method for macro address access needs to solve two problems: 1) User random collision and information recovery; 2) The channel noise impact. The system of the cascade code adopts a two-stage coding structure to carry out grading to respectively process the two problems.

Specifically, the concatenated code structure is a system structure based on a non-cooperative mode, a non-coordinated mechanism is adopted for sending user code words, overlapping collision exists on the same resource, and a BAC (Binary additive Channel) Channel is formed. Therefore, in order to cope with collision superposition on the channel, the user information payload at the transmitting end first enters a multiple access encoder (first-stage encoder), and after multiple access encoding, the receiver at the receiving end can recover the respective messages of the users participating in collision superposition. The second-stage coder is a channel reliability coder and is used for resisting noise in a channel and reducing the error probability of a user data packet in the transmission process of the noisy channel. At a receiving end, firstly, sending the received code words into a channel decoder to complete a computer-and-Forward (CoF) stage; and secondly, reversely recovering respective code words of multiple users which are superposed together through a multiple-access decoder, namely a BAC stage.

As a possible implementation, w is shown in fig. 1 and 2 ₁ And w ₂ The information respectively transmitted by user 1 and user 2 is coded by multiple access coder and then its multiple access code component alpha is respectively output ₁ And alpha ₂ The components are respectively sent to a channel reliability encoder to obtain a code word component c after the cascade encoding is finished ₁ And c ₂ . Where α and c are both binary bit sequences, and modulation is required for transmission in the actual channel. Obtaining a symbol sequence x after modulation ₁ And x ₂ While simultaneously transmitted symbol sequences are directly superimposed in the channel (user timing alignment), passing through a fading channel (in fig. 1 and 2)Omitting the channel h) and superimposing the 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 detection

I.e. the multi-user information sequence in the superimposed state. Decoding the code word which is regarded as the channel reliability code, and obtaining the multi-user code sequence in the superposition state at the output end of the decoder after completing error correction

Sending it to multi-address decoder to obtain the information detection results of user 1 and user 2

And

thus, it can be seen that the above system is designed with several features: (1) Although different users carry out independent coding, the coder configurations of the users participating in transmission are completely the same, and a distributed multi-user coding system is arranged at the transmitting end; (2) The non-cooperation of the system enables the sending of the user information of the sending end to be randomly carried out on the time domain, so that the problem of multi-user collision can be generated when the user information is transmitted on a channel, namely, the information of a plurality of users is simultaneously transmitted on the channel, and meanwhile, the information of the collision and superposition of the plurality of users is centrally processed by a unified receiver at the receiving end and is recovered from the superposed signal; (3) From the collision point of view, since the collision is generated on the channel, the superposition of the signals needs to be completed on the channel, and the superposition is at the symbol level, so the signals received by the receiving end are multilevel symbols, and the multi-user signals in the superposition state must be detected and then sent to the channel reliability decoder when the receiving end performs decoding and demodulation, thereby starting the two-stage decoding process of the concatenated codes.

In the embodiment of the application, the slotted ALOHA mechanism adopted in the macro access forms the random collision of multi-user superpositionAnd (4) colliding with the channel. As shown in fig. 3, the vertical axis is K _a Individual users (i.e., the number of active users). The data frame with length of n on the horizontal axis is divided into

An

A long sub-frame block (i.e. the length of the information block transmitted by the user in each time slot is equal to

) Each subframe block is an orthogonal resource block, and V is the number of allocated time slots. Random subframe transmission selected by each user

A long codeword. Due to the randomness of the information sent by the users, it may happen that the same timeslot is occupied by multiple users at the same time.

In FIG. 3, user m is shown with the 2 nd time slot as an example ₁ And m ₂ Separately transmitted symbol sequences x ₁ And x ₂ A superposition collision occurs to produce a multi-level signal u. Further discovering the code word bit c after the cascade coding ₁ And c ₂ Parity results formed by binary field collisions between

A certain mapping rule f exists between multi-level symbols u formed by superimposing BPSK (Binary Phase Shift Keying) symbols, and the distribution of each level on the symbol domain is a priori unequal probability. Since the symbol superposition is performed symbol by symbol, only a single-symbol superposition collision channel model needs to be considered. One element in the vector is represented by the sign of the positive variable corresponding to the sign of the bold italic vector (i.e. a single sign, e.g. u, replaces one sign in the multilevel sign vector u).

Specifically, in the symbol collision channel, the code word of the user is not in a direct modulo-two addition relationship, but in a symbol superposition relationship:

wherein, assuming that the fading channel h is equalized, y represents a single received symbol, x _l Indicating that the l-th user transmits superimposed BPSK symbols participating in the symbol position of the slot, u is the resulting multi-level symbol,

AWGN (Additive White Gaussian Noise).

Suppose that user l sends two levels of coded bits c _l Then, the codeword superposition result of each user at this position shall be:

after BPSK modulation, the user code word generates a transmission symbol x _l Belongs to { +1, -1}, and satisfies x _l ＝2c _l -1. The parity check relation formed by the binary domain collision between the bits can be obtained by the channel model

Multi-level symbol formed by superposition with BPSK symbols

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 needs to be designed according to the distribution characteristics of the received symbol y, and the conversion of the parity check relationship formed by the collision of the received multilevel symbol y and the binary domain is completed according to the mapping rule. Namely:

aiming at the binary domain equivalent decision algorithm, adopting equivalenceTransform g (y), modulo Yu Oujian which maps the received symbol y back to [0,2) yields the equivalent symbol:

wherein the content of the first and second substances,

is a binary field bit, so the modulo two result remains unchanged while the AWGN noise n is also mapped to the modulo interval of [0,2 ]. In this case, the decision device needs to be based on

Distribution characteristic pair of

The values of (a) are divided into decision domains. Regardless of the prior distribution of multilevel symbols, the decision domain is generated according to the minimum euclidean distance criterion (i.e., the ML criterion) by the following rules:

thus, the mapping from the symbol domain to the binary domain is complete,

namely, the channel reliability coding code words which are completed by superposition in the binary domain equivalent channel can be directly sent to the next-stage decoder to complete decoding. It is to be noted that

In operation, it has been assumed that the receiving end knows the number of users L superimposed on the channel at this time.

The multi-user signal detection method firstly performs equivalent mapping on the multi-user signal to return to the modulo two domain, then performs binary decision according to the mapping equivalent relation between the binary domain and the 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 the equivalent mapping link in the method, the multi-user signal superposition result under the influence of Gaussian noise is merged into a binary signal through equivalent mapping for processing, and the performance loss exists in comparison with a direct processing scheme of a multi-level judgment mode; for another example, the method does not consider the characteristic of the prior distribution of the multi-user signal, obviously, the prior probability distribution of the mapping from the multilevel to the binary domain is not equal probability, in the multi-user signal detection algorithm, the prior distribution of the symbols will influence the correct division of the decision domain, that is, the rule of the received signal mapping back to the bit information domain, and the decision domain division method of the binary domain equivalent decision algorithm assumes the prior equal probability of the user information, and does not consider the characteristic of the prior unequal probability of the symbols after being superposed in the channel, therefore, the binary domain equivalent decision algorithm is not the algorithm that minimizes the error rate of the multi-user signal detection, for example, because the channel reliability decoder adopts the algorithm of soft information input and soft information output (referred to as soft-in soft-out) for iterative decoding, the initial input information of the iteration is the soft information output from the multi-user information detection module, but because the output of the binary domain equivalent decision algorithm adopted in the classical scheme is the hard decision result, the performance of the high-performance soft-in soft-out algorithm is greatly reduced.

In order to solve the above problems, the present application provides a multi-user signal detection method, which determines multi-level prior distribution information or multi-level information according to the number of activated users in a channel, and obtains a received symbol, and determines 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, so that the multi-user signal is input to a channel decoder for processing based on the obtained log-likelihood ratio for multi-user signal detection, and the information loss generated by directly performing detection decision on the multi-level is minimized, thereby maximally ensuring correct detection of the multi-user information, and fully utilizing the prior distribution information of the multi-level symbol, so that the division of a multi-level decision domain is more reasonable, and the error probability of the multi-user signal detection is minimized.

A 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 illustrating 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, acquiring the number of active users in a channel.

Step 102, determining multilevel prior distribution information or multilevel information according to the number of users.

And 103, acquiring a received symbol, and determining the log-likelihood ratio of the binary domain superposition codeword of each user on the received symbol according to the multilevel prior distribution information or the multilevel information.

It should be noted that, the present application mainly aims at the improvement of the module for performing symbol hard decision and converting the decision result into the log-likelihood ratio described in fig. 2, and does not relate to the improvement of other modules, for example, the decoding algorithms such as the existing low density check code can be adopted for multiple access decoding and reliable decoding.

In the embodiment of the application, a multi-level symbol decision is directly performed by adopting a multi-level decision method. That is to say, according to the characteristics of the multi-user signal, the multi-level symbols with unequal probability prior distribution are superposed on the same time slot, and the multi-level symbols and the superposed multi-user information in the bit binary domain have a direct mapping relation. 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 maximum extent.

As an example, the method of acquiring the number of active users in the channel includes acquiring a received signal in the channel, extracting a pilot sequence signal from the received signal, and determining the number of active users according to the pilot sequence signal, that is, the pilot sequence may be obtained through pilot sequence correlation detection, so as to obtain the number of superimposed users. It should be noted that, in the foregoing method, 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 multilevel symbol.

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

In a first example, noise information in a channel is acquired, a first comparison table including multilevel prior distribution information is acquired, and l is acquired by calculating according to a preset first algorithm or a second algorithm, a preset first parameter, the noise information and the first comparison table ₀ ，l ₁ When the value of (2) is greater than the number of users, the first log likelihood ratio component and the second log likelihood ratio component are obtained, and the log likelihood ratio of the binary domain superposition code word is 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 multilevel 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 l is greater 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 obtained, multilevel prior distribution information or multilevel information is determined according to the number of users, a received symbol is obtained, and a log-likelihood ratio of a binary domain superposition codeword of each user on the received symbol is determined according to the multilevel prior distribution information or the multilevel information. Therefore, the success rate of the receiving end for recovering the multi-user superposition information is improved, and the access error probability of the whole macro-address access cascade code system is minimized.

Based on the above description of the embodiments, there are various ways to obtain the received symbols and the log-likelihood ratio of the binary-domain superposition code word according to the multilevel prior distribution information, and the above first example is described in detail below with reference to fig. 5.

Fig. 5 is a flowchart illustrating another method for detecting a multiuser signal according to an embodiment of the application.

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

step 201, noise information in a channel is obtained, and a first look-up table including multilevel prior 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, as illustrated below.

A first example according to

And

mapping rules and the number L of users superimposed on the channel, and selecting and combining

The corresponding probability center, i.e. the generated first look-up table including the multilevel prior distribution information, is shown in table 1, and the first look-up table with the odd number of users L:

table 1 first comparison table 1

A first example according to

And

Corresponding centers of probability, i.e.The generated first look-up table including multi-level prior distribution information is shown in table 2, and the first look-up table with an even number of users L:

table 2 first comparison table 2

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

Therefore, the prior distribution information of the multilevel symbols can be fully utilized, wherein the multilevel symbols generated by superposition in the channel obey the binomial distribution with the test probability of 1/2 and the test times of the number L of the superposed users. That is to say, as long as the receiving end knows the number of users superimposed on the time slot, the level value and the prior probability of each symbol in the multi-level symbol set can be accurately calculated, so that the division of the symbol decision domain can be more reasonable by using 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 Is greater than the first and second log-likelihood ratio components for the number of users.

And step 203, obtaining the log likelihood ratio of the binary domain superposition code word according to the first log likelihood ratio component and the second log likelihood ratio component.

In the implementation of the present application, different algorithms can be selected differently for calculation, and as a possible implementation manner, 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 When the value of (2) is greater than the number of users, the first log likelihood ratio component and the second log likelihood ratio component are obtained, and the log likelihood ratio of the binary domain superposition code word is obtained according to the first log likelihood ratio component and the second log likelihood ratio component. More particularly, toThe ground:

presetting a first parameter, including: l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0; wherein l ₀ Is composed of

Number of multilevel symbols,/, of ₁ Is composed of

The number of the multi-level symbols of (a),

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of; inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

generated for superposition of multiuser signals ₁ Multiple multilevel symbols, y being the received symbols, δ ² The first mapping table is a mapping table containing multi-level prior distribution information for the noise variance in the channel;inquiring the first comparison table to obtain the corresponding prior probability

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ Updating LLR according to a first algorithm based on prior probabilities of multiple multilevel symbols ₀ And LLR ₁ And then will l ₀ ，l ₁ Respectively add 2, judge l ₀ ，l ₁ If the values of the first and second pairs are greater than the user number, acquiring a first log likelihood ratio component and a second log likelihood ratio component, and otherwise, updating the calculation conditional probability and the prior probability; wherein the first algorithm is

Obtaining a 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, and l is obtained ₀ ，l ₁ Is greater than the first and second log-likelihood ratio components for the number of users. More specifically:

Multiple electric power generatorNumber of plain symbols, l ₁ Is composed of

The number of the multi-level symbols of (a),

Log-likelihood ratio component of (LLR) ₁ Is composed of

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

generated for superposition of multiuser signals ₁ Multiple multilevel symbols, y being the received symbols, δ ² For the noise variance of the channel, a first comparison table is inquired to obtain the corresponding prior probability

And

and calculating its logarithmic form

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ The prior probability of multiple multilevel symbols, the first mapping table is a mapping table containing multilevel prior distribution information, and the prior LLR is compared respectively ₀ ， LLR ₁ In relation to the calculation of LLR values by the second algorithm, larger values are respectively assigned to the LLRs ₀ ，LLR ₁ Then l is added ₀ ，l ₁ Respectively adding 2, judging ₀ ，l ₁ If the values of the two are all larger than the number of users, acquiring a first log likelihood ratio component and a second log likelihood ratio component, and otherwise, updating the calculation conditional probability and the prior probability; 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, including:

in summary, in the embodiment of the present application, a first lookup table including multi-level prior distribution information is generated, and a log-likelihood ratio is obtained through calculation based on the first lookup table, so that the obtained log-likelihood ratio is input to a channel decoder for processing to perform multi-user signal detection, and the minimum information loss caused by directly performing detection decision on multiple levels is achieved, thereby ensuring correct detection of multi-user information to the maximum extent, and making full use of the prior distribution information of multi-level symbols, so that the division of multi-level decision domains is more reasonable, and the error probability of multi-user signal detection is minimized.

As an example, as shown in fig. 6, the number L of users superimposed on the channel is input, the received symbols 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 multilevel prior distribution information according to the number of users, selecting an LLS algorithm according to a symbol s, for example, s is 0, initializing a parameter l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0, calculating the corresponding conditional probability

And

look-up tables 1 and 2 for prior probabilities

And

the LLR values for the codewords of 0 and 1 are updated,

further will l ₀ ，l ₁ Respectively adding 2, judging ₀ ，l ₁ If the number of users is larger than the number of users, obtaining a first log likelihood ratio component and a second log likelihood ratio component, otherwise, updating the calculation conditional probability and the prior probability, and finally calculating

For another example, s is 1, initialize parameter, l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0， LLR ₁ =0, calculating the corresponding conditional probability

And

look-up tables 1 and 2 for prior probabilities

And

and calculating its logarithmic form

And

compare existing LLRs ₀ ，LLR ₁ And

and

relation of LLR values calculated, larger values being assigned to LLRs ₀ ， LLR ₁ Then l is added ₀ ，l ₁ Respectively adding 2, judging ₀ ，l ₁ If the number of users is larger than the number of users, the first log likelihood ratio component and the second log likelihood ratio component are obtained, the calculation conditional probability and the prior probability are updated, and finally, the calculation is carried out

From this, it is derived

Then the signal can be directly sent to a channel reliability decoder to finish decoding for multi-user signal detection, and therefore, the multi-level symbol judgment method based on Bayesian detection fully considers

The prior distribution of the Bayesian detection rules influences the division of the decision domain, and the division of the decision domain is adjusted according to the distribution of the likelihood function from the angle of the Bayesian detection rules. That is, the symbol jump points of the LLR are divided by the multi-level symbol decision based on Bayesian detectionAnd judging the domain boundary. Therefore, the prior distribution information of the multilevel symbols is fully utilized, the division of the multilevel decision domain can be more reasonable, and the error probability of the multi-user signal detection is minimized.

Based on the above description of the embodiments, the present application also removes a method for detecting a hard information multiuser signal, which is described below with reference to fig. 7.

Fig. 7 is a flowchart illustrating another multiuser signal detection method according to an embodiment of the application.

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

step 301, noise information in a channel is obtained, and a second look-up table including multi-level information is obtained.

In the embodiment of the application, a multi-level symbol decision is directly performed by adopting a multi-level decision method. That is to say, according to the characteristics of the multi-user signal, the multi-level symbols with unequal probability prior distribution are superposed on the same time slot, and the multi-level symbols and the multi-user information superposed in the bit binary domain have a direct mapping relation. 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 maximum extent.

In the embodiment of the present application, the second comparison table is generated according to the number of users, where the second comparison table generated when the number of users is even or odd is different, which is illustrated as follows.

A first example according to

And

mapping rules and the number L of users superimposed on the channel to obtain different decision level values mu _l That is, the generated second lookup table including the multi-level prior distribution information is shown in table 3, and the second lookup table with the odd number of users L:

TABLE 3 second COMPARATIVE TABLE 3

A first example according to

And

mapping rules and the number L of users superimposed on the channel to obtain different decision level values mu _l That is, the generated second look-up table including the multilevel prior distribution information is shown in table 4, and the second look-up table with the even number of users L:

TABLE 4 second COMPARATIVE TABLE 4

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 domain superposition code word when the value of l is greater than the number of users.

And 303, acquiring the log-likelihood ratio of the binary domain superposition code word according to the detection value and the noise information.

In the embodiment of the application, the hard information detection is used for carrying out multi-level symbol decision to obtain LLR, and the LLR comprises updating of three aspects of multi-level decision, multi-level information utilization and hard information output.

In this embodiment, as a possible implementation manner, the presetting of the second parameter includes: l =0, and the ratio of the total of the components,

wherein l is the number of the multilevel symbol,

to receive the binary field superposition codeword for the symbol,

is the current minimum normalized euclidean distance; querying the second lookup table for μ _l Calculating conditional probabilities

Where y is the received symbol, δ ² Is the variance of the noise in the channel, mu _l The first multi-level symbol generated for the multi-user signal superposition; updating according to a third algorithm

Querying the second lookup table to obtain corresponding

L = L +1, if L is more than or equal to L, the detection value of the binary domain superposition code word when the value of L is more than the number of users is obtained, otherwise, the conditional probability sum is updated and calculated

Wherein the third algorithm is if

Then

If it is

Then

Obtaining the log-likelihood of the binary domain superposition code word according to the detection value and the noise informationThe method comprises the following steps:

wherein the content of the first and second substances,

is composed of

Detected value of δ ² Is the noise variance in the channel.

From this, it is derived

And then the signal can be directly sent to a channel reliability decoder to finish decoding, and the signal is input to the channel decoder to be processed based on the obtained log-likelihood ratio to carry out multi-user signal detection. The decision domain is directly divided according to the distribution of P { y } for multi-level symbol decision based on hard decision, which solves the problem to a certain extent

The prior unequal probability problem when L is an odd number.

In summary, in the embodiment of the present application, the second lookup table including the multilevel prior distribution information is generated, and the log-likelihood ratio is obtained through calculation based on the second lookup table, so that the obtained log-likelihood ratio is input to the channel decoder for processing to perform multi-user signal detection, and the minimum information loss generated by directly performing detection decision on the multilevel is realized, thereby ensuring the correct detection of the multi-user information to the maximum extent, and making full use of the prior distribution information of the multilevel symbols, so that the division of the multilevel decision domain can be more reasonable, and the error probability of the multi-user signal detection is minimized.

As an example, as shown in fig. 8, the number L of users superimposed on the channel, the received symbols y, the noise variance y are input, a look-up table including information of a multi-level prior distribution is generated according to the number of users, initialization parameters, L =0,

calculating conditional probabilities

Assigning a smaller value to

Querying the second lookup table to obtain corresponding

L = L +1, if L is larger than or equal to L, the detection value of the binary domain superposition code word is obtained, otherwise, the conditional probability sum is updated and calculated

According to the detection value and the noise variance, the log-likelihood ratio of the binary domain superposition code word is obtained as

And output.

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

Based on the description of the above embodiments, as a possible implementation manner of the present application, the present application further provides a soft-hard information hybrid detection, that is, a calculation manner of obtaining noise measurement values of a plurality of noise signals, performing calculation according to the noise measurement values, obtaining a noise variance, and determining a log likelihood ratio of a binary domain superposition codeword of each user on a 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 hybrid detection algorithm performs multi-level symbol decision to obtain LLR, and includes updating of five aspects of multi-level decision, prior distribution information utilization, soft information output, hard information output, information output selection and the like.

As an example, the number of active users L on the channel, the received symbols y, the noise variance σ ² Variance of threshold value

The LLR algorithm selects the symbol s _h (s _h Value of 0 or 1), the noise variance σ is judged ² Variance with threshold

The magnitude relationship of (1), if

Soft information detection of multi-user signals: according to

And

mapping rule and number L of users superimposed on channel are selected and

corresponding mu _l When L is an odd number, it is shown in Table 1, and when L is an odd number, it is shown in Table 2. Selecting LLS algorithm according to symbol s, such as s value is 0, initializing parameter l ₀ ＝0，l ₁ ＝1，LLR ₀ ＝0，LLR ₁ =0, calculating the corresponding conditional probability

And

look-up tables 1 and 2 for prior probabilities

And

updating LLR values for codewords of 0 and 1

Further will l ₀ ，l ₁ Respectively adding 2 to the mixture, adding the mixture,judgment of l ₀ ，l ₁ If the values are all larger than the number of users, obtaining a first log likelihood ratio component and a second log likelihood ratio component, otherwise, updating the calculation conditional probability and the prior probability, and finally calculating

And

look-up tables 1 and 2 for prior probabilities

And

and calculating its logarithmic form

And with

Compare existing LLRs ₀ ，LLR ₁ And

and

calculating the relation of LLR values, assigning larger value to LLR ₀ ，LLR ₁ Then l is added ₀ ，l ₁ Respectively adding 2, judging ₀ ，l ₁ If the values of the two are all larger than the number of users, obtaining a first log likelihood ratio component and a second log likelihood ratio component, updating the calculation conditional probability and the prior probability, and finally calculating

From this, it is derived

Then can be directly sent into a channel reliability decoder to finish decoding and carry out multi-user signal detection, therefore, the method for carrying out multi-level symbol judgment based on Bayesian detection fully considers the method

The prior distribution of the Bayesian detection rules influences the division of the decision domain, and the division of the decision domain is adjusted according to the distribution of the likelihood function from the angle of the Bayesian detection rules. That is, the symbol jump point of the LLR is a decision domain boundary into which multi-level symbol decisions are divided based on bayesian detection. Therefore, the prior distribution information of the multilevel symbols is fully utilized, the division of the multilevel decision domain can be more reasonable, and the error probability of the multi-user signal detection is minimized.

If it is not

Soft information detection of multi-user signals: according to

And

mapping rule and number L of users superimposed on channel are selected and

corresponding mu _l When L is an odd number, as shown in table 3, and when L is an odd number, as shown in table 4, the initialization parameter, L =0,

calculating conditional probabilities

Assigning a smaller value to

Querying the second lookup table to obtain corresponding

L = L +1, if L is larger than or equal to L, the detection value of the binary domain superposition code word is obtained when the value of L is larger than the number of users, otherwise, the conditional probability sum is updated and calculated

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

Specifically, the data frame length: 3600bit, system code rate:

number of active users: k _a ＝50、 K _a =100, maximum number of allowed collisions: t =10,ldpc code rate:

BCH code rate:

number of bits per frame per user: m =12. The simulation result is shown in fig. 9, and from the overall performance point of view, the soft information detection scheme is compared with the existing detection scheme

The system energy efficiency is improved by 1.8dB under the code rate

Improved by 1.7dB at code rate

Therefore, the method improves the recovery success rate of the receiving end to multi-user superposition information through a multi-level judgment method, combines prior information and soft information output, minimizes the access error probability of the whole macro-address access cascade code system, properly simplifies a soft information detection algorithm through a multi-level judgment method and a method of converting hard information into soft symbol output, still improves the overall performance of the soft information detection algorithm to a certain extent under the condition of high signal-to-noise ratio compared with the traditional scheme, and finally can flexibly switch the two calculation methods through setting a receiving signal-to-noise ratio threshold value to further improve the flexibility of multi-user signal detection.

In order to implement the foregoing embodiment, the present application further provides a network side device.

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

The network device according to the embodiment of the present application may be configured to exchange a received air frame with an Internet Protocol (IP) packet, and may be used 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 embodiment of the present application may be a Base Transceiver Station (BTS) in a Global System for Mobile communications (GSM) or a Code Division Multiple Access (CDMA), may also be a network device (NodeB) in a Wideband Code Division Multiple Access (WCDMA), may also be a evolved Node B (eNB or e-NodeB) in a Long Term Evolution (LTE) System, a 5G Base Station (gNB) in a 5G network architecture (next generation System), may also be a Home evolved Node B (HeNB), a relay Node (relay Node), a Home Base Station (femto), a pico Base Station (pico) and the like, which are not limited in the embodiments of the present application. In some network configurations, a 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 can be applied to various systems, especially 5G systems. For example, the applicable System may be a Global System for Mobile communications (GSM) System, a Code Division Multiple Access (CDMA) System, a Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (General Packet Radio Service, GPRS) System, a Long Term Evolution (Long Term Evolution, LTE) System, a LTE Frequency Division Duplex (FDD) System, a LTE Time Division Duplex (TDD) System, a Long Term Evolution (Long Term Evolution, LTE-a) System, a Universal Mobile telecommunications System (Universal Mobile telecommunications System, UMTS), a Worldwide Interoperability for Microwave Access (WiMAX) System, or a New Radio network (NR 5, wiMAX) System. These various systems include terminal devices and base stations. The System may further include a core network portion, such as an Evolved Packet System (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.

Where in fig. 10, the bus architecture may include any number of interconnected buses and bridges, with one or more processors represented by processor 1010 and various circuits of memory represented by memory 1020 being linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 1000 may be a number of elements including a transmitter and a receiver that provide a means for communicating with various other apparatus over a transmission medium including wireless channels, wired channels, fiber optic cables, and the like. 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 (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD), and may also have a multi-core architecture.

The processor 1010, by calling a memory stored computer program, performs the following operations:

acquiring the number of active users in a channel;

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

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 Is greater than the first log likelihood ratio for the number of usersA component and a second log likelihood ratio component;

and obtaining the log-likelihood ratio of the binary domain superposition code word according to the first log-likelihood ratio component and the second log-likelihood ratio component.

Optionally, 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 When the values of (a) are both greater than the number of users, a first log likelihood ratio component and a second log likelihood ratio component, comprising:

Number of multilevel symbols, l ₁ Is composed of

The number of the multi-level symbols of (a),

Log likelihood ratio component, LLR ₁ Is composed of

A log likelihood ratio component of;

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

inquiring the first comparison table to obtain corresponding prior probability

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ A prior probability of a number of multilevel symbols;

optionally, 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 When the values of (a) are both greater than the number of users, a first log likelihood ratio component and a second log likelihood ratio component, comprising:

Number of multilevel symbols, l ₁ Is composed of

The number of the multi-level symbols of (a),

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of;

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein，

inquiring the first comparison table to obtain corresponding prior probability

And

and calculating its logarithmic form

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

compare existing LLRs separately ₀ ，LLR ₁ A relation with the second algorithm to calculate LLR values, larger values being respectively given to the LLRs ₀ ，LLR ₁ Then l is added ₀ ，l ₁ Respectively adding 2, judging ₀ ，l ₁ If the values of the first log likelihood ratio component and the second log likelihood ratio component are both larger than the user number, acquiring the first log likelihood ratio component and the second log likelihood ratio component, and if not, updating and calculating the conditional probability and the prior probability; wherein the second algorithmIs composed of

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

Optionally, as another embodiment, the 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 greater than the number of the users includes:

wherein l is the number of the multilevel symbol,

to receive the binary field superposition codeword corresponding to the symbol,

is the current minimum normalized euclidean distance;

querying the second lookup table for μ _l Calculating conditional probability

Where y is the received symbol, δ ² Is the noise variance, mu, of the channel _l The first multi-level symbol generated for the multi-user signal superposition;

updating according to the third algorithm

Inquiring the second comparison table to obtain the corresponding

Wherein the third algorithm is if

Then

If it is

Then

wherein the content of the first and second substances,

is composed of

Detected value of δ ² Is the noise variance in the channel.

Optionally, as another embodiment, the method for detecting multiple user signals further includes: obtaining noise measurements of a plurality of noise signals; calculating according to the noise measurement value to obtain a noise variance; and determining a calculation mode of the log-likelihood ratio of the binary domain superposition code word of each user on the received symbol according to the multi-level prior distribution information or the multi-level information according to a comparison result of the noise variance and a 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 by the foregoing method embodiment, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those in the method embodiment are omitted here.

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

Fig. 11 is a schematic structural diagram of a multi-user signal detection apparatus 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.

The obtaining module 1110 is configured to obtain the number of active users in a channel.

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

An obtaining and determining module 1130, configured to obtain a received symbol, and determine, according to the multilevel prior distribution information or the multilevel information, a log-likelihood ratio of a binary domain superposition codeword of each user on the received symbol.

Therefore, the number of activated users in a channel is obtained, multilevel prior distribution information or multilevel information is determined according to the number of the users, a received symbol is obtained, and the log-likelihood ratio of binary domain superposition code words of each user on the received symbol is determined according to the multilevel prior distribution information or the multilevel information, so that the obtained log-likelihood ratio is input into a channel decoder to be processed for multi-user signal detection, the minimum information loss generated by directly carrying out detection judgment on the multilevel is realized, the correct detection of the multi-user information is ensured to the maximum extent, the prior distribution information of the multilevel symbol is fully utilized, the division of a plurality of level judgment domains is more reasonable, and the error probability of the multi-user signal detection is minimized.

It should be noted that the multi-user signal detection apparatus provided in the embodiment of the present application can implement all the method steps implemented in the method embodiments shown in fig. 1 to 9, and can achieve the same technical effect, and detailed descriptions of the same parts and beneficial effects as those of the method embodiments in this embodiment are not repeated herein.

It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

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

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

On the other hand, the embodiment of the present application further provides a processor-readable storage medium, where a computer program is stored, and the computer program is used to enable a processor to execute the method shown in the embodiment of fig. 5 of the present application.

The 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 memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO), etc.), optical memory (e.g., CD, DVD, BD, HVD, etc.), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid State Disk (SSD)), etc.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

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

acquiring the number of active users in a channel;

2. The multi-user signal detection method of claim 1, wherein said determining log-likelihood ratios of binary-domain superposition codewords for respective users over the received symbols based on the multi-level prior distribution information comprises:

3. The multi-user signal detection method of claim 2, wherein the calculating according to the preset first algorithm, the preset first parameter, the noise information and the first look-up table obtains/ ₀ ，l ₁ The first and second log likelihood ratio components when both of the values of (a) and (b) are greater than the number of users, comprising:

Number of multilevel symbols, l ₁ Is composed of

The sequence number of the multi-level symbol of (c),

for binary domain superposition codeword, LLR corresponding to received symbol ₀ Is composed of

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of;

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

inquiring the first comparison table to obtain corresponding prior probability

And

wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ A prior probability of a number of multilevel symbols;

4. the multi-user signal detection method of claim 2, wherein the calculating according to the preset second algorithm, the preset first parameter, the noise information and the first look-up table obtains/ ₀ ，l ₁ When the values of (a) are both greater than the number of users, a first log likelihood ratio component and a second log likelihood ratio component, comprising:

Number of multilevel symbols, l ₁ Is composed of

The number of the multi-level symbols of (a),

Log likelihood ratio component, LLR ₁ Is composed of

A log likelihood ratio component of;

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein, the first and the second end of the pipe are connected with each other,

inquiring the first comparison table to obtain corresponding prior probability

And

and calculating its logarithmic form

And

is the first ₁ The prior probability of a number of multi-level symbols,

5. the multi-user signal detection method of claim 1 wherein said determining log-likelihood ratios of binary-domain superposition codewords for respective users over said received symbols based on said multi-level information comprises:

6. The multi-user signal detection method of claim 5, wherein the obtaining the detection value of the binary field superposition code word when the value of/is greater than the number of the users according to the calculation performed by the preset third algorithm, the preset second parameter and the second comparison table comprises:

wherein l is the number of the multilevel symbol,

to receive the binary field superposition codeword for the symbol,

is the current minimum normalized euclidean distance;

querying the second lookup table to obtain μ _l Calculating conditional probabilities

updating according to the third algorithm

Inquiring the second comparison table to obtain the corresponding

Wherein the third algorithm is if

Then

If it is

Then

wherein the content of the first and second substances,

is composed of

Detected value of (d), δ ² For noise in the channelAnd (4) poor.

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

obtaining noise measurements of a plurality of noise signals;

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

acquiring the number of active users in a channel;

9. The network-side device of claim 8, wherein the determining log-likelihood ratios of the binary-domain superposition codewords for the users over the received symbols according to the multilevel prior 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 All values of are greater thanA first log-likelihood ratio component and a second log-likelihood ratio component for the number of users;

10. The network-side device of claim 9, wherein the calculating according to the preset first algorithm, the preset first parameter, the noise information, and the first lookup table obtains/ ₀ ，l ₁ When the values of (a) are both greater than the number of users, a first log likelihood ratio component and a second log likelihood ratio component, comprising:

Number of multilevel symbols,/, of ₁ Is composed of

The number of the multi-level symbols of (a),

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of (a);

inquiring the first comparison table to obtain

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

generated for superposition of multiuser signals ₁ A number of multilevel symbols, y being the received symbol, δ ² The first mapping table is a mapping table containing multi-level prior distribution information and is the noise variance of a channel;

inquiring the first comparison table to obtain corresponding prior probability

And

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ A prior probability of a number of multilevel symbols;

11. the network-side device of claim 9, wherein the calculating according to the preset second algorithm, the preset first parameter, the noise information, and the first lookup table obtains/ ₀ ，l ₁ When the values of (a) are both greater than the number of users, a first log likelihood ratio component and a second log likelihood ratio component, comprising:

Number of multilevel symbols, l ₁ Is composed of

The number of the multi-level symbols of (a),

Log-likelihood ratio component of (LLR) ₁ Is composed of

A log likelihood ratio component of;

query the first pairLook-up table acquisition

Calculating corresponding conditional probabilities

And

wherein the content of the first and second substances,

inquiring the first comparison table to obtain corresponding prior probability

And

and calculating its logarithmic form

And with

Wherein the content of the first and second substances,

is the first ₁ The prior probability of a number of multi-level symbols,

is the first ₀ A plurality ofThe first mapping table is a mapping table containing multi-level prior distribution information;

12. the network-side device of claim 8, wherein the determining log-likelihood ratios of the binary-domain superposition codewords for the users over the received symbols according to the multi-level information comprises:

acquiring noise information in a channel and acquiring a second contrast table comprising the multilevel information;

13. The network-side device of claim 12, wherein the calculating according to a preset third algorithm, a preset second parameter, and the second lookup table to obtain the detection value of the binary field superposition codeword when the value of/is greater than the number of the users comprises:

presetting the second parameter, including: l =0, and the sum of the total weight of the steel,

wherein l is the number of the multilevel symbol,

to receive the binary field superposition codeword for the symbol,

is the current minimum normalized euclidean distance;

querying the second lookup table for μ _l Calculating conditional probability

updating according to the third algorithm

Querying the second comparison table to obtain corresponding

Wherein the third algorithm is if

Then

If it is

Then

wherein the content of the first and second substances,

is composed of

Detected value of δ ² Is the noise variance in the channel.

14. The network-side device of claims 8-13, further comprising:

obtaining noise measurements of a plurality of noise signals;

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

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

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