Beamforming method and device based on massive MIMO

Abstract

The present invention discloses a beamforming method and device based on large-scale MIMO, the method comprising: obtaining matrix data of a target matrix for beamforming, and obtaining communication data of a current scene, disassembling the target matrix according to the matrix data to obtain matrix sub-data, determining the matrix calculation result of the target matrix through a pre-set first target algorithm and all matrix sub-data, generating a target beam of the current scene based on the target matrix calculation result and the communication data and a pre-set second target algorithm, and realizing beamforming based on the target beam. It can be seen that the implementation of the present invention can be beneficial to improving the calculation efficiency of the matrix in beamforming, and thus is beneficial to improving the generation efficiency of beamforming in a large-scale MIMO system.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a beamforming method and device based on large-scale MIMO. Background Art

[0002] With the continuous development of scientific progress, the usage of mobile networks and mobile data is also growing. Massive multiple-input multiple-output systems (also known as MIMO systems) can have extremely high spectrum utilization efficiency by configuring large-scale antenna arrays for base stations. On the basis of fully utilizing existing spectrum resources, they can obtain gains in reliability and effectiveness by utilizing spatial resources. However, the cost is to increase the processing complexity of the transmitter and receiver, and the commonly used spatial diversity technologies in MIMO systems mainly include time-space block coding technology and beamforming technology. Among them, beamforming technology uses the correlation of array elements with small spacing to form interference and concentrate energy in a specific direction to maximize the signal strength of the beam pointing to the user. At present, the calculation method for beamforming vectors is usually calculated by the maximum eigenvalue vector or by the MUSIC algorithm, but the existing methods have the problem of low calculation efficiency, which leads to low efficiency of beamforming. It can be seen that it is particularly important to provide a new beamforming method to improve the efficiency of beamforming. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a beamforming method and device based on large-scale MIMO, which can help improve the calculation efficiency of the matrix in beamforming, and further help improve the generation efficiency of beamforming in large-scale MIMO systems.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a beamforming method based on massive MIMO, the method comprising:

[0005] Obtaining matrix data of a target matrix for beamforming, and obtaining communication data of a current scene;

[0006] According to the matrix data, the target matrix is ​​disassembled to obtain matrix sub-data;

[0007] Determine the matrix calculation result of the target matrix by using a preset first target algorithm and all the matrix sub-data;

[0008] According to the target matrix calculation result and the communication data, based on a preset second target algorithm, a target beam of the current scene is generated, and beamforming is implemented based on the target beam.

[0009] As an optional implementation, in the first aspect of the present invention, after determining the matrix calculation result of the target matrix by a preset first target algorithm and all the matrix sub-data, before generating the target beam of the current scene based on a preset second target algorithm according to the target matrix calculation result and the communication data, the method further includes:

[0010] According to a preset target quantity, a grouping operation is performed on all target data included in the target matrix calculation result to obtain at least one target data group, wherein the quantity of the target data included in each target data group is equal;

[0011] Performing precision conversion operations on all the target data included in all the target data groups to obtain target precision data that meets preset precision conditions, and updating each target data group according to all the target precision data;

[0012] The step of generating the target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm includes:

[0013] According to all the target data groups and the communication data, based on a preset second target algorithm, a target beam for the current scene is generated.

[0014] As an optional implementation, in the first aspect of the present invention, after performing a grouping operation on all target data included in the target matrix calculation result according to a preset target number to obtain at least one target data group, before performing a precision conversion operation on all the target data included in all the target data groups to obtain target precision data that meets a preset precision condition, the method further includes:

[0015] For each of the target data groups, performing a data alignment operation on all the target data included in the target data group to obtain a first target data group after data alignment;

[0016] Each of the target data sets is updated based on all of the first target data sets.

[0017] As an optional implementation, in the first aspect of the present invention, the first preset target algorithm is a Strassen algorithm;

[0018] The step of determining the matrix calculation result of the target matrix by using the preset first target algorithm and all the matrix sub-data includes:

[0019] Determine a matrix sub-calculation result by using the Strassen algorithm and all the matrix sub-data, wherein the number of the matrix sub-calculation results is multiple;

[0020] The matrix calculation result of the target matrix is ​​determined according to all the matrix sub-calculation results.

[0021] As an optional implementation, in the first aspect of the present invention, generating the target beam of the current scene based on all the target data groups and the communication data and based on a preset second target algorithm includes:

[0022] Determine a received signal of a target user according to all the target data groups and the communication data, wherein the target user is a user corresponding to a terminal receiving a target beam in the current scene;

[0023] A target beam for the current scene is generated according to the received signal of the target user and a preset second target algorithm.

[0024] As an optional implementation, in the first aspect of the present invention, the target data group includes an analog precoder matrix and a digital precoder matrix;

[0025] The communication data of the current scene includes one or more of data on the number of users of the current scene, data on the number of data streams required for each user of the current scene, data on the number of antenna segments of the base station of the current scene, data on the number of receiving antenna segments for each user of the current scene, data on the number of RF chains of the base station of the current scene, and data on the number of RF chains for each user of the current scene.

[0026] As an optional implementation manner, in the first aspect of the present invention, determining the received signal of the target user according to all the target data groups and the communication data includes:

[0027] Determine the receiving signal of the target user by a preset signal algorithm according to all the target data groups and the communication data;

[0028] Among them, the pre-set signal algorithm includes:

[0029]

[0030] Among them, y k is the received signal of the target user, H k is the complex channel matrix from the base station to the target user, F is the analog precoder matrix, B k is the digital precoder matrix of the target user, S k is the traffic of the target user, B lis the digital precoder matrix of the neighboring users of the target user, S l is the traffic of the neighboring users of the target user, n k is the signal noise of the target user.

[0031] A second aspect of the present invention discloses a beamforming device based on massive MIMO, the device comprising:

[0032] An acquisition module, used to acquire matrix data of a target matrix for beamforming and to acquire communication data of a current scene;

[0033] A disassembly module, used for disassembling the target matrix according to the matrix data to obtain matrix sub-data;

[0034] A determination module, used to determine the matrix calculation result of the target matrix by using a preset first target algorithm and all the matrix sub-data;

[0035] A generation module is used to generate a target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm, and to implement beamforming based on the target beam.

[0036] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0037] A grouping module, configured to perform a grouping operation on all target data included in the target matrix calculation result according to a preset target number, to obtain at least one target data group, wherein the number of target data included in each target data group is equal, after the determination module determines the matrix calculation result of the target matrix by using a preset first target algorithm and all the matrix sub-data, and before the generation module generates the target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm;

[0038] A conversion module, used for performing a precision conversion operation on all the target data included in all the target data groups to obtain target precision data that meets a preset precision condition;

[0039] An updating module, used for updating each of the target data groups according to all the target accuracy data;

[0040] The generation module generates the target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm, specifically including:

[0041] According to all the target data groups and the communication data, based on a preset second target algorithm, a target beam for the current scene is generated.

[0042] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0043] An alignment module is used for, after the grouping module performs a grouping operation on all the target data included in the target matrix calculation result according to a preset target number to obtain at least one target data group, and before the conversion module performs a precision conversion operation on all the target data included in all the target data groups to obtain target precision data that meets a preset precision condition, performing a data alignment operation on all the target data included in the target data group for each target data group to obtain a first target data group after data alignment;

[0044] The updating module is further configured to update each of the target data groups according to all of the first target data groups.

[0045] As an optional implementation, in the second aspect of the present invention, the first target algorithm preset is a Strassen algorithm;

[0046] The determination module determines the matrix calculation result of the target matrix by using the preset first target algorithm and all the matrix sub-data, specifically including:

[0047] Determine a matrix sub-calculation result by using the Strassen algorithm and all the matrix sub-data, wherein the number of the matrix sub-calculation results is multiple;

[0048] The matrix calculation result of the target matrix is ​​determined according to all the matrix sub-calculation results.

[0049] As an optional implementation, in the second aspect of the present invention, the generation module generates the target beam of the current scene according to all the target data groups and the communication data based on a preset second target algorithm, specifically including:

[0050] Determine a received signal of a target user according to all the target data groups and the communication data, wherein the target user is a user corresponding to a terminal receiving a target beam in the current scene;

[0051] A target beam for the current scene is generated according to the received signal of the target user and a preset second target algorithm.

[0052] As an optional implementation, in the second aspect of the present invention, the target data group includes an analog precoder matrix and a digital precoder matrix;

[0053] The communication data of the current scene includes one or more of data on the number of users of the current scene, data on the number of data streams required for each user of the current scene, data on the number of antenna segments of the base station of the current scene, data on the number of receiving antenna segments for each user of the current scene, data on the number of RF chains of the base station of the current scene, and data on the number of RF chains for each user of the current scene.

[0054] As an optional implementation manner, in the second aspect of the present invention, the generation module determines the receiving signal of the target user according to all the target data groups and the communication data, specifically comprising:

[0055] Determine the receiving signal of the target user by a preset signal algorithm according to all the target data groups and the communication data;

[0056] Among them, the pre-set signal algorithm includes:

[0057]

[0058] Among them, y k is the received signal of the target user, H k is the complex channel matrix from the base station to the target user, F is the analog precoder matrix, B k is the digital precoder matrix of the target user, S k is the traffic of the target user, B l is the digital precoder matrix of the neighboring users of the target user, S l is the traffic of the neighboring users of the target user, n k is the signal noise of the target user.

[0059] The third aspect of the present invention discloses another beamforming device based on massive MIMO, the device comprising:

[0060] A memory storing executable program code;

[0061] a processor coupled to the memory;

[0062] The processor calls the executable program code stored in the memory to execute the beamforming method based on large-scale MIMO disclosed in the first aspect of the present invention.

[0063] The fourth aspect of the present invention discloses a computer storable medium, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the beamforming method based on large-scale MIMO disclosed in the first aspect of the present invention.

[0064] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0065] In an embodiment of the present invention, matrix data of a target matrix for beamforming is obtained, and communication data of a current scene is obtained. According to the matrix data, the target matrix is ​​disassembled to obtain matrix sub-data. The matrix calculation result of the target matrix is ​​determined by a pre-set first target algorithm and all matrix sub-data. According to the target matrix calculation result and the communication data, a target beam of the current scene is generated based on a pre-set second target algorithm, and beamforming is implemented based on the target beam. It can be seen that the implementation of the present invention can help improve the calculation efficiency of the matrix in beamforming, and thus help improve the generation efficiency of beamforming in a large-scale MIMO system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0067] Figure 1 It is a flowchart of a beamforming method based on massive MIMO disclosed in an embodiment of the present invention;

[0068] Figure 2 It is a flowchart of another beamforming method based on massive MIMO disclosed in an embodiment of the present invention;

[0069] Figure 3 It is a structural schematic diagram of a beamforming device based on massive MIMO disclosed in an embodiment of the present invention;

[0070] Figure 4 is a structural schematic diagram of another beamforming device based on massive MIMO disclosed in an embodiment of the present invention;

[0071] Figure 5 It is a structural schematic diagram of another beamforming device based on massive MIMO disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0074] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0075] The present invention discloses a beamforming method and device based on massive MIMO, which can help improve the calculation efficiency of the matrix in beamforming, and further help improve the generation efficiency of beamforming in massive MIMO system. The following are detailed descriptions.

[0076] Embodiment 1

[0077] See also Figure 1 , Figure 1 is a flow chart of a beamforming method based on massive MIMO disclosed in an embodiment of the present invention. Figure 1 The described beamforming method based on massive MIMO can be applied to a beamforming device based on massive MIMO, and can also be applied to a cloud server or a local server based on massive MIMO beamforming, which is not limited in the embodiments of the present invention. Figure 1 As shown, the beamforming method based on massive MIMO may include the following operations:

[0078] 101. Obtain matrix data of a target matrix for beamforming, and obtain communication data of a current scene.

[0079] In the embodiment of the present invention, the target matrix is ​​a square matrix. Further, the target matrix is ​​a square matrix with two rows and two columns.

[0080] In the embodiment of the present invention, optionally, the matrix data of the target matrix may include each value in the target matrix.

[0081] In an embodiment of the present invention, optionally, obtaining matrix data of a target matrix for beamforming and obtaining communication data of a current scene may be obtained in real time, may be obtained periodically according to a preset time period, or may be obtained according to a received beamforming instruction, which is not limited in the embodiment of the present invention.

[0082] In the embodiment of the present invention, the current scenario includes a 5G massive MIMO scenario. It should be noted that the full name of massive MIMO is: Massive Multiple Input Multiple Output, i.e., multiple-input multiple-output technology, which refers to the use of multiple transmitting antennas and receiving antennas at the transmitting end and the receiving end respectively, so that the signal is transmitted and received through multiple antennas at the transmitting end and the receiving end, thereby improving the communication quality. It can make full use of spatial resources, realize multiple transmission and multiple reception through multiple antennas, and can multiply the system channel capacity without increasing spectrum resources and antenna transmission power, showing obvious advantages and being regarded as the core technology of the next generation of mobile communications.

[0083] 102. According to the matrix data, the target matrix is ​​disassembled to obtain matrix sub-data.

[0084] In the embodiment of the present invention, optionally, according to the matrix data, the target matrix is ​​decomposed to obtain the matrix sub-data, which may include: according to the matrix data, extracting each data in the target matrix, and taking each data in the target matrix as the matrix sub-data. For example, when the target matrix is , A11, A12, A21, and A22 are all matrix sub-data of the target matrix.

[0085] 103. Determine the matrix calculation result of the target matrix through the preset first target algorithm and all matrix sub-data.

[0086] In the embodiment of the present invention, the pre-set first target algorithm may be, optionally, a Strassen algorithm. It should be noted that the Strassen algorithm is a Strassen algorithm, which is an algorithm for calculating matrix multiplication.

[0087] 104. According to the target matrix calculation result and the communication data, based on a preset second target algorithm, a target beam of the current scene is generated, and beamforming is implemented based on the target beam.

[0088] In the embodiments of the present invention, it should be noted that beamforming, also known as beamforming and spatial filtering, is a signal processing technology that uses a sensor array to send and receive signals in a directional manner. The beamforming technology adjusts the parameters of the basic unit of the phase array so that signals at certain angles obtain constructive interference, while signals at other angles obtain destructive interference. Beamforming can be used for both signal transmitting and signal receiving ends.

[0089] In the embodiment of the present invention, optionally, the target beam may include a target output wave and / or a target receiving wave.

[0090] It can be seen that implementation Figure 1 The described beamforming method based on large-scale MIMO can obtain matrix data of a target matrix used for beamforming, as well as communication data of a current scene. According to the matrix data, the target matrix is ​​decomposed to obtain matrix sub-data. The matrix calculation result of the target matrix is ​​determined by a pre-set first target algorithm and all matrix sub-data. According to the target matrix calculation result and the communication data, a target beam for the current scene is generated based on a pre-set second target algorithm. Based on the target beam, beamforming is implemented, which can help improve the calculation efficiency of the matrix in beamforming, and thus help improve the generation efficiency of beamforming in large-scale MIMO systems.

[0091] Embodiment 2

[0092] See also Figure 2 , Figure 2 is a flow chart of a beamforming method based on massive MIMO disclosed in an embodiment of the present invention. Figure 2 The described beamforming method based on massive MIMO can be applied to a beamforming device based on massive MIMO, and can also be applied to a cloud server or a local server based on massive MIMO beamforming, which is not limited in the embodiments of the present invention. Figure 2 As shown, the beamforming method based on massive MIMO may include the following operations:

[0093] 201. Obtain matrix data of a target matrix for beamforming, and obtain communication data of a current scene.

[0094] 202. According to the matrix data, the target matrix is ​​decomposed to obtain matrix sub-data.

[0095] 203. Determine a matrix calculation result of a target matrix through a preset first target algorithm and all matrix sub-data.

[0096] In the embodiment of the present invention, for other descriptions of step 201 to step 203, please refer to the detailed description of step 101 to step 103 in the first embodiment, and the embodiment of the present invention will not be repeated.

[0097] 204. Perform a grouping operation on all target data included in the target matrix calculation result according to a preset target quantity to obtain at least one target data group, wherein the quantity of target data included in each target data group is equal.

[0098] In the embodiment of the present invention, optionally, the preset target number may be 4.

[0099] In the embodiment of the present invention, optionally, the target data included in each target data group is different.

[0100] 205. Perform precision conversion operations on all target data included in all target data groups to obtain target precision data that meets preset precision conditions, and update each target data group according to all target precision data.

[0101] In the embodiment of the present invention, optionally, performing a precision conversion operation on all target data included in all target data groups to obtain target precision data that meets a preset precision condition may include: performing a precision conversion operation on all target data included in all target data groups so that all target data included in all target data groups are double-precision floating point numbers to obtain target precision data that meets the preset precision condition. The preset precision condition may be a double-precision floating point condition.

[0102] In the embodiment of the present invention, optionally, updating each target data group according to all target accuracy data may include: updating all data in all target data groups to target accuracy data that meets preset accuracy conditions. In this way, the accuracy of all target data included in all target data groups can be converted into target accuracy data that meets preset accuracy conditions, which is conducive to improving the accuracy of subsequent generation of target beams for the current scene, and is conducive to improving the efficiency of subsequent generation of target beams for the current scene, thereby facilitating the improvement of the accuracy and efficiency of beamforming.

[0103] 206. Generate a target beam for the current scene according to all target data groups and communication data based on a preset second target algorithm, and implement beamforming based on the target beam.

[0104] It can be seen that implementation Figure 2The described beamforming method based on large-scale MIMO can obtain matrix data of a target matrix for beamforming, as well as communication data of a current scene, and according to the matrix data, decompose the target matrix to obtain matrix sub-data, determine the matrix calculation result of the target matrix through a preset first target algorithm and all matrix sub-data, and perform a grouping operation on all target data included in the target matrix calculation result according to a preset target number to obtain at least one target data group, perform a precision conversion operation on all target data included in all target data groups to obtain target precision data that meets preset precision conditions, and update each target data group according to all target precision data, and generate the target beam of the current scene based on a preset second target algorithm according to all target data groups and communication data, which can improve the calculation efficiency of the matrix in beamforming, and can improve the intelligence and efficiency of processing all target data, thereby facilitating the improvement of the efficiency and accuracy of generating the target beam of the current scene, and further facilitating the improvement of the generation efficiency of beamforming in large-scale MIMO systems.

[0105] In an optional embodiment, after performing a grouping operation on all target data included in the target matrix calculation result according to a preset target number to obtain at least one target data group, performing a precision conversion operation on all target data included in all target data groups to obtain target precision data that meets a preset precision condition, the method further includes:

[0106] For each target data group, performing a data alignment operation on all target data included in the target data group to obtain a first target data group after data alignment;

[0107] Each target data set is updated based on all first target data sets.

[0108] In this optional embodiment, optionally, for each target data group, performing a data alignment operation on all target data included in the target data group to obtain a first target data group after data alignment may include: for each target data group, analyzing the data type in the target data group, determining an alignment method according to the data type in the target data group, and performing a data alignment operation on all target data included in the target data group according to the determined alignment method to obtain the first target data group after data alignment. In this way, the alignment method of the target data group can be determined according to the data type in the target data group, and the data alignment operation can be performed on the target data group according to the alignment method, which can improve the intelligence of performing the data alignment operation, and is conducive to improving the accuracy and efficiency of performing the data alignment operation.

[0109] In this optional embodiment, optionally, performing a data alignment operation on all target data included in the target data group may include: performing a data alignment operation on all target data included in the target data group through a preset target alignment instruction. Wherein, the preset target alignment instruction may include a MOVAPD instruction. It should be noted that the MOVAPD instruction is to move aligned compressed double-precision floating-point values, and a double quad-word containing two compressed double-precision floating-point values ​​is moved from the source operand (the second operand) to the target operand (the first operand). This instruction can be used to load the contents of a 128-bit memory location into an XMM register, store the contents of an XMM register into a 128-bit memory location, or move data between two XMM registers. Further, when the source operand or the target operand is a memory operand, the operand must be aligned to a 16-byte boundary. Furthermore, when it is necessary to move in and / or out a double-precision floating-point value in an unaligned memory location, the preset target alignment instruction may be a MOVUPD instruction. For example, the target data group includes variables a, b, c, d, R, and I. These variables are aligned to 32 bytes through the ALIGN32 instruction and the MOVAPD instruction, so that the data in the target data group are all aligned. In this way, by performing data alignment operations on all target data included in the target data group, data reading and writing between memory and registers can be accelerated, thereby improving the efficiency of generating the target beam of the current scene, which is conducive to improving the efficiency of beamforming.

[0110] In this optional embodiment, further optionally, for each target data group, each target data group includes 4 target data, the four registers of YMMO-3 are used as buffers, and each target data included in the target data group is loaded into the four registers respectively, so that the four registers can calculate or process the data at the same time. In this way, the number of times the variable is loaded into the CPU can be reduced to 1, which is conducive to speeding up the reading and writing of data between the memory and the register, and is conducive to improving the efficiency of processing data, thereby improving the efficiency of generating the target beam of the current scene, and further conducive to improving the efficiency of realizing beamforming.

[0111] It can be seen that implementing this optional embodiment can perform data alignment operations on all target data included in each target data group to obtain the first target data group after data alignment, and update each target data group based on all first target data groups, which can speed up the reading and writing of data between the memory and the register, thereby improving the efficiency of generating the target beam of the current scene, and further helping to improve the efficiency of realizing beamforming.

[0112] In another optional embodiment, the preset first target algorithm is the Strassen algorithm;

[0113] The matrix calculation result of the target matrix is ​​determined by the preset first target algorithm and all matrix sub-data, including:

[0114] Determine the matrix sub-calculation result through the Strassen algorithm and all matrix sub-data, and the number of the matrix sub-calculation results is multiple;

[0115] The matrix calculation result of the target matrix is ​​determined based on all matrix sub-calculation results.

[0116] In this optional embodiment, optionally, determining the matrix sub-calculation result through the Strassen algorithm and all the matrix sub-data may include: dividing the two multiplied target matrices into matrix blocks of two rows and two columns through the Strassen algorithm and all the matrix sub-data, and determining the matrix sub-calculation result based on each matrix block and the matrix sub-data.

[0117] In this optional embodiment, for example, C=A×B, where, Wherein, determining the matrix sub-calculation result through the Strassen algorithm and all matrix sub-data may include:

[0118] P1 = (A11 + A22) (B11 + B22);

[0119] P2=(A21+A22)B11;

[0120] P3 = A11 (B12 - B22);

[0121] P4 = A22 (B21 - B11);

[0122] P5 = (A11 + A12) B22;

[0123] P6 = (A21 - A11) (B11 + B12);

[0124] P7 = (A12 - A22) (B21 + B22);

[0125] C11=P1+P4-P5+P7;

[0126] C12=P3+P5;

[0127] C21=P2+P4;

[0128] C22=P1+P3-P2+P6;

[0129] Among them, P1, P2, P3, P4, P5, P6, and P7 are matrix sub-calculation results, and C11, C12, C21, and C22 are matrix calculation results.

[0130] It can be seen that the implementation of this optional embodiment can determine the matrix sub-calculation results through the Strassen algorithm and all the matrix sub-data, and determine the matrix calculation results of the target matrix based on all the matrix sub-calculation results, and can improve the efficiency of calculating the matrix sub-calculation results through the Strassen algorithm, and can improve the accuracy of calculating the matrix sub-calculation results through the Strassen algorithm, which is beneficial to improve the efficiency and accuracy of generating the target beam, and further beneficial to improve the efficiency and accuracy of beamforming.

[0131] In yet another optional embodiment, generating a target beam for the current scene based on all target data groups and communication data and based on a preset second target algorithm includes:

[0132] Determine the receiving signal of the target user according to all target data groups and communication data, where the target user is the user corresponding to the terminal receiving the target beam in the current scene;

[0133] A target beam for the current scene is generated according to the received signal of the target user and a preset second target algorithm.

[0134] In this optional embodiment, optionally, generating a target beam for the current scene based on a received signal of the target user and a pre-set second target algorithm may include: inputting the received signal of the target user into a pre-set target model to obtain a target output result, and generating the target beam for the current scene based on the target output result.

[0135] In this optional embodiment, optionally, the number of target users may be one.

[0136] In this optional embodiment, for example, through the AVX instruction of the SIMD upgraded version, every four target data included in the target data group are packed into a YMM register, and the data processing operation is performed, so that the four target data can be calculated at the same time by executing once. It should be noted that the target data are all double data, that is, double-precision floating point numbers, and the number of YMM registers can be multiple, which is not limited in the embodiment of the present invention. It should be noted that SIMD stands for Single Instruction Multiple Data, a set of instructions that can copy multiple operands and pack them in large registers, and can execute the same instruction at the same time in a synchronous manner. The AVX instruction set is a new instruction set under the Sandy Bridge and Larrabee architectures. AVX is a single instruction multiple data stream that is extended from the previous 128 bits to 256 bits. While the single instruction multiple data stream calculation unit of Sandy Bridge is extended to 256 bits, data transmission is also improved, so theoretically, the floating-point operation performance of the CPU core is improved by 2 times. Intel AVX instruction set, while enhancing SIMD computing performance, also uses the MMX / SSE instruction set. However, the difference from MMX / SSE is that the enhanced AVX instructions have undergone significant changes in the instruction format. The prefix (Prefix) is added to the x86 (IA-32 / Intel 64) architecture, so new commands are implemented, and more complex instructions can be implemented, thereby improving the performance of the x86 CPU.

[0137] It can be seen that implementing this optional embodiment can determine the received signal of the target user based on all target data groups and communication data, and generate the target beam of the current scene based on the received signal of the target user and a pre-set second target algorithm, which can improve the efficiency of generating the target beam of the current scene, and can improve the accuracy of generating the target beam of the current scene, which can help improve the efficiency of beamforming, and help improve the accuracy of beamforming.

[0138] In yet another optional embodiment, the target data set includes an analog precoder matrix, a digital precoder matrix;

[0139] The communication data of the current scene includes one or more of data on the number of users in the current scene, data on the number of data streams required by each user in the current scene, data on the number of antenna segments of the base station in the current scene, data on the number of receiving antenna segments for each user in the current scene, data on the number of RF chains of the base station in the current scene, and data on the number of RF chains for each user in the current scene.

[0140] In this optional embodiment, optionally, the digital precoder matrix can be The analog precoder matrix can be

[0141] In this optional embodiment, optionally, the data stream quantity data required by each user in the current scene can be N s , the number of antenna segments of the base station in the current scene can be N BS , the number of receiving antenna segments for each user in the current scene can be N MS , the radio frequency chain number data of the base station in the current scene can be M MS , the radio frequency chain number data of each user in the current scene can be M BS .

[0142] In this optional embodiment, further, the target data group may also include a complex channel matrix from the base station to the user in the current scene. It should be noted that the complex channel matrix from the base station to the user in the current scene may be

[0143] In this optional embodiment, further optionally, the communication data of the current scene may also include user noise of the target user, wherein the user noise of the target user may be n k .

[0144] It can be seen that implementing this optional embodiment can jointly determine the target user's receiving signal based on the target data group and the multiple types of communication data included in the current scene, so that the target beam of the current scene can be generated according to the target data group and the multiple types of communication data included in the current scene, and the accuracy of determining the target user's receiving signal can be improved, which is beneficial to improving the accuracy of generating the target beam of the current scene, and further beneficial to improving the accuracy of beamforming.

[0145] In yet another optional embodiment, determining the received signal of the target user according to all target data groups and communication data includes:

[0146] Determine the receiving signal of the target user through a pre-set signal algorithm according to all target data groups and communication data;

[0147] Among them, the pre-set signal algorithm includes:

[0148]

[0149] Among them, y k is the received signal of the target user, H k is the complex channel matrix from the base station to the target user, F is the analog precoder matrix, B k is the digital precoder matrix of the target user, Sk is the traffic of the target user, B l is the digital precoder matrix of the neighboring users of the target user, S l is the traffic of the neighboring users of the target user, n k is the signal noise of the target user.

[0150] In this optional embodiment, optionally, the simulated precoding matrix may be The digital precoder matrix can be Among them, N BS is the number of antenna segments of the base station in the current scenario, M BS is the number of radio frequency chains of each user in the current scenario, K NS The number of data streams required for the target user.

[0151] It can be seen that the implementation of this optional embodiment can determine the target user's receiving signal through a pre-set signal algorithm based on the target data group and the communication data, which can improve the accuracy and reliability of determining the target user's receiving signal, thereby improving the accuracy and reliability of generating the target beam for the current scene, which in turn is beneficial to improving the accuracy and reliability of beamforming.

[0152] Embodiment 3

[0153] See also Figure 3 , Figure 3 FIG. 1 is a schematic diagram of the structure of another beamforming device based on massive MIMO disclosed in an embodiment of the present invention. Figure 3 As shown, the beamforming device based on massive MIMO may include:

[0154] The acquisition module 301 is used to acquire matrix data of a target matrix for beamforming and to acquire communication data of a current scene.

[0155] The disassembly module 302 is used to disassemble the target matrix according to the matrix data to obtain matrix sub-data.

[0156] The determination module 303 is used to determine the matrix calculation result of the target matrix through a preset first target algorithm and all matrix sub-data.

[0157] The generation module 304 is used to generate a target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm, and implement beamforming based on the target beam.

[0158] It can be seen that implementation Figure 3The described device can obtain matrix data of a target matrix for beamforming, and obtain communication data of a current scene. According to the matrix data, the target matrix is ​​disassembled to obtain matrix sub-data. The matrix calculation result of the target matrix is ​​determined by a predetermined first target algorithm and all matrix sub-data. According to the target matrix calculation result and the communication data, a target beam of the current scene is generated based on a predetermined second target algorithm. Based on the target beam, beamforming is implemented, which can help improve the calculation efficiency of the matrix in beamforming, and further help improve the generation efficiency of beamforming in large-scale MIMO systems.

[0159] In an optional embodiment, if Figure 4 As shown, the device also includes:

[0160] The grouping module 305 is used to perform a grouping operation on all target data included in the target matrix calculation result according to a preset target number, so as to obtain at least one target data group, wherein the number of target data included in each target data group is equal, after the determination module 303 determines the matrix calculation result of the target matrix through a preset first target algorithm and all matrix sub-data, and before the generation module 304 generates the target beam of the current scene according to the target matrix calculation result and communication data based on a preset second target algorithm.

[0161] The conversion module 306 is used to perform precision conversion operations on all target data included in all target data groups to obtain target precision data that meets preset precision conditions.

[0162] An updating module 307, used to update each target data group according to all target accuracy data;

[0163] The generation module 304 generates the target beam of the current scene according to the target matrix calculation result and the communication data based on the preset second target algorithm, specifically including:

[0164] According to all target data groups and communication data, based on a preset second target algorithm, a target beam for the current scene is generated.

[0165] It can be seen that implementation Figure 4The described device can obtain matrix data of a target matrix for beamforming, as well as communication data of a current scene, and according to the matrix data, decompose the target matrix to obtain matrix sub-data, determine the matrix calculation result of the target matrix through a preset first target algorithm and all matrix sub-data, and perform a grouping operation on all target data included in the target matrix calculation result according to a preset target number to obtain at least one target data group, perform a precision conversion operation on all target data included in all target data groups to obtain target precision data that meets preset precision conditions, and update each target data group according to all target precision data, and generate the target beam of the current scene based on a preset second target algorithm according to all target data groups and communication data, which can improve the calculation efficiency of the matrix in beamforming, and can improve the intelligence and efficiency of processing all target data, thereby facilitating the improvement of the efficiency and accuracy of generating the target beam of the current scene, and further facilitating the improvement of the generation efficiency of beamforming in a large-scale MIMO system.

[0166] In another optional embodiment, Figure 4 As shown, the device also includes:

[0167] The alignment module 308 is used for performing a grouping operation on all target data included in the target matrix calculation result according to a preset target number in the grouping module 305 to obtain at least one target data group, and before the conversion module 306 performs a precision conversion operation on all target data included in all target data groups to obtain target precision data that meets a preset precision condition, performing a data alignment operation on all target data included in the target data group for each target data group to obtain a first target data group after data alignment;

[0168] The updating module 307 is further configured to update each target data group according to all the first target data groups.

[0169] It can be seen that implementation Figure 4 The described device can perform data alignment operations on all target data included in each target data group to obtain the first target data group after data alignment, and update each target data group based on all first target data groups. It can speed up the reading and writing of data between the memory and the register, thereby improving the efficiency of generating the target beam of the current scene, which is beneficial to improving the efficiency of realizing beamforming.

[0170] In yet another optional embodiment, Figure 4 As shown, the preset first target algorithm is the Strassen algorithm;

[0171] The determination module 303 determines the matrix calculation result of the target matrix by using the preset first target algorithm and all matrix sub-data, specifically including:

[0172] Determine the matrix sub-calculation result through the Strassen algorithm and all matrix sub-data, and the number of the matrix sub-calculation results is multiple;

[0173] The matrix calculation result of the target matrix is ​​determined based on all matrix sub-calculation results.

[0174] It can be seen that implementation Figure 4 The described device can determine the matrix sub-calculation results through the Strassen algorithm and all the matrix sub-data, and determine the matrix calculation results of the target matrix based on all the matrix sub-calculation results. It can improve the efficiency of calculating the matrix sub-calculation results through the Strassen algorithm, and can improve the accuracy of calculating the matrix sub-calculation results through the Strassen algorithm, thereby helping to improve the efficiency and accuracy of generating the target beam, and further helping to improve the efficiency and accuracy of beamforming.

[0175] In yet another optional embodiment, Figure 4 As shown, the generation module 304 generates the target beam of the current scene according to all target data groups and communication data based on a preset second target algorithm, specifically including:

[0176] Determine the receiving signal of the target user according to all target data groups and communication data, where the target user is the user corresponding to the terminal receiving the target beam in the current scene;

[0177] A target beam for the current scene is generated according to the received signal of the target user and a preset second target algorithm.

[0178] It can be seen that implementation Figure 4 The described device can determine the received signal of the target user based on all target data groups and communication data, and generate the target beam of the current scene based on the received signal of the target user and a pre-set second target algorithm, which can improve the efficiency of generating the target beam of the current scene, and can improve the accuracy of generating the target beam of the current scene, which can help improve the efficiency of beamforming, and help improve the accuracy of beamforming.

[0179] In yet another optional embodiment, Figure 4 As shown, the target data group includes an analog precoder matrix and a digital precoder matrix;

[0180] The communication data of the current scene includes one or more of data on the number of users in the current scene, data on the number of data streams required by each user in the current scene, data on the number of antenna segments of the base station in the current scene, data on the number of receiving antenna segments for each user in the current scene, data on the number of RF chains of the base station in the current scene, and data on the number of RF chains for each user in the current scene.

[0181] It can be seen that implementation Figure 4 The described device can jointly determine the receiving signal of the target user based on the target data group and the multiple types of communication data included in the current scene, so as to generate the target beam of the current scene according to the target data group and the multiple types of communication data included in the current scene, and can improve the accuracy of determining the receiving signal of the target user, which is beneficial to improve the accuracy of generating the target beam of the current scene, and further beneficial to improve the accuracy of beamforming.

[0182] In yet another optional embodiment, Figure 4 As shown, the generation module 304 determines the receiving signal of the target user according to all target data groups and communication data, specifically including:

[0183] Determine the receiving signal of the target user through a pre-set signal algorithm according to all target data groups and communication data;

[0184] Among them, the pre-set signal algorithm includes:

[0185]

[0186] Among them, y k is the received signal of the target user, H k is the complex channel matrix from the base station to the target user, F is the analog precoder matrix, B k is the digital precoder matrix of the target user, S k is the traffic of the target user, B l is the digital precoder matrix of the neighboring users of the target user, S l is the traffic of the neighboring users of the target user, n k is the signal noise of the target user.

[0187] It can be seen that implementation Figure 4 The described device can determine the received signal of the target user according to the target data group and the communication data through a preset signal algorithm, and can improve the accuracy and reliability of determining the received signal of the target user, thereby improving the accuracy and reliability of generating the target beam of the current scene, which is beneficial to improving the accuracy and reliability of beamforming.

[0188] Embodiment 4

[0189] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another beamforming device based on massive MIMO disclosed in an embodiment of the present invention. Figure 5 As shown, the beamforming device based on massive MIMO may include:

[0190] A memory 401 storing executable program codes;

[0191] a processor 402 coupled to the memory 401;

[0192] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the beamforming method based on massive MIMO described in the first embodiment of the present invention or the second embodiment of the present invention.

[0193] Embodiment 5

[0194] An embodiment of the present invention discloses a computer storable medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the beamforming method based on large-scale MIMO described in Embodiment 1 or Embodiment 2 of the present invention.

[0195] Embodiment 6

[0196] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the beamforming method based on large-scale MIMO described in Embodiment 1 or Embodiment 2.

[0197] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0198] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, a magnetic disk storage, a magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0199] Finally, it should be noted that the beamforming method and device based on massive MIMO disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A beamforming method based on massive MIMO, characterized in that: The method comprises: Obtaining matrix data of a target matrix for beamforming, and obtaining communication data of the current scene; According to the matrix data, the target matrix is ​​disassembled to obtain matrix sub-data; Determine the matrix calculation result of the target matrix by using a preset first target algorithm and all the matrix sub-data; According to the target matrix calculation result and the communication data, based on a preset second target algorithm, a target beam of the current scene is generated, and beamforming is implemented based on the target beam; After determining the matrix calculation result of the target matrix by using the preset first target algorithm and all the matrix sub-data, and before generating the target beam of the current scene based on the preset second target algorithm according to the target matrix calculation result and the communication data, the method further includes: According to a preset target quantity, a grouping operation is performed on all target data included in the target matrix calculation result to obtain at least one target data group, wherein the quantity of the target data included in each target data group is equal; Performing precision conversion operations on all the target data included in all the target data groups to obtain target precision data that meets preset precision conditions, and updating each target data group according to all the target precision data; The step of generating the target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm includes: Generate a target beam for the current scene based on all the target data groups and the communication data and based on a preset second target algorithm; And, generating the target beam of the current scene according to all the target data groups and the communication data based on a preset second target algorithm, comprises: Determine a received signal of a target user according to all the target data groups and the communication data, wherein the target user is a user corresponding to a terminal receiving a target beam in the current scene; Generate a target beam for the current scene according to a received signal of the target user and a preset second target algorithm; The step of generating the target beam of the current scene according to the received signal of the target user and a preset second target algorithm includes: The received signal of the target user is input into a preset target model to obtain a target output result, and a target beam of the current scene is generated according to the target output result.

2. The beamforming method based on massive MIMO according to claim 1, characterized in that: After performing a grouping operation on all target data included in the target matrix calculation result according to a preset target number to obtain at least one target data group, and before performing a precision conversion operation on all the target data included in all the target data groups to obtain target precision data that meets a preset precision condition, the method further includes: For each of the target data groups, performing a data alignment operation on all the target data included in the target data group to obtain a first target data group after data alignment; Each of the target data sets is updated based on all of the first target data sets.

3. The beamforming method based on massive MIMO according to claim 2, characterized in that: The first target algorithm is preset as the Strassen algorithm; The step of determining the matrix calculation result of the target matrix by using the preset first target algorithm and all the matrix sub-data includes: Determine a matrix sub-calculation result by using the Strassen algorithm and all the matrix sub-data, wherein the number of the matrix sub-calculation results is multiple; The matrix calculation result of the target matrix is ​​determined according to all the matrix sub-calculation results.

4. The beamforming method based on massive MIMO according to claim 3, characterized in that: The target data group includes an analog precoder matrix and a digital precoder matrix; The communication data of the current scene includes one or more of data on the number of users of the current scene, data on the number of data streams required for each user of the current scene, data on the number of antenna segments of the base station of the current scene, data on the number of receiving antenna segments for each user of the current scene, data on the number of RF chains of the base station of the current scene, and data on the number of RF chains for each user of the current scene.

5. The beamforming method based on massive MIMO according to claim 4, characterized in that: Determining the received signal of the target user according to all the target data groups and the communication data includes: Determine the receiving signal of the target user by a preset signal algorithm according to all the target data groups and the communication data; Among them, the pre-set signal algorithm includes: Among them, y k is the received signal of the target user, H k is the complex channel matrix from the base station to the target user, F is the analog precoder matrix, B k is the digital precoder matrix of the target user, S k is the traffic of the target user, B l is the digital precoder matrix of the neighboring users of the target user, S l is the traffic of the neighboring users of the target user, n k is the signal noise of the target user, k is used to represent the number of the target user, and K is used to represent the total number of all the target users.

6. A beamforming device based on massive MIMO, characterized in that: The device comprises: An acquisition module, used to acquire matrix data of a target matrix for beamforming and to acquire communication data of a current scene; A disassembly module, used for disassembling the target matrix according to the matrix data to obtain matrix sub-data; A determination module, used to determine the matrix calculation result of the target matrix by using a preset first target algorithm and all the matrix sub-data; A generating module, configured to generate a target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm, and implement beamforming based on the target beam; A grouping module, configured to perform a grouping operation on all target data included in the target matrix calculation result according to a preset target number, to obtain at least one target data group, wherein the number of target data included in each target data group is equal, after the determination module determines the matrix calculation result of the target matrix by using a preset first target algorithm and all the matrix sub-data, and before the generation module generates the target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm; A conversion module, used for performing a precision conversion operation on all the target data included in all the target data groups to obtain target precision data that meets a preset precision condition; An updating module, used for updating each of the target data groups according to all the target accuracy data; The generation module generates the target beam of the current scene according to the target matrix calculation result and the communication data based on a preset second target algorithm, specifically including: Generate a target beam for the current scene based on all the target data groups and the communication data and based on a preset second target algorithm; The generation module generates the target beam of the current scene according to all the target data groups and the communication data based on a preset second target algorithm, specifically including: Determine a received signal of a target user according to all the target data groups and the communication data, wherein the target user is a user corresponding to a terminal receiving a target beam in the current scene; Generate a target beam for the current scene according to a received signal of the target user and a preset second target algorithm; The specific manner in which the generating module generates the target beam of the current scene according to the received signal of the target user and a preset second target algorithm includes: The received signal of the target user is input into a preset target model to obtain a target output result, and a target beam of the current scene is generated according to the target output result.

7. A beamforming device based on massive MIMO, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the beamforming method based on massive MIMO as described in any one of claims 1-5.

8. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the beamforming method based on large-scale MIMO as described in any one of claims 1-5.

Images