Beamforming method, network device, apparatus, and storage medium

Abstract

Embodiments of the present application provide a beamforming method, network device, apparatus and storage medium, wherein the method comprises: determining a third matrix corresponding to a target user equipment based on a first matrix corresponding to the target user equipment and a second matrix corresponding to the target user equipment in paired users; the first matrix is a channel estimation matrix corresponding to the target user equipment, the second matrix is determined based on power iteration vectors corresponding to multiple data streams of the target user equipment, and the third matrix is an equivalent channel matrix corresponding to the target user equipment; performing orthogonalization processing on the third matrix corresponding to the target user equipment based on third matrices corresponding to other user equipments except the target user equipment in the paired users; updating the second matrix corresponding to the target user equipment based on the third matrix after the orthogonalization processing; iterating the foregoing steps, and determining a beamforming factor of multiple data streams of the target user equipment and performing beamforming on the target user equipment when a preset iteration number is reached.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a beamforming method, network equipment, apparatus, and storage medium. Background Technology

[0002] Non-codebook beamforming is used in multi-user multi-input multi-output (MU-MIMO) systems to achieve spatial multiplexing of user equipment, enabling paired user equipment to conduct data communication simultaneously on the same frequency.

[0003] Traditional MU MIMO non-codebook beamforming algorithms consist of two steps: first, beamforming of a single user equipment is performed based on the eigenvalue-based beamforming (EBB) method, and then interference suppression between user equipment is performed based on algorithms such as zero forcing, minimizing mean square error, and signal-to-noise ratio.

[0004] However, these algorithms separate the selection of the maximum gain sub-channel for a single user equipment from the channel decorrelation process between user equipments. This means that the beamforming factor after interference suppression cannot simultaneously meet the requirements of maximizing the channel gain of a single user equipment and minimizing interference between user equipments, thus failing to maximize the downlink throughput of MU-MIMO. Summary of the Invention

[0005] To address the problems existing in the prior art, embodiments of this application provide a beamforming method, network device, apparatus, and storage medium.

[0006] In a first aspect, embodiments of this application provide a beamforming method, including:

[0007] The third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users.

[0008] Wherein, the first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment, the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment, and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment;

[0009] The third matrix corresponding to the target user device is orthogonalized based on the third matrix corresponding to the other user devices among the paired users besides the target user device.

[0010] The second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix;

[0011] The steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment are iterated. When a preset number of iterations is reached, the shaping factors of multiple data streams of the target user equipment are determined.

[0012] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the target user equipment.

[0013] Optionally, the orthogonalization process of the third matrix corresponding to the target user device based on the third matrix corresponding to other user devices among the paired users besides the target user device includes:

[0014] A fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device; the fourth matrix corresponding to the target user device is the interference matrix corresponding to the target user device.

[0015] The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment.

[0016] Optionally, the fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device, satisfying the following calculation formula:

[0017] P j =[G 1,k ,...,G s,k ,...,G J,k ]

[0018] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], where J is the number of user devices in the paired users, and P j This represents the fourth matrix corresponding to the target user equipment in the k-th iteration, [G 1,k ,...,G s,k ,...,G J,k ] represents the matrix formed by the third matrix corresponding to the other user devices among the paired users besides the target user device in the k-th iteration.

[0019] Optionally, the third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment, satisfying the following calculation formula:

[0020] Gt j,k =Gj,k -P j (P j H *P j ) - 1P j H G j,k

[0021] Where j represents the sequence number of the target user equipment in the paired users, P j G represents the fourth matrix corresponding to the target user device. j,k Let Gt represent the third matrix corresponding to the target user equipment in the k-th iteration. j,k G represents the orthogonalized version. j,k The superscript H indicates that the matrix is ​​conjugate transpose.

[0022] Optionally, determining the shaping factors of multiple data streams of the target user equipment after reaching a preset number of iterations includes:

[0023] If the preset number of iterations is reached, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment.

[0024] Optionally, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of multiple data streams of the target user equipment, satisfying the following calculation formula:

[0025] w = Gt j

[0026] Where w represents the shaping factor of multiple data streams of the target user equipment, j represents the sequence number of the target user equipment in the paired users, and Gt j This represents the third matrix corresponding to the target user device after orthogonalization, obtained after a preset number of iterations.

[0027] Optionally, after updating the second matrix corresponding to the target user equipment based on the orthogonalized third matrix, the method further includes:

[0028] The updated second matrix corresponding to the target user equipment is subjected to Schmitt orthogonalization.

[0029] Optionally, the beamforming of the target user equipment based on the beamforming factors of multiple data streams of the target user equipment includes:

[0030] The shaping factors of multiple data streams of the target user equipment are subjected to power normalization processing.

[0031] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the user equipment after normalization.

[0032] Optionally, the shaping factors of the multiple data streams of the target user equipment are subjected to power normalization processing, satisfying the following calculation formula:

[0033]

[0034] Among them, w i This represents the i-th column vector in the beamforming factor w of multiple data streams of the target user equipment, where i is any integer in the interval [1, L], L is the number of beamforming streams of the target user equipment, and w i ′ represents the power normalization result of w i ,||w i || indicates w i The modulus.

[0035] Optionally, the third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users, satisfying the following calculation formula:

[0036] G j,k =H j v j,k

[0037] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], J is the number of user devices in the paired users, and G j,k H represents the third matrix corresponding to the target user equipment in the k-th iteration. j The first matrix, v, represents the target user device. j,k Let v represent the second matrix corresponding to the target user equipment in the k-th iteration. j,k The number of columns is the same as the number of beamforming streams of the target user equipment.

[0038] Optionally, the second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix, satisfying the following calculation formula:

[0039] v j,k+1 =H j Gt j,k

[0040] Where j represents the sequence number of the target user equipment in the paired users, H j The first matrix, Gt, represents the target user equipment. j,k v represents the third matrix of the target user equipment in the k-th iteration after orthogonalization.j,k+1 This represents the second matrix corresponding to the target user equipment in the (k+1)th iteration.

[0041] Secondly, embodiments of this application also provide a network device, including a memory, a transceiver, and a processor:

[0042] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations:

[0043] The third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users.

[0044] Wherein, the first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment, the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment, and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment;

[0045] The third matrix corresponding to the target user device is orthogonalized based on the third matrix corresponding to the other user devices among the paired users besides the target user device.

[0046] The second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix;

[0047] The steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment are iterated. When a preset number of iterations is reached, the shaping factors of multiple data streams of the target user equipment are determined.

[0048] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the target user equipment.

[0049] Optionally, the orthogonalization process of the third matrix corresponding to the target user device based on the third matrix corresponding to other user devices among the paired users besides the target user device includes:

[0050] A fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device; the fourth matrix corresponding to the target user device is the interference matrix corresponding to the target user device.

[0051] The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment.

[0052] Optionally, the fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device, satisfying the following calculation formula:

[0053] P j =[G 1,k ,...,G s,k ,...,G J,k ]

[0054] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], where J is the number of user devices in the paired users, and P j This represents the fourth matrix corresponding to the target user equipment in the k-th iteration, [G 1,k ,...,G s,k ,...,G J,k ] represents the matrix formed by the third matrix corresponding to the other user devices among the paired users besides the target user device in the k-th iteration.

[0055] Optionally, the third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment, satisfying the following calculation formula:

[0056] Gt j,k =G j,k -P j (P j H *P j ) -1 P j H G j,k

[0057] Where j represents the sequence number of the target user equipment in the paired users, P j G represents the fourth matrix corresponding to the target user device. j,k Let Gt represent the third matrix corresponding to the target user equipment in the k-th iteration. j,k G represents the orthogonalized version. j,k The superscript H indicates that the matrix is ​​conjugate transpose.

[0058] Optionally, determining the shaping factors of multiple data streams of the target user equipment after reaching a preset number of iterations includes:

[0059] If the preset number of iterations is reached, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment.

[0060] Optionally, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of multiple data streams of the target user equipment, satisfying the following calculation formula:

[0061] w = Gt j

[0062] Where w represents the shaping factor of multiple data streams of the target user equipment, j represents the sequence number of the target user equipment in the paired users, and Gt j This represents the third matrix corresponding to the target user device after orthogonalization, obtained after a preset number of iterations.

[0063] Optionally, after updating the second matrix corresponding to the target user equipment based on the orthogonalized third matrix, the method further includes:

[0064] The updated second matrix corresponding to the target user equipment is subjected to Schmitt orthogonalization.

[0065] Optionally, the beamforming of the target user equipment based on the beamforming factors of multiple data streams of the target user equipment includes:

[0066] The shaping factors of multiple data streams of the target user equipment are subjected to power normalization processing.

[0067] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the user equipment after normalization.

[0068] Optionally, the shaping factors of the multiple data streams of the target user equipment are subjected to power normalization processing, satisfying the following calculation formula:

[0069]

[0070] Among them, w i This represents the i-th column vector in the beamforming factor w of multiple data streams of the target user equipment, where i is any integer in the interval [1, L], L is the number of beamforming streams of the target user equipment, and w i ′ represents the power normalization result of w i ,||w i || indicates w i The modulus.

[0071] Optionally, the third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users, satisfying the following calculation formula:

[0072] G j,k =H j v j,k

[0073] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], J is the number of user devices in the paired users, and G j,k H represents the third matrix corresponding to the target user equipment in the k-th iteration. j The first matrix, v, represents the target user device. j,k Let v represent the second matrix corresponding to the target user equipment in the k-th iteration. j,k The number of columns is the same as the number of beamforming streams of the target user equipment.

[0074] Optionally, the second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix, satisfying the following calculation formula:

[0075] v j,k+1 =H j Gt j,k

[0076] Where j represents the sequence number of the target user equipment in the paired users, H j The first matrix, Gt, represents the target user equipment. j,k v represents the third matrix of the target user equipment in the k-th iteration after orthogonalization. j,k+1 This represents the second matrix corresponding to the target user equipment in the (k+1)th iteration.

[0077] Thirdly, embodiments of this application also provide a beamforming device, comprising:

[0078] The first determining unit is configured to determine the third matrix corresponding to the target user device based on the first matrix corresponding to the target user device in the paired users and the second matrix corresponding to the target user device;

[0079] Wherein, the first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment, the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment, and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment;

[0080] The first orthogonalization processing unit is used to orthogonalize the third matrix corresponding to the target user device based on the third matrix corresponding to the other user devices in the paired users besides the target user device.

[0081] The update unit is used to update the second matrix corresponding to the target user equipment based on the orthogonalized third matrix;

[0082] The second determining unit is used to iterate over the steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization processing on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment, and to determine the shaping factor of multiple data streams of the target user equipment when a preset number of iterations is reached.

[0083] A beamforming unit is used to beamform the target user equipment based on beamforming factors of multiple data streams of the target user equipment.

[0084] Fourthly, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the beamforming method provided in the first aspect above.

[0085] Fifthly, embodiments of this application also provide a computer-readable storage medium storing a computer program for causing a computer to perform the steps of the beamforming method provided in the first aspect as described above.

[0086] In a sixth aspect, embodiments of this application also provide a communication device readable storage medium storing a computer program for causing the communication device to perform the steps of the beamforming method provided in the first aspect as described above.

[0087] In a seventh aspect, embodiments of this application also provide a chip product readable storage medium storing a computer program for causing the chip product to perform the steps of the beamforming method provided in the first aspect as described above.

[0088] The beamforming method, network device, apparatus, and storage medium provided in this application construct an equivalent channel matrix for the target user equipment (User Equipment) by using a second matrix determined from the power-law iteration vectors of multiple data streams of the User Equipment in a paired user system and the channel estimation matrix of the User Equipment. The equivalent channel matrix of the User Equipment is orthogonalized based on the equivalent channel matrices of other User Equipments. The second matrix is ​​then updated based on the orthogonalized equivalent channel matrix. When the update of the second matrix reaches a preset number of iterations, the beamforming factor of the User Equipment is determined. This achieves synchronous joint multidimensional power-law iteration for multiple users in a MU-MIMO system. The beamforming factors of multiple data streams of the User Equipment are obtained in one cycle of the multidimensional power-law iteration, ensuring channel orthogonality between User Equipments in each iteration and guaranteeing that all User Equipments tend to select the sub-channel with the maximum gain after the cycle ends. Attached Figure Description

[0089] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0090] Figure 1 This is one of the flowcharts illustrating the beamforming method provided in the embodiments of this application;

[0091] Figure 2 This is a second schematic flowchart of the beamforming method provided in the embodiments of this application;

[0092] Figure 3 This is a schematic diagram of the network device provided in the embodiments of this application;

[0093] Figure 4 This is a schematic diagram of the beamforming device provided in the embodiments of this application. Detailed Implementation

[0094] In 5G mobile communication, massive MIMO (Massively Interactive) technology provides the technological foundation for fully utilizing spatial information. Through beamforming, massive MIMO generates dedicated high-gain beams directed at users. The beams for different users are spatially differentiated, enabling different users to conduct data communication simultaneously on the same frequency within the same cell, effectively improving the utilization rate of time and frequency resources.

[0095] Downlink beamforming includes both codebook and non-codebook methods. Non-codebook beamforming, which maximizes the utilization of channel space diversity gain, is a superior transmission method compared to codebook beamforming. Non-codebook beamforming effectively enables user spatial multiplexing in MU-MIMO systems, allowing paired user equipment to communicate simultaneously on the same frequency, thus improving the utilization of time and frequency resources.

[0096] Non-codebook beamforming algorithms based on uplink sounding channel estimation involve transmitting uplink pilots, calculating the MU beamforming factor, and transmitting downlink beams. Classic beamforming algorithms include EBB, Zero Forcing (ZF), Minimum Mean-Squared Error (MMSE), Signal-to-Leakage and Noise Ratio (SLNR), and Minimum Variance Distortionless Response (MVDR).

[0097] First, single-user beamforming is performed based on EBB, followed by inter-user interference suppression based on ZF, MMSE, SLNR, MVDR, etc. Specifically, this includes:

[0098] Step 1: For a single user, the principle of EBB beamforming is to orthogonally decompose the user's channel space, such as singular value decomposition, to obtain orthogonal sub-channels. The singular vectors corresponding to the centrally orthogonal sub-channels are selected as beamforming factors. Different streams are mapped to different sub-channels. Due to the orthogonality of these sub-channels, the data from different streams received at the user end can be transmitted in parallel, independently and without interference, in space.

[0099] Step 2: For different users in the paired MU user group, the shaping factor of the single user after EBB processing needs to be orthogonalized again to achieve interference suppression between users, for example, by using algorithms such as ZF, MMSE, SLNR, MVDR, etc.

[0100] However, these algorithms separate the selection of the maximum gain sub-channel for a single user from the channel decorrelation process between users. After inter-user interference suppression, the orthogonality of the shaping factor obtained after EBB processing in step 1 will be destroyed. At the same time, the shaping factor after interference suppression is no longer the spatial vector corresponding to the maximum gain sub-channel.

[0101] Therefore, the beamforming factor after interference suppression cannot simultaneously meet the requirements of maximizing channel gain for a single user and minimizing interference between users, thus failing to maximize downlink throughput of MU-MIMO.

[0102] To address the aforementioned problems in the existing technology, this application provides a beamforming method, apparatus, and storage medium that performs synchronous joint multidimensional power iteration on paired users in a MU-MIMO system. This simplifies the traditional single-stream power iteration with multiple cycles into eigenvalue decomposition implemented in a single cycle, simultaneously obtaining eigenvectors of multiple streams within one cycle. Furthermore, a channel orthogonality processing mechanism is introduced between users during the multidimensional power iteration cycle, ensuring channel orthogonality between users in each power iteration. After multiple power iteration cycles, all paired users select the sub-channel with the highest gain, effectively maximizing the downlink throughput of the MU-MIMO system.

[0103] 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 cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0104] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0105] The technical solutions provided in this application can be applied to various systems, especially 5G systems. For example, applicable systems include Global System for Mobile Communication (GSM), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Long Term Evolution Advanced (LTE-A), Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and 5G New Radio (NR). All of these systems include terminal equipment and network equipment. The system may also include a core network component, such as the Evolved Packet System (EPS) or the 5G system (5GS).

[0106] The network device involved in this application embodiment can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, a base station may also be called an access point, or a device in an access network that communicates with a wireless terminal device through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with 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 may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network equipment involved in the embodiments of this application can be a base transceiver station (BTS) in a Global System for Mobile Communications (GSM) or Code Division Multiple Access (CDMA) system, a NodeB in a Wide-band Code Division Multiple Access (WCDMA) system, an evolved Node B (eNB or e-NodeB) in a Long Term Evolution (LTE) system, a 5G base station (gNB) in a Next Generation System, a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc., and is not limited in the embodiments of this application. In some network structures, the network equipment may include centralized unit (CU) nodes and distributed unit (DU) nodes, and the centralized unit and distributed unit may be geographically separated.

[0107] The terminal devices involved in the embodiments of this application can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. The names of the terminal devices may differ in different systems; for example, in a 5G system, a terminal device can be called User Equipment (UE). Wireless terminal devices can communicate with one or more core networks (CNs) via a Radio Access Network (RAN). Wireless terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, for example, portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the RAN. Examples include Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station, remote station, access point, remote terminal, access terminal, user terminal, user agent, or user device, but this application embodiment does not limit the terminology.

[0108] Network devices and terminal devices can each use one or more antennas for multiple-input multiple-output (MIMO) transmission. MIMO transmission can be single-user MIMO (SU-MIMO) or multiple-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, and can also be diversity transmission, precoding transmission, or beamforming transmission, etc.

[0109] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0110] The beamforming method provided in this application can be applied to the beamforming process of multiple users in a MU-MIMO system. This application example illustrates its application to a MU-MIMO system. In a MU-MIMO system, the number of base station antennas is M, the number of terminals is K, each terminal contains N antennas, and the channel estimation matrix H for each user equipment is... j It is an M×N matrix, where the subscript j represents the sequence number of the user equipment, and the number of data streams for each user is L≤N. In the embodiments of this application, "user" and "between users" actually refer to "user equipment" and "between user equipment", and "channel matrix" and "channel space" have the same meaning.

[0111] Figure 1 This is one of the flowcharts illustrating the beamforming method provided in the embodiments of this application, such as... Figure 1 As shown, this method can be applied to network devices, and the method includes at least the following steps:

[0112] Step 101: Determine the third matrix corresponding to the target user device based on the first matrix and the second matrix corresponding to the target user device among the paired users;

[0113] The first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment; the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment; and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment.

[0114] Specifically, downlink data transmission in LTE systems supports MU-MIMO. In a MU-MIMO system, network devices transmit multiple data streams on the same time-frequency resources to at least two user equipments. Generally, these at least two user equipments are referred to as each other's paired users.

[0115] The first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment in the paired users, and the third matrix is ​​the equivalent channel matrix corresponding to the target user equipment.

[0116] The second matrix is ​​a pre-defined known matrix, determined by the power iteration vectors corresponding to multiple data streams from the target user equipment. These power iteration vectors are column vectors, and the power iteration vectors from multiple data streams form a matrix. The second matrix can be this matrix or a normalized version of it. The number of columns in the second matrix is ​​the same as the number of data streams from the target user equipment, i.e., the same as the number of beamforming streams.

[0117] Based on the first and second matrices corresponding to the target user equipment, the third matrix of the target user equipment is determined. The third matrix is ​​the equivalent channel matrix corresponding to the target user equipment. All stream shaping spaces of the target user equipment are initialized using the second matrix corresponding to the target user equipment, i.e., the original channel estimation matrix, to determine the equivalent channel matrix corresponding to the target user equipment, which is used for subsequent orthogonalization processing between users.

[0118] Step 102: Perform orthogonalization on the third matrix corresponding to the target user device based on the third matrix corresponding to the other user devices in the paired users besides the target user device.

[0119] Specifically, in a MU-MIMO system, an interference matrix corresponding to the target user equipment is constructed based on the third matrix corresponding to the other user equipments in the paired users (excluding the target user equipment). The equivalent channel matrices between users are orthogonalized based on the interference matrix, and the equivalent channel matrix of the target user equipment is updated.

[0120] Step 103: Update the second matrix corresponding to the target user device based on the orthogonalized third matrix.

[0121] Specifically, by performing a conjugate multiplication between the orthogonalized third matrix and the first matrix, the updated second matrix is ​​obtained, thus completing one exponentiation iteration.

[0122] Step 104: Iterate through the steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment. When the preset number of iterations is reached, determine the shaping factors of multiple data streams of the target user equipment.

[0123] Specifically, steps 101 to 103 are iterated. When the preset number of iterations is reached, the third matrix after orthogonalization in the last iteration is output as the shaping factor of multiple data streams of the target user device.

[0124] Step 105: Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the target user equipment.

[0125] Specifically, after determining the beamforming factors of multiple data streams of the target user equipment, beamforming of the MU-MIMO system is completed.

[0126] The beamforming method provided in this application constructs an equivalent channel matrix for the target user equipment (User Equipment) based on a second matrix determined by the power-law iteration vectors of multiple data streams of the User Equipment in a paired user system and the channel estimation matrix of the User Equipment. It then orthogonally processes the equivalent channel matrix of the User Equipment based on the equivalent channel matrices of other User Equipments (User Equipments), and updates the second matrix based on the orthogonalized equivalent channel matrix. When the update of the second matrix reaches a preset number of iterations, the beamforming factor of the User Equipment is determined. This achieves synchronous joint multidimensional power-law iteration for multiple users in a MU-MIMO system. The beamforming factors of multiple data streams of the User Equipment are obtained in one cycle of the multidimensional power-law iteration, ensuring channel orthogonality between User Equipments in each iteration and guaranteeing that all User Equipments tend to select the sub-channel with the maximum gain after the cycle ends.

[0127] Optionally, a third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users, satisfying the following calculation formula:

[0128] G j,k =H j v j,k

[0129] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], J is the number of user devices in the paired users, and G j,k H represents the third matrix corresponding to the target user equipment in the k-th iteration. j The first matrix, v, represents the target user device. j,k Let v represent the second matrix corresponding to the target user equipment in the k-th iteration. j,k The number of columns is the same as the number of beamforming streams of the target user equipment.

[0130] Specifically, the channel estimation matrix corresponding to the target user equipment is multiplied by the second matrix to obtain the equivalent channel matrix corresponding to the target user equipment.

[0131] Before the iteration begins, the initial power iteration matrix corresponding to the user equipment can be normalized to reduce the complexity of the beamforming process. The normalized initial power iteration matrix is ​​the second matrix before the iteration begins.

[0132] Taking a two-stream system with N=4 terminal antennas as an example, after normalizing the initial matrix of the power iteration, we obtain the second matrix v before the iteration begins. j,0 for:

[0133]

[0134] Taking the three-stream method as an example, after normalizing the initial matrix of the power iteration, we obtain the second matrix v before the iteration begins. j,0 for:

[0135]

[0136] Taking the four-stream method as an example, after normalizing the initial matrix of the power iteration, we obtain the second matrix v before the iteration begins. j,0 for:

[0137]

[0138] Here, the subscript j represents the index of the target user equipment, and the subscript 0 indicates that the iteration number is 0. The second matrix represents the space where multiple data streams of the target user equipment reside, and its number of columns is the same as the number of beamforming streams of the target user equipment, that is, the number of multiple data streams.

[0139] Based on the second matrix of each user equipment in the paired users and the corresponding original channel estimation matrix, the equivalent channel matrix of different user equipment can be obtained.

[0140] The second matrix, determined by the power-iteration vectors of multiple data streams of the target user equipment, enables the beamforming eigenvalue decomposition of multiple data streams of the target user equipment to be achieved in one loop. The eigenvectors of multiple data streams of the target user equipment are obtained in one loop, without having to obtain the eigenvectors of each data stream of the target user equipment separately through multiple loops, thus reducing the complexity of beamforming eigenvalue decomposition for a single user.

[0141] Optionally, the third matrix corresponding to the target user device is orthogonalized based on the third matrix corresponding to the other user devices among the paired users, excluding the target user device, including:

[0142] Construct a fourth matrix corresponding to the target user device based on the third matrix corresponding to the other user devices in the paired users (excluding the target user device); the fourth matrix corresponding to the target user device is the interference matrix corresponding to the target user device.

[0143] The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment.

[0144] Specifically, in a MU-MIMO system, the orthogonalization of the target user's equivalent channel matrix is ​​achieved by using the equivalent channel matrices of other user equipments in the paired user group. Based on the equivalent channel matrices among the users, the channel space selected by the user equipment in each iteration is orthogonalized.

[0145] First, the fourth matrix corresponding to the target user equipment is constructed, namely the interference matrix. The interference matrix of the target user equipment is composed of the equivalent channel matrices of the other user equipments in the paired users, excluding the target user equipment.

[0146] Optionally, a fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users, excluding the target user device, satisfying the following calculation formula:

[0147] P j =[G 1,k ,...,G s,k ,...,G J,k ]

[0148] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], where J is the number of user devices in the paired users, and P j This represents the fourth matrix corresponding to the target user equipment in the k-th iteration, [G 1,k ,...,G s,k ,...,G J,k ] represents the matrix formed by the third matrix corresponding to the user devices other than the target user device among the paired users in the k-th iteration.

[0149] After constructing the fourth matrix, based on the principle of orthogonal projection, the third matrix corresponding to the target user equipment is orthogonalized according to the fourth matrix corresponding to the target user equipment, the channel matrices between users are orthogonalized, and the third matrix of the target user equipment is updated to ensure that the feature spaces selected by each user equipment are orthogonal during the iteration process.

[0150] Optionally, the third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment, satisfying the following calculation formula:

[0151] Gt j,k =G j,k -P j (P j H *P j ) -1 P j H G j,k

[0152] Where j represents the sequence number of the target user equipment in the paired users, P j G represents the fourth matrix corresponding to the target user device. j,k Let Gt represent the third matrix corresponding to the target user equipment in the k-th iteration. j,kG represents the orthogonalized version. j,k The superscript H indicates that the matrix is ​​conjugate transpose.

[0153] The beamforming method provided in this application embodiment, through inter-user stream orthogonalization processing, constructs the interference matrix of the target user equipment by using the equivalent channel matrix of other user equipment in the paired users, and performs orthogonalization processing on the equivalent channel matrix of the target user equipment according to the interference matrix, ensuring that the channels between users are orthogonal in each power iteration process.

[0154] After orthogonalizing the third matrix of the target user equipment, the second matrix of the target user equipment is updated with the orthogonalized third matrix.

[0155] Optionally, the second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix, satisfying the following calculation formula:

[0156] v j,k+1 =H j Gt j,k

[0157] Where j represents the sequence number of the target user equipment in the paired users, H j The first matrix, Gt, represents the target user equipment. j,k v represents the third matrix of the target user equipment in the k-th iteration after orthogonalization. j,k+1 This represents the second matrix corresponding to the target user equipment in the (k+1)th iteration.

[0158] To ensure that the power iteration does not overflow or iterate to zero, the column vectors of the second matrix are normalized by modulus. Modulus normalization can be achieved by Schmitt orthogonalization.

[0159] Optionally, after updating the second matrix corresponding to the target user equipment based on the orthogonalized third matrix, the method further includes:

[0160] The second matrix corresponding to the updated target user equipment is subjected to Schmitt orthogonalization.

[0161] The Schmidt orthogonalization process follows the calculation formula:

[0162] vj ，k+1 ′=Schmidt(v j，k+1 )

[0163] Among them, v j,k+1 Let v represent the second matrix corresponding to the target user equipment in the (k+1)th iteration. j，k+1 ′ indicates that the value of v is... j，k+1 The matrix obtained after performing Schmidt orthogonalization.

[0164] Taking two streams as an example, the orthogonalization process can be represented as:

[0165] v j，1，k+1 =v j，1，k+1 / ||v j，1，k+1 ||

[0166]

[0167] v j，2，k+1 =v j，2，k+1 / ||v j，2，k+1

[0168] First, the first column is normalized by modulus. Then, the second column is orthogonalized to the first column, and then normalized by modulus again. Here, the left side of the equation represents the normalized column vector, and the right side represents the process of normalizing the column vector. v j，1，k+1 v represents the column vector of the first column of the second matrix of the target user device in the (k+1)th iteration. j，2，k+1 This represents the column vector of the second column of the second matrix of the target user device in the (k+1)th iteration.

[0169] Determine if the iteration termination condition has been met. If not, repeat the steps of updating the second and third matrices. If the iteration termination condition has been met, output the orthogonalized third matrix corresponding to the target user device during the last iteration as the shaping factor for the multiple data streams of the target user device. The number of columns in the matrix corresponding to the final output shaping factor is the same as the number of data streams of the target user device.

[0170] Optionally, after reaching a preset number of iterations, the shaping factors of multiple data streams of the target user device are determined, including:

[0171] If the preset number of iterations is reached, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment.

[0172] Specifically, the iteration termination condition can be reaching a preset number of iterations.

[0173] Optionally, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment, satisfying the following calculation formula:

[0174] w = Gt j

[0175] Where w represents the shaping factor of multiple data streams of the target user equipment, j represents the sequence number of the target user equipment in the paired users, and Gt jThis represents the third matrix corresponding to the target user device after orthogonalization, obtained after a preset number of iterations.

[0176] Optionally, beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the target user equipment, including:

[0177] Power normalization processing is performed on the shaping factors of multiple data streams of the target user equipment;

[0178] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams from the user equipment after normalization.

[0179] Specifically, after reaching a preset number of iterations, the beamforming factors of multiple data streams of the target user equipment are output. The beamforming factors of the multiple data streams of the target user equipment are then subjected to power normalization. Beamforming is then performed using the power-normalized beamforming factors, which can further reduce the complexity of beamforming.

[0180] Optionally, the shaping factors of multiple data streams of the target user equipment are power normalized to satisfy the following calculation formula:

[0181]

[0182] Among them, w i This represents the i-th column vector in the beamforming factor w of multiple data streams of the target user equipment, where i is any integer in the interval [1, L], and L is the number of beamforming streams of the target user equipment. i This represents the power after normalization. i ,||w i || indicates w i The modulus.

[0183] Taking a two-stream system as an example, the shaping factor can be normalized by power processing as follows:

[0184]

[0185] Where w1 represents the first column vector of the shaping factor w of the multiple data streams of the target user equipment, w2 represents the second column vector of the shaping factor w of the multiple data streams of the target user equipment, w′1 represents w1 after power normalization, w2′ represents w2 after power normalization, and the double vertical lines represent modulo operation.

[0186] The beamforming method provided in this application simplifies the single-stream power iteration algorithm with multiple loops into beamforming feature value decomposition in one loop through a multidimensional power iteration algorithm. In one loop, the beamforming factors of multiple data streams of the target user equipment are obtained. Furthermore, channel orthogonality processing between users is introduced, which ensures that the channels between user equipment are orthogonal in each power iteration. At the same time, after the multidimensional power iteration loop, it is ensured that all user equipment tends to select the sub-channel with the largest gain, effectively improving the downlink throughput maximization of MU-MIMO.

[0187] The technical solutions of the embodiments of this application are further described below through several specific examples.

[0188] Example 1: Figure 2 This is a second schematic flowchart of the beamforming method provided in the embodiments of this application, as shown below. Figure 2 As shown, in a MU-MIMO system, there are M base station antennas, K terminals, and each terminal contains N antennas. The original channel estimation spatial matrix H for each user is... j Given an M×N matrix, and assuming the number of streams per user is L = 2 ≤ N, this method includes at least:

[0189] Step 201: Import the user's original channel estimation matrix.

[0190] Step 202: Randomly generate the vector v of all streams for each user, and initialize the power iteration initial matrix. For 2 streams, initialize the power iteration initial matrix to obtain:

[0191]

[0192] Step 203: Calculate the equivalent channel space matrix G:

[0193] G j,k =H j v j,k

[0194] Step 204: Construct the interference matrix P:

[0195] P j =[G 1,k ,...,G s,k ,...,G J,k ]

[0196] Step 205: Inter-user orthogonal projection, update the equivalent channel space matrix G:

[0197] Gt j,k =G j,k -P j (P j H *P j )-1 P j H G j,k

[0198] Step 206: Update v and perform Schmidt orthogonalization on v.

[0199] Update v:

[0200] v j,k+1 =H j Gt j,k

[0201] Perform Schmidt orthogonalization on v:

[0202] v j，1，k+1 =v j，1，k+1 / ||v j，1，k+1 ||

[0203]

[0204] v j，2，k+1 =v j，2，k+1 / ||v j，2，k+1 ||

[0205] Step 207: Determine whether the iteration termination condition has been met. If it has, end the iteration; otherwise, return to step 203.

[0206] Step 208: Output the shaping factors for all streams:

[0207] w = Gt j

[0208] The shaping factors for all streams have been normalized. For stream 2, the specific normalization is as follows:

[0209]

[0210] The explanation of all parameters in this example can be found in the description in the foregoing embodiments.

[0211] Steps 201 to 208 above can be divided into three implementation modules:

[0212] All Stream Initialization Module: Initializes all stream shaping spaces for each user using known fixed vectors, determines the corresponding equivalent channel space, and is used for orthogonalization processing between users.

[0213] Inter-user flow orthogonalization module: Based on the equivalent channel space between users, the feature space selected by the user in each iteration is orthogonalized. The orthogonalization process is implemented based on the principle of orthogonal projection.

[0214] The power iteration update stream module performs a conjugate multiplication between the orthogonalized equivalent channel space and the original channel estimation space to complete one power iteration, and updates the user's power iteration matrix through Schmitt orthogonalization.

[0215] Figure 3 This is a schematic diagram of the network device provided in the embodiments of this application, such as... Figure 3 As shown, the network device includes a memory 301, a transceiver 302, and a processor 303, wherein:

[0216] The memory 301 is used to store computer programs; the transceiver 302 is used to send and receive data under the control of the processor 303.

[0217] Specifically, transceiver 302 is used to receive and send data under the control of processor 303.

[0218] Among them, Figure 3 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 303) and memory (memory 301). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 302 can be multiple elements, including transmitters and receivers, providing units for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor 303 is responsible for managing the bus architecture and general processing, and the memory 301 can store data used by the processor 303 during operation.

[0219] The processor 303 can 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 can also adopt a multi-core architecture.

[0220] Processor 303 is used to read the computer program in memory 301 and perform the following operations:

[0221] The third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users.

[0222] Wherein, the first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment, the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment, and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment;

[0223] The third matrix corresponding to the target user device is orthogonalized based on the third matrix corresponding to the other user devices among the paired users besides the target user device.

[0224] The second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix;

[0225] The steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment are iterated. When a preset number of iterations is reached, the shaping factors of multiple data streams of the target user equipment are determined.

[0226] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the target user equipment.

[0227] Optionally, the orthogonalization process of the third matrix corresponding to the target user device based on the third matrix corresponding to other user devices among the paired users besides the target user device includes:

[0228] A fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device; the fourth matrix corresponding to the target user device is the interference matrix corresponding to the target user device.

[0229] The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment.

[0230] Optionally, the fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device, satisfying the following calculation formula:

[0231] P j =[G 1,k ,...,G s,k ,...,G J,k ]

[0232] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], where J is the number of user devices in the paired users, and P j This represents the fourth matrix corresponding to the target user equipment in the k-th iteration, [G 1,k ,...,G s,k ,...,G J,k ] represents the matrix formed by the third matrix corresponding to the other user devices among the paired users besides the target user device in the k-th iteration.

[0233] Optionally, the third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment, satisfying the following calculation formula:

[0234] Gt j,k =G j,k -P j (P j H *P j ) -1 P j H G j,k

[0235] Where j represents the sequence number of the target user equipment in the paired users, P j G represents the fourth matrix corresponding to the target user device. j,k Let Gt represent the third matrix corresponding to the target user equipment in the k-th iteration. j,k G represents the orthogonalized version. j,k The superscript H indicates that the matrix is ​​conjugate transpose.

[0236] Optionally, determining the shaping factors of multiple data streams of the target user equipment after reaching a preset number of iterations includes:

[0237] If the preset number of iterations is reached, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment.

[0238] Optionally, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of multiple data streams of the target user equipment, satisfying the following calculation formula:

[0239] w = Gt j

[0240] Where w represents the shaping factor of multiple data streams of the target user equipment, j represents the sequence number of the target user equipment in the paired users, and Gt jThis represents the third matrix corresponding to the target user device after orthogonalization, obtained after a preset number of iterations.

[0241] Optionally, after updating the second matrix corresponding to the target user equipment based on the orthogonalized third matrix, the method further includes:

[0242] The updated second matrix corresponding to the target user equipment is subjected to Schmitt orthogonalization.

[0243] Optionally, the beamforming of the target user equipment based on the beamforming factors of multiple data streams of the target user equipment includes:

[0244] The shaping factors of multiple data streams of the target user equipment are subjected to power normalization processing.

[0245] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the user equipment after normalization.

[0246] Optionally, the shaping factors of the multiple data streams of the target user equipment are subjected to power normalization processing, satisfying the following calculation formula:

[0247]

[0248] Among them, w i This represents the i-th column vector in the beamforming factor w of multiple data streams of the target user equipment, where i is any integer in the interval [1, L], and L is the number of beamforming streams of the target user equipment. i This represents the power after normalization. i ,||w i || indicates w i The modulus.

[0249] Optionally, the third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users, satisfying the following calculation formula:

[0250] G j,k =H j v j,k

[0251] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], J is the number of user devices in the paired users, and G j,k H represents the third matrix corresponding to the target user equipment in the k-th iteration. j The first matrix, v, represents the target user device. j,kLet v represent the second matrix corresponding to the target user equipment in the k-th iteration. j,k The number of columns is the same as the number of beamforming streams of the target user equipment.

[0252] Optionally, the second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix, satisfying the following calculation formula:

[0253] v j,k+1 =H j Gt j,k

[0254] Where j represents the sequence number of the target user equipment in the paired users, H j The first matrix, Gt, represents the target user equipment. j,k v represents the third matrix of the target user equipment in the k-th iteration after orthogonalization. j,k+1 This represents the second matrix corresponding to the target user equipment in the (k+1)th iteration.

[0255] It should be noted that the network device provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0256] Figure 4 This is a schematic diagram of the beamforming device provided in the embodiments of this application, as shown below. Figure 4 As shown, the device includes:

[0257] The first determining unit 401 is used to determine the third matrix corresponding to the target user equipment based on the first matrix and the second matrix corresponding to the target user equipment in the paired users;

[0258] The first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment; the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment; and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment.

[0259] The first orthogonalization processing unit 402 is used to orthogonalize the third matrix corresponding to the target user device based on the third matrix corresponding to the other user devices in the paired users besides the target user device.

[0260] The update unit 403 is used to update the second matrix corresponding to the target user equipment based on the orthogonalized third matrix;

[0261] The second determining unit 404 is used to iterate the steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization processing on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment, and determine the shaping factors of multiple data streams of the target user equipment when a preset number of iterations is reached.

[0262] Beamforming unit 405 is used to beamform the target user equipment based on the beamforming factors of multiple data streams of the target user equipment.

[0263] Optionally, the first orthogonalization processing unit is also used for:

[0264] Construct a fourth matrix corresponding to the target user device based on the third matrix corresponding to the other user devices in the paired users (excluding the target user device); the fourth matrix corresponding to the target user device is the interference matrix corresponding to the target user device.

[0265] The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment.

[0266] Optionally, a fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users, excluding the target user device, satisfying the following calculation formula:

[0267] P j =[G 1,k ,...,G s,k ,...,G J,k ]

[0268] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], where J is the number of user devices in the paired users, and P j This represents the fourth matrix corresponding to the target user equipment in the k-th iteration, [G 1,k ,...,G s,k ,...,G J,k ] represents the matrix formed by the third matrix corresponding to the user devices other than the target user device among the paired users in the k-th iteration.

[0269] Optionally, the third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment, satisfying the following calculation formula:

[0270] Gt j,k =G j,k -P j (P j H *P j ) -1 Pj H G j,k

[0271] Where j represents the sequence number of the target user equipment in the paired users, P j G represents the fourth matrix corresponding to the target user device. j,k Let Gt represent the third matrix corresponding to the target user equipment in the k-th iteration. j,k G represents the orthogonalized version. j,k The superscript H indicates that the matrix is ​​conjugate transpose.

[0272] Optionally, the second determining unit is further configured to:

[0273] If the preset number of iterations is reached, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment.

[0274] Optionally, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment, satisfying the following calculation formula:

[0275] w = Gt j

[0276] Where w represents the shaping factor of multiple data streams of the target user equipment, j represents the sequence number of the target user equipment in the paired users, and Gt j This represents the third matrix corresponding to the target user device after orthogonalization, obtained after a preset number of iterations.

[0277] Optionally, the device further includes:

[0278] The second orthogonalization processing unit is used to perform Schmitt orthogonalization processing on the second matrix corresponding to the updated target user equipment.

[0279] Optionally, the beamforming unit is also used for:

[0280] Power normalization processing is performed on the shaping factors of multiple data streams of the target user equipment;

[0281] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams from the user equipment after normalization.

[0282] Optionally, the shaping factors of multiple data streams of the target user equipment are power normalized to satisfy the following calculation formula:

[0283]

[0284] Among them, w iThis represents the i-th column vector in the beamforming factor w of multiple data streams of the target user equipment, where i is any integer in the interval [1, L], L is the number of beamforming streams of the target user equipment, and w i ′ represents the power normalization result of w i ,||w i || indicates w i The modulus.

[0285] Optionally, a third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users, satisfying the following calculation formula:

[0286] G j,k =H j v j,k

[0287] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], J is the number of user devices in the paired users, and G j,k H represents the third matrix corresponding to the target user equipment in the k-th iteration. j The first matrix, v, represents the target user device. j,k Let v represent the second matrix corresponding to the target user equipment in the k-th iteration. j,k The number of columns is the same as the number of beamforming streams of the target user equipment.

[0288] Optionally, the second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix, satisfying the following calculation formula:

[0289] v j,k+1 =H j Gt j,k

[0290] Where j represents the sequence number of the target user equipment in the paired users, H j The first matrix, Gt, represents the target user equipment. j,k v represents the third matrix of the target user equipment in the k-th iteration after orthogonalization. j,k+1 This represents the second matrix corresponding to the target user equipment in the (k+1)th iteration. The methods and apparatuses provided in the various embodiments of this application are based on the same application concept. Since the principles by which the methods and apparatuses solve the problem are similar, the implementations of the apparatuses and methods can refer to each other, and repeated details will not be repeated.

[0291] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0292] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0293] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0294] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to execute the beamforming methods provided in the above embodiments, including:

[0295] The third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users.

[0296] The first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment; the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment; and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment.

[0297] The third matrix corresponding to the target user device is orthogonalized based on the third matrix corresponding to the other user devices in the paired users besides the target user device.

[0298] The second matrix corresponding to the target user device is updated based on the orthogonalized third matrix;

[0299] The steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment are iterated. When the preset number of iterations is reached, the shaping factors of multiple data streams of the target user equipment are determined.

[0300] Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams from the target user equipment.

[0301] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0302] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0303] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0304] 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 particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0305] These processors can execute instructions that can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0306] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A beamforming method, characterized in that, include: The third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users. Wherein, the first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment, the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment, and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment; The third matrix corresponding to the target user device is orthogonalized based on the third matrix corresponding to the other user devices among the paired users besides the target user device. The second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix; The steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment are iterated. When a preset number of iterations is reached, the shaping factors of multiple data streams of the target user equipment are determined. Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the target user equipment.

2. The beamforming method according to claim 1, characterized in that, The orthogonalization process of the third matrix corresponding to the target user device based on the third matrix corresponding to the other user devices among the paired users (excluding the target user device) includes: A fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device; the fourth matrix corresponding to the target user device is the interference matrix corresponding to the target user device. The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment.

3. The beamforming method according to claim 2, characterized in that, The fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device, satisfying the following calculation formula: P j =[G 1，k ,...,G s，k ,...,G J，k ] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], where J is the number of user devices in the paired users, and P j This represents the fourth matrix corresponding to the target user equipment in the k-th iteration, [G 1，k , ..., G s，k , ..., G J，k ] represents the matrix formed by the third matrix corresponding to the other user devices among the paired users besides the target user device in the k-th iteration.

4. The beamforming method according to claim 2, characterized in that, The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment, satisfying the following calculation formula: Gt j，k =G j，k -P j (P j H *P j ) -1 P j H G j，k Where j represents the sequence number of the target user equipment in the paired users, P j G represents the fourth matrix corresponding to the target user device. j，k Gt represents the third matrix corresponding to the target user equipment in the k-th iteration. j，k G represents the orthogonalized version. j，k The superscript H indicates that the matrix is ​​conjugate transpose.

5. The beamforming method according to claim 1, characterized in that, The step of determining the shaping factors of multiple data streams of the target user equipment after reaching a preset number of iterations includes: If the preset number of iterations is reached, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment.

6. The beamforming method according to claim 5, characterized in that, The determination of the third matrix corresponding to the target user equipment after orthogonalization in the last iteration as the shaping factor of the multiple data streams of the target user equipment satisfies the following calculation formula: w=Gt j Where w represents the shaping factor of multiple data streams of the target user equipment, j represents the sequence number of the target user equipment in the paired users, and Gt j This represents the third matrix corresponding to the target user device after orthogonalization, obtained after a preset number of iterations.

7. The beamforming method according to claim 1, characterized in that, After updating the second matrix corresponding to the target user equipment based on the orthogonalized third matrix, the method further includes: The updated second matrix corresponding to the target user equipment is subjected to Schmitt orthogonalization.

8. The beamforming method according to claim 1, characterized in that, The beamforming of the target user equipment based on the beamforming factors of multiple data streams of the target user equipment includes: The shaping factors of multiple data streams of the target user equipment are subjected to power normalization processing. Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the user equipment after normalization.

9. The beamforming method according to claim 8, characterized in that, The shaping factors of the multiple data streams of the target user equipment are power normalized to satisfy the following calculation formula: Among them, w i This represents the i-th column vector in the beamforming factor w of multiple data streams of the target user equipment, where i is any integer in the interval [1, L], and L is the number of beamforming streams of the target user equipment. i This represents the power after normalization. i ,||w i || indicates w i The modulus.

10. The beamforming method according to claim 1, characterized in that, The third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among paired users, satisfying the following calculation formula: G j，k =H j v j，k Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], J is the number of user devices in the paired users, and G j，k H represents the third matrix corresponding to the target user equipment in the k-th iteration. j The first matrix, v, represents the target user device. j，k Let v represent the second matrix corresponding to the target user equipment in the k-th iteration. j，k The number of columns is the same as the number of beamforming streams of the target user equipment.

11. The beamforming method according to claim 1, characterized in that, The second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix, satisfying the following calculation formula: v j，k+1 =H j Gt j，k Where j represents the sequence number of the target user equipment in the paired users, H j The first matrix, Gt, represents the target user equipment. j，k v represents the third matrix of the target user equipment in the k-th iteration after orthogonalization. j，k+1 This represents the second matrix corresponding to the target user equipment in the (k+1)th iteration.

12. A network device, comprising a memory, a transceiver, and a processor; characterized in that: A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: The third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among the paired users. Wherein, the first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment, the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment, and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment; The third matrix corresponding to the target user device is orthogonalized based on the third matrix corresponding to the other user devices among the paired users besides the target user device. The second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix; The steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment are iterated. When a preset number of iterations is reached, the shaping factors of multiple data streams of the target user equipment are determined. Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the target user equipment.

13. The network device according to claim 12, characterized in that, The orthogonalization process of the third matrix corresponding to the target user device based on the third matrix corresponding to the other user devices among the paired users (excluding the target user device) includes: A fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device; the fourth matrix corresponding to the target user device is the interference matrix corresponding to the target user device. The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment.

14. The network device according to claim 13, characterized in that, The fourth matrix corresponding to the target user device is constructed based on the third matrix corresponding to the other user devices among the paired users besides the target user device, satisfying the following calculation formula: P j =[G 1，k ,...,G s，k ,...,G J，k ] Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], where J is the number of user devices in the paired users, and P j This represents the fourth matrix corresponding to the target user equipment in the k-th iteration, [G 1，k , ..., G s，k , ..., G J，k ] represents the matrix formed by the third matrix corresponding to the other user devices among the paired users besides the target user device in the k-th iteration.

15. The network device according to claim 13, characterized in that, The third matrix corresponding to the target user equipment is orthogonalized based on the fourth matrix corresponding to the target user equipment, satisfying the following calculation formula: Gt j，k =G j，k -P j (P j H *P j ) -1 P j H G j，k Where j represents the sequence number of the target user equipment in the paired users, P j G represents the fourth matrix corresponding to the target user device. j，k Gt represents the third matrix corresponding to the target user equipment in the k-th iteration. j，k G represents the orthogonalized version. j，k The superscript H indicates that the matrix is ​​conjugate transpose.

16. The network device according to claim 12, characterized in that, The step of determining the shaping factors of multiple data streams of the target user equipment after reaching a preset number of iterations includes: If the preset number of iterations is reached, the third matrix corresponding to the target user equipment after orthogonalization in the last iteration is determined as the shaping factor of the multiple data streams of the target user equipment.

17. The network device according to claim 16, characterized in that, The determination of the third matrix corresponding to the target user equipment after orthogonalization in the last iteration as the shaping factor of the multiple data streams of the target user equipment satisfies the following calculation formula: w=Gt j Where w represents the shaping factor of multiple data streams of the target user equipment, j represents the sequence number of the target user equipment in the paired users, and Gt j This represents the third matrix corresponding to the target user device after orthogonalization, obtained after a preset number of iterations.

18. The network device according to claim 12, characterized in that, After updating the second matrix corresponding to the target user equipment based on the orthogonalized third matrix, the method further includes: The updated second matrix corresponding to the target user equipment is subjected to Schmitt orthogonalization.

19. The network device according to claim 12, characterized in that, The beamforming of the target user equipment based on the beamforming factors of multiple data streams of the target user equipment includes: The shaping factors of multiple data streams of the target user equipment are subjected to power normalization processing. Beamforming is performed on the target user equipment based on the beamforming factors of multiple data streams of the user equipment after normalization.

20. The network device according to claim 19, characterized in that, The shaping factors of the multiple data streams of the target user equipment are power normalized to satisfy the following calculation formula: Among them, w i This represents the i-th column vector in the beamforming factor w of multiple data streams of the target user equipment, where i is any integer in the interval [1, L], and L is the number of beamforming streams of the target user equipment. i This represents the power after normalization. i ,||w i || indicates w i The modulus.

21. The network device according to claim 12, characterized in that, The third matrix corresponding to the target user device is determined based on the first matrix and the second matrix corresponding to the target user device among paired users, satisfying the following calculation formula: G j，k =H j v j，k Where j represents the sequence number of the target user device in the paired users, and the value of j is any integer in the interval [1, J], J is the number of user devices in the paired users, and G j，k H represents the third matrix corresponding to the target user equipment in the k-th iteration. j The first matrix, v, represents the target user device. j，k Let v represent the second matrix corresponding to the target user equipment in the k-th iteration. j，k The number of columns is the same as the number of beamforming streams of the target user equipment.

22. The network device according to claim 12, characterized in that, The second matrix corresponding to the target user equipment is updated based on the orthogonalized third matrix, satisfying the following calculation formula: v j，k+1 =H j Gt j，k Where j represents the sequence number of the target user equipment in the paired users, H j The first matrix, Gt, represents the target user equipment. j，k v represents the third matrix of the target user equipment in the k-th iteration after orthogonalization. j，k+1 This represents the second matrix corresponding to the target user equipment in the (k+1)th iteration.

23. A beamforming device, characterized in that, include: The first determining unit is configured to determine the third matrix corresponding to the target user device based on the first matrix corresponding to the target user device in the paired users and the second matrix corresponding to the target user device; Wherein, the first matrix is ​​the channel estimation matrix for communication between the target user equipment and the network equipment, the second matrix is ​​determined based on the power iteration vectors corresponding to multiple data streams of the target user equipment, and the third matrix corresponding to the target user equipment is the equivalent channel matrix corresponding to the target user equipment; The first orthogonalization processing unit is used to orthogonalize the third matrix corresponding to the target user device based on the third matrix corresponding to the other user devices in the paired users besides the target user device. The update unit is used to update the second matrix corresponding to the target user equipment based on the orthogonalized third matrix; The second determining unit is used to iterate over the steps of determining the third matrix corresponding to the target user equipment, performing orthogonalization processing on the third matrix corresponding to the target user equipment, and updating the second matrix corresponding to the target user equipment, and to determine the shaping factor of multiple data streams of the target user equipment when a preset number of iterations is reached. A beamforming unit is used to beamform the target user equipment based on beamforming factors of multiple data streams of the target user equipment.

24. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that causes a computer to perform the method according to any one of claims 1 to 11.

Images