An interference suppression method and device for MU-MIMO beamforming

Abstract

An embodiment of the present application provides an interference suppression method and device for MU-MIMO beamforming, which relates to the field of communication technologies. The method includes: obtaining a first beamforming weight vector corresponding to a first user equipment and an interference space matrix, where the interference space matrix is composed of channel estimation matrices of one or more second user equipments, and the one or more second user equipments are user equipments that cause interference to the first user equipment in a multi-user MU-multi-input multi-output MIMO system; constructing a zero-forcing algorithm ZF transformation matrix corresponding to the first user equipment according to the first beamforming weight vector corresponding to the first user equipment and the interference space matrix, where the ZF transformation matrix is used to implement interference suppression of the first user equipment on one or more second user equipments. The embodiment of the present application achieves the purpose of improving the interference suppression effect of the MU-MIMO system in a frequency-selective channel.

Description

Technical Field

[0001] The present application relates to the field of communication technologies. Specifically, the present application relates to an interference suppression method and apparatus for MU-MIMO beamforming. Background Art

[0002] In traditional multi-user MU-Massive In Massive Out (MIMO) non-codebook beamforming algorithms, during the beamforming process, the channel estimates at different frequency points within the band are usually mathematically averaged according to the configured beamforming bandwidth (i.e., sub-band) of the system to obtain the channel estimate within the sub-band. Then, beamforming is performed for each user, and the interference between each other is suppressed. However, traditional beamforming algorithms are not applicable to frequency-selective channels with large differences in channel frequency characteristics, and there will be an obvious phenomenon of insufficient inter-user interference suppression. This is because the method of averaging the channel estimates will cause loss of channel information, and the interference suppression space is quite different from the actual channel space. After interference suppression, there will still be a relatively large amount of inter-user interference remaining, resulting in a poor beam interference suppression effect between users. Summary of the Invention

[0003] The present application provides an interference suppression method and apparatus for MU-MIMO beamforming, which are used to solve the technical problem of poor interference suppression effect in the MU-MIMO system under frequency-selective channels.

[0004] In a first aspect, an interference suppression method for MU-MIMO beamforming is provided. The method includes:

[0005] Obtain a first beamforming weight vector corresponding to a first user equipment and an interference space matrix, where the interference space matrix is composed of channel estimation matrices of one or more second user equipments, and the one or more second user equipments are user equipments that cause interference to the first user equipment in a multi-user MU-Massive In Massive Out (MIMO) system;

[0006] Construct a zero-forcing (ZF) transform matrix corresponding to the first user equipment according to the first beamforming weight vector corresponding to the first user equipment and the interference space matrix, where the ZF transform matrix is used to implement interference suppression of the first user equipment on the one or more second user equipments.

[0007] In a second aspect, an interference suppression apparatus for MU-MIMO beamforming is provided. The apparatus includes:

[0008] An obtaining module, configured to obtain a first beamforming weight vector corresponding to a first user equipment and an interference space matrix, where the interference space matrix is composed of channel estimation matrices of one or more second user equipments, and the second user equipments are user equipments that cause interference to the first user equipment in a multi-user (MU)-multiple input multiple output (MIMO) system;

[0009] A constructing module, configured to construct a zero-forcing (ZF) transform matrix corresponding to the first user equipment according to the first beamforming weight vector corresponding to the first user equipment and the interference space matrix, where the ZF transform matrix is used to implement...

[0010] In a third aspect, there is provided an interference suppression device for MU-MIMO beamforming, and the device includes:

[0011] A memory, configured to store a computer program;

[0012] A transceiver, configured to transmit and receive data under the control of the processor;

[0013] A processor, configured to read the computer program in the memory and execute the interference suppression method for MU-MIMO beamforming shown in the first aspect of this application.

[0014] In a fourth aspect, there is provided a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program is used to cause a computer to execute the interference suppression method for MU-MIMO beamforming shown in the first aspect of this application when executed.

[0015] In a fifth aspect, there is provided an interference suppression apparatus for MU-MIMO beamforming, and the apparatus includes: a memory and a processor. Codes and data are stored in the memory, the memory is coupled to the processor, and the processor runs the codes and data in the memory so that the apparatus executes the interference suppression method for MU-MIMO beamforming shown in the first aspect of this application when executed.

[0016] In a sixth aspect, there is provided a computer program product, and when the computer program product runs on a network device, it causes the network device to execute the interference suppression method for MU-MIMO beamforming shown in the first aspect of this application when executed.

[0017] The beneficial effects brought by the technical solution provided in this application are:

[0018] By constructing a ZF transformation matrix corresponding to the first user equipment according to the first beamforming weight vector corresponding to the first user equipment and the interference space matrix formed based on the channel estimation matrix corresponding to the second user equipment that interferes with the first user equipment, and based on this ZF transformation matrix, interference suppression of the first user equipment on the second user equipment is achieved. Since the transformation matrix of the traditional ZF algorithm is reconstructed, the interference suppression effect of the MU-MIMO system in a frequency-selective channel can be improved. Brief Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the accompanying drawings required for describing the embodiments of the present application will be briefly introduced below.

[0020] Figure 1 Schematic diagram of the transformation matrix of the traditional ZF algorithm;

[0021] Figure 2 Flow schematic diagram of an interference suppression method for MU-MIMO beamforming provided by an embodiment of the present application;

[0022] Figure 3 Flow schematic diagram of an interference suppression method for MU-MIMO beamforming provided by another embodiment of the present application;

[0023] Figure 4 Schematic diagram of the transformation matrix of a reconstructed ZF algorithm provided by an embodiment of the present application;

[0024] Figure 5 Structural schematic diagram of an interference suppression device for MU-MIMO beamforming provided by an embodiment of the present application;

[0025] Figure 6 Structural schematic diagram of an interference suppression device for MU-MIMO beamforming provided by an embodiment of the present application. Detailed Description of the Embodiments

[0026] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and should not be construed as a limitation of the present invention.

[0027] Those skilled in the art can understand that, unless specifically stated, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of this application means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0028] To make the objectives, technical solutions and advantages of this application clearer, the following will further describe the embodiments of this application in detail with reference to the accompanying drawings.

[0029] The technical solutions provided by the embodiments of this application can be applied to a variety of systems, especially 5G systems. For example, the applicable systems can be the global system of mobile communication (GSM) system, code division multiple access (CDMA) system, wideband code division multiple access (WCDMA) general packet radio service (GPRS) system, long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD) system, long term evolution advanced (LTE-A) system, universal mobile telecommunication system (UMTS), worldwide interoperability for microwave access (WiMAX) system, 5G new radio (NR) system, etc. Both terminal devices and network-side devices are included in these various systems. The core network part may also be included in the system, such as the evolved packet system (EPS), 5G system (5GS), etc.

[0030] First, several terms related to this application are introduced and explained:

[0031] The terminal device involved in the embodiments of this application can be a device that provides voice and / or data connectivity to users, such as a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem, etc. In different systems, the name of the terminal device may also be different. For example, in a 5G system, the terminal device can be called a User Equipment (UE). The wireless terminal device can communicate with one or more core networks (CN) via a Radio Access Network (RAN). The wireless terminal device can be a mobile terminal device, such as a mobile phone (or a "cellular" phone) and a computer with a mobile terminal device. For example, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with the wireless access network. For example, devices such as Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). The wireless terminal device can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, an access point, a remote terminal device, an access terminal device, a user terminal device, a user agent, a user device, which is not limited in the embodiments of this application.

[0032] The network-side device involved in the embodiments of this application can be a base station, which can include multiple cells that provide services to terminals. Depending on the specific application scenarios, the base station can also be referred to as an access point, or it can be a device in the access network that communicates with wireless terminal devices through one or more sectors over the air interface, or other names. The network-side device can be used to mutually replace the received air frames and Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network can include an Internet Protocol (IP) communication network. The network-side device can also coordinate the attribute management of the air interface. For example, the network-side device involved in the embodiments of this application can be a network-side device (Base Transceiver Station, BTS) in a Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), or it can be a network-side device (NodeB) in a Wide-band Code Division Multiple Access (WCDMA), or it can also be an evolved network-side device (evolutional 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), or it can be a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc. The embodiments of this application do not limit this. In some network architectures, the network-side device can include a centralized unit (centralized unit, CU) node and a distributed unit (distributed unit, DU) node, and the centralized unit and the distributed unit can also be geographically separated and arranged.

[0033] The network-side device and the terminal device can each use one or more antennas for multi-input multi-output (MIMO) transmission. The MIMO transmission can be single-user MIMO (SU-MIMO) or multi-user MIMO (MU-MIMO). According to the form and quantity of the antenna combinations, the MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO or massive-MIMO, or it can also be diversity transmission, precoding transmission, beamforming transmission, etc.

[0034] For beamforming, when the base station performs downlink beamforming, it usually uses multiple antennas to transmit signals. For each user signal, when the base station transmits through subcarriers, different shaping vectors are used on different antennas, and the shaped signal is transmitted in a certain beam shape. Therefore, this operation is called beamforming.

[0035] Shaping granularity: In the actual channel environment, due to multipath propagation causing frequency selectivity of the channel, the channel correlation matrices corresponding to different subcarriers are different, resulting in differences in the shaping vectors used for different subcarriers. In the actual system implementation, considering the implementation complexity, the base station does not set different shaping vectors for each subcarrier separately, but uses the same shaping vector to shape the data carried by multiple subcarriers on the same antenna to reduce the computational complexity and storage space of the shaping vector. The shaping vector is set in units of physical resource blocks (PRBs). A PRB is the unit for resource allocation in the system. In the Long Term Evolution (LTE) system, one PRB contains 12 adjacent subcarriers in the frequency domain. The subcarriers within the same PRB use the same shaping vector, and the number of consecutive PRBs using the same shaping vector is called the shaping granularity.

[0036] Frequency-selective fading channel, hereinafter referred to as: frequency-selective channel. Due to multipath and Doppler frequency shift, the wireless channel exhibits frequency-selective fading in the frequency domain. When the signal propagates through multiple paths, the distance between the first-arriving signal and the last-arriving signal is fixed, but the frequency wavelengths are different, so the phase differences are different, resulting in some frequencies fading and some frequencies enhancing, thus causing the channel to exhibit selective fading in the frequency domain.

[0037] Maximum signal leakage ratio principle: In a MU-MIMO system, if multiple user equipment that transmit data simultaneously and on the same frequency do not leak energy into each other's channel spaces, then the multiple user equipment satisfies the maximum signal leakage ratio principle.

[0038] The EBB algorithm, the eigenvector based beam (EBB) method, obtains the weight vector by decomposing the eigenvalues of the spatial correlation matrix.

[0039] For the traditional multi-user MU-Massive MIMO (Multiuser-Massive In Massive Out) non-codebook beamforming algorithm, the transformation matrix of the traditional ZF interference suppression algorithm is as Figure 1 shown. Figure 1 The transformation matrix composed of vectors of 4 user equipments is shown, and the number of data streams simultaneously transmitted by each user equipment is L = 4.

[0040] In the transformation matrix of the traditional ZF interference suppression algorithm, the EBB shaping factor of each user equipment characterizes the channel space vector corresponding to the subband. When the channel space where the user is located has obvious frequency selective characteristics, due to the large difference between the interference suppression space and the actual channel space, after interference suppression, there will still be a lot of inter-user interference remaining, and there will be an obvious phenomenon that the inter-user interference suppression is insufficient, resulting in limited downlink rate.

[0041] The reason for the above situation is that the common practice in the prior art is to separately process the channel estimations on different physical resource blocks (PRBs) within the subband, without averaging the channel estimations between PRBs, but retaining the process of averaging the channel estimations between frequency points within the PRB. In this way, it not only retains the noise suppression effect of channel estimation averaging, but also, to a certain extent, combats the problem of poor interference suppression effect caused by the frequency selective channel.

[0042] However, after the above processing, there will still be a problem that the signal-to-noise ratio of the received signal detected at the receiving end is high at the middle frequency point and low at the two side frequency points. The main reason is that the traditional MU-MIMO interference suppression algorithm does not fully utilize the channel estimation information of all frequency points within the shaping subband. After the channel estimation averaging, the interference suppression effect tends to be more matched to the center frequency point of the subband, while at the subband edge, the interference suppression effect is poor, affecting the overall downlink rate.

[0043] Therefore, in order to overcome the above problems, this application proposes a method for reconstructing and optimizing the transformation matrix of the traditional zero-forcing algorithm (ZF, Zero Force) based on the maximum signal leakage ratio principle, which can effectively improve the interference suppression effect and the overall downlink rate of the MU-MIMO system in the frequency selective channel.

[0044] The technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments. The following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0045] In an embodiment of the present application, a method for interference suppression for MU-MIMO beamforming is provided. As Figure 2 shown, the method includes:

[0046] S110. Obtain a first beamforming weight vector corresponding to a first user equipment and an interference space matrix, where the interference space matrix is composed of channel estimation matrices of one or more second user equipments, and the one or more second user equipments are user equipments that cause interference to the first user equipment in a multi-user MU-multi-input multi-output MIMO system;

[0047] S120. Construct a zero-forcing algorithm ZF transformation matrix corresponding to the first user equipment according to the first beamforming weight vector corresponding to the first user equipment and the interference space matrix, where the ZF transformation matrix is used to implement interference suppression of the first user equipment on the one or more second user equipments.

[0048] That is to say, in this embodiment, by constructing a ZF transformation matrix corresponding to the first user equipment according to the first beamforming weight vector corresponding to the first user equipment and the interference space matrix composed of the channel estimation matrices corresponding to the second user equipments that cause interference to the first user equipment, and based on this ZF transformation matrix, interference suppression of the first user equipment on the second user equipment is realized. Since the transformation matrix of the traditional ZF algorithm is reconstructed, when using this transformation matrix to realize interference suppression of the first user equipment on the second user equipment, the difference between the interference suppression space and the actual channel space can be reduced, so that the interference suppression effect of the MU-MIMO system under frequency-selective channels can be improved.

[0049] Specifically, in this embodiment, the process of realizing interference suppression of the first user equipment on the second user equipment based on this ZF transformation matrix may include:

[0050] Update the first beamforming weight vector corresponding to the first user equipment according to the second beamforming weight vector corresponding to the first user equipment obtained based on the ZF transformation matrix corresponding to the first user equipment, so as to realize interference suppression of the first user equipment on the second user equipment.

[0051] Specifically, in this embodiment, the second beamforming weight vector corresponding to the first user equipment can be obtained from the matrix after the zero-forcing operation on the ZF transformation matrix corresponding to the first user equipment; then, the first beamforming weight vector corresponding to the first user equipment can be updated by using the second beamforming weight vector corresponding to the first user equipment.

[0052] In some embodiments, S120 can specifically be:

[0053] Concatenate the first beamforming weight vector corresponding to the first user equipment on the first subband and the interference space matrix to construct the ZF transformation matrix corresponding to the first user equipment on the first subband, where the first subband is any subband shared by the first user equipment and the one or more second user equipment.

[0054] Specifically, in this embodiment, the ZF transformation matrix T corresponding to the first user equipment on the first subband can be constructed by using the following formula m :

[0055] T m =[w m , H m

[0056] where w m is the first beamforming weight vector corresponding to the first user equipment on the first subband m, H m is the interference space matrix corresponding to the first user equipment on the first subband m, and m is a positive integer greater than or equal to 1.

[0057] That is to say, in this embodiment, the first beamforming weight vector w m corresponding to the first user equipment on the first subband m and the interference space matrix H m corresponding to the first user equipment on the first subband m can be concatenated by column vectors to construct the ZF transformation matrix T m . In the process of reconstructing the ZF transformation matrix, the channel space information corresponding to the interfering user equipment (the second user equipment) on the subband is fully considered, and then the interference suppression of the interfering user by the desired user equipment (the first user equipment) is realized based on this transformation matrix, so that the effect of inter-user interference suppression in the frequency-selective channel is better.

[0058] In some embodiments, the step of obtaining the interference space matrix corresponding to the first user equipment in S110 includes:

[0059] ​S111. Determine the channel estimation matrices corresponding to multiple physical resource blocks (PRBs) of the one or more second user equipments within the first sub-band based on the shaping granularity of the first sub-band, where the shaping granularity is used to indicate the number of PRBs included in the first sub-band;

[0060] S112. Construct an interference space matrix corresponding to the first user equipment on the first sub-band based on the channel estimation matrices corresponding to the multiple PRBs of the one or more second user equipments within the first sub-band.

[0061] Specifically, in this embodiment, S112 may specifically include:

[0062] When the number of second user equipments in the MU-MIMO system is F, the interference space matrix corresponding to the first user equipment on the first sub-band is constructed by the following formula:

[0063]

[0064] where H m is the interference space matrix corresponding to the first user equipment on the first sub-band m, is the channel estimation matrix corresponding to the f-th second user equipment among the F second user equipments on the first sub-band m, F is a positive integer greater than or equal to 1, and f = 1, 2,..., F.

[0065] In this embodiment, S111 may specifically include:

[0066] For each of the F second user equipments, use the following formula to obtain the channel estimation matrix corresponding to multiple PRBs within the first sub-band:

[0067]

[0068] where is the channel estimation matrix corresponding to the f-th second user equipment among the F second user equipments on Q PRBs within the first sub-band m, Q is the shaping granularity of the first sub-band m, and Q takes a positive integer greater than or equal to 2.

[0069] That is to say, in this embodiment, when constructing the interference space matrix corresponding to the first user equipment on the first sub-band, the channel space information corresponding to the interfering user equipment (the second user equipment) on each PRB is fully considered. That is to say, the channel estimation information of all frequency points within the shaping sub-band is considered. Compared with the prior art method of averaging the channel estimations of the PRBs in different sub-bands and then obtaining the EBB shaping factor, the granularity of the channel estimation is finer, that is, the interference space matrix is constructed from a finer frequency domain space perspective. The EBB shaping factor obtained on this basis can ensure that the desired user equipment does not leak energy to the channel space of the interfering user equipment on each PRB, and can satisfy the maximum signal-to-leakage ratio principle of the desired user equipment.

[0070] Specifically, in the prior art, in the method of averaging the channel estimations of the PRBs in different sub-bands and then obtaining the EBB shaping factor, it is for the channel estimation matrix corresponding to the entire sub-band, and the granularity is coarser. Due to the large difference between the interference suppression space of the frequency-selective channel and the actual channel space, after using the prior art method for interference suppression, there will still be a lot of inter-user interference remaining, and the interference suppression effect is not good. However, the method in the embodiment of the present application starts from the channel estimation matrix on each PRB within the sub-band, and the granularity is finer, which can make the interference suppression space of the frequency-selective channel match the actual channel space. The user equipment after interference suppression can satisfy the maximum signal-to-leakage ratio principle, effectively improving the interference suppression effect.

[0071] It should be noted that when the first user equipment and the second user equipment share multiple sub-bands to send data, for each sub-band, the above method needs to be executed to ensure that multiple user equipments in MU-MIMO have interference suppression capabilities on each shared sub-band.

[0072] In some embodiments, the step of obtaining the first beamforming weight vector corresponding to the first user equipment in S110 includes:

[0073] S113. For the first sub-band, using the eigenvector method EBB algorithm for the obtained channel estimation matrix of the first user equipment, to obtain the first beamforming weight vector corresponding to the first user equipment on the first sub-band.

[0074] Specifically, in this embodiment, the following formula is used to obtain the first beamforming weight vector corresponding to the first user equipment on the first sub-band:

[0075]

[0076] where the function svd represents the channel estimation matrix of the first user equipment on the first sub-band m Perform singular value decomposition, and select the left singular vectors corresponding to the top L singular values from the singular values sorted from large to small obtained from the decomposition, where L is the number of data streams simultaneously transmitted by the first user equipment.

[0077] Specifically, in this embodiment, based on the sounding reference signal SRS, the channel estimation matrix corresponding to the first user equipment on the first subband m can be obtained. Then, the EBB algorithm is applied to this channel estimation matrix. Specifically, singular value decomposition is performed on this channel estimation matrix, the obtained singular values are sorted, and the singular value vectors corresponding to the top L large singular values are selected as the EBB shaping factors, so as to obtain the first beamforming weight vector corresponding to the first user equipment on the first subband m.

[0078] In some embodiments, it may further include:

[0079] S130. Perform a zero-forcing operation on the ZF transformation matrix corresponding to the first user equipment on the first subband to obtain the matrix after the zero-forcing operation;

[0080] S140. Obtain the second beamforming weight vector corresponding to the first user equipment according to the matrix after the zero-forcing operation;

[0081] S150. Use the second beamforming weight vector corresponding to the first user equipment to update the first beamforming weight vector corresponding to the first user equipment to suppress interference to one or more second user equipments.

[0082] That is to say, in this embodiment, from the matrix after the zero-forcing operation on the re-ZF transformation matrix using the EBB shaping factor (the first beamforming weight vector) corresponding to the first user equipment on the first subband and the interference space matrix, the vector corresponding to the first user equipment (the second beamforming weight vector) is selected to update the EBB shaping factor. The updated EBB shaping factor has the characteristic of being orthogonal to the channel space matrix of the interfering user equipment (the second user equipment), thereby realizing the interference suppression of the desired user equipment to the interfering user equipment and not leaking energy to the channel space of the interfering user equipment.

[0083] Specifically, in this embodiment, S130 may specifically be:

[0084] Use the following formula to perform a zero-forcing operation on the ZF transformation matrix T m to obtain the matrix after the zero-forcing operation

[0085]

[0086] where (T m ) H is Tm The conjugate transpose matrix of, [(T m ) H *T m -1 is [(T m ) H *T m 's inverse matrix.

[0087] It should be noted that the matrix T before the zero-forcing operation m and the matrix after the zero-forcing operation have the same dimension, and the position of the column vector corresponding to each user equipment in the matrices before and after the zero-forcing operation is the same. For example: if the first user equipment is A, and the second user equipment interfering with A in the MU-MIMO system is B and C, and the column vectors in the transformation matrix before the zero-forcing operation are, in sequence, the EBB shaping weight vector of A, the channel estimation matrix of B, and the channel estimation matrix of C, then the column vectors in the matrix after the zero-forcing operation still correspond to A, B, and C in sequence.

[0088] In this embodiment, S140 may specifically include:

[0089] Select the second beamforming weight vector corresponding to the first user equipment on the first subband from the matrix after the zero-forcing operation. The position of the elements of the second shaping weight vector in the matrix after the zero-forcing operation is the same as the position of the elements of the first beamforming weight vector in the ZF transformation matrix.

[0090] Specifically, the second beamforming weight vector corresponding to the first user equipment can be obtained by using the following formula

[0091]

[0092] where, means selecting L column vectors from , and the position of the L column vectors in is the same as the position of w m in T m .

[0093] Specifically, S133 can be updated by using the following formula:

[0094]

[0095] where w m is the first beamforming weight vector, is the second beamforming weight vector.

[0096] ​Specifically, in this embodiment, the position of the column vector selected from the matrix after the zero-forcing operation as the second beamforming weight vector is the same as the position of the original EBB shaping factor (the first beamforming weight vector) in the transformation matrix before the zero-forcing operation. For example, if the first user equipment is A, and the second user equipment that interferes with A in the MU-MIMO system are B and C, since the column vectors in the transformation matrix before the zero-forcing operation are, in sequence, the EBB shaping weight vector of A, the channel estimation matrix of B, and the channel estimation matrix of C, therefore, a column vector corresponding to A is selected from the matrix after the zero-forcing operation to update the EBB shaping weight vector of A.

[0097] Since the column vector selected from the matrix after the zero-forcing operation as the second beamforming weight vector has the characteristic of being orthogonal to the channel space matrix of the interfering user equipment, therefore, the original EBB shaping factor is updated using the EBB shaping factor (the second beamforming weight vector) obtained from the matrix after the zero-forcing operation. The updated EBB shaping factor has the characteristic of being orthogonal to the channel space matrix of the interfering user equipment, thereby enabling the desired user equipment to suppress interference from the interfering user equipment.

[0098] The above combines the attached Figure 2 A detailed description has been given to the technical solution of an interference suppression method for MU-MIMO beamforming provided by an embodiment of the present application. Next, in combination with the attached Figure 3 A further description is given to the technical solution of the above method provided by an embodiment of the present application.

[0099] In the MU-MIMO system, assume that user equipments 1, 2, 3, and 4 simultaneously transmit data on the same frequency. Then, when user equipment 1 (i.e., the first user equipment in the above text) transmits data, the user equipments that interfere with it are user equipments 2, 3, and 4 (i.e., the second user equipments in the above text, and the number of second user equipments F = 3). Assume that user equipments 1, 2, 3, and 4 transmit data on the same sub-band m, and the shaping granularity Q of this sub-band m is 2, that is: the number of physical resource blocks PRBs in this sub-band m is 2. Assume that the number of antennas of the base station in this MU-MIMO system is N = 64, and the number of antennas of user equipment 1 is L = 2, that is: the number of ports for simultaneously transmitting data is 2. Then, the number of data streams transmitted simultaneously is 2.

[0100] Next, in combination with the attached Figure 3 A detailed description is given to the specific process of implementing interference suppression of user equipment 2, 3, and 4 by user equipment 1 by executing the above method of an embodiment of the present application for user equipment 1.

[0101] As Figure 3 shown, an interference suppression method for MU-MIMO beamforming includes:

[0102] S201. Obtain the channel estimation matrix corresponding to user equipment 1 on sub-band m based on the sounding reference signal (SRS). The dimension of this matrix is 64 * 2.

[0103] S202. Adopt the EBB algorithm to obtain the first beamforming weight vector w corresponding to user equipment 1 on sub-band m. m :

[0104]

[0105] Among them, the function svd represents the singular value decomposition of the channel estimation matrix of user equipment 1 on sub-band m. Perform singular value decomposition and select the left singular vectors corresponding to the top 2 singular values from the singular values sorted from large to small obtained from the decomposition.

[0106] Specifically, in this embodiment, for Performing singular value decomposition can obtain 64 singular values and their corresponding 64 singular value vectors. After arranging the 64 singular values in descending order, select the left singular vectors corresponding to the top 2 large singular values as the EBB shaping factor of user equipment 1, that is: the first beamforming weight vector w. m The dimension of this vector is 64 * 2.

[0107] S203. Respectively obtain the channel estimation matrices corresponding to user equipment 2, 3, and 4 on 2 PRBs within sub-band m:

[0108]

[0109]

[0110]

[0111] Among them, is the channel estimation matrix corresponding to user equipment 2 on 2 PRBs within sub-band m. Assuming that the number of antennas of interfering user equipment 2, 3, and 4 is 2, the dimension of the channel estimation matrix corresponding to the PRBs is 62 * 2. Therefore, has a dimension of 64 * 4. is the channel estimation matrix corresponding to user equipment 3 on 2 PRBs within sub-band m, with a dimension of 64 * 4. is the channel estimation matrix corresponding to user equipment 4 on 2 PRBs within sub-band m, with a dimension of 64 * 4.

[0112] S204. Based on the channel estimation matrices of user equipment 2, 3, and 4 Construct the interference space matrix H corresponding to user equipment 1 on sub-band m. m:

[0113]

[0114] Among them, the interference space matrix T m has a dimension of 64 * 12.

[0115] S205. Concatenate the first beamforming weight vector w m corresponding to the user equipment 1 on the sub - band m and the interference space matrix H m to construct the ZF transformation matrix T m corresponding to the user equipment 1 on the sub - band m:

[0116] T m = [w m , H m ,

[0117] Among them, the ZF transformation matrix T m has a dimension of 64 * 14.

[0118] S206. Perform a zero - forcing operation on the ZF transformation matrix T m corresponding to the user equipment 1 on the sub - band m to obtain the matrix after the zero - forcing operation

[0119]

[0120] Among them, the matrix after the zero - forcing operation has a dimension of 64 * 14.

[0121] S207. Select the second beamforming weight vector corresponding to the user equipment 1 on the sub - band m from the matrix after the zero - forcing operation

[0122]

[0123] Among them, means selecting L = 2 column vectors from , and the positions of the 2 column vectors in are the same as the positions of w m in T m , that is: the elements of the second beamforming weight vector in the matrix after the zero - forcing operation are the same as the positions of the elements of the first beamforming weight vector w m in the ZF transformation matrix T m .

[0124] Specifically, the position of w m in T m is the first 2 column vectors, and since the matrix T before the zero - forcing operationm and the matrix after the zero-forcing operation have the same dimension. Therefore, select the first two column vectors in

[0125] S208. Use the second beamforming weight vector corresponding to user equipment 1 to update the first beamforming weight vector w corresponding to user equipment 1 m to suppress interference to user equipment 2, 3, and 4.

[0126] Specifically,

[0127] In this embodiment, according to the first beamforming weight vector corresponding to user equipment 1 on subband m, and the interference space matrix constructed based on the channel estimation matrices corresponding to multiple PRBs within subband m of interfering user equipment 2, 3, and 4, construct the ZF transformation matrix corresponding to user equipment 1 on subband m, perform zero-forcing operation based on this ZF transformation matrix, and obtain the second beamforming weight vector from the matrix after the zero-forcing operation. Since the second beamforming weight vector is orthogonal to the channel space matrices of user equipment 2, 3, and 4, therefore, using the second beamforming weight vector to update the first beamforming weight vector can achieve interference suppression of user equipment 1 to user equipment 2, 3, and 4.

[0128] It should be noted that if there are multiple subbands shared by user equipment 1, 2, 3, and 4, then for each subband, the above method needs to be executed so that user equipment 1 has the ability to suppress interference to user equipment 2, 3, and 4 when sending data on each subband.

[0129] It should also be understood that for user equipment 1, 2, 3, and 4 sending data on the shared subband, it is necessary to traverse each user equipment and execute the above method so that there is interference suppression between user equipment 1, 2, 3, and 4. That is: in the case where user equipment 1 is used as the first user equipment, after achieving interference suppression of user equipment 1 to user equipment 2, 3, and 4, the above method can be executed for the cases where user equipment 2, 3, and 4 are respectively used as the first user equipment to achieve mutual interference suppression between user equipment 1, 2, 3, and 4.

[0130] Next, in combination with the attached Figure 4 , taking the vector after ZF as an example, describe the principle of interference suppression involved in the embodiments of the present application. As Figure 4 shown, the dotted lines therein are orthogonal terms and the solid lines are correlation terms. The number of data streams of user equipment 1, 2, 3, and 4 is L = 4, that is: the subscripts 1 to 4 represent the values of L.

[0131] Taking the user equipment 1 as an example of the expected user, assume that the channel estimation matrix of the user equipment 1 is H1, and the optimal weight after interference suppression is To meet the maximum signal-to-leakage ratio principle, it should be orthogonal to the spaces where the channel estimation matrices H2, H3, and H4 corresponding to multiple PRBs in the sub-band of other user equipments 2, 3, and 4 are located, that is, the signal of the user equipment 1 will not leak energy into the channel spaces of other user equipments. For the user equipment 1 itself, it should make the L vectors V1 to V L after passing through the channel H1 orthogonal, so that the interference between data streams can be zero, that is:

[0132]

[0133] where, V i and V j are any two vectors among the vectors V1 to V L , and are two shaping factors after interference suppression. Since interference suppression is performed and the space W1 is rotated, the new weight should consist of two parts. Among them, one part belongs to the original space H1 (represented by ), and the other part belongs to the null space of H1 (represented by ), and the combination of the two is orthogonal to the channel estimation matrices H2, H3, and H4 of other users. Therefore, rewrite the above formula as:

[0134]

[0135] It can be seen that when is the eigenvector of the spatial correlation matrix R1, the above formula holds, which is a sufficient condition. Further select two terms from the above formula and reorganize the formula to obtain:

[0136]

[0137] It can be seen that the vector W1 j before interference suppression is orthogonal to the vector and after interference suppression (that is: the vectors after interference suppression are orthogonal to each other), and is orthogonal to H2, H3, and H4.

[0138] In the above text, in combination with Appendix Figures 2-4 a method for interference suppression for MU-MIMO beamforming provided by an embodiment of the present application is described in detail. Next, in combination with Appendix Figure 5 and 6A detailed description is given respectively to an interference suppression device and apparatus for MU-MIMO beamforming provided by this application.

[0139] An embodiment of this application provides an interference suppression device for MU-MIMO beamforming, as Figure 5 shown. The device 50 includes: a memory 501, a transceiver 502, and a processor 503. Among them,

[0140] The memory 501 is used to store computer programs;

[0141] The transceiver 502 is used to transmit and receive data under the control of the processor 503;

[0142] The processor 503 is used to read the computer programs stored in the memory 501 and execute the methods shown in any of the above embodiments.

[0143] For the content not detailed in the device 50 provided by the embodiment of this application, reference may be made to the methods provided in the above embodiments. The beneficial effects that the device 50 provided by the embodiment of this application can achieve are the same as those of the methods provided in the above embodiments, and will not be elaborated herein.

[0144] It should be understood that in the above embodiments, Figure 5 the bus architecture may include any number of interconnected buses and bridges. Specifically, various circuits represented by one or more processors represented by the processor 503 and the memory represented by the memory 501 are linked together. The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art. Therefore, they will not be further described herein. The bus interface provides an interface. The transceiver 502 may be multiple components, that is, including a transmitter and a receiver, and provides a unit for communicating with various other devices on a transmission medium, and these transmission mediums include wireless channels, wired channels, optical fiber cables, and other transmission mediums. The processor 503 is responsible for managing the bus architecture and general processing, and the memory 501 can store the data used by the processor 503 when performing operations.

[0145] The processor 503 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). The processor may also adopt a multi-core architecture.

[0146] It should be understood that the device 50 provided in the embodiments of the present application may be a network-side device, for example: a base station.

[0147] Based on the same inventive concept, the embodiments of the present application further provide an interference suppression device for MU-MIMO beamforming, as Figure 6 shown, the device 60 may include: an acquisition module 601 and a construction module 602, wherein,

[0148] The acquisition module 601 is configured to acquire a first beamforming weight vector corresponding to a first user equipment and an interference space matrix, wherein the interference space matrix is composed of channel estimation matrices of one or more second user equipments, and the second user equipment is a user equipment that interferes with the first user equipment in a multi-user MU - multiple input multiple output MIMO system;

[0149] The construction module 602 is configured to construct a zero-forcing algorithm ZF transformation matrix corresponding to the first user equipment according to the first beamforming weight vector and the interference space matrix corresponding to the first user equipment, wherein the ZF transformation matrix is used to implement interference suppression of the first user equipment on the one or more second user equipments.

[0150] In some embodiments, the construction module 602 is specifically configured to: splice the first beamforming weight vector and the interference space matrix corresponding to the first user equipment on a first subband to form a ZF transformation matrix corresponding to the first user equipment on the first subband, wherein the first subband is any subband shared by the first user equipment and the one or more second user equipments.

[0151] In some embodiments, the construction module 602 constructs the ZF transformation matrix T corresponding to the first user equipment on the first subband by using the following formula m :

[0152] T m =[w m , H w

[0153] wherein, w m is the first beamforming weight vector corresponding to the first user equipment on the first subband m, H m is the interference space matrix corresponding to the first user equipment on the first subband m, and m is a positive integer greater than or equal to 1.

[0154] In some embodiments, when the acquisition module 601 acquires the interference space matrix corresponding to the first user equipment, it specifically includes:

[0155] A determination unit, configured to determine a channel estimation matrix corresponding to a plurality of physical resource blocks (PRBs) of the one or more second user equipments in the first sub-band based on the shaping granularity of the first sub-band, where the shaping granularity is used to indicate the number of PRBs included in the first sub-band;

[0156] A construction unit, configured to construct an interference space matrix corresponding to the first user equipment in the first sub-band based on the channel estimation matrix corresponding to the plurality of PRBs of the one or more second user equipments in the first sub-band.

[0157] In some embodiments, the construction unit is specifically configured to: when the number of the second user equipments in the MU-MIMO system is F, the interference space matrix corresponding to the first user equipment in the first sub-band is constructed by the following formula:

[0158]

[0159] where, H m is the interference space matrix corresponding to the first user equipment on the first sub-band m, is the channel estimation matrix corresponding to the f-th second user equipment among the F second user equipments on the first sub-band m, F is a positive integer greater than or equal to 1, and f = 1, 2,..., F.

[0160] In some embodiments, the determination unit is specifically configured to: for each of the F second user equipments, obtain a channel estimation matrix corresponding to a plurality of PRBs in the first sub-band by using the following formula:

[0161]

[0162] where, is the channel estimation matrix corresponding to the f-th second user equipment among the F second user equipments on Q PRBs in the first sub-band m, Q is the shaping granularity of the first sub-band m, and Q takes a positive integer greater than or equal to 2.

[0163] In some embodiments, when the obtaining module 601 obtains the first beamforming weight vector corresponding to the first user equipment, it is specifically configured to:

[0164] For the first sub-band, perform an eigenvector method EBB algorithm on the obtained channel estimation matrix of the first user equipment to obtain the first beamforming weight vector corresponding to the first user equipment on the first sub-band.

[0165] In some embodiments, the obtaining module 601 is specifically configured to obtain the first beamforming weight vector corresponding to the first user equipment on the first sub-band by using the following formula:

[0166]

[0167] where the function svd represents performing singular value decomposition on the channel estimation matrix of the first user equipment on the first sub-band m and selecting the left singular vectors corresponding to the first L singular values from the singular values sorted from large to small obtained from the decomposition, where L is the number of data streams simultaneously transmitted by the first user equipment.

[0168] In some embodiments, it further includes: a processing module 603 and an updating module 604, where

[0169] The processing module 603 is configured to perform a zero-forcing operation on the zero-forcing transform matrix corresponding to the first user equipment on the first sub-band to obtain a matrix after the zero-forcing operation;

[0170] The obtaining module 601 is further configured to obtain a second beamforming weight vector corresponding to the first user equipment according to the matrix after the zero-forcing operation;

[0171] The updating module 604 is configured to update the first beamforming weight vector corresponding to the first user equipment by using the second beamforming weight vector corresponding to the first user equipment to perform interference suppression on the one or more second user equipments.

[0172] In some embodiments, the processing module 603 is specifically configured to: perform a zero-forcing operation on the zero-forcing transform matrix T corresponding to the first user equipment by using the following formula m to obtain a matrix after the zero-forcing operation

[0173]

[0174] where (T m ) H is the conjugate transpose matrix of T m .

[0175] In some embodiments, the obtaining module 601 is specifically configured to: select the second beamforming weight vector corresponding to the first user equipment on the first sub-band from the matrix after the zero-forcing operation, and the positions of the elements of the second shaping weight vector in the matrix after the zero-forcing operation are the same as the positions of the elements of the first beamforming weight vector in the zero-forcing transform matrix.

[0176] In some embodiments, the obtaining module 601 is configured to obtain the second beamforming weight vector corresponding to the first user equipment by using the following formula

[0177]

[0178] where denotes selecting L column vectors from The positions of the L column vectors in are the same as the positions of w m in T m .

[0179] For the content not described in detail in the apparatus 60 provided in the embodiments of the present application, reference may be made to the method provided in the above embodiments. The beneficial effects that the apparatus 60 provided in the embodiments of the present application can achieve are the same as those of the method provided in the above embodiments, and will not be elaborated herein

[0180] It should be understood that the apparatus 60 provided in the embodiments of the present application can be applied to network-side devices, such as: base stations

[0181] The embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored. When it runs on a computer, the computer can execute the corresponding content in the foregoing method embodiments

[0182] The embodiments of the present application further provide an interference suppression apparatus for MU-MIMO beamforming. The apparatus includes: a memory and a processor. Codes and data are stored in the memory. The memory is coupled to the processor. The processor runs the codes and data in the memory so that the apparatus executes the corresponding content in the foregoing method embodiments

[0183] The embodiments of the present application further provide a computer program product. When the computer program product runs on a network device, the network device is enabled to execute the corresponding content in the foregoing method embodiments

[0184] Compared with the prior art, the embodiments of the present application propose a ZF interference suppression algorithm based on the maximum signal leakage ratio principle and applicable to frequency-selective channels. On the basis of the traditional ZF interference suppression algorithm, the traditional ZF transformation matrix is reconstructed. The reconstructed transformation matrix is composed of the EBB shaping factor corresponding to the desired user equipment and the channel estimation matrix on the PRB corresponding to the undesired user equipment (interfering user equipment). Since the channel estimation matrix on the PRB corresponding to the undesired user contains richer frequency-domain information, the algorithm has a good interference suppression effect on frequency-selective channels

[0185] It should be noted that the division of units in the embodiments of the present application is illustrative, only a logical function division, and there may be other division methods in actual implementation. In addition, in each embodiment of the present application, each functional unit may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0186] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a processor-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product 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 device, etc.) or a processor to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0187] Those skilled in the art should understand that the disclosed embodiments of the present application can be provided in the form of methods, systems, or computer program products, etc. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) containing computer-usable program codes.

[0188] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer-executable instructions. These computer-executable instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0189] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to work in a particular manner, such that the instructions stored in the processor-readable memory produce a manufacture including an instruction device that implements the functions specified in one process Figure 1 one process or a plurality of processes and / or blocks Figure 1 specified in one block or a plurality of blocks.

[0190] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or a plurality of processes and / or blocks Figure 1 specified in one block or a plurality of blocks.

[0191] The above are only partial embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An interference suppression method for MU-MIMO beamforming, characterized in that The method includes: Obtaining a first beamforming weight vector corresponding to a first user equipment and an interference space matrix, where the interference space matrix is composed of channel estimation matrices of one or more second user equipments, and the one or more second user equipments are user equipments that cause interference to the first user equipment in a multi-user (MU)-multiple input multiple output (MIMO) system; According to the first beamforming weight vector corresponding to the first user equipment on a first sub-band and the interference space matrix splicing, constructing a zero-forcing (ZF) transform matrix corresponding to the first user equipment on the first sub-band, where the ZF transform matrix is used to implement interference suppression of the first user equipment on the one or more second user equipments, and the first sub-band is any sub-band shared by the first user equipment and the one or more second user equipments.

2. The method according to claim 1, wherein The splicing the first beamforming weight vector corresponding to the first user equipment on a first sub-band and the interference space matrix to construct the ZF transform matrix corresponding to the first user equipment on the first sub-band includes: Construct the ZF transformation matrix corresponding to the first user equipment on the first sub-band by using the following formula :[[]] Among them, is the first beamforming weight vector corresponding to the first user equipment on the first sub-band m, is the interference space matrix corresponding to the first user equipment on the first sub-band m, where m is a positive integer greater than or equal to 1.

3. The method according to claim 1, characterized in that The obtaining the interference space matrix corresponding to the first user equipment includes: Based on the shaping granularity of the first sub-band, determining channel estimation matrices corresponding to the one or more second user equipments on multiple physical resource blocks (PRBs) within the first sub-band, where the shaping granularity is used to indicate the number of PRBs included in the first sub-band; Based on the channel estimation matrices corresponding to the one or more second user equipments on multiple PRBs within the first sub-band, constructing the interference space matrix corresponding to the first user equipment on the first sub-band.

4. The method according to claim 3, wherein The constructing the interference space matrix corresponding to the first user equipment on the first sub-band based on the channel estimation matrices corresponding to the one or more second user equipments on multiple PRBs within the first sub-band includes: When the number of second user equipments in the MU-MIMO system is F, the interference space matrix corresponding to the first user equipment on the first sub-band is constructed by the following formula: Among them, is the interference space matrix corresponding to the first user equipment on the first sub-band m, is the channel estimation matrix corresponding to the f-th second user equipment among the F second user equipments on the first sub-band m, where F is a positive integer greater than or equal to 1, and f = 1, 2,..., F.

5. The method according to claim 4, wherein The determining the channel estimation matrix of the second user equipment on multiple PRBs within the first sub-band based on the shaping granularity of the first sub-band includes: For each of the F second user equipments, obtaining the channel estimation matrix corresponding to the multiple PRBs within the first sub-band by using the following formula: Among them, is the channel estimation matrix corresponding to the Q PRBs in the first sub-band m for the f-th second user equipment among the F second user equipments, where Q is the shaping granularity of the first sub-band m, and Q is a positive integer greater than or equal to 2.

6. The method according to any one of claims 1-5, characterized in that, The obtaining the first beamforming weight vector corresponding to the first user equipment includes: For the first sub-band, using the eigenvector method (EBB algorithm) for the obtained channel estimation matrix of the first user equipment to obtain the first beamforming weight vector corresponding to the first user equipment on the first sub-band.

7. The method according to claim 6, characterized in that, The using the eigenvector method (EBB algorithm) for the obtained channel estimation matrix of the first user equipment for the first sub-band to obtain the first beamforming weight vector corresponding to the first user equipment on the first sub-band includes: Obtaining the first beamforming weight vector corresponding to the first user equipment on the first sub-band by using the following formula: Among them, the function svd represents the channel estimation matrix of the first user equipment on the first sub-band m to perform singular value decomposition, and select the left singular vectors corresponding to the first L singular values from the singular values sorted from large to small obtained from the decomposition, where L is the number of data streams simultaneously transmitted by the first user equipment.

8. The method according to any one of claims 2-5 and 7, characterized in that, The method further includes: Perform a zero-forcing operation on the ZF transformation matrix corresponding to the first user equipment on the first sub-band to obtain a matrix after the zero-forcing operation; Obtain a second beamforming weight vector corresponding to the first user equipment according to the matrix after the zero-forcing operation; Update the first beamforming weight vector corresponding to the first user equipment by using the second beamforming weight vector corresponding to the first user equipment.

9. The method according to claim 8, wherein Performing a zero-forcing operation on the ZF transformation matrix corresponding to the first user equipment to obtain a matrix after the zero-forcing operation includes: Perform a zero-forcing operation on the ZF transformation matrix corresponding to the first user equipment using the following formula to obtain the matrix after the zero-forcing operation : Among them, is the conjugate transpose matrix of.

10. The method according to claim 9, wherein The obtaining a second beamforming weight vector corresponding to the first user equipment according to the matrix after the zero-forcing operation includes: Select, from the matrix after the zero-forcing operation, a second beamforming weight vector corresponding to the first user equipment on the first sub-band, where the positions of the elements of the second beamforming weight vector in the matrix after the zero-forcing operation are the same as the positions of the elements of the first beamforming weight vector in the ZF transformation matrix.

11. The method according to claim 10, wherein The selecting, from the matrix after the zero-forcing operation, a second beamforming weight vector corresponding to the first user equipment on the first sub-band includes: The second beamforming weight vector corresponding to the first user equipment is obtained by using the following formula :[[]]END]] Among them, denotes selecting L column vectors from . The positions of the L column vectors in are the same as the positions of in .

12. An interference suppression device for MU-MIMO beamforming, characterized in that, including: A memory for storing a computer program; A transceiver for transmitting and receiving data under the control of a processor; A processor for reading the computer program in the memory and executing the method according to any one of claims 1 to 11.

13. An interference suppression device for MU-MIMO beamforming, characterized in that, including: An obtaining module for obtaining a first beamforming weight vector and an interference space matrix corresponding to a first user equipment, where the interference space matrix is composed of channel estimation matrices of one or more second user equipments, and the second user equipments are user equipments that cause interference to the first user equipment in a multi-user MU - multiple input multiple output MIMO system; A constructing module for constructing a zero-forcing algorithm ZF transformation matrix corresponding to the first user equipment on the first sub-band according to the first beamforming weight vector and the interference space matrix corresponding to the first user equipment on the first sub-band, where the ZF transformation matrix is used to implement interference suppression of the first user equipment on the one or more second user equipments, and the first sub-band is any sub-band shared by the first user equipment and the one or more second user equipments.

14. The device according to claim 13, characterized in that, The building block constructs the ZF transform matrix corresponding to the first user equipment on the first sub-band by using the following formula :[[]]END]] Wherein, is the first beamforming weight vector corresponding to the first user equipment on the first sub-band m, is the interference space matrix corresponding to the first user equipment on the first sub-band m, where m is a positive integer greater than or equal to 1.

15. The device according to claim 13, characterized in that, When the obtaining module obtains the interference space matrix corresponding to the first user equipment, it specifically includes: A determining unit for determining, based on the shaping granularity of the first sub-band, channel estimation matrices corresponding to the one or more second user equipments on multiple physical resource blocks (PRBs) within the first sub-band, where the shaping granularity is used to indicate the number of PRBs included in the first sub-band; A constructing unit for constructing the interference space matrix corresponding to the first user equipment on the first sub-band based on the channel estimation matrices corresponding to the one or more second user equipments on multiple PRBs within the first sub-band.

16. The device according to claim 15, characterized in that, The constructing unit is specifically used for: when the number of the second user equipments in the MU - MIMO system is F, the interference space matrix corresponding to the first user equipment on the first sub-band is constructed by the following formula: Among them, is the interference space matrix corresponding to the first user equipment on the first sub-band m, is the channel estimation matrix corresponding to the f-th second user equipment among the F second user equipment on the first sub-band m, where F is a positive integer greater than or equal to 1, and f = 1, 2,..., F.

17. The device according to claim 16, characterized in that, The determining unit is specifically configured to: for each of the F second user devices, use the following formula to obtain a corresponding channel estimation matrix on multiple PRBs in the first sub-band: Among them, is the channel estimation matrix corresponding to the f-th second user equipment among the F second user equipments on the Q PRBs in the first sub-band m, where Q is the shaping granularity of the first sub-band m, and Q is a positive integer greater than or equal to 2.

18. The device according to any one of claims 13-17, characterized in that, When the obtaining module obtains the first beamforming weight vector corresponding to the first user device, it is specifically configured to: For the first sub-band, use the eigenvector method EBB algorithm on the obtained channel estimation matrix of the first user device to obtain the first beamforming weight vector corresponding to the first user device on the first sub-band.

19. The device according to claim 18, wherein The obtaining module is specifically configured to use the following formula to obtain the first beamforming weight vector corresponding to the first user device on the first sub-band: Among them, the function svd represents the channel estimation matrix of the first user equipment on the first sub-band m to perform singular value decomposition, and select the left singular vectors corresponding to the first L singular values from the singular values sorted from large to small obtained from the decomposition, where L is the number of data streams simultaneously transmitted by the first user equipment.

20. The device according to any one of claims 14 - 17, 19, characterized in that, The apparatus further includes: a processing module and an updating module, where The processing module is configured to perform a zero-forcing operation on the ZF transformation matrix corresponding to the first user device on the first sub-band to obtain a matrix after the zero-forcing operation; The obtaining module is further configured to obtain a second beamforming weight vector corresponding to the first user device according to the matrix after the zero-forcing operation; The updating module is configured to update the first beamforming weight vector corresponding to the first user device by using the second beamforming weight vector corresponding to the first user device.

21. The device according to claim 20, wherein The processing module is specifically configured to perform a zero-forcing operation on the ZF transform matrix corresponding to the first user equipment by using the following formula to obtain a matrix after the zero-forcing operation : wherein, is the conjugate transpose matrix of.

22. The device according to claim 21, characterized in that, The obtaining module is specifically configured to: select the second beamforming weight vector corresponding to the first user device on the first sub-band from the matrix after the zero-forcing operation, and the positions of the elements of the second beamforming weight vector in the matrix after the zero-forcing operation are the same as the positions of the elements of the first beamforming weight vector in the ZF transformation matrix.

23. The device according to claim 22, characterized in that, The obtaining module is used to obtain the second beamforming weight vector corresponding to the first user equipment by using the following formula :[[]]END]] Among them, denotes selecting L column vectors from , and the positions of the L column vectors in are the same as the positions of in .

24. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is used to cause a computer to execute the method according to any one of claims 1 to 11.

25. An interference suppression device for MU-MIMO beamforming, the device comprising: A memory and a processor, where the memory stores code and data, the memory is coupled to the processor, and the processor runs the code and data in the memory to cause the apparatus to execute the method according to any one of claims 1 to 11.

26. A computer program product, characterized in that, When the computer program product runs on a network device, it causes the network device to execute the method according to any one of claims 1 to 11.

Images