Beamforming method, device and network equipment

Abstract

The present application provides a beamforming method, apparatus and network equipment, relating to the field of communication technology. The method is executed by a network device, comprising: performing eigenvalue decomposition on the channel estimation matrix of at least two physical resource blocks (PRBs) contained in the subband of the terminal to be beamformed, and obtaining characteristic information, wherein the characteristic information includes: eigenvalues ​​and eigenvectors; and obtaining the beamforming weights of each of the at least two PRBs according to the characteristic information. The above scheme obtains the beamforming weights of each of the at least two PRBs according to the characteristic information obtained by performing eigenvalue decomposition on the channel estimation matrix of at least two PRBs contained in the subband, so that the obtained beamforming weights are accurately matched with the channel transmission matrix on each PRB within the beamforming granularity, thereby ensuring the reliability of system performance.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a beamforming method, apparatus, and network equipment. Background Art

[0002] Traditional beamforming methods use the same beamforming weights for different physical resource blocks (PRBs) within their binding range. This makes it impossible to precisely match the beamforming weights with the channel transmission matrix on each PRB within the beamforming granularity, thus affecting the performance of the communication system. Summary of the Invention

[0003] The embodiments of the present application provide a beamforming method, apparatus, and network device to address the problem that, under existing beamforming methods, different PRBs use the same beamforming weights, making it impossible to accurately match the beamforming weights with the channel transmission matrix on each PRB within the beamforming granularity, thereby affecting the performance of the communication system.

[0004] In order to solve the above technical problems, an embodiment of the present application provides a beamforming method, including:

[0005] Performing eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of the terminal to be shaped to obtain characteristic information, where the characteristic information includes: eigenvalues ​​and eigenvectors;

[0006] According to the characteristic information, a shaping weight of each PRB in the at least two PRBs is obtained.

[0007] In a possible implementation, performing eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of the terminal to be shaped to obtain characteristic information includes:

[0008] Obtain a channel space matrix of a subband of a terminal to be shaped, where the channel space matrix includes a channel space matrix of each of at least two PRBs in the subband;

[0009] Obtaining an autocorrelation matrix of each PRB in the at least two PRBs according to a channel space matrix of a subband of the terminal to be shaped;

[0010] According to the autocorrelation matrix of each PRB in the at least two PRBs, generalized eigenvalue decomposition is performed on the autocorrelation matrix of the at least two PRBs to obtain characteristic information.

[0011] In a possible implementation manner, obtaining, according to the channel space matrix of the subband of the terminal to be shaped, an autocorrelation matrix of each PRB in the at least two PRBs includes:

[0012] An autocorrelation matrix of each of the at least two PRBs is determined according to the channel space matrix of each of the at least two PRBs.

[0013] In a possible implementation manner, obtaining, according to the characteristic information, a shaping weight of each PRB in the at least two PRBs includes:

[0014] Determining a reference PRB among the at least two PRBs;

[0015] Determining a shaping weight of the reference PRB based on a feature vector in the feature information;

[0016] Determining a shaping weight for a first PRB based on an eigenvalue and an eigenvector in the feature information;

[0017] The first PRB is the other PRB among the at least two PRBs except the reference PRB.

[0018] In a possible implementation manner, determining the shaping weight of the reference PRB based on the feature vector in the feature information includes:

[0019] Determine a shaping weight of the reference PRB according to the channel space matrix of the reference PRB and the eigenvector.

[0020] In a possible implementation, determining the shaping weight of the first PRB based on the eigenvalue and the eigenvector in the feature information includes:

[0021] Determine a shaping weight of the first PRB according to the channel space matrix, the eigenvalue, and the eigenvector of the first PRB.

[0022] In a possible implementation manner, the channel space matrix is ​​an uplink channel space matrix.

[0023] The embodiment of the present application further provides a network device, including a memory, a transceiver, and a processor:

[0024] A memory for storing a computer program; a transceiver for transmitting and receiving data under the control of the processor; and a processor for reading the computer program in the memory and performing the following operations:

[0025] Performing eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of the terminal to be shaped to obtain characteristic information, where the characteristic information includes: eigenvalues ​​and eigenvectors;

[0026] According to the characteristic information, a shaping weight of each PRB in the at least two PRBs is obtained.

[0027] In a possible implementation, the processor is configured to read the computer program in the memory and perform the following operations:

[0028] Obtain a channel space matrix of a subband of a terminal to be shaped, where the channel space matrix includes a channel space matrix of each of at least two PRBs in the subband;

[0029] Obtaining an autocorrelation matrix of each PRB in the at least two PRBs according to a channel space matrix of a subband of the terminal to be shaped;

[0030] According to the autocorrelation matrix of each PRB in the at least two PRBs, generalized eigenvalue decomposition is performed on the autocorrelation matrix of the at least two PRBs to obtain characteristic information.

[0031] In a possible implementation, the processor is configured to read the computer program in the memory and perform the following operations:

[0032] An autocorrelation matrix of each of the at least two PRBs is determined according to the channel space matrix of each of the at least two PRBs.

[0033] In a possible implementation, the processor is configured to read the computer program in the memory and perform the following operations:

[0034] Determining a reference PRB among the at least two PRBs;

[0035] Determining a shaping weight of the reference PRB based on a feature vector in the feature information;

[0036] Determining a shaping weight for a first PRB based on an eigenvalue and an eigenvector in the feature information;

[0037] The first PRB is the other PRB among the at least two PRBs except the reference PRB.

[0038] In a possible implementation, the processor is configured to read the computer program in the memory and perform the following operations:

[0039] Determine a shaping weight of the reference PRB according to the channel space matrix of the reference PRB and the eigenvector.

[0040] In a possible implementation, the processor is configured to read the computer program in the memory and perform the following operations:

[0041] Determine a shaping weight of the first PRB according to the channel space matrix, the eigenvalue, and the eigenvector of the first PRB.

[0042] In a possible implementation manner, the channel space matrix is ​​an uplink channel space matrix.

[0043] The present application also provides a beamforming device, which is applied to a network device and includes:

[0044] a decomposition unit, configured to perform eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of a terminal to be shaped, to obtain characteristic information, wherein the characteristic information includes: eigenvalues ​​and eigenvectors;

[0045] An acquiring unit is configured to acquire, based on the characteristic information, an assignment weight of each PRB in the at least two PRBs.

[0046] An embodiment of the present application further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the above method.

[0047] The above scheme obtains the shaping weights of each PRB in at least two PRBs based on the characteristic information obtained by eigenvalue decomposition of the channel estimation matrix of at least two PRBs contained in the subband, so that the obtained shaping weights are accurately matched with the channel transmission matrix on each PRB within the shaping granularity, thereby ensuring the reliability of system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0049] Figure 1 A structural diagram showing a network system applicable to an embodiment of the present application;

[0050] Figure 2 A schematic diagram showing a flow chart of a beamforming method according to an embodiment of the present application;

[0051] Figure 3 A detailed flow chart showing an embodiment of the present application;

[0052] Figure 4 A schematic diagram showing a unit of a beamforming device according to an embodiment of the present application;

[0053] Figure 5 A structural diagram of a network device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0055] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein, for example, are implemented in a sequence other than those illustrated or described herein. In addition, the terms "comprise" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0056] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship. In the embodiments of this application, the term "plurality" refers to two or more, and other quantifiers are similar.

[0057] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0058] The following describes embodiments of the present application with reference to the accompanying drawings. The mode indication method, terminal device, and network device provided in the embodiments of the present application can be applied to a wireless communication system. The wireless communication system can be a system using fifth-generation (5G) mobile communication technology (hereinafter referred to as a 5G system). Those skilled in the art will appreciate that the 5G NR system is only an example and not a limitation.

[0059] See also Figure 1 , Figure 1 This is a structural diagram of a network system that can be applied in the embodiment of the present application, such as Figure 1 As shown, it includes a user terminal 11 and a base station 12, wherein the user terminal 11 can be a user equipment (UE), for example, it can be a terminal-side device such as a mobile phone, a tablet personal computer, a laptop computer, a personal digital assistant (PDA), a mobile Internet device (MID) or a wearable device. It should be noted that the specific type of the user terminal 11 is not limited in the embodiment of the present application. The above-mentioned base station 12 can be a base station of 5G or later versions (for example, gNB, 5G NR NB), or a base station in other communication systems, or referred to as a node B. It should be noted that in the embodiment of the present application, only a 5G base station is taken as an example, but the specific type of the base station 12 is not limited.

[0060] First, some concepts related to the embodiments of the present application are explained as follows.

[0061] Beam forming is one of the main technologies of 5G. It can effectively improve the user's receiving power and improve power efficiency. In addition, it can realize spatial division multiplexing of multiple users and improve spectrum efficiency. In the time division duplex (TDD) transmission mode, the user equipment (UE) usually measures the transmission matrix of the channel by sending a sounding reference signal (SRS) and calculates the corresponding beamforming weights on the base station side. The granularity of weight calculation is in resource blocks (RBs). On different RBs, due to different frequencies, the weights calculated by the SRS method are discontinuous in the frequency domain, resulting in the UE's equivalent channel (that is, the channel observed on the UE side after the base station side adopts weight shaping) being discontinuous between RBs, affecting communication performance.

[0062] To address this issue, the 5G communication protocol specifies the granularity of resource block bundling for the physical downlink shared channel (PDSCH) and the physical downlink control channel (PDCCH). PDSCH specifies three types of PRB bundling granularities: 2RB, 4RB, and wideband. PDCCH specifies three types of PRB bundling granularities: 2RB, 3RB, and 6RB. After the PRB bundling granularity is specified, the UE assumes that the base station uses the same shaping weights on all frequency domain subcarriers within the PRB bundling granularity, and performs joint channel estimation on all frequency points within this PRB bundling granularity to obtain an equivalent channel estimate within the shaping granularity. Using the same shaping weights within the PRB bundling granularity can ensure the continuity of the UE's equivalent channel within the PRB bundling granularity and improve the accuracy of its channel estimation.

[0063] However, when channel frequency selection changes rapidly or multiple UEs are spatially multiplexed, a beamforming granularity of 2 RBs or greater can lead to inaccurate beamforming weights, resulting in inaccurate beamforming and a decrease in beamforming performance. In the case of multiple UEs spatially multiplexed, inaccurate beamforming can also increase mutual interference between UEs, affecting the throughput of the communication cell. Furthermore, within the PRB binding range, different RBs use different beamforming weights. While this improves beamforming accuracy, it can cause discontinuity in the UE's equivalent channel within the PRB binding range, affecting the UE's downlink reception estimation, thereby reducing traffic and cell throughput. This scenario is not supported by the protocol, and the UE cannot perform equivalent channel estimation based on PRB granularity.

[0064] The embodiments of the present application provide a beamforming method, apparatus, and network device to address the problem that, under existing beamforming methods, different PRBs use the same beamforming weights, making it impossible to accurately match the beamforming weights with the channel transmission matrix on each PRB within the beamforming granularity, thereby affecting the performance of the communication system.

[0065] Among them, the method and the device are based on the same application concept. Since the principles of solving problems by the method and the device are similar, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0066] like Figure 2 As shown, an embodiment of the present application provides a beamforming method, which is performed by a network device and includes:

[0067] Step S201, performing eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) contained in a subband of a terminal to be shaped to obtain characteristic information;

[0068] It should be noted that the characteristic information includes: characteristic values ​​and characteristic vectors.

[0069] It should also be noted here that the at least two mentioned in the embodiments of the present application refers to multiple, that is, two or more.

[0070] Step S202: Obtain, according to the characteristic information, a shaping weight of each of the at least two PRBs.

[0071] It should be noted that the embodiment of the present application mainly provides a method for obtaining the shaping weight for a single PRB, so as to avoid the situation where the same shaping weight is used for different PRBs, and the shaping weight cannot be accurately matched with the channel transmission matrix on each PRB within the shaping granularity, resulting in the performance of the communication system being affected.

[0072] It should be noted that in the embodiment of the present application, when performing eigenvalue decomposition on the PRBs contained in the subband, it can be performed based on part or all of the PRBs contained in the subband. If the eigenvalue decomposition is performed on part of the PRBs, then the final result obtained is also the assignment weight of each PRB in the part of the PRBs. If the eigenvalue decomposition is performed on all PRBs in the subband, then the final result obtained is also the assignment weight of each PRB in all PRBs in the subband.

[0073] Optionally, the specific implementation of step S201 of the present application is as follows:

[0074] Step S2011: Obtain the channel space matrix of the subband of the terminal to be shaped;

[0075] It should be noted that the channel space matrix includes the channel space matrix of each PRB of at least two PRBs in a subband.

[0076] Specifically, the channel space matrix is ​​an uplink channel space matrix.

[0077] Step S2012: Obtain an autocorrelation matrix of each of the at least two PRBs according to the channel space matrix of the subband of the terminal to be shaped;

[0078] Optionally, the specific method for acquiring the autocorrelation matrix of the PRB is: determining the autocorrelation matrix of each PRB in the at least two PRBs according to the channel space matrix of each PRB in the at least two PRBs.

[0079] Step S2013: performing generalized eigenvalue decomposition on the autocorrelation matrix of the at least two PRBs according to the autocorrelation matrix of each PRB in the at least two PRBs to obtain characteristic information;

[0080] That is, in this case, the autocorrelation matrix of the at least two PRBs is the channel estimation matrix of the at least two PRBs.

[0081] It should be noted that since the generalized eigenvalue decomposition is performed in this application, the feature information obtained can also be called generalized feature information, and the eigenvalues ​​included in the corresponding generalized feature information are called generalized eigenvalues, and the eigenvectors are called generalized eigenvectors.

[0082] It should be noted that the embodiment of the present application converts the PRBs on the sub-bands of the terminal in the MIMO communication system into different shaping weights on different PRBs based on generalized eigenvalue decomposition, and matches their respective channel space matrices, so that the accuracy of beamforming is refined from the sub-band level to the PRB level, making the beamforming more accurate and the performance better.

[0083] Optionally, after obtaining the generalized eigenvalues ​​and generalized eigenvectors, the PRB shaping weights are obtained. Optionally, the implementation of step S202 includes:

[0084] Step S2021: Determine a reference PRB from the at least two PRBs;

[0085] It should be noted that the reference PRB does not need to be corrected by using generalized eigenvalues ​​when performing generalized eigenvalue decomposition.

[0086] Step S2022: Determine a shaping weight of the reference PRB based on a feature vector in the feature information;

[0087] Optionally, this step is further implemented as follows: determining a shaping weight of the reference PRB according to the channel space matrix and the eigenvector of the reference PRB;

[0088] That is to say, when obtaining the shaping weight of the reference PRB, it is only necessary to obtain it based on the generalized eigenvector.

[0089] Step S2023: Determine a shaping weight of the first PRB based on the eigenvalue and eigenvector in the feature information;

[0090] It should be noted that the first PRB is the other PRB in the at least two PRBs except the reference PRB. That is to say, after the other PRBs except the reference PRB obtain the shaping weight (which can be the initial shaping weight) in the manner of the reference PRB, it is necessary to use the generalized eigenvalue to correct the shaping weight to obtain the final usable shaping weight.

[0091] Optionally, this step is further implemented as follows: determining the shaping weight of the first PRB based on the channel space matrix, the eigenvalue and the eigenvector of the first PRB.

[0092] The following describes the specific implementation of this application by taking the acquisition of the shaping weights of all PRBs in a subband as an example.

[0093] Assume that in a multiple-input multiple-output (MIMO) communication system, there are K user devices (i.e., terminals), the number of base station antennas is M, the user device has N antennas, and the transmission channel space matrix of the kth user device on the i-th PRB is is an M×N matrix, where I band Indicates the number of PRBs contained in the channel bandwidth occupied by the UE. Taking user equipment 1 as the desired user, the application of generalized eigenvalue decomposition in beamforming is explained.

[0094] like Figure 3 As shown, the main implementation process of this application is:

[0095] Step S301: obtaining a channel space matrix of a subband of a user equipment to be configured;

[0096] It should be noted that the channel space matrix of the subband can be obtained through channel estimation or MU-MIMO interference suppression.

[0097] Step S302, obtaining the autocorrelation matrix of each PRB;

[0098] It should be noted that the matrix dimension of the autocorrelation matrix is ​​N×N.

[0099] Step S303, performing generalized eigenvalue decomposition on the autocorrelation matrix of all PRBs in the subband;

[0100] It should be noted that, through step S303 , the generalized eigenvalue and the generalized eigenvector can be obtained respectively.

[0101] Step S304, using the generalized eigenvector to calculate the shaping weight of each PRB;

[0102] Step S305 : The shaping weights of the PRBs other than the reference PRB are modified using the generalized eigenvalues ​​to obtain the corresponding final shaping weights.

[0103] It should be noted that, since the embodiments of the present application are mainly aimed at the protocol limitation that cannot be performed with more precision than the sub-band level, the implementation process of generalized eigenvalue decomposition is explained below using the lowest shaping granularity (one sub-band contains 2 PRBs) as an example.

[0104] First, the uplink channel space matrix of the current beamforming subband of the terminal to be beamformed (e.g., the kth user) is obtained by channel estimation or multi-user multiple input multiple output (MU-MIMO) interference suppression. The beamforming subband contains 2 PRBs, and the channel space matrix on each PRB is recorded as It should be noted that when the terminal to be processed is a single-user multiple-input multiple-output (SU-MIMO) user, Derived from uplink channel estimation, when the user to be processed is a MU-MIMO user, Derived from MU interference suppression, that is is the channel space matrix of a terminal after inter-user interference suppression. The dimension is M×N, the number of base station antennas is M, and the terminal has N antennas. Usually, the number of ports for uplink channel estimation is equal to the number of antennas on the UE side, that is, M=N.

[0105] based on Calculate the autocorrelation matrix R of each PRB on the current subband i , i∈(1,2), where the upper label k of user k is omitted, that is:

[0106]

[0107] Among them, R i The dimension is N×N, (*) H The superscript H indicates the conjugate transpose.

[0108] Furthermore, the autocorrelation matrix of each PRB in the subband is decomposed together to obtain the generalized eigenvalue and generalized eigenvector, namely:

[0109] [U,λ]=GSVD(R1,R2)

[0110] Where GSVD represents the generalized eigenvalue decomposition function, U represents the generalized eigenvector, and λ represents the generalized eigenvalue.

[0111] From the property of generalized eigenvalue R1U=λR2U, we know that U=λR1 -1 R2U, so the above formula can be rewritten as follows:

[0112] [U,λ]=SVD((R1) -1 R2)

[0113] Here, SVD represents the general eigenvalue decomposition function.

[0114] Assume that the number of shaping streams of the terminal (it should be noted that the shaping stream number is pre-agreed and is used to indicate how many beams the terminal transmits) is L, L ≤ N, and the shaping weights of PRB1 (it should be noted that, due to the properties of the generalized eigenvalue, it is known that the inverse matrix of R1 is performed, so PRB1 is used as the reference PRB) and PRB2 on the current subband are W respectively. 1,1～L , W 2,1～L , then we have the following formula:

[0115]

[0116]

[0117] It should be noted that this embodiment of the present application obtains the shaping weight value in a manner that can also make the equivalent channel estimate seen by the UE continuous on two different PRBs of the subband. The following proof of the continuity of the equivalent channel estimate seen by the UE on two different PRBs of the subband is as follows (Note: the stream marks 1 to L are omitted in the proof process):

[0118] First, the equivalent channel estimation on PRB1 / PRB2 seen by the UE The calculations are as follows:

[0119]

[0120]

[0121] From the property of generalized eigenvalue R1U=λR2U, we can know that Therefore, the equivalent channel estimates seen by the UE are continuous and completely equal on two different PRBs in the subband.

[0122] It should be noted that the beamforming method based on generalized orthogonal decomposition proposed in the embodiment of the present application enables the beam weight to be matched with the channel of each PRB within the shaping granularity, resulting in better shaping accuracy and better link performance.

[0123] The technical solution provided in the embodiment of the present application can be applicable to a variety of systems, especially 5G systems. For example, the applicable system can be a global system of mobile communication (GSM) system, a code division multiple access (CDMA) system, a wideband code division multiple access (WCDMA) general packet radio service (GPRS) system, a long term evolution (LTE) system, a LTE frequency division duplex (FDD) system, a LTE time division duplex (TDD) system, an advanced long term evolution (LTE-A) system, a universal mobile telecommunication system (UMTS), a world-wide interoperability for microwave access (WiMAX) system, a 5G new air interface (NR) system, etc. These various systems include terminal equipment and network equipment. The system may also include a core network part, such as an evolved packet system (EPS), a 5G system (5GS), etc.

[0124] The terminal involved in the embodiments of the present application may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connection function, or other processing devices connected to a wireless modem. In different systems, the name of the terminal device may also be different. For example, in a 5G system, the terminal device may be called a user equipment (UE). A wireless terminal device can communicate with one or more core networks (CN) via a radio access network (RAN). The wireless terminal device may be a mobile terminal device, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal device. For example, it may be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device that exchanges language and / or data with a radio access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), and other devices. The wireless terminal device may also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, an access point, a remote terminal device, an access terminal device, a user terminal device, a user agent, or a user device, but is not limited in the embodiments of the present application.

[0125] The network device involved in the embodiments of the present application may be a base station, which may include multiple cells providing services to terminals. Depending on the specific application scenario, the base station may also be called an access point, or may be a device in an access network that communicates with a wireless terminal device through one or more sectors on an air interface, or may be named otherwise. The network device may be used to interchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, wherein the rest of the access network may include an Internet Protocol (IP) communication network. The network device may also coordinate attribute management of the air interface. For example, the network device involved in the embodiments of the present application may be a network device (Base Transceiver Station, BTS) in the Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), or a network device (NodeB) in Wide-band Code Division Multiple Access (WCDMA), or an evolutionary network device (eNB or e-NodeB) in the Long Term Evolution (LTE) system, a 5G base station (gNB) in the 5G network architecture (next generation system), or a home evolved Node B (HeNB), a relay node, a home base station (femto), a pico base station (pico), etc., and is not limited in the embodiments of the present application. In some network structures, the network device may include a centralized unit (CU) node and a distributed unit (DU) node, and the centralized unit and the distributed unit may also be geographically separated.

[0126] Network devices and terminal devices can each use one or more antennas for Multiple Input Multiple Output (MIMO) transmission. MIMO transmission can be either Single User MIMO (SU-MIMO) or Multi User MIMO (MU-MIMO). Depending on the configuration and number of antenna combinations, MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO, or Massive-MIMO. It can also use diversity transmission, precoding, or beamforming.

[0127] like Figure 4 As shown, an embodiment of the present application provides a beamforming device 400, which is applied to a network device, including:

[0128] The decomposition unit 401 is configured to perform eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of a terminal to be configured to obtain characteristic information, where the characteristic information includes eigenvalues ​​and eigenvectors.

[0129] The acquiring unit 402 is configured to acquire, according to the characteristic information, a shaping weight of each of the at least two PRBs.

[0130] Optionally, the decomposition unit 401 is specifically configured to:

[0131] Obtain a channel space matrix of a subband of a terminal to be shaped, where the channel space matrix includes a channel space matrix of each of at least two PRBs in the subband;

[0132] Obtaining an autocorrelation matrix of each PRB in the at least two PRBs according to a channel space matrix of a subband of the terminal to be shaped;

[0133] According to the autocorrelation matrix of each PRB in the at least two PRBs, generalized eigenvalue decomposition is performed on the autocorrelation matrix of the at least two PRBs to obtain characteristic information.

[0134] Optionally, the decomposition unit 401 executes, based on the channel space matrix of the subband of the terminal to be shaped, obtaining the autocorrelation matrix of each PRB in the at least two PRBs, specifically implementing:

[0135] An autocorrelation matrix of each of the at least two PRBs is determined according to the channel space matrix of each of the at least two PRBs.

[0136] Optionally, the acquiring unit 402 specifically implements:

[0137] Determining a reference PRB among the at least two PRBs;

[0138] Determining a shaping weight of the reference PRB based on a feature vector in the feature information;

[0139] Determining a shaping weight for a first PRB based on an eigenvalue and an eigenvector in the feature information;

[0140] The first PRB is the other PRB among the at least two PRBs except the reference PRB.

[0141] Optionally, the acquiring unit 402 determines the shaping weight of the reference PRB based on the feature vector in the feature information, specifically implementing:

[0142] Determine a shaping weight of the reference PRB according to the channel space matrix of the reference PRB and the eigenvector.

[0143] Optionally, the acquiring unit 402 determines the shaping weight of the first PRB based on the eigenvalue and the eigenvector in the feature information, specifically implementing:

[0144] Determine a shaping weight of the first PRB according to the channel space matrix, the eigenvalue, and the eigenvector of the first PRB.

[0145] Optionally, the channel space matrix is ​​an uplink channel space matrix.

[0146] It should be noted that the network device embodiment is a network device that corresponds one-to-one to the above method embodiment. All implementation methods in the above method embodiment are applicable to the embodiment of the network device and can achieve the same technical effects.

[0147] It should be noted that the division of units in the embodiments of the present application is schematic and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0148] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0149] like Figure 5 As shown, an embodiment of the present application further provides a network device, including a processor 500, a transceiver 510, a memory 520, and a program stored in the memory N20 and executable on the processor 500; wherein the transceiver 510 is connected to the processor 500 and the memory 520 via a bus interface, wherein the processor 500 is configured to read the program in the memory and execute the following process:

[0150] Performing eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of the terminal to be shaped to obtain characteristic information, where the characteristic information includes: eigenvalues ​​and eigenvectors;

[0151] According to the characteristic information, a shaping weight of each PRB in the at least two PRBs is obtained.

[0152] The transceiver 510 is configured to receive and send data under the control of the processor 500 .

[0153] Among them, Figure 5In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 500 and memory represented by memory 520. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are all well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The transceiver 510 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, such as a wireless channel, a wired channel, an optical cable, and the like. The processor 500 is responsible for managing the bus architecture and general processing, and the memory 520 may store data used by the processor 500 when performing operations.

[0154] The processor 500 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.

[0155] Furthermore, the processor is configured to read the computer program in the memory and perform the following operations:

[0156] Obtain a channel space matrix of a subband of a terminal to be shaped, where the channel space matrix includes a channel space matrix of each of at least two PRBs in the subband;

[0157] Obtaining an autocorrelation matrix of each PRB in the at least two PRBs according to a channel space matrix of a subband of the terminal to be shaped;

[0158] According to the autocorrelation matrix of each PRB in the at least two PRBs, generalized eigenvalue decomposition is performed on the autocorrelation matrix of the at least two PRBs to obtain characteristic information.

[0159] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0160] An autocorrelation matrix of each of the at least two PRBs is determined according to the channel space matrix of each of the at least two PRBs.

[0161] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0162] Determining a reference PRB among the at least two PRBs;

[0163] Determining a shaping weight of the reference PRB based on a feature vector in the feature information;

[0164] Determining a shaping weight for a first PRB based on an eigenvalue and an eigenvector in the feature information;

[0165] The first PRB is the other PRB among the at least two PRBs except the reference PRB.

[0166] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0167] Determine a shaping weight of the reference PRB according to the channel space matrix of the reference PRB and the eigenvector.

[0168] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0169] Determine a shaping weight of the first PRB according to the channel space matrix, the eigenvalue, and the eigenvector of the first PRB.

[0170] Optionally, the channel space matrix is ​​an uplink channel space matrix.

[0171] It should be noted here that the above-mentioned network device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0172] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of a beamforming method applied to a network device. The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as a floppy disk, hard disk, magnetic tape, magneto-optical disk (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0173] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. 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 magnetic disk storage and optical storage, etc.) that contain computer-usable program code.

[0174] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] 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 operate in a specific manner, so that the instructions stored in the processor-readable memory produce an article of manufacture comprising an instruction device that implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0176] These processor-executable instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0177] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A beamforming method, characterized in that: include: Performing eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of the terminal to be shaped to obtain characteristic information, where the characteristic information includes: eigenvalues ​​and eigenvectors; Obtaining, according to the characteristic information, a shaping weight for each of the at least two PRBs; Wherein, obtaining, according to the characteristic information, a shaping weight of each PRB in the at least two PRBs includes: Determining a reference PRB among the at least two PRBs; Determining a shaping weight of the reference PRB based on a feature vector in the feature information; Determining a shaping weight for a first PRB based on an eigenvalue and an eigenvector in the feature information; The first PRB is the other PRB among the at least two PRBs except the reference PRB.

2. The method according to claim 1, characterized in that The performing eigenvalue decomposition on the channel estimation matrix of at least two physical resource blocks (PRBs) contained in the subband of the terminal to be shaped to obtain characteristic information includes: Obtain a channel space matrix of a subband of a terminal to be shaped, where the channel space matrix includes a channel space matrix of each of at least two PRBs in the subband; Obtaining an autocorrelation matrix of each PRB in the at least two PRBs according to a channel space matrix of a subband of the terminal to be shaped; According to the autocorrelation matrix of each PRB in the at least two PRBs, generalized eigenvalue decomposition is performed on the autocorrelation matrix of the at least two PRBs to obtain characteristic information.

3. The method according to claim 2, characterized in that The acquiring, according to the channel space matrix of the subband of the terminal to be shaped, an autocorrelation matrix of each PRB in the at least two PRBs includes: An autocorrelation matrix of each of the at least two PRBs is determined according to the channel space matrix of each of the at least two PRBs.

4. The method according to claim 1, wherein The determining, based on the feature vector in the feature information, a shaping weight of the reference PRB, includes: Determine a shaping weight of the reference PRB according to the channel space matrix of the reference PRB and the eigenvector.

5. The method according to claim 1, wherein The determining, based on the eigenvalue and the eigenvector in the feature information, a shaping weight of the first PRB includes: Determine a shaping weight of the first PRB according to the channel space matrix, the eigenvalue, and the eigenvector of the first PRB.

6. The method according to claim 2, characterized in that The channel space matrix is ​​an uplink channel space matrix.

7. A network device, characterized in that: Including memory, transceiver, processor: A memory for storing a computer program; a transceiver for transmitting and receiving data under the control of the processor; and a processor for reading the computer program in the memory and performing the following operations: Performing eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of the terminal to be shaped to obtain characteristic information, where the characteristic information includes: eigenvalues ​​and eigenvectors; Obtaining, according to the characteristic information, a shaping weight for each of the at least two PRBs; The processor is configured to read the computer program in the memory and perform the following operations: Determining a reference PRB among the at least two PRBs; Determining a shaping weight of the reference PRB based on a feature vector in the feature information; Determining a shaping weight for a first PRB based on an eigenvalue and an eigenvector in the feature information; The first PRB is the other PRB among the at least two PRBs except the reference PRB.

8. The network device according to claim 7, wherein: The processor is configured to read the computer program in the memory and perform the following operations: Obtain a channel space matrix of a subband of a terminal to be shaped, where the channel space matrix includes a channel space matrix of each of at least two PRBs in the subband; Obtaining an autocorrelation matrix of each PRB in the at least two PRBs according to a channel space matrix of a subband of the terminal to be shaped; According to the autocorrelation matrix of each PRB in the at least two PRBs, generalized eigenvalue decomposition is performed on the autocorrelation matrix of the at least two PRBs to obtain characteristic information.

9. The network device according to claim 8, characterized in that The processor is configured to read the computer program in the memory and perform the following operations: An autocorrelation matrix of each of the at least two PRBs is determined according to the channel space matrix of each of the at least two PRBs.

10. The network device according to claim 7, wherein: The processor is configured to read the computer program in the memory and perform the following operations: Determine a shaping weight of the reference PRB according to the channel space matrix of the reference PRB and the eigenvector.

11. The network device according to claim 7, wherein: The processor is configured to read the computer program in the memory and perform the following operations: Determine a shaping weight of the first PRB according to the channel space matrix, the eigenvalue, and the eigenvector of the first PRB.

12. The network device according to claim 9, wherein: The channel space matrix is ​​an uplink channel space matrix.

13. A beamforming device, applied to a network device, characterized in that: include: a decomposition unit, configured to perform eigenvalue decomposition on a channel estimation matrix of at least two physical resource blocks (PRBs) included in a subband of a terminal to be shaped, to obtain characteristic information, wherein the characteristic information includes: eigenvalues ​​and eigenvectors; an acquiring unit, configured to acquire, based on the characteristic information, a shaping weight of each of the at least two PRBs; Wherein, the acquisition unit is used to: Determining a reference PRB among the at least two PRBs; Determining a shaping weight of the reference PRB based on a feature vector in the feature information; Determining a shaping weight for a first PRB based on an eigenvalue and an eigenvector in the feature information; The first PRB is the other PRB among the at least two PRBs except the reference PRB.

14. A processor-readable storage medium, characterized in that: The processor-readable storage medium stores a computer program, and the computer program is configured to cause the processor to execute the method according to any one of claims 1 to 6.

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