Multi-user detection method and device without cell MIMO

Abstract

The embodiment of the present disclosure provides a multi-user detection method and device without cell MIMO, which comprises the following steps: a central processing unit (CPU) of a cell MIMO system receives MRC combined data symbol streams S of K users transmitted back by an access point (AP) MRC =H H Y, multiplies the S MRC by a conjugate transpose matrix to obtain a K*K matrix, performs singular value decomposition on the matrix to obtain a unitary matrix V and a diagonal matrix A, obtains a diagonal matrix Lambda through the diagonal matrix A, and estimates data symbols of the K users through a formula. In the present disclosure, the CPU at the receiving end does not need to obtain channel information of each user, and can estimate the data symbols only by using the MRC combined data symbol streams transmitted back by the AP, so that the CPU can realize optimal MMSE multi-user detection without increasing the front transmission bandwidth from the AP to the CPU.

Description

Technical Field

[0001] This disclosure relates to the field of communications, and more specifically, to a multi-user detection method and apparatus for cell-free MIMO. Background Technology

[0002] When multiple user terminals (UEs) simultaneously transmit data to a cell-free MIMO network, the central processing unit (CPU) of the cell-free MIMO system needs to perform multi-user detection. Initially, the CPU of cell-free MIMO performed multi-user detection based on Maximum Ratio Combining (MRC) because it was the simplest and did not require increasing the fronthaul bandwidth from the access point (AP) to the CPU in the cell-free MIMO system. However, MRC-based multi-user detection is generally not optimal in performance because it does not consider inter-user interference. In particular, the performance of MRC multi-user detection deteriorates as the number of simultaneously accessing users increases. Therefore, a better-performing multi-user detection method needs to be designed for cell-free MIMO systems. However, for current cell-free MIMO systems to achieve better multi-user detection than MRC multi-user detection, each AP needs to transmit the channel information from the access user back to the CPU. Only then can the CPU consider the correlation of user spatial channels based on this transmitted channel information, and thus perform multi-user detection that better suppresses multi-user interference, such as multi-user detection based on zero forcing (ZF) or minimum mean square error (MMSE), to achieve better performance than MRC multi-user detection.

[0003] It is evident that current industry-standard ZF or MMSE methods have a drawback compared to MRC multi-user detection: they require each access point (AP) to transmit the channel information from the access user to its CPU. In contrast, the original MRC multi-user detection did not require the AP to transmit this information back to the CPU. Requiring the AP to transmit channel information back to the CPU undoubtedly increases the fronthaul bandwidth between the AP and the CPU, leading to an increase in the cost of cell-free MIMO systems. Summary of the Invention

[0004] This disclosure provides a multi-user detection method and apparatus without cell MIMO, which at least solves the problem in related technologies where the AP needs to transmit channel information back to the CPU during multi-user detection, resulting in an increase in the AP-to-CPU fronthaul bandwidth.

[0005] According to an embodiment of this disclosure, a multi-user detection method without cell MIMO is provided, the method comprising:

[0006] The receiver of a cellless MIMO system receives a data symbol stream S from K users transmitted back by the access point (AP). MRC =H H Y, where S MRC =H H Y is a K*L matrix, where each row represents L data symbols of a user, and K and L are both positive integers;

[0007] S MRC Multiply by its conjugate transpose Obtain a K*K matrix

[0008] For the matrix Performing Singular Value Decomposition (SVD) yields a unitary matrix V = [v1, v2, ... v2]. K and K real numbers a1 a2 … a K , making Among them, V H Let V be the transpose of the unitary matrix V. Alternatively, by calculating the matrix or K eigenvalues ​​a1 a2 … a K and the corresponding K feature vectors v1, v2, ... v K , making

[0009] Obtain the diagonal matrix in, or c is a real number greater than 1, σ 2 The noise variance on the received signal of the AP;

[0010] Through formula For data symbol stream S MRC The corresponding K users' transmitting data symbols are estimated.

[0011] In an exemplary embodiment, before obtaining the diagonal array Λ, the method may further include: the receiving end receiving the noise variance σ transmitted by the AP.2 Alternatively, the receiving CPU may determine the noise variance σ based on the attributes of the AP. 2 .

[0012] According to embodiments of this disclosure, a multi-user detection method without cell MIMO is also provided, the method comprising:

[0013] The receiver of a cell-free MIMO system receives a data symbol stream S from K users transmitted back by the access point (AP). MRC =H H Y, where S MRC =H H Y is a K*L matrix, where each row represents L data symbols of a user, and K and L are both positive integers;

[0014] For the matrix S MRC Perform singular value decomposition to obtain a unitary matrix V = [v1, v2, ... v K ] and K real numbers ω1, ω2, ... ω K , making Among them, U H Let Ω be the transpose of the unitary matrix U, and let Ω be a K*L matrix:

[0015]

[0016] Obtain the diagonal matrix in, or c is a real number greater than 1, σ 2 Let a be the noise variance on the received signal of the AP. k =|ω k | 2 ;

[0017] Through formula For data symbol stream S MRC The corresponding K users' transmitting data symbols are estimated, where V H Let V be the transpose of the unitary matrix V.

[0018] In an exemplary embodiment, before obtaining the diagonal array Λ, the method may further include: the receiving end receiving the noise variance σ transmitted by the AP. 2 Alternatively, the receiving CPU may determine the noise variance σ based on the attributes of the AP. 2 .

[0019] According to one embodiment of the present disclosure, a multi-user detection device for cell-free MIMO is provided. The device is located within the CPU of a cell-free MIMO system, and the multi-user detection device includes:

[0020] The receiving module is used to receive the data symbol stream S of K users transmitted back from the access point (AP). MRC =H H Y, where S MRC =H H Y is a K*L matrix, where each row represents L data symbols of a user, and K and L are both positive integers;

[0021] The first acquisition module is used to acquire S MRC Multiply by its conjugate transpose Obtain a K*K matrix

[0022] Decomposition module, used for decomposing the matrix Perform singular value decomposition to obtain a unitary matrix V = [v1, v2, ... v K and K real numbers a1 a2 … a K , making Among them, V H Let V be the transpose of the unitary matrix V. Alternatively, by calculating the matrix or K eigenvalues ​​a1a2 … a K and the corresponding K feature vectors v1, v2, ... v K , making

[0023] The second acquisition module is used to acquire the diagonal matrix. in, or c is a real number greater than 1, σ 2 The noise variance on the received signal of the AP;

[0024] The estimation module is used to estimate the formula. For data symbol stream S MRC The corresponding K users' transmitting data symbols are estimated.

[0025] In an exemplary embodiment, the receiving module is further configured to receive the noise variance σ transmitted by the AP, or determine the noise variance σ based on the properties of the AP. 2 .

[0026] According to embodiments of this disclosure, a cell-free MIMO multi-user detection device is also provided. This device is located within the CPU of a MIMO-free system and includes:

[0027] The receiving module is used to receive the data symbol stream S of K users transmitted back from the access point (AP). MRC =H HY, where S MRC =H H Y is a K*L matrix, where each row represents L data symbols of a user, and K and L are both positive integers;

[0028] Decomposition module, used for decomposing the matrix S MRC Perform singular value decomposition to obtain a unitary matrix V = [v1, v2, ... v K ] and K real numbers ω1, ω2, ... ω K , making Among them, U H Let Ω be the transpose of the unitary matrix U, and let Ω be a K*L matrix:

[0029]

[0030] The acquisition module is used to acquire the diagonal matrix. in, or c is a real number greater than 1, σ 2 Let a be the noise variance on the received signal of the AP. k =|ω k | 2 ;

[0031] The estimation module is used to estimate the formula. For data symbol stream S MRC The corresponding K users' transmitting data symbols are estimated, V H Let V be the transpose of the unitary matrix V.

[0032] In one exemplary embodiment, the receiving module is further configured to receive the noise variance σ transmitted by the AP. 2 Or, the noise variance σ can be determined based on the properties of the AP. 2 .

[0033] According to yet another embodiment of this disclosure, a cell-free MIMO system is also provided, which includes the multi-user detection device described in the preceding embodiments.

[0034] According to yet another embodiment of this disclosure, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0035] According to yet another embodiment of this disclosure, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0036] In the above embodiments of this disclosure, the CPU does not need to obtain the channel information of each user. It can estimate the data symbols based solely on the MRC combined data symbols returned by the AP. Thus, without increasing the fronthaul bandwidth from the AP to the CPU, the CPU can achieve optimal MMSE multi-user detection and optimal uplink multi-user transmission in the cell-free MIMO system. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of data symbol transmission in a cellless MIMO system based on relevant technologies;

[0038] Figure 2 This is a flowchart of a multi-user detection method without cell MIMO according to an embodiment of the present disclosure;

[0039] Figure 3 This is a flowchart of a multi-user detection method without cell MIMO according to another embodiment of the present disclosure;

[0040] Figure 4 This is a structural diagram of a multi-user detection device module according to an embodiment of the present disclosure;

[0041] Figure 5 This is a structural diagram of a multi-user detection device module according to another embodiment of the present disclosure without cell MIMO;

[0042] Figure 6 This is a schematic diagram of the structure of a cellless MIMO system according to an embodiment of the present disclosure;

[0043] Figure 7 This is a flowchart of a cell-free MIMO multi-user detection method according to Embodiment 1 of this disclosure;

[0044] Figure 8 This is a flowchart of a cell-free MIMO multi-user detection method according to Embodiment 2 of this disclosure. Detailed Implementation

[0045] The embodiments of this disclosure will be described in detail below with reference to the accompanying drawings and examples.

[0046] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0047] Figure 1 This is a schematic diagram of data symbol transmission in a cell-free MIMO system based on related technologies, such as... Figure 1As shown, a cell-free MIMO system contains many access nodes (APs). Each circle in the diagram represents an access node. These access nodes are typically deployed in a distributed manner and connected to the central processing unit (CPU) via a specific connection method (topology). For example... Figure 1 As shown in (a), N access nodes are connected to a central processing unit via a chain or strip connection. Figure 1 As shown in (b), a portion of the N access nodes are connected to a central processing unit via a chain or strip connection, while another portion is connected to the central processing unit via another chain or strip connection.

[0048] In cell-free MIMO systems, both wireless signal transmission and reception are accomplished through access nodes (APs). Uplink multi-user transmission... Figure 1 Let's take (a) as an example. Suppose K UEs send data to the AP (also known as uplink data transmission). Assume that the L symbols transmitted by each user traverse the same radio channel (the channels traversed by symbols from different users are independent), then the AP... m The received L data symbols are:

[0049]

[0050] Among them, y m = [y m,1 ,y m,2 ,...y m,L ] is AP m The vector consisting of the received L data symbols is a line vector of length L; s k =[s k,1 ,s k,2 ,...s k,L ] is UE k The vector consisting of the L transmitted data symbols is a row vector; h mk It is UE k To AP m The wireless channel is a scalar, n m It is AP m Additive white Gaussian noise (AWGN) on the vector is also a row vector of length L.

[0051] Furthermore, the signals received by the M APs can be written in matrix form:

[0052]

[0053] in, It is an M*L matrix with M rows, and the m-th row represents y.m ;

[0054] It is an L*L matrix, and the m-th row is y. m ;

[0055] h k =[h 1,k ,h 2,k ,...h M,k ] T It is the spatial channel vector of user k to M APs, which is an M-length column vector with M rows. H = [h1, h2, ... h K ] is an M*K matrix. AWGN noise N is an M*L matrix.

[0056] The multi-user detection process based on MRC in a cell-free MIMO system is as follows:

[0057] AP m via UE k The reference signal is used to estimate the UE. k To AP m wireless channel h mk Then use h mk The conjugate of, i.e. To weight the received data symbol y m To obtain data symbols related to user k Then, and the previous AP (i.e., AP) m-1 The data symbols related to user k transmitted. Added together, that is get Then this accumulated signal is transmitted to the next AP (i.e., AP). m+1 ), and so on, until the Mth AP transmits the data symbol related to user k to the CPU.

[0058] The CPU has signals related to user k. It can also be written as in, It is h k The conjugate transpose of a vector. Furthermore, It can be seen that what the CPU obtains It's a maximum ratio merging of user k symbols, if for middle user k symbol s k Normalization yields the normalized MRC merge of user k symbols, denoted as...

[0059] In this MRC reception method described above, each AP only needs to transmit K data symbols to the next AP, without transmitting other information. For example, AP m Simply stream K data symbols of length L. k = 1...K, pass to AP m+1 However, from the above formula... It appears that MRC merging does not consider inter-user interference, therefore there will be a large user interference term in the user k data symbol. Therefore, its performance is suboptimal.

[0060] The relevant MRC data symbols received by the CPU from user k are The MRC data symbols of K users can be combined into S. MRC =H H Y.

[0061] Although existing cell-free MIMO multi-user detection technologies also employ higher-performance ZF or MMSE multi-user detection, each AP still needs to transmit all access user channel information to the CPU. Only then can the CPU utilize the correlation of user spatial channels to perform multi-user detection that can suppress multi-user interference, such as multi-user detection based on zero-forcing (ZF) or minimum mean square error (MMSE), to achieve better performance than MRC multi-user detection.

[0062] Specifically, the CPU receives the merged MRC data symbols from the AP for K users:

[0063] S MRC =H H Y = H H (HS+N)=H H HS+H H N.

[0064] S MRC =H H Y is a K*L matrix, where each row represents the MRC merged symbol stream of a user. If the AP simultaneously transmits H to the CPU, the CPU, knowing the H matrix, can use the MMSE criterion to estimate S, i.e., S0. MMSE =(H H H+σ 2 I) -1 H H Y = (H H H+σ 2 I) -1 S MRC This means that after the CPU knows H, it estimates the MMSE of S from Y. Here, σ 2This is the variance of the AWGN on the received signals of each AP. MMSE estimation scenarios all assume that the CPU has known σ. 2 of.

[0065] It is evident that existing zero-forcing or MMSE methods have a drawback compared to MRC multi-user detection: each AP needs to transmit the channel information from the access user to its CPU, while the original MRC multi-user detection does not require user channel information. This undoubtedly increases the fronthaul bandwidth from the AP to the CPU, leading to an increase in the cost of the cellless MIMO system. This patent proposes a novel method that eliminates the need for APs to transmit the channel information from the access user to its CPU, thus avoiding the increase in AP-to-CPU fronthaul bandwidth. While still using the original MRC fronthaul, the CPU can achieve optimal MMSE multi-user detection.

[0066] Therefore, this disclosure provides a novel MMSE multi-user detection method. In this embodiment, without increasing the fronthaul bandwidth from the AP to the CPU, the CPU can achieve optimal MMSE multi-user detection and optimal uplink multi-user transmission in the cell-free MIMO system.

[0067] In this embodiment, the original MRC fronthaul is used. Without the AP transmitting the channel information of each user back, the CPU cannot directly obtain the channel information of each user; for example, the CPU does not know H. Without channel information, the CPU cannot estimate the data symbols S of each user according to the traditional MMSE method. In this embodiment, the CPU relies solely on the MRC-combined data symbols S transmitted back by the AP. MRC =H H Y = H H (HS+N)=H H HS+H H N is used to obtain matrix H H H+σ 2 I, and then through H H H+σ 2 I and S MRC Obtain MMSE estimation of S. MMSE =(H H H+σ 2 I) -1 H H Y = (H H H+σ 2 I) -1 S MRC

[0068] In this embodiment of the disclosure, it is assumed that the CPU knows the noise variance σ on the AP. 2Yes. This can be achieved in two ways, for example, as follows. The first way is for AP to transfer σ 2 Or σ 2 The relevant information is transmitted back to the CPU. The second method is for the CPU to determine σ based on the AP's attributes. 2 No AP backhaul required 2 Or σ 2 Relevant information.

[0069] This embodiment provides a multi-user detection method without cell MIMO. Figure 2 This is a flowchart according to an embodiment of the present disclosure, such as... Figure 2 As shown, the process includes the following steps:

[0070] In step S202, the CPU of the Cell-free MIMO system receives the data symbol stream S of K users transmitted back from the access point (AP). MRC =H H Y, where S MRC =H H Y is a K*L matrix, where each row represents L data symbols of a user, and K and L are both positive integers;

[0071] Step S204, S MRC Multiply by its conjugate transpose Obtain a K*K matrix

[0072] Step S206, for the matrix Perform singular value decomposition to obtain a unitary matrix V = [v1, v2, ... v K and K real numbers a1 a2 … a K , making Among them, V H Let V be the transpose of the unitary matrix V. In this step, the matrix can also be calculated. or K eigenvalues ​​a1a2…a K and the corresponding K feature vectors v1, v2, ..., v K , making Composed of K eigenvectors v k Construct a K*K unitary matrix V = [v1, v2, ... v K ];

[0073] Step SS208: Obtain the diagonal matrix in, or c is a real number greater than 1, σ 2 The noise variance on the received signal of the AP;

[0074] Step S210, using the formula For data symbol stream S MRC The corresponding K users' transmitting data symbols are estimated.

[0075] This embodiment also provides a cell-free MIMO multi-user detection method. Figure 3 This is a flowchart according to an embodiment of the present disclosure, such as... Figure 3 As shown, the process includes the following steps:

[0076] Step S302: The receiver of the Cell-free MIMO system receives the data symbol stream S of K users transmitted back from the access point (AP). MRC =H H Y, where S MRC =H H Y is a K*L matrix, where each row represents L data symbols of a user, and K and L are both positive integers;

[0077] Step S304, for the matrix S MRC Perform singular value decomposition to obtain a unitary matrix V = [v1, v2, ... v K ] and K real numbers ω1, ω2, ... ω K , making Among them, U H Let Ω be the transpose of the unitary matrix U, and let Ω be a K*L matrix:

[0078]

[0079] Step S306, Obtain the diagonal matrix in, or c is a real number greater than 1, σ 2 Let a be the noise variance on the received signal of the AP. k =ω k 2 ;

[0080] Step S308, using the formula For data symbol stream S MRC The corresponding K users' transmitting data symbols are estimated, V H Let V be the transpose of the unitary matrix V.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0082] This embodiment also provides a cell-free MIMO multi-user detection device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0083] Figure 4 This is a structural block diagram of a cell-free MIMO multi-user detection device according to an embodiment of the present disclosure. The device can be located within the CPU of a cell-free MIMO system, such as... Figure 4 As shown, the device includes:

[0084] Receiver module 10 is used to receive the data symbol stream S of K users transmitted back from the access point (AP). MRC =H H Y, where S MRC =H H Y is a K*L matrix, where each row represents L data symbols of a user, and K and L are both positive integers;

[0085] The first acquisition module 20 is used to acquire S MRC Multiply by its conjugate transpose Obtain a K*K matrix

[0086] Decomposition module 30 is used to decompose the matrix Perform singular value decomposition to obtain a unitary matrix V = [v1, v2, ... v K and K real numbers a1 a2 … a K , making Among them, V H Let V be the transpose of the unitary matrix V.

[0087] The second acquisition module 40 is used to acquire the diagonal matrix. in, or c is a real number greater than 1, σ 2 The noise variance on the received signal of the AP;

[0088] Estimation module 50, used to estimate by formula For data symbol stream S MRC The corresponding K users' transmitting data symbols are estimated.

[0089] In this embodiment, the decomposition module 30 can also calculate the matrix. or K eigenvalues ​​a1 a2 … a K and the corresponding K feature vectors v1, v2, ..., v K , making Composed of K eigenvectors v k This forms a K*K unitary matrix V = [v1, v2, ... v K ].

[0090] Figure 5 This is a structural block diagram of a multi-user detection apparatus for cell-free MIMO according to another embodiment of the present disclosure. The apparatus is located within the CPU of a cell-free MIMO system. Figure 5 As shown, the device includes:

[0091] Receiver module 60 is used to receive the data symbol stream S of K users transmitted back from the access point (AP). MRC =H H Y, where S MRC =H H Y is a K*L matrix, where each row represents L data symbols of a user, and K and L are both positive integers;

[0092] Decomposition module 70 is used to decompose the matrix S MRC Perform singular value decomposition to obtain a unitary matrix V = [v1, v2, ... v K ] and K real numbers ω1, ω2, ... ω K , making Among them, U H Let Ω be the transpose of the unitary matrix U, and let Ω be a K*L matrix:

[0093]

[0094] Module 80 is used to obtain the diagonal matrix. in, or c is a real number greater than 1, σ 2Let a be the noise variance on the received signal of the AP. k =ω k 2 ;

[0095] Estimation module 90 is used to estimate the formula For data symbol stream S MRC The corresponding K users' transmitting data symbols are estimated, where V H Let V be the transpose of the unitary matrix V.

[0096] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0097] Figure 6 This is a cell-free MIMO system according to embodiments of this disclosure. For example... Figure 6 As shown, the system includes the multi-user detection device described in the previous embodiment.

[0098] To facilitate understanding of the technology provided in this disclosure, a detailed description is provided below with reference to specific scenario embodiments.

[0099] Example 1

[0100] This embodiment provides a cell-free MIMO multi-user detection device. In this embodiment, during cell-free MIMO uplink transmission, the CPU receives the user data symbol stream S transmitted back from the AP. MRC =H H Y after (S) MRC The matrix has K rows, each row has L symbols, and each row corresponds to the MRC merge of L data symbols of one user, from S MRC Obtain matrix H H H+σ 2 I, and then through H H H+σ 2 I and S MRC Obtain the data symbol S for each user and perform MMSE estimation. For example... Figure 7 As shown, the method in this embodiment includes the following steps:

[0101] Step S702, transfer the data symbol matrix S MRC Multiply by its conjugate transpose We obtain a K*K matrix.

[0102] Step S704, for square matrix or or The matrix formed by multiplying a matrix by any constant is... Where c is a constant, SVD decomposition yields a K*K unitary matrix V and K real numbers a1 a2 … a K , making That is to say Here, A represents K real numbers a1 a2 … a K A K*K diagonal matrix with diagonal elements, i.e. All off-diagonal elements of a diagonal matrix are 0.

[0103] In this embodiment, it can also be achieved by calculating a square matrix. or K eigenvalues ​​a1a2 … a K and the corresponding K feature vectors v1, v2, ..., v K , making This is satisfied for k = 1, 2, ..., K. It consists of K eigenvectors v. k Construct a K*K unitary matrix V = [v1, v2, ... v K ].

[0104] Step S706, calculate a new diagonal matrix. or c is a real number greater than 1.

[0105] Step S708, Calculate This allows for the estimation of data symbols for the corresponding transmitters of K users.

[0106] Example 2

[0107] This embodiment provides a cell-free MIMO multi-user detection device. In this embodiment, during cell-free MIMO uplink transmission, the CPU receives the user data symbol stream S transmitted back from the AP. MRC =H H Y after (S) MRC The matrix has K rows, each row has L symbols, and each row corresponds to the MRC merge of L data symbols of one user, from S MRC Obtain matrix H H H+σ 2 I, and then through H H H+σ 2 I and S MRC Obtain the data symbol S for each user and perform MMSE estimation, such as... Figure 8 As shown, the process includes the following steps:

[0108] Step S802, for the K*L data symbol matrix S MRC or Performing SVD decomposition yields a K*K unitary matrix V and K real numbers ω1, ω2, ... ω. K , making or Where Ω is composed of K real numbers ω1, ω2, ... ω K The generated K*L matrix, U H U is a K*K transpose matrix, and its uniqueness can be determined by SVD decomposition. H :

[0109]

[0110] Step S804: Generate a new diagonal matrix. in or c is a real number greater than 1, a k =ω k 2 ;

[0111] Step S804, calculate This allows for the estimation of data symbols for the corresponding transmitters of K users.

[0112] To facilitate understanding of the above embodiments of this disclosure, the principles upon which the embodiments of this disclosure are based will be described in detail below.

[0113] With M access points (APs) and K users connecting simultaneously, each user sending L data symbols, the CPU receives the following symbol matrix:

[0114] S MRC =H H Y = H H (HS+N)=H H HS+H H N,

[0115] in, It is an M*L matrix with M rows, and the m-th row represents y. m ; refers to the L data symbols received by the m-th AP.

[0116] It is a K*L matrix, where the k-th row represents y. k ; refers to the L data symbols sent by the k-th UE.

[0117] H = [h0, h1, ... h K-1 ] is an M*K matrix, where the k-th column is...

[0118] h k =[h 0,k ,h 1,k ,...h M-1,k ]T The spatial channel vectors for users k to M APs are M-length column vectors with M rows. The AWGN noise N is an M*L matrix.

[0119] Therefore, the CPU receives the symbol matrix S. MRC It is a K*L matrix.

[0120] If you know H or H H H, then from S MRC =H H Y performs MMSE estimation on the data symbols S for each user, i.e., S MMSE =(H H H+σ 2 I) -1 H H Y = (H H H+σ 2 I) -1 S MRC .

[0121] Where I is a K*K identity matrix, and it is assumed that the modulation symbols transmitted by the user are of normalized energy, and the variance of the AWGN on each AP is σ. 2 .

[0122] But the CPU doesn't know H, nor does it know H. H H, therefore, neither MMSE nor the above can be implemented. However, it can be seen that the CPU obtains the data symbol S. MRC =H H After Y, as long as it is possible to obtain data from symbol S MRC H was obtained from H H can then be used to obtain the H required for MMSE. H H+σ 2 I, thereby achieving this MMSE.

[0123] The following explains how to extract data symbol S MRC H was obtained from H H+σ 2 I:

[0124] 1) Due to S MRC =H H Y = H H HS+H H N, then S MRC The relevant array is:

[0125]

[0126] Since AWGNs on different APs are independent and unrelated, and data symbols on different users are also independent and unrelated, as the number of symbols L increases, It will increasingly approach a K*K unit matrix, and It will increasingly approach a K*K identity matrix multiplied by σ 2 Therefore, as long as the number of symbols L is large enough, (H H HH H H+σ 2 H H H) can be used To replace it.

[0127] 2) Furthermore, due to H H Let H be a complex symmetric matrix. H H = VXV H ,

[0128] It is a diagonal matrix, with 0 as the off-diagonal element and x as the diagonal element. k It is a non-negative real number that is not less than 0, i.e., x k ≥0. V is a unitary matrix, V H It is a conjugate symmetric matrix of V.

[0129] The following explains how to obtain (H) H HH H H+σ 2 H H H) Obtain V and X:

[0130] H H H = VXV H Substitute (H) H HH H H+σ 2 H H H), we can obtain

[0131]

[0132] Furthermore, on That is (H) H HH H H+σ 2 H H The SVD decomposition of H) is:

[0133] H H HH H H+σ 2 H H H = VAV H ,in

[0134] Since the singular values ​​of a matrix are unique, and assuming they are already sorted by size, we can obtain k = 1…K. These K relations are essentially quadratic equations in one variable, each with two solutions; however, due to x… k Since ≥0, negative solutions are meaningless, and therefore we can solve for:

[0135]

[0136] Therefore H H H = VXV H Both V and X in the equation can be obtained by... The SVD decomposition is obtained.

[0137] Furthermore, H can be obtained. H H+σ 2 I = V(X + σ) 2 I)V H Let Λ = (X + σ) 2 I) -1 Right now

[0138]

[0139] Therefore, ultimately from S MRC =H H Y performs MMSE estimation on the data symbols S for each user, i.e.: S MMSE =(H H H+σ 2 I) -1 H H Y = (H H H+σ 2 I) -1 S MRC =V(X+σ) 2 I) -1 V H S MRC =VΛV H S MRC .

[0140] The following explains how to extract data symbol S MRC H was obtained from H H+σ 2 Another way to do it:

[0141] 1) Due to S MRC =H H Y = H H HS+H H N, then S MRC The relevant array is:

[0142]

[0143] Since AWGNs on different APs are independent and unrelated, and data symbols on different users are also independent and unrelated, as the number of symbols L increases, It will increasingly approach a K*K unit matrix, and It will increasingly approach a K*K identity matrix multiplied by σ 2 Therefore, as long as the number of symbols L is large enough, (H H HH H H+σ 2 H H H) can be used To replace it.

[0144] 2) Further from but That is to say so, Therefore, we can further conclude that:

[0145] Therefore, ultimately from S MRC =H H Y performs MMSE estimation on the data symbols S for each user, i.e.:

[0146] Embodiments of this disclosure also provide a computer-readable storage medium storing a computer program configured to perform the steps in any of the above method embodiments when executed.

[0147] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0148] Embodiments of this disclosure also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0149] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0150] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0151] It is obvious to those skilled in the art that the modules or steps of this disclosure described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this disclosure is not limited to any particular combination of hardware and software.

[0152] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A multi-user detection method without cell MIMO, characterized in that, include: The central processing unit (CPU) of a cellless MIMO system receives the MRC merged data symbol streams of K users transmitted from the access point (AP). ,in, It is a K A matrix of length L, where each row represents L data symbols for a user, where K and L are both positive integers; Y is a matrix of length M. L is a matrix representing the signals received by M APs; H is a matrix of M... The matrix K represents the spatial channel vectors from K users to M APs respectively; Will Multiply by its conjugate transpose Obtain a K The matrix of K ; For the matrix Perform singular value decomposition to obtain a unitary matrix. and K real numbers , making ,in, For the unitary matrix The transpose of the matrix, It is composed of the K real numbers As a diagonal matrix composed of diagonal elements, or by calculating the matrix or K eigenvalues and the corresponding K feature vectors , making ; Obtain the diagonal matrix ,in, ,or c is a real number greater than 1. The noise variance on the received signal of the AP; Through formula For data symbol stream The corresponding K users' transmitting data symbols are estimated.

2. The method according to claim 1, characterized in that, Obtain the diagonal matrix Previously, it also included: The central processing unit (CPU) receives the noise variance transmitted by the AP. Or, the central processing unit (CPU) determines the noise variance based on the attributes of the AP. .

3. A multi-user detection method without cell MIMO, characterized in that, include: The central processing unit (CPU) of a cellless MIMO system receives the MRC merged data symbol streams of K users transmitted from the access point (AP). ,in, It is a K A matrix of length L, where each row represents L data symbols for a user, where K and L are both positive integers; Y is a matrix of length M. L is a matrix representing the signals received by M APs; H is a matrix of M... The matrix K represents the spatial channel vectors from K users to M APs respectively; For the matrix Perform singular value decomposition to obtain a unitary matrix. and K real numbers , making ,in, For the unitary matrix The transpose of the matrix, It is a K L matrix: ； Obtain the diagonal matrix ,in, ,or c is a real number greater than 1. The noise variance of the received signal by the AP. ; Through formula For data symbol stream The corresponding K users' transmitting data symbols are estimated, where, For the unitary matrix The transpose of .

4. The method according to claim 3, characterized in that, Obtain the diagonal matrix Previously, it also included: The central processing unit (CPU) receives the noise variance transmitted by the AP. Or, the central processing unit (CPU) determines the noise variance based on the attributes of the AP. .

5. A multi-user detection device for cell-free MIMO, located in the central processing unit (CPU) of a cell-free MIMO system, characterized in that, include: The receiving module is used to receive data symbol streams from K users transmitted back by the access point (AP). ,in, It is a K A matrix of length L, where each row represents L data symbols for a user, where K and L are both positive integers; Y is a matrix of length M. L is a matrix representing the signals received by M APs; H is a matrix of M... The matrix K represents the spatial channel vectors from K users to M APs respectively; The first acquisition module is used to obtain... Multiply by its conjugate transpose Obtain a K The matrix of K ; Decomposition module, used for decomposing the matrix Perform singular value decomposition to obtain a unitary matrix. and K real numbers , making ,in, For the unitary matrix The transpose of the matrix, It is a diagonal matrix composed of the K real numbers as diagonal elements, or, by calculating the matrix... or K eigenvalues and the corresponding K feature vectors , making ; The second acquisition module is used to acquire the diagonal matrix. ,in, ,or c is a real number greater than 1. The noise variance on the received signal of the AP; The estimation module is used to estimate the formula. For data symbol stream The corresponding K users' transmitting data symbols are estimated.

6. The apparatus according to claim 5, characterized in that, The receiving module is also configured to receive the noise variance transmitted by the AP. Or determine the noise variance based on the properties of the AP. .

7. A multi-user detection device for cell-free MIMO, located in the central processing unit (CPU) of a cell-free MIMO system, characterized in that, include: The receiving module is used to receive data symbol streams from K users transmitted back by the access point (AP). ,in, It is a K A matrix of length L, where each row represents L data symbols for a user, where K and L are both positive integers; Y is a matrix of length M. L is a matrix representing the signals received by M APs; H is a matrix of M... The matrix K represents the spatial channel vectors from K users to M APs respectively; Decomposition module, used for decomposing the matrix Perform singular value decomposition to obtain a unitary matrix. and K real numbers , making ,in, For the unitary matrix The transpose of the matrix, It is a K L matrix: ； The acquisition module is used to acquire the diagonal matrix. ,in, ,or c is a real number greater than 1. The noise variance of the received signal by the AP. ; The estimation module is used to estimate the formula. For data symbol stream The corresponding K users' transmitting data symbols are estimated, where, For the unitary matrix The transpose of .

8. The apparatus according to claim 7, characterized in that, The receiving module is also configured to receive the noise variance transmitted by the AP. Or determine the noise variance based on the properties of the AP. .

9. A cell-free MIMO system, characterized in that, The multi-user detection device includes any one of claims 5 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of claims 1 to 4.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 4.

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