Beam forming matrix determination method and device, equipment, storage medium and product

Abstract

The invention discloses a beam forming matrix determination method and device, equipment, a storage medium and a product, and relates to the technical field of data processing, and the beam forming matrix determination method comprises the steps: determining an optimization problem model under the reachable security and rate of a maximized legal base station; converting the optimization problem model through an auxiliary variable to obtain a block convex problem model; and iteratively solving the block convex problem model based on a preset alternating optimization algorithm to obtain a beam forming matrix. As the optimization problem model under the reachable safety and rate of the maximized legal base station is converted and then iteratively solved to obtain the optimal beam forming matrix, compared with an existing mode of determining the beam forming matrix through a continuous convex approximation algorithm, the mode of the application can avoid direct optimization of logarithmic determinants, and can improve the beam forming efficiency. And the calculation complexity is reduced.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a beamforming matrix determination method, apparatus, device, storage medium and product. Background Art

[0002] In the multiple-input-multiple-output (MIMO) communication and computing integrated network, the design of secure beamforming is carried out, that is, the design of the corresponding optimal communication beam, computing beam and artificial noise beam according to the current channel conditions of the network, so as to better utilize the limited wireless resources to simultaneously ensure the security of network information transmission and improve computing performance. The secure beamforming design under the criterion of achievable security and rate maximization involves the maximization of the difference of logarithmic determinants. The existing MIMO secure beamforming design algorithms, such as the continuous convex approximation algorithm, convexly approximate the non-convex terms in the original objective function through the first-order Taylor expansion, thereby converting the non-convex problem into a convex problem for solution. However, in the converted problem, the optimization variables are still contained in the logarithmic determinant, and the complexity of solving the optimized logarithmic determinant is extremely high, which brings great challenges to the determination of the beamforming matrix. Summary of the invention

[0003] The main purpose of the present application is to provide a beamforming matrix determination method, apparatus, device, storage medium and product, aiming to solve the technical problem of low efficiency in existing beamforming matrix determination.

[0004] To achieve the above object, the present application proposes a beamforming matrix determination method, the beamforming matrix determination method comprising:

[0005] Determine the optimization problem model for maximizing the reachability safety and rate of legal base stations;

[0006] The optimization problem model is transformed by auxiliary variables to obtain a block convex problem model;

[0007] The block convex problem model is iteratively solved based on a preset alternating optimization algorithm to obtain a beamforming matrix.

[0008] Optionally, the step of transforming the optimization problem model through auxiliary variables to obtain a block convex problem model includes:

[0009] Perform variable substitution on the optimization problem model based on auxiliary variables to obtain an equivalent problem model corresponding to the optimization problem model;

[0010] The equivalent problem model is transformed using the matrix-expanded Lagrange dual transformation to obtain a block convex problem model.

[0011] Optionally, the step of iteratively solving the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix includes:

[0012] Determine an initial variable, and determine a first auxiliary variable based on the initial variable;

[0013] Determine a second auxiliary variable according to the first auxiliary variable and a preset solution algorithm;

[0014] The block convex problem model is solved using the CVX toolkit and the second auxiliary variable to obtain a beamforming matrix.

[0015] Optionally, the step of using the CVX toolkit and the second auxiliary variable to solve the block convex problem model to obtain a beamforming matrix includes:

[0016] Solving the block convex problem model using the CVX toolkit and the second auxiliary variable to obtain a solution result;

[0017] Determine whether the solution result converges;

[0018] When the solution result converges, a beamforming matrix is ​​determined according to the solution result.

[0019] Optionally, the step of determining an optimization problem model for maximizing the achievable safety and rate of a legal base station includes:

[0020] Determine the achievable safety and rate at which legitimate base stations can decode the communication signals of all sensors;

[0021] Identify constraints;

[0022] An optimization problem model is determined based on the achievable safety and rate and the constraint conditions.

[0023] Optionally, the beamforming matrix includes: a communication beamforming matrix, a computational beamforming matrix and an artificial noise beamforming matrix.

[0024] In addition, to achieve the above-mentioned purpose, the present application also proposes a beamforming matrix determination device, wherein the beamforming matrix determination device comprises:

[0025] A determination module, used to determine an optimization problem model for maximizing the reachable safety and rate of a legal base station;

[0026] A transformation module, used to transform the optimization problem model through auxiliary variables to obtain a block convex problem model;

[0027] The solution module is used to iteratively solve the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix.

[0028] In addition, to achieve the above-mentioned purpose, the present application also proposes a beamforming matrix determination device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the beamforming matrix determination method as described above.

[0029] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the beamforming matrix determination method as described above are implemented.

[0030] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the beamforming matrix determination method as described above are implemented.

[0031] The present application determines an optimization problem model for maximizing the achievable safety and rate of legal base stations; transforms the optimization problem model through auxiliary variables to obtain a block convex problem model; iteratively solves the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix. Since the present application iteratively solves the optimization problem model for maximizing the achievable safety and rate of legal base stations after transformation to obtain the optimal beamforming matrix, compared to the existing method of determining the beamforming matrix through a continuous convex approximation algorithm, the above method of the present application can avoid directly optimizing the logarithmic determinant and reduce the computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0034] Figure 1 A schematic diagram of a flow chart of a method for determining a beamforming matrix according to a first embodiment of the present application;

[0035] Figure 2 A schematic diagram of a system model provided for Embodiment 1 of the method for determining a beamforming matrix of the present application;

[0036] Figure 3 A schematic diagram of a flow chart of Embodiment 2 of the method for determining a beamforming matrix of the present application;

[0037] Figure 4 A schematic diagram of an optimization solution provided for Embodiment 2 of the beamforming matrix determination method of the present application;

[0038] Figure 5 This is a schematic diagram of the module structure of the beamforming matrix determination device according to an embodiment of the present application;

[0039] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the beamforming matrix determination method in the embodiment of the present application.

[0040] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0042] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0043] The main solution of the embodiment of the present application is: determine the optimization problem model under maximizing the achievable safety and rate of legal base stations; transform the optimization problem model through auxiliary variables to obtain a block convex problem model; iteratively solve the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix. Since the present application is to iteratively solve the optimization problem model under maximizing the achievable safety and rate of legal base stations after transformation to obtain the optimal beamforming matrix, compared with the existing method of determining the beamforming matrix through a continuous convex approximation algorithm, the above method of the present application can avoid directly optimizing the logarithmic determinant and reduce the computational complexity.

[0044] Explanation of important professional terms involved in this application:

[0045] The communication and computing integrated network refers to a network that has ubiquitous intelligent communication and computing capabilities. Through the collaboration and sharing of synergistic computing software and hardware resources, each network element device in the network achieves deep integration and mutual enhancement of collaborative communication and intelligent computing functions, thereby enabling the network to have the ability of new information flow intelligent interaction and processing and wide-area intelligent collaboration.

[0046] Communication beamforming is the abbreviation for beamforming designed for communication signals.

[0047] Computational beamforming is the abbreviation for beamforming designed for computational signals.

[0048] Artificial noise beamforming is the abbreviation of beamforming designed for artificial noise signals.

[0049] Beamforming literally means shaping the shape of a beam. In the field of wireless communications, by using multiple antennas and controlling the signal transmitted by each antenna, the beam shape of the wireless signal can be changed. Beamforming technology is a technology that sends signals to wireless terminals in an energy-concentrated and directional manner, which can comprehensively improve the signal quality received by wireless terminals and increase throughput.

[0050] Matrix fraction: tr(X H Y -1 X), tr(·) is the matrix trace operator.

[0051] Block convex problem: When some variables are fixed, the optimization problem is convex with respect to other variables.

[0052] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a multi-input multi-output system, etc. The following takes a multi-input multi-output system as an example to illustrate this embodiment and the following embodiments.

[0053] Based on this, the embodiment of the present application provides a beamforming matrix determination method, referring to Figure 1 , Figure 1 FIG. 1 is a flow chart of a first embodiment of a method for determining a beamforming matrix according to the present application.

[0054] In this embodiment, the beamforming matrix determination method includes steps S10 to S30:

[0055] Step S10, determining an optimization problem model for maximizing the reachable safety and rate of the legal base station;

[0056] It should be noted that for a secure communication and computing integrated network, secure communication and computing have different performance indicators. The secure communication performance is characterized by achievable security and rate, while the computing performance is measured by calculating the mean square error. Taking into account the competitive relationship between the two functions of secure communication and high-precision computing in terms of communication resources, in order to take into account the performance requirements of both functions, this embodiment can jointly optimize the design of communication beams, computing beams, and artificial noise beamforming, thereby maximizing the achievable security and rate of legal base stations under the constraints of the transmitter's transmission power and the calculation mean square error of legal base stations. The problem is modeled as a mathematical optimization problem, that is, the optimization problem model that maximizes the achievable security and rate of legal base stations is obtained.

[0057] Specifically, in order to maximize the achievable safety and rate of the legitimate base station, the step S10 may include: determining the achievable safety and rate of the legitimate base station decoding the communication signals of all sensors;

[0058] Identify constraints;

[0059] An optimization problem model is determined based on the achievable safety and rate and the constraint conditions.

[0060] It should be noted that, please refer to Figure 2 , Figure 2 A schematic diagram of a system model provided for the first embodiment of the beamforming matrix determination method of the present application; Consider an uplink MIMO communication and computing integrated system, such as Figure 2 As shown in the figure, it contains K multi-antenna dual-function sensors, a multi-antenna legal communication and computing integrated base station and a multi-antenna potential eavesdropper. Each sensor is equipped with N s Root transmitting antennas, legal base stations are equipped with N b Root receiving antennas, the eavesdropper is equipped with N e The channel coefficients between the kth sensor and the legitimate base station and the eavesdropper are expressed as and

[0061] Each sensor carries N d Heterogeneous computing data and N c communication symbols, which will be sent to the legitimate base station (i.e., the above-mentioned multi-antenna legitimate communication and computing integrated base station) through the uplink in a superposition coding manner. Due to the broadcast characteristics of the wireless channel, the data sent by the sensor may be intercepted by the eavesdropper. In order to ensure the communication security between the sensor and the legitimate base station, the sensor will send artificial noise while sending its own signal to interfere with the eavesdropper's eavesdropping. Therefore, the superposition signal sent by the kth sensor is expressed by the following formula (1):

[0062]

[0063]

[0064] The signal beamforming matrix, the computational beamforming matrix and the artificial noise beamforming matrix. Nd The representation dimension is the unit matrix of Nd, and Ns represents the number of sensor transmitting antennas.

[0065] Since the transmission power of real communication networks is always limited, the sensor's transmit power must satisfy the power constraint corresponding to the following formula (2):

[0066]

[0067] Correspondingly, the received signals of the legitimate base station and the eavesdropper are expressed as formulas (3) and (4) respectively:

[0068]

[0069] in and Represent the additive Gaussian white noise at the legitimate base station and the eavesdropper respectively. For simplicity, the following block matrix is ​​defined:

[0070] H=[H1,…,H K ],W=[W1,…,W K ],F=[F1,…,F K ],

[0071] V=[V1,…,V K ]

[0072] Among them, H k Characterizes the channel from the kth sensor to the base station, σ 2 Used to characterize noise power.

[0073] According to Shannon's formula, we can get the decoded signal s of the legitimate base station and the eavesdropper k The achievable rates are given by formulas (5) and (6) respectively.

[0074]

[0075] in,

[0076]

[0077] I N Represents the N×N identity matrix. According to the Wyner eavesdropping channel model, the achievable security and rate of the legitimate base station decoding the communication signals of all sensors can be expressed as:

[0078]

[0079] Where [a] + =max{a,0}.

[0080] Without loss of generality, consider that the legal base station objective function is the arithmetic mean of the calculated data, that is

[0081]

[0082] To improve the computational performance, the legitimate base station first uses the data aggregation beamforming matrix The received signal is processed, and the calculation performance and the complexity of the receiver design are comprehensively weighed. In this embodiment, an MMSE receiver is used to process the calculation data. The corresponding data aggregation beamforming matrix and the calculation result are given by the following formulas (9) and (10):

[0083]

[0084] Here, i is used to represent the index of the sum traversal.

[0085] This embodiment uses the mean square error between the calculation result and the objective function to measure the calculation performance. The mean square error is a function of the beamforming matrix W, F, V and U, which is given by formula (11):

[0086]

[0087] In order to simultaneously ensure the communication performance and computing performance of the communication and computing integrated network, this embodiment proposes to jointly optimize the design of the communication beam, computing beam and artificial noise beamforming matrix under the transmission power constraint of each sensor and the computational mean square error constraint of the legitimate base station, so as to maximize the achievable safety and rate of the legitimate base station. The corresponding optimization problem is modeled as follows: Formula (12), that is, the optimization problem model under the maximization of the achievable safety and rate of the legitimate base station:

[0088]

[0089] stMSE(W,F,V,U ★ )≤ξ,(12b)

[0090]

[0091] In the above problem, ξ is the maximum tolerable mean square error of the legal base station, constraint (12b) is the mean square error constraint of the legal base station, P k is the maximum transmit power of the kth sensor, and constraint (12c) represents the transmit power constraint of each sensor. In problem (12), the objective function contains the difference of logarithmic determinants, and the logarithmic determinant also has a matrix inversion operation embedded inside, which makes the problem highly non-convex.

[0092] Step S20, transforming the optimization problem model through auxiliary variables to obtain a block convex problem model;

[0093] It should be noted that the transformation of the optimization problem model through auxiliary variables to obtain the block convex problem model may be to introduce auxiliary variables to transform the original problem into an equivalent problem that is easier to handle. Specifically, the step S20 may include: performing variable substitution on the optimization problem model based on the auxiliary variables to obtain an equivalent problem model corresponding to the optimization problem model;

[0094] The equivalent problem model is transformed using the matrix-expanded Lagrange dual transformation to obtain a block convex problem model.

[0095] It should be noted that the equivalent problem model corresponding to the optimization problem model obtained by performing variable substitution on the optimization problem model based on the auxiliary variables can be: the auxiliary variable Ω first introduced k , Φ k ,Ψ k Perform variable substitution and rewrite the objective function (12a) and constraint (12b). Perform variable substitution defined by the following formula (13):

[0096]

[0097] Define the block matrix:

[0098] Ω=[Ω1,…,Ω K ],Φ=[Φ1,…,Φ K ],Ψ=[Ψ1,…,Ψ K ]

[0099] The objective function (12a) can be rewritten as the following formula (14):

[0100]

[0101] Among them, formulas (14b) and (14c) are equivalent forms of formulas (5) and (6), respectively, and (14a) is an equivalent form of (7).

[0102] Substituting formula (9) into formula (10) and combining the variable substitution defined in formula (13), constraint (12b) becomes:

[0103]

[0104] In this way, problem (12) is equivalently transformed into the following equivalent problem model:

[0105]

[0106] stMSE(F,Ω,Φ,Ψ)≤ξ,(15b)

[0107]

[0108] Among them, (15a) corresponds to the objective function (12a), (15b) corresponds to the constraint (12b), (15c) corresponds to the constraint (12c), and (15d) and (15e) are equivalent forms of the first two equations in (13). Problems (15) and (12) are completely equivalent, so we only need to solve problem (15).

[0109] It should be noted that the Lagrange dual transformation using matrix expansion is used to transform the equivalent problem model to obtain a block convex problem model, which can be: using the Lagrange dual transformation using matrix expansion to transform the optimization variables to the outside of the logarithmic determinant to obtain an equivalent matrix fraction optimization problem, and then using a secondary transformation to transform the matrix fraction optimization problem obtained in one step into a block convex problem model. Specifically, the Lagrange dual transformation using matrix expansion is used to transform the optimization variables to the outside of the logarithmic determinant to obtain an equivalent matrix fraction optimization problem, which includes:

[0110] The objective function (15a) is equivalently transformed using the matrix-expanded Lagrange dual transformation, where the equivalent transformations of (14b) and (14c) are given by formulas (16a) and (16b) respectively:

[0111]

[0112] where Y k and is the auxiliary variable introduced in the equivalent transformation process. For simplicity, the block matrix Y is defined as [Y1,…,Y K ], Y that makes (14b) and (14c) equivalent to (16a) and (16b) k and They are given by formulas (17) and (18) respectively:

[0113]

[0114] Correspondingly, the objective function (15a) is equivalently transformed into

[0115]

[0116] It can be seen that after the Lagrange dual transformation of the matrix expansion, the function and The optimization variables in (the optimization variables refer to the auxiliary variables W, Ω, Φ, Ψ) have been transformed outside the logarithmic determinant and only appear in the matrix fraction (Formulas (16c) and (16d) are called matrix fractions), which simplifies the solution of the problem. In this way, after the proposed matrix extended Langrange dual transformation, problem (15) will become its equivalent problem (20), that is, the matrix fraction optimization problem (20):

[0117]

[0118] st(15b),(15c),(15d),(15e).

[0119] Furthermore, it is necessary to use a quadratic transformation to transform the resulting matrix fraction optimization problem into a block convex problem. Specifically, in problem (20), the objective function involves maximizing the difference of matrix fractions, and the constraint (15b) contains a negative matrix fraction on the left side of the inequality sign. Since the matrix fraction function tr(X H Y -1 X) is a convex function, so the optimization problem (20) is still not convex. Next, we will use the quadratic transformation to process it. Specifically, function (16c) can be equivalently converted to:

[0120]

[0121] M k is an auxiliary variable introduced by equivalent transformation, M=[M1,…,M K ]. To make (21) and (16c) equivalent, the auxiliary variable M k The value of is:

[0122]

[0123] Similarly, constraint (15b) can be equivalently transformed into:

[0124]

[0125] The auxiliary variable value that makes (23) and (15b) equivalent is:

[0126]

[0127] Finally, we need to use the first-order Taylor expansion to make a convex approximation to the difference convex constraint (15e). Define the difference convex function g(x,z)=tr(zx H x), which is at point X t The first-order Taylor expansion at is:

[0128]

[0129] real(·) represents the real part operation. Finally, the exact penalty function method is used to transform the problem (20) into a block convex problem (25):

[0130]

[0131] (15c),(15d).

[0132] Where α is a preset penalty factor.

[0133]

[0134] Step S30, iteratively solving the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix.

[0135] It should be noted that when the variable When fixed, problem (25) becomes about Y and The optimal solution of the convex problem is given by closed-form expressions (17) and (18); similarly, when When fixed, its relationship with the variables M and is also convex, and the optimal solution is given by expressions (22) and (24); when When fixed, the problem becomes a convex problem with respect to the variables (W, F, Ω, Φ, Ψ), which can be solved using the optimization toolkit CVX. The beamforming matrix is ​​obtained.

[0136] This embodiment proposes a method for determining a beamforming matrix. The method first introduces appropriate auxiliary variables to transform problem (12) into a problem that is easier to handle. Then, the transformed easier problem is equivalently transformed into a matrix fraction optimization problem by using the Lagrange dual transformation of matrix expansion. Then, the matrix fraction optimization problem is transformed into a block convex problem by using a quadratic transformation. Finally, the block convex problem is iteratively solved by an alternating optimization algorithm to obtain the corresponding beamforming matrix.

[0137] This embodiment determines an optimization problem model for maximizing the achievable safety and rate of legal base stations; transforms the optimization problem model through auxiliary variables to obtain a block convex problem model; iteratively solves the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix. Since this embodiment is to iteratively solve the optimization problem model for maximizing the achievable safety and rate of legal base stations after transformation to obtain the optimal beamforming matrix, compared to the existing method of determining the beamforming matrix through a continuous convex approximation algorithm, the above method of this embodiment can avoid directly optimizing the logarithmic determinant and reduce the computational complexity.

[0138] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , Figure 3 The flowchart of Embodiment 2 of the method for determining a beamforming matrix of the present application is provided, wherein the step S30 further includes the following steps:

[0139] Step S2301: determining an initial variable, and determining a first auxiliary variable based on the initial variable;

[0140] It should be noted that the initial variables may be given initial variables W0, F0, V0, and the first auxiliary variable determined based on the initial variables may be calculated according to formula (13): 0 and Φ 0 .

[0141] Step S302: determining a second auxiliary variable according to the first auxiliary variable and a preset solution algorithm;

[0142] It should be noted that the second auxiliary variable can be determined according to the first auxiliary variable and the preset solution algorithm by updating the formula (17) and the formula (18). and Y. Update according to formulas (22) and (24) and M.

[0143] Step S303: using the CVX toolkit and the second auxiliary variable to solve the block convex problem model and obtain a beamforming matrix.

[0144] It should be noted that the use of the CVX toolkit and the second auxiliary variable to solve the block convex problem model to obtain the beamforming matrix can be to use the CVX toolkit to solve the problem (25 to obtain its optimal solution (W, F, Ω, Φ, Ψ).

[0145] Further, the step S303 may include: solving the block convex problem model using the CVX toolkit and the second auxiliary variable to obtain a solution result;

[0146] Determine whether the solution result converges;

[0147] When the solution result converges, a beamforming matrix is ​​determined according to the solution result.

[0148] In the specific implementation, please refer to Figure 4 , Figure 4 The schematic diagram of the optimization solution provided in the second embodiment of the beamforming matrix determination method of the present application is as follows: after solving the block convex problem model using the CVX toolkit and the second auxiliary variable to obtain the solution result, it is determined whether the solution result converges. If not, it returns to step 1 and updates according to formula (17) and formula (18). and Y. If the solution result converges, a beamforming matrix is ​​determined according to the solution result. The beamforming matrix includes a communication beamforming matrix, a computational beamforming matrix and an artificial noise beamforming matrix.

[0149] This embodiment determines the initial variables, and determines the first auxiliary variables based on the initial variables; determines the second auxiliary variables according to the first auxiliary variables and a preset solution algorithm; uses the CVX toolkit and the second auxiliary variables to solve the block convex problem model to obtain a beamforming matrix. In order to avoid directly optimizing the logarithmic determinant and reduce the computational complexity, this embodiment proposes a matrix-expanded Lagrange dual transformation algorithm, which can transform the optimized variables to the outside of the logarithmic determinant through an equivalent transformation, thereby obtaining a block convex problem equivalent to the original problem. Compared with existing algorithms, the algorithm proposed in this embodiment has lower computational complexity and greater practical application value.

[0150] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the beamforming matrix determination method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0151] The present application also provides a beamforming matrix determination device, please refer to Figure 5 , the beamforming matrix determining device comprises:

[0152] A determination module 10 is used to determine an optimization problem model that maximizes the achievable safety and rate of a legal base station;

[0153] A transformation module 20, used to transform the optimization problem model through auxiliary variables to obtain a block convex problem model;

[0154] The solving module 30 is used to iteratively solve the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix.

[0155] This embodiment determines an optimization problem model for maximizing the achievable safety and rate of legal base stations; transforms the optimization problem model through auxiliary variables to obtain a block convex problem model; iteratively solves the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix. Since this embodiment is to iteratively solve the optimization problem model for maximizing the achievable safety and rate of legal base stations after transformation to obtain the optimal beamforming matrix, compared to the existing method of determining the beamforming matrix through a continuous convex approximation algorithm, the above method of this embodiment can avoid directly optimizing the logarithmic determinant and reduce the computational complexity.

[0156] The beamforming matrix determination device provided by the present application adopts the beamforming matrix determination method in the above-mentioned embodiment, which can solve the technical problem of low efficiency of existing beamforming matrix determination. Compared with the prior art, the beneficial effects of the beamforming matrix determination device provided by the present application are the same as the beneficial effects of the beamforming matrix determination method provided by the above-mentioned embodiment, and other technical features in the beamforming matrix determination device are the same as the features disclosed in the above-mentioned embodiment method, which will not be described in detail here.

[0157] The present application provides a beamforming matrix determination device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the beamforming matrix determination method in the above-mentioned embodiment 1.

[0158] Reference below Figure 6 , which shows a schematic diagram of the structure of a beamforming matrix determination device suitable for implementing an embodiment of the present application. The beamforming matrix determination device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The beamforming matrix determination device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0159] like Figure 6As shown, the beamforming matrix determination device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Onl y Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the beamforming matrix determination device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 may allow the beamforming matrix determination device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a beamforming matrix determination device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0160] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0161] The beamforming matrix determination device provided by the present application adopts the beamforming matrix determination method in the above embodiment, which can solve the technical problem of low efficiency of existing beamforming matrix determination. Compared with the prior art, the beneficial effects of the beamforming matrix determination device provided by the present application are the same as the beneficial effects of the beamforming matrix determination method provided by the above embodiment, and other technical features in the beamforming matrix determination device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0162] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0163] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0164] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the beamforming matrix determination method in the above-mentioned embodiment.

[0165] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0166] The computer-readable storage medium may be included in the beamforming matrix determination device; or may exist independently without being assembled into the beamforming matrix determination device.

[0167] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the beamforming matrix determination device, the beamforming matrix determination device performs the beamforming matrix determination method.

[0168] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0169] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0170] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0171] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned beamforming matrix determination method, and can solve the technical problem of low efficiency in the existing beamforming matrix determination. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the beamforming matrix determination method provided in the above-mentioned embodiment, and will not be repeated here.

[0172] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned beamforming matrix determination method when executed by a processor.

[0173] The computer program product provided by the present application can solve the technical problem of low efficiency in determining the existing beamforming matrix. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the beamforming matrix determination method provided by the above embodiment, which will not be described in detail here.

[0174] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A beamforming matrix determination method, characterized in that: The beamforming matrix determination method comprises the following steps: Determine the optimization problem model for maximizing the reachability safety and rate of legal base stations; The optimization problem model is transformed by auxiliary variables to obtain a block convex problem model; The block convex problem model is iteratively solved based on a preset alternating optimization algorithm to obtain a beamforming matrix.

2. The beamforming matrix determination method according to claim 1, characterized in that: The step of transforming the optimization problem model through auxiliary variables to obtain a block convex problem model includes: Perform variable substitution on the optimization problem model based on auxiliary variables to obtain an equivalent problem model corresponding to the optimization problem model; The equivalent problem model is transformed using the matrix-expanded Lagrange dual transformation to obtain a block convex problem model.

3. The beamforming matrix determination method according to claim 1, characterized in that: The step of iteratively solving the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix includes: Determine an initial variable, and determine a first auxiliary variable based on the initial variable; Determine a second auxiliary variable according to the first auxiliary variable and a preset solution algorithm; The block convex problem model is solved using the CVX toolkit and the second auxiliary variable to obtain a beamforming matrix.

4. The beamforming matrix determination method according to claim 3, characterized in that: The step of using the CVX toolkit and the second auxiliary variable to solve the block convex problem model to obtain a beamforming matrix includes: Solving the block convex problem model using the CVX toolkit and the second auxiliary variable to obtain a solution result; Determine whether the solution result converges; When the solution result converges, a beamforming matrix is ​​determined according to the solution result.

5. The beamforming matrix determination method according to any one of claims 1 to 4, characterized in that: The step of determining the optimization problem model under maximizing the achievable safety and rate of the legal base station includes: Determine the achievable safety and rate at which legitimate base stations can decode the communication signals of all sensors; Identify constraints; An optimization problem model is determined based on the achievable safety and rate and the constraint conditions.

6. The beamforming matrix determination method according to any one of claims 1 to 4, characterized in that: The beamforming matrix includes: a communication beamforming matrix, a computational beamforming matrix and an artificial noise beamforming matrix.

7. A beamforming matrix determination device, characterized in that: The beamforming matrix determining device comprises: A determination module, used to determine an optimization problem model for maximizing the reachable safety and rate of a legal base station; A transformation module, used to transform the optimization problem model through auxiliary variables to obtain a block convex problem model; The solution module is used to iteratively solve the block convex problem model based on a preset alternating optimization algorithm to obtain a beamforming matrix.

8. A beamforming matrix determination device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the beamforming matrix determination method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the beamforming matrix determination method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the beamforming matrix determination method according to any one of claims 1 to 6 are implemented.

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