Antenna Beamforming Method and Energy-Harvesting Communication and Sensing Integrated System

Abstract

The embodiment of the present application provides an antenna beamforming method and an integrated energy communication perception system, which relates to the field of communication technology. The total transmission power of the system, the received signal interference-noise ratio of each communication receiving terminal, and the collection power of the energy receiving terminal are obtained through the initial value of the beamforming vector, and then a first constraint condition set is constructed according to the total transmission power, the received signal interference-noise ratio, and the collection power. Then, the initial value of the beamforming vector is reduced in rank based on the first constraint condition set to obtain a linear equation group containing the initial value of the beamforming vector, and the optimized value of the beamforming vector is obtained according to the linear equation group. The convergence of the beamforming matrix optimization process is improved by reducing the rank, thereby improving the optimization performance of the beamforming matrix. At the same time, there is no need to use manual experience to adjust the parameters, thereby improving the optimization efficiency of the beamforming matrix, and being able to improve the communication perception and energy transmission performance of the integrated energy communication perception system including multiple energy receiving terminals and multiple communication receiving terminals.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an antenna beamforming method and an integrated energy-carrying communication and perception system. Background Art

[0002] Integrated energy-carrying communication and perception refers to the integration of perception, communication, and energy transmission. During the energy-carrying communication and perception process, energy and information are simultaneously transmitted from one or more transmitters to one or more receivers, enabling radar targets to be perceived. Receivers include information receivers and energy receivers. Receivers can be deployed in different locations or in the same location. Receivers in the same location can simultaneously collect energy and receive information. For separate receivers, the information receiver and energy receiver are separate devices, with the former receiving information and the latter collecting energy.

[0003] The communication, perception, and energy transfer performance of an integrated energy-carrying, communication, and perception system is limited by the parameter design of the beamformer. Related technologies utilize the alternating direction multiplier algorithm to solve beamforming problems that meet different beam shape requirements, such as wide mainlobe beamforming. However, this approach lacks guaranteed algorithm convergence and requires manual parameter adjustments during the algorithm tuning process, which impacts the optimization performance and efficiency of the beamforming matrix. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose an antenna beamforming method and an integrated energy-carrying communication and perception system to improve the optimization performance and optimization efficiency of the beamforming matrix.

[0005] To achieve the above-mentioned objectives, the first aspect of the embodiments of the present application proposes an antenna beamforming method, which is applied to an integrated energy-carrying communication and perception system. The integrated energy-carrying communication and perception system includes a beamformer and multiple transmitting antennas. The integrated energy-carrying communication and perception system uses the transmitting antennas to perceive the perception target, sends communication information to the communication receiving end, and supplies energy to the energy receiving end. The integrated energy-carrying communication and perception system includes a communication channel corresponding to the communication receiving end and an energy channel corresponding to the energy receiving end. The method includes:

[0006] Obtain the total transmit power of the system; the total transmit power is calculated based on the initial value w of the beamforming vector of the beamformer at each communication receiving end. k The calculated initial value of the beamforming vector is a component value of the beamforming matrix of the beamformer in the direction of each communication receiving end;

[0007] Obtaining an initial value of a communication channel matrix of each of the communication channels, and calculating a received signal interference-to-noise ratio of each of the communication receiving ends based on the initial value of the communication channel matrix and the initial value of the beamforming vector;

[0008] Obtaining an initial value of the channel gain of each of the energy channels, and calculating the collection power of the energy receiving end according to the energy collection coefficient, the initial value of the energy channel matrix, and the initial value of the beamforming vector;

[0009] A first constraint condition set is constructed according to the total transmit power, the received signal interference-to-noise ratio, and the acquisition power, wherein a first optimization objective of the first constraint condition set is to minimize a Cramer-Rao bound parameter of a target azimuth angle;

[0010] The initial value of the beamforming vector is rank-reduced based on the first constraint condition set to obtain a linear equation system including the initial value of the beamforming vector, and an optimized value of the beamforming vector is obtained according to the linear equation system.

[0011] In one embodiment, the rank reduction of the initial value of the beamforming vector based on the first set of constraints to obtain a system of linear equations containing the initial value of the beamforming vector includes:

[0012] transforming the first constraint condition set into a second constraint condition set according to the Schur complement condition;

[0013] Using the semi-definite relaxation principle, the second set of constraints is relaxed to obtain a third set of constraints;

[0014] Obtaining the dual variable of the third constraint set to obtain a complementary condition set of the third constraint condition set;

[0015] The initial value of the beamforming vector is decomposed, and the linear equation group is obtained according to the complementary condition set.

[0016] In one embodiment, the integrated energy-carrying, communication, and sensing system includes multiple receiving antennas; and constructing a first constraint set based on the total transmit power, the received signal interference-to-noise ratio, and the collection power includes:

[0017] Acquire a transmitting antenna steering vector of the transmitting antenna at the target azimuth angle, and a receiving antenna steering vector of the receiving antenna at the target azimuth angle;

[0018] Calculating the Cramer-Rao bound parameter of the target azimuth angle according to the transmitting antenna steering vector, the receiving antenna steering vector, and the initial value of the beamforming vector;

[0019] Establishing a first transmit power constraint condition based on the preset transmit power and the total transmit power;

[0020] Establishing a first signal-to-interference-plus-noise ratio constraint condition based on a signal-to-interference-plus-noise ratio threshold and the received signal-to-interference-plus-noise ratio;

[0021] A first collection power constraint condition is constructed based on a preset collection power and the collection power.

[0022] In one embodiment, converting the first set of constraints into the second set of constraints according to the Schur complement condition includes:

[0023] According to the Schur complement condition, the first optimization objective is changed to a second optimization objective, where the second optimization objective includes: all elements in a first optimization matrix are not less than zero, the first optimization matrix is generated according to the transmit antenna steering vector, the receive antenna steering vector, and the initial value of the beamforming vector;

[0024] The second set of constraints is obtained according to the second optimization objective and the first set of constraints.

[0025] In one embodiment, the method of relaxing the second set of constraints using the semi-definite relaxation principle to obtain the third set of constraints includes:

[0026] The second constraint condition set is updated using the communication channel matrix parameter, the beamforming vector parameter, and the channel gain parameter to obtain a second constraint condition on transmit power, a second constraint condition on signal-to-interference-and-noise ratio, and a second constraint condition on acquisition power;

[0027] Updating the first optimization matrix using communication channel matrix parameters, beamforming vector parameters, and channel gain parameters to obtain a second optimization matrix;

[0028] Generating a third optimization objective according to the second optimization matrix, the third optimization objective including: the rank of the beamforming vector parameter is a preset value, and all elements in the second optimization matrix are not less than zero;

[0029] The third constraint condition set is generated according to the third optimization objective, the second transmit power constraint condition, the second signal to interference and noise ratio constraint condition, and the second collection power constraint condition.

[0030] In one embodiment, the complementary condition set includes: a first expression, a second expression, a third expression, a fourth expression, and a fifth expression; and obtaining the dual variable of the third constraint set to obtain the complementary condition set of the third constraint condition set includes:

[0031] Acquire a first number of the communication receiving terminals and a second number of the energy receiving terminals, and generate a first dual variable, a second dual variable, and a third dual variable according to the first number and the second number;

[0032] Obtain a first expression according to the first dual variable and the second signal to interference noise ratio constraint;

[0033] Obtain a second expression according to the second dual variable and the second constraint condition of the collection power;

[0034] Obtain a third expression according to the first dual variable and the second constraint condition of the transmit power;

[0035] generating a fourth expression according to the beamforming vector parameter and the third dual variable;

[0036] A fifth expression is generated according to the second optimization matrix and the third dual variable.

[0037] In one embodiment, decomposing the initial value of the beamforming vector and obtaining the linear equation system according to the complementary condition set includes:

[0038] Obtain the rank vector of the beamforming vector parameter, generate a Hermitian matrix of the rank vector, and decompose the beamforming vector parameter according to the rank vector to obtain a third optimization matrix V k ;

[0039] constructing a first linear equation based on the third optimization matrix, the Hermitian matrix, and the first expression;

[0040] constructing a second linear equation based on the third optimization matrix, the Hermitian matrix, and the second expression;

[0041] constructing a third system of linear equations based on the third optimization matrix, the Hermitian matrix, the third expression, the fourth expression, and the fifth expression;

[0042] The linear equation group is obtained based on the first linear equation, the second linear equation and the third linear equation group.

[0043] In one embodiment, obtaining the optimized value of the beamforming vector according to the linear equation system includes:

[0044] Calculating the eigenvalues of the Hermitian matrix, and calculating the maximum eigenvalue based on the eigenvalues;

[0045] reducing the rank of the beamforming matrix of the beamformer based on the maximum eigenvalue, the third optimization matrix, and the Hermitian matrix to obtain a reduced rank matrix;

[0046] Solving the reduced rank matrix to obtain the optimized value of the beamforming vector.

[0047] To achieve the above-mentioned objectives, the second aspect of the embodiments of the present application proposes an antenna beamforming device, which is applied to an integrated energy-carrying communication and perception system. The integrated energy-carrying communication and perception system includes a beamformer and multiple transmitting antennas. The integrated energy-carrying communication and perception system uses the transmitting antennas to sense the perception target, send communication information to the communication receiving end, and supply energy to the energy receiving end; the integrated energy-carrying communication and perception system includes a communication channel corresponding to the communication receiving end and an energy channel corresponding to the energy receiving end; the device includes:

[0048] a total transmit power acquisition module, configured to acquire the total transmit power of the system; the total transmit power is calculated based on an initial value of a beamforming vector of the beamformer at each of the communication receiving ends, where the initial value of the beamforming vector is a component value of a beamforming matrix of the beamformer in the direction of each of the communication receiving ends;

[0049] a signal-to-interference-and-noise ratio acquisition module, configured to acquire an initial value of a communication channel matrix of each communication channel, and calculate a received signal-to-interference-and-noise ratio of each communication receiving end based on the initial value of the communication channel matrix and the initial value of the beamforming vector;

[0050] an energy collection module, configured to obtain an initial value of a channel gain of each of the energy channels, and calculate the collection power of the energy receiving end based on an energy collection coefficient, an initial value of the energy channel matrix, and an initial value of the beamforming vector;

[0051] A first constraint condition construction module is configured to construct a first constraint condition set according to the total transmit power, the received signal interference-to-noise ratio, and the acquisition power, wherein a first optimization objective of the first constraint condition set is to minimize a Cramer-Rao bound parameter of a target azimuth angle;

[0052] A rank reduction optimization module is used to reduce the rank of the initial value of the beamforming vector based on the first set of constraints to obtain a linear equation system including the initial value of the beamforming vector, and obtain an optimized value of the beamforming vector according to the linear equation system.

[0053] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an integrated energy-carrying communication and perception system, wherein the system includes a beamformer, and the optimized value of the beamforming vector of the beamformer is calculated according to the antenna beamforming method described in any one of the first aspects.

[0054] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0055] To achieve the above-mentioned purpose, the fifth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the method described in the first aspect is implemented.

[0056] The antenna beamforming method and the integrated energy communication and perception system proposed in the embodiment of the present application obtain the total transmission power of the system, the received signal interference-noise ratio of each communication receiving end, and the collection power of the energy receiving end through the initial value of the beamforming vector, and then construct a first constraint condition set based on the total transmission power, the received signal interference-noise ratio, and the collection power. Then, based on the first constraint condition set, the initial value of the beamforming vector is reduced in rank to obtain a linear equation group containing the initial value of the beamforming vector, and the optimized value of the beamforming vector is obtained according to the linear equation group. The embodiment of the present application improves the convergence of the beamforming matrix optimization process by reducing the rank, thereby improving the optimization performance of the beamforming matrix. At the same time, there is no need to use manual experience to adjust parameters, thereby improving the optimization efficiency of the beamforming matrix, and can improve the communication perception and energy transmission performance of the integrated energy communication and perception system including multiple energy receiving ends and multiple communication receiving ends. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the structure of the integrated energy-carrying communication and perception system provided in an embodiment of the present application.

[0058] Figure 2 This is a flow chart of the antenna beamforming method provided in an embodiment of the present application.

[0059] Figure 3 yes Figure 2 Flowchart of step S140 in .

[0060] Figure 4 yes Figure 2 Flowchart of step S150 in .

[0061] Figure 5 yes Figure 4 Flowchart of step S152 in .

[0062] Figure 6 yes Figure 4 Flowchart of step S153 in .

[0063] Figure 7 yes Figure 4 Flowchart of step S154 in .

[0064] Figure 8 This is a schematic diagram of the relationship between the antenna beamforming method provided by another embodiment of the present application and the method in the related art under the same simulation environment between the CRB and the preset transmission power of the access point AP.

[0065] Figure 9 This is a schematic diagram of the relationship between the antenna beamforming method provided in another embodiment of the present application and the method in the related art, under the same simulation environment, the CRB changes with the preset transmission power of the access point AP.

[0066] Figure 10 This is a schematic diagram of the relationship between the antenna beamforming method provided in another embodiment of the present application and the method in the related art, in the same simulation environment, in which the CRB changes with the number of transmitting antennas.

[0067] Figure 11 This is a schematic diagram of the relationship between the CRB and the signal-to-interference-and-noise ratio threshold in the same simulation environment for the antenna beamforming method provided by another embodiment of the present application and the method in the related art.

[0068] Figure 12 This is a structural block diagram of an antenna beamforming device provided in another embodiment of the present application.

[0069] Figure 13 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0071] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flowchart.

[0072] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0073] Energy-carrying communication and perception integration refers to the integration of communication perception and energy transmission. During the energy-carrying communication and perception process, energy and information are simultaneously transmitted from one or more transmitters to one or more receivers, allowing radar targets to be sensed. This improves radio resource efficiency and enables data collection from massive amounts of low-power devices, ultimately achieving the integration of wireless energy-carrying communication and perception. Receivers include information receivers and energy receivers, which can be deployed in different locations or in the same location. Receivers in the same location can simultaneously collect energy and receive information. For separate receivers, the information receiver and energy receiver are separate devices, with the former receiving information and the latter collecting energy.

[0074] The communication, perception, and energy transfer performance of an integrated energy-carrying, communication, and perception system is limited by the parameter design of the beamformer. Related technologies utilize the alternating direction multiplier algorithm to solve beamforming problems that meet different beam shape requirements, such as wide mainlobe beamforming. However, this approach lacks guaranteed algorithm convergence and requires manual parameter adjustments during the algorithm tuning process, which impacts the optimization performance and efficiency of the beamforming matrix.

[0075] Based on this, the embodiments of the present application provide an antenna beamforming method and an integrated energy-carrying communication and perception system, which improves the convergence of the beamforming matrix optimization process by reducing the rank, thereby improving the optimization performance of the beamforming matrix. At the same time, there is no need to use manual experience to adjust parameters, thereby improving the optimization efficiency of the beamforming matrix, and can improve the communication perception and energy transmission performance of the integrated energy-carrying communication and perception system including multiple energy receiving ends and multiple communication receiving ends.

[0076] The embodiments of the present application provide an antenna beamforming method and an integrated energy-carrying communication and perception system, which are specifically illustrated by the following embodiments. First, the antenna beamforming method in the embodiments of the present application is described.

[0077] First, the integrated energy-carrying, communication, and perception system in the embodiment of the present application is described.

[0078] Reference Figure 1 The integrated energy communication and sensing system 10 includes: an access point (AP), a sensing target (ST), K single-antenna communication receiving ends (IR) and M single-antenna energy receiving ends (ER).

[0079] Among them, the access point AP is equipped with N t Transmitting antenna (TA), used to send communication signals to the communication receiving end IR, send energy signals to the energy receiving end ER, and send perception signals to the perception target ST, and equipped with N r A receiving antenna (RA) is used to receive the echo signal reflected by the sensing target ST, thereby realizing the detection of the sensing target ST. In one embodiment, in order to ensure the communication quality, the number K of the communication receiving end IR is set to be less than the number N of the transmitting antennas. t The integrated energy communication and perception system forms a corresponding communication channel with the communication receiving end, and forms a corresponding energy channel with the energy receiving end.

[0080] In one embodiment, the integrated energy-carrying communication and perception system also includes a beamformer. The beamformer is a device that uses an antenna sensor array to implement beamforming and spatial filtering. It is a signal processing technology used for directional transmission or reception. It is implemented by combining elements in the antenna array. Beamforming is achieved by using the principle that signals at specific angles are subject to correlated interference, while other signals are subject to interference cancellation. Beamforming can be used at both the transmitting and receiving ends to achieve spatial selectivity. The embodiments of the present application use beamforming to improve the signal-to-noise ratio of the received signal, eliminate undesirable interference sources, and focus the transmitted signal to a specific location.

[0081] Reference Figure 1 In this embodiment, it is assumed that the access point AP is in T>N t The transmitted signal within a symbol interval is: C represents a set of real numbers, and the transmission signal is expressed as:

[0082] X=W D S C

[0083] W D =[w1,w2,...,w K ]∈C (Nt×K)

[0084] S C =[s1,s2,...,s K ]∈C (K×T)

[0085] Among them, W D represents the beamforming matrix of the beamformer, w k Represents the initial value of the beamforming vector of the kth communication receiving end IR, that is, the beamforming matrix W D The component value in the IR direction of the kth communication receiving end, S C represents the data flow matrix transmitted to K communication receiving terminals IR, s k represents the data stream transmitted to the kth communication receiving end IR. The data streams of each communication receiving end IR are independent of each other, that is, the data streams satisfy:

[0086]

[0087] Among them, E represents expectation, represents the conjugate transpose of the data flow matrix, I K Represents the identity matrix of dimension K.

[0088] Reference Figure 1 , for the system's transmitting antenna, its total transmitting power is expressed as:

[0089]

[0090] Among them, tr() represents the trace of the calculation matrix, Represents the conjugate transpose of the initial value of the beamforming vector. It can be seen that the total transmit power is based on the initial value w of the beamforming vector of the beamformer at each communication receiving end IR. k Calculated.

[0091] For the communication receiving end IR, a communication channel is formed between each communication receiving end IR and the access point AP, and the received signal matrix Y C Expressed as:

[0092] Y C =HX+N C

[0093] Y C =[y C1 ,y C2 ,...,y CK ]∈C (K×T)

[0094]

[0095] Among them, y Ck represents the received signal of the kth communication receiving end IR, H represents the communication channel matrix, h k N represents the initial value of the communication channel matrix of the communication channel corresponding to the kth communication receiving end IR, C The communication channel matrix can be calculated from the output signal of the access point AP and the input signal of the communication receiving end IR to represent the channel gain. It can be understood that the communication channel matrix of the access point AP can be obtained as a priori information.

[0096] In one embodiment, the noise matrix N C is additive white Gaussian noise, the noise matrix N C The contrast of each element in is expressed as

[0097] According to the above received signals, for each communication receiving end IR, its communication performance is measured by the ratio of the signal to the interference noise in its received signal, that is, the signal to interference plus noise ratio (SINR). The received signal to interference plus noise ratio γ of the communication receiving end is k Expressed as:

[0098]

[0099] It can be seen that the received signal interference-to-noise ratio γ at the communication receiving end is k According to the initial value of the communication channel matrix hk and the initial value of the beamforming vector w k Calculated.

[0100] In one embodiment, referring to Figure 1 , at the access point AP, the echo signal reflected by the target ST is sensed Expressed as:

[0101] Y R =GX+N R

[0102]

[0103] Among them, N R represents the additive white Gaussian noise matrix with variance , and G represents the target response matrix between the access point AP and the sensing target ST.

[0104] In one embodiment, assuming that the perceived target ST is a distant point target, the target response matrix G is set to satisfy the following conditions:

[0105] G=αA(θ)

[0106] A(θ)=b(θ)a(θ) H

[0107]

[0108]

[0109] Wherein, α represents the reflection coefficient, which can be set according to the actual scenario, θ represents the target azimuth angle of the sensing target ST relative to the access point AP, a(θ) represents the transmitting antenna steering vector, and b(θ) represents the receiving antenna steering vector.

[0110] For the energy transfer process, refer to Figure 1 , the signal y received by the mth energy receiving end m ∈C (1×T) Expressed as:

[0111] y m =c m X+n m

[0112]

[0113] Among them, c m represents the initial value of the channel gain of the energy channel between the access point AP and the mth energy receiving end ER, n m represents the noise matrix.

[0114] In the above embodiment, the noise matrix N C, noise matrix N R and the noise matrix n m All are AWGN vectors.

[0115] Since the power of noise is negligible compared to the power of the signal, the collection power E of the mth energy receiving end calculated based on the received signal is m It can be expressed as:

[0116]

[0117] Among them, β m ∈[0,1] represents the energy harvesting coefficient.

[0118] The above describes the communication parameters of the integrated energy-carrying, communication and perception system in the embodiment of the present application. The following describes the antenna beamforming method in the embodiment of the present application in combination with the communication parameters.

[0119] Figure 2 This is an optional flowchart of the antenna beamforming method provided in an embodiment of the present application. Figure 2 The method may include but is not limited to steps S110 to S150. It is also understood that this embodiment is Figure 2 The order of step S110 to step S150 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0120] Step S110: Obtain the total transmit power of the system.

[0121] In one embodiment, the total transmit power is calculated based on the initial value of the beamforming vector of the beamformer at each communication receiving end. The initial value of the beamforming vector is the component value of the beamforming matrix of the beamformer in the direction of each communication receiving end. The total transmit power is expressed as:

[0122]

[0123] Step S120: obtaining an initial value of a communication channel matrix of each communication channel, and calculating a received signal interference-to-noise ratio of each communication receiving end based on the initial value of the communication channel matrix and the initial value of the beamforming vector.

[0124] In one embodiment, the received signal interference-to-noise ratio γ of the communication receiving end is k Expressed as:

[0125]

[0126] Step S130: obtaining an initial value of the channel gain of each energy channel, and calculating the collection power of the energy receiving end according to the energy collection coefficient, the initial value of the energy channel matrix and the initial value of the beamforming vector.

[0127] In one embodiment, the collection power E of the mth energy receiving end is calculated based on the received signal. m It can be expressed as:

[0128]

[0129] Step S140: constructing a first constraint condition set according to the total transmit power, the received signal interference-to-noise ratio, and the acquisition power.

[0130] In one embodiment, the beamforming matrix is the target to be optimized in the embodiment of the present application. The antenna beamforming method of the embodiment of the present application ultimately calculates the optimal solution of the beamforming matrix. Therefore, by setting: the preset transmit power P at the access point AP, the signal-to-interference-and-noise ratio threshold {η k} and the preset collection power {Q m}, the optimization problem of the beamforming matrix is transformed into the optimization problem of minimizing the Cramer-Rao bound parameter of the target azimuth angle, and minimizing the Cramer-Rao bound parameter of the target azimuth angle is taken as the first optimization goal.

[0131] The above optimization process is recorded as P1, and the optimization problem P1 is expressed as: The initial value of the beamforming vector w is obtained when the Cramer-Rao bound parameter CRB(θ) at the target azimuth angle θ is minimized. k For the optimized result.

[0132] The purpose of the first set of constraints is to ensure that the total transmit power at the access point AP is less than or equal to the preset transmit power P, and the received signal interference-to-noise ratio at the communication receiving end IR is greater than or equal to the required signal interference-to-noise ratio threshold {η k} and the energy receiving end ER's collection power is greater than or equal to the preset collection power required {Q m}.

[0133] In one embodiment, referring to Figure 3 , is a specific implementation flowchart of step S140 shown in an embodiment. In this embodiment, the step of constructing a first constraint condition set based on the total transmit power, the received signal interference-to-noise ratio, and the acquisition power includes:

[0134] Step S141: Acquire a transmitting antenna steering vector of a target azimuth angle at a transmitting antenna and a receiving antenna steering vector of a target azimuth angle at a receiving antenna.

[0135] Step S142: Calculate the Cramer-Rao bound parameters of the target azimuth angle based on the transmitting antenna steering vector, the receiving antenna steering vector, and the initial value of the beamforming vector.

[0136] In one embodiment, assuming that the transmitting antenna and the receiving antenna are uniform linear arrays (ULA) with a half-wavelength antenna spacing, and the center of the ULA antenna is selected as the reference point, the transmitting antenna steering vector a(θ) and the receiving antenna steering vector b(θ) are expressed as follows:

[0137]

[0138]

[0139] Therefore, the Cramer-Rao bound parameter CRB(θ) of the target azimuth angle θ is obtained according to the transmitting antenna steering vector a(θ) and the receiving antenna steering vector b(θ), which is expressed as:

[0140]

[0141]

[0142]

[0143]

[0144] Step S143: constructing a first transmit power constraint condition based on the preset transmit power and the total transmit power.

[0145] In one embodiment, the first transmission power constraint is expressed as:

[0146]

[0147] Step S144: constructing a first signal-to-interference-plus-noise ratio constraint condition based on the signal-to-interference-plus-noise ratio threshold and the received signal-to-interference-plus-noise ratio.

[0148] In one embodiment, the first signal to interference plus noise ratio constraint is expressed as:

[0149]

[0150] Among them, st is the abbreviation of suchthat, which is used to introduce restrictive conditions.

[0151] Step S145: A first collection power constraint condition is constructed based on the preset collection power and the collection power.

[0152] In one embodiment, the first constraint condition of the collection power is expressed as:

[0153]

[0154] From the above, we can see that the first set of constraints is expressed as:

[0155]

[0156] Due to the fractional structure of the first constraint of the signal-to-interference-noise ratio, it can be seen that this constraint is non-convex.

[0157] Step S150: reducing the rank of the initial value of the beamforming vector based on the first constraint condition set to obtain a linear equation system including the initial value of the beamforming vector, and obtaining the optimized value of the beamforming vector according to the linear equation system.

[0158] In one embodiment, referring to Figure 4 , is a specific implementation flowchart of step S150 shown in one embodiment, including:

[0159] Step S151: converting the first constraint condition set into the second constraint condition set according to the Schur complement condition.

[0160] In one embodiment, the optimization problem P1 is equivalent to the optimization problem P2 through the Schur complement condition, specifically including: changing the first optimization objective of the optimization problem P1 to the second optimization objective of the optimization problem P2, and then obtaining the second constraint condition set based on the second optimization objective and the first constraint condition set.

[0161] In one embodiment, the second optimization goal of the optimization problem P2 is: all elements in the first optimization matrix are not less than zero, where the first optimization matrix is generated according to the initial values of the transmitting antenna steering vector, the receiving antenna steering vector and the beamforming vector.

[0162] In one embodiment, the optimization problem P2 is expressed as:

[0163]

[0164]

[0165] Where t represents the variable introduced by the Schur complement condition and is the upper bound of the Cramer-Rao bound parameter CRB(θ) of the target azimuth θ in the first optimization objective. It means that the matrix is semi-positive definite, that is, every element in the matrix is not less than zero, and Ω1 represents the first optimization matrix.

[0166] The above optimization problem P2 represents the initial value of the beamforming vector w obtained when t is minimized. k For the optimized result.

[0167] The second set of constraints obtained by optimizing problem P2 is expressed as:

[0168]

[0169]

[0170]

[0171] and

[0172] Step S152: Using the semi-definite relaxation principle, relax the second constraint condition set to obtain a third constraint condition set.

[0173] In one embodiment, since the constraints of the optimization problem P2 are still non-convex, the second constraint set is relaxed using the semidefinite relaxation principle (SDR) to obtain the third constraint set.

[0174] In one embodiment, referring to Figure 5 , is a specific implementation flowchart of step S152 shown in one embodiment, including:

[0175] Step S1521: The second constraint condition set is updated using the communication channel matrix parameters, the beamforming vector parameters, and the channel gain parameters to obtain the second constraint condition of the transmit power, the second constraint condition of the signal to interference and noise ratio, and the second constraint condition of the acquisition power.

[0176] In one embodiment, the communication channel matrix parameters are defined as follows: Beamforming vector parameters and channel gain parameters

[0177] The second constraint on transmit power is expressed as:

[0178]

[0179] The second constraint of the signal-to-interference-noise ratio is expressed as:

[0180]

[0181] The second constraint of the collection power is expressed as:

[0182]

[0183] Step S1522: The first optimization matrix is updated using the communication channel matrix parameters, the beamforming vector parameters, and the channel gain parameters to obtain a second optimization matrix.

[0184] Step S1523: Generate a third optimization target according to the second optimization matrix.

[0185] In one embodiment, the third optimization objective includes: the rank of the beamforming vector parameter is a preset value, and all elements in the second optimization matrix are not less than zero.

[0186] Step S1524: Generate a third constraint set according to the third optimization objective, the second transmit power constraint, the second signal to interference and noise ratio constraint, and the second collection power constraint.

[0187] In one embodiment, the optimization problem P2 is transformed into the optimization problem P3, and the optimization objective of the optimization problem P3 is the third optimization objective.

[0188] In one embodiment, the third optimization objective includes: the beamforming vector parameter W k The rank of is a preset value, for example, the preset value is 1, then rank(W k )=1 and And all elements in the second optimization matrix are not less than zero.

[0189] Here, by giving up the beamforming vector parameter W k The rank constraint of , the optimization problem P3 is expressed as:

[0190]

[0191]

[0192] Wherein, Ω2 represents the second optimization matrix.

[0193] The third set of constraints obtained by optimizing problem P3 is expressed as:

[0194]

[0195]

[0196]

[0197]

[0198]

[0199] Step S153: Obtain the dual variables of the third constraint set to obtain the complementary condition set of the third constraint condition set.

[0200] In one embodiment, since the third constraint set may not satisfy the rank constraint of the optimization problem P1, to extract a feasible solution, eigendecomposition may be performed on the beamforming vector parameters. The eigenvector corresponding to the maximum eigenvalue is used as the optimal solution for the beamforming vector. If the rank of the beamforming vector parameters is already 1, eigendecomposition is directly performed to obtain the optimal solution to the optimization problem P1. However, since the rank of the beamforming vector parameters is not necessarily the minimum rank, rank reduction is required until it reaches the minimum rank before eigendecomposition is directly performed.

[0201] In one embodiment, the rank reduction process can be performed using step S153, referring to Figure 6 , step S153 includes:

[0202] Step S1531: Acquire a first number of communication receiving ends and a second number of energy receiving ends, and generate a first dual variable, a second dual variable, and a third dual variable according to the first number and the second number.

[0203] Step S1532: Obtain a first expression according to the first dual variable and the second constraint condition of the signal to interference and noise ratio.

[0204] Step S1533: Obtain a second expression according to the second dual variable and the second constraint condition of the acquisition power.

[0205] Step S1534: Obtain a third expression based on the first dual variable and the second constraint condition of the transmission power.

[0206] Step S1535: Generate a fourth expression according to the beamforming vector parameters and the third dual variable.

[0207] Step S1536: Generate a fifth expression based on the second optimization matrix and the third dual variable.

[0208] In one embodiment, the first number is K and the second number is M, then K+1 first dual variables {z1, z2, ..., z K+1}, generate M second dual variables {v1,v2,...,v M}, and generate K+1 third dual variables {Z1, Z2, ..., Z K+1}.

[0209] Assume that {z1,z2,...,z K+1}、{v1,v2,...,v M} and, the beamforming matrix W K ={w1,w2,...,w K} can achieve the optimality, then the complementary condition set is satisfied, where the complementary condition set includes: the first expression, the second expression, the third expression, the fourth expression and the fifth expression.

[0210] The first expression is:

[0211]

[0212] The second expression is:

[0213]

[0214] The third expression is:

[0215]

[0216] The fourth expression is:

[0217]

[0218] The fifth expression is:

[0219]

[0220] Wherein, Ω2 represents the second optimization matrix.

[0221] Step S154: Decompose the initial value of the beamforming vector and obtain a linear equation system according to the complementary condition set.

[0222] In one embodiment, referring to Figure 7 , step S154 includes:

[0223] Step S1541: Obtain the rank vector of the beamforming vector parameters, generate a Hermitian matrix of the rank vector, and decompose the beamforming vector parameters according to the rank vector to obtain a third optimization matrix.

[0224] In one embodiment, the rank vector of the beamforming vector parameters is expressed as: By decomposition V k Represents the third optimization matrix, satisfying: You can get: Where Ω′ is an arbitrary given matrix.

[0225] Step S1542: Construct a first linear equation based on the third optimization matrix, the Hermitian matrix and the first expression.

[0226] In one embodiment, the first linear equation is expressed as:

[0227]

[0228] Among them, Δ k Represents R k ×R k Hermitian matrix.

[0229] Step S1543: Construct a second linear equation based on the third optimization matrix, the Hermitian matrix and the second expression.

[0230] In one embodiment, the second linear equation is expressed as:

[0231]

[0232] Step S1544: Construct a third linear equation system based on the third optimization matrix, the Hermitian matrix, the third expression and the fourth expression.

[0233] In one embodiment, the third linear equation group includes four linear equations, which are expressed as follows:

[0234]

[0235]

[0236]

[0237]

[0238] Step S1545: Obtain a linear equation group based on the first linear equation, the second linear equation and the third linear equation group.

[0239] In one embodiment, the linear equation group can be obtained by summarizing the first linear equation, the second linear equation and the third linear equation group. It can be seen that the linear equation group contains real-valued unknown variables and K+M+4 linear equations.

[0240] After obtaining the linear equations, the optimized value of the beamforming vector can be obtained according to the linear equations. Specifically, the Hermitian matrix Δ k The eigenvalues of:

[0241]

[0242] And calculate the maximum eigenvalue δ based on the eigenvalue max , expressed as:

[0243] δ max =argmax{|δ kl |,k=1,...,L,l=1,...,R k}

[0244] Then, based on the maximum eigenvalue, the third optimization matrix, and the Hermitian matrix, the beamforming matrix of the beamformer is reduced in rank to obtain the reduced rank matrix W′ k , expressed as:

[0245]

[0246] Finally, the reduced rank matrix is solved to obtain the optimized value of the beamforming vector.

[0247] In one embodiment, the reduced rank matrix W′ k The rank and beamforming vector parameter W k The rank satisfies the following relationship: It can be seen that due to δ maxSelection of the updated reduced rank matrix W′ k Rank reduction is achieved, and the rank of the solution is reduced by at least 1.

[0248] The solution of the reduced rank matrix described below is the optimized value of the beamforming vector in the embodiment of the present application.

[0249] definition is a solution of the linear system, so it satisfies:

[0250]

[0251] a,b≥0

[0252] When the optimal solution is reached, it satisfies at-|c| 2 b -1 =0, which means:

[0253]

[0254] Denote the updated dual variables as {z′1,z′2,...,z K+1 ′}, {v′1,v′2,...,v M ′} and {Z′1,Z′2,...,Z K+1 ′}, by keeping z′ k =z k ,k=1,...K, can satisfy the complementary condition of the signal-to-interference-noise ratio threshold constraint, by keeping v m ′=v m ,m=1,...M, can satisfy the complementary condition of the preset acquisition power constraint. In addition, by letting Z′ k =Z k ,k=1,...K, we can get the third complementary condition:

[0255]

[0256] Given that W k and Z k The positive semi-definite property of , we can get:

[0257]

[0258] So we have:

[0259]

[0260] This shows that Z′ k =Z k and W′ k ,k=1,...,the complementary condition of K still holds.

[0261] Then Z k+1' is processed and the conditions are met On the premise that:

[0262]

[0263] Then Substituting the third complementary condition does not change the values of a, b, and c above, so the target value -t remains unchanged.

[0264] It can be equivalent to: Z k+1 ′=Z k+1 , in this case we can conclude that: k+1 The complementary condition and semidefinite constraint of ′ still hold, so the upper bound t of the Cramer-Rao bound parameter CRB(θ) of the target azimuth angle θ in the first optimization objective remains unchanged.

[0265] In summary, we can get It is the optimal solution to the reduced-rank optimization problem P1.

[0266] In one embodiment, it is then determined If yes, repeat the above rank reduction process until And rank(W k )≥1.

[0267] Therefore the rank vector satisfies the condition: This formula can ensure that the sum of the squares of the matrix rank vectors is no less than K and no greater than K+M+4, but it cannot guarantee that the matrix rank is 1. If the rank obtained at this time is greater than 1, the eigendecomposition method is used to extract a feasible solution to obtain the optimized value of the beamforming vector.

[0268] In one embodiment, the technical performance corresponding to the optimized value of the beamforming vector obtained by the antenna beamforming method of the embodiment of the present application is evaluated through simulation.

[0269] During the simulation, the performance metric is the Cramer-Rao bound parameter of the radar sensor. The access point (AP) is equipped with 10 transmit antennas and 10 receive antennas, transmitting information to K = 8 communication receivers (IR) and M = 6 energy receivers (ER) within a symbol interval of T = 20. The default transmit power is P = 1 W. The radar perceives the target azimuth angle θ = 0, and the path loss during transmission is α = 0.01. The required signal-to-interference-and-noise ratio threshold for the kth communication receiver (IR) follows η. k ∈(1,10)dB uniform distribution. The energy collection coefficient of the mth energy receiving end ER follows the uniform distribution of βm∈(0,1), and the preset collection power follows the uniform distribution of Qm∈(0,0.5)W. Assume that all channels obey independent and identically distributed Rician fading, modeled as independent and identically distributed with non-zero mean μ=1 and variance σ 2= 1. The noise power in the communication channel and radar sensor channel is σ C 2 =0dBm and σ R 2 =10dBm.

[0270] In order to compare the performance, three traditional schemes in the related technology are selected for performance comparison, namely: the EDRR scheme (the scheme of the embodiment of the present application) is to verify the communication performance of the optimized value of the beamforming vector obtained by eigendecomposition after rank reduction; the EDCVX scheme is to verify the communication performance of the optimized value of the beamforming vector obtained by eigendecomposition after solving the optimization problem P3 using the CVX toolbox; the CVX toolbox uses the solution of the optimization problem P3 exported by the CVX Toolbox to calculate the CRB to characterize the lower bound.

[0271] Reference Figure 8 , showing a schematic diagram of the relationship between CRB and the preset transmit power of the access point AP. It can be seen that the CRB obtained by the EDRR scheme and the CVX scheme decreases with the increase of transmit power. This is because a larger transmit power will lead to a higher signal-to-noise ratio, thereby reducing the perception error. However, the CRB obtained by the DCVX scheme fluctuates with the increase of transmit power. This is due to the optimality loss caused by ED. In addition, the CRB obtained by the EDRR scheme is almost close to the lower bound characterized by the CVX scheme, with only a slight deviation at P = 1W, indicating that the solution obtained by the reduced rank scheme is The rank of usually reaches 1, thereby verifying the effectiveness of the embodiment of the present application.

[0272] Reference Figure 9 , is a diagram showing the relationship between CRB and the preset transmit power of the access point AP. As can be seen from the figure, the EDRR solution and the CVX solution decrease as the number of transmit antennas increases. The reason is that more transmit antennas will expand the optimization dimension of the transmit beamformer, thereby improving the perception accuracy. When N t When ≥12, the performance of the EDRR scheme overlaps with the lower bound determined by the CVX scheme, which indicates that when there are more transmit antennas, the EDRR scheme is more likely to convert the rank-reduced solution into a The rank of is reduced to 1.

[0273] Reference Figure 10 , is a diagram showing the relationship between CRB and the number of transmitting antennas. As can be seen from the figure, the required collection power for all ERs increases uniformly from 0.01W to 0.4W. The CRB obtained by the EDRR scheme and the CVX scheme increases with the required collection power, which means that more energy can be transmitted at the expense of sensing accuracy. mWhen the power consumption is ≥0.05W, the performance of the EDRR scheme overlaps with the lower bound of the CVX scheme, which shows that the EDRR scheme can successfully obtain a rank-1 solution under high collection power requirements.

[0274] Reference Figure 11 , is a diagram showing the relationship between CRB and SINR threshold, where the SINR threshold level required for all IRs increases uniformly from 1 dB to 10 dB. It can be seen that the CRB obtained by the EDRR scheme and the CVX scheme increases with the increase of the SINR threshold, which illustrates the trade-off between sensing and communication performance. When all η k When SINR is ≥2dB, the performance of the EDRR scheme overlaps with the lower bound determined by the CVX scheme, which shows that the EDRR scheme can successfully obtain a rank-1 solution under high SINR requirements.

[0275] From the above, it can be seen that the embodiment of the present application proposes an antenna beamforming method for an integrated energy-carrying communication and perception system for multi-user scenarios, and studies the beamforming design for multiple communication receiving ends and energy receiving ends. Compared with related technologies, it improves the perception, communication and energy transmission performance in multi-user scenarios, and improves the utilization rate of spectrum resources.

[0276] The antenna beamforming method proposed in the embodiment of the present application obtains the total transmission power of the system, the received signal interference-noise ratio of each communication receiving end, and the collection power of the energy receiving end through the initial value of the beamforming vector, and then constructs a first constraint condition set based on the total transmission power, the received signal interference-noise ratio, and the collection power. The initial value of the beamforming vector is then reduced in rank based on the first constraint condition set to obtain a linear equation group containing the initial value of the beamforming vector, and the optimized value of the beamforming vector is obtained based on the linear equation group. The embodiment of the present application improves the convergence of the beamforming matrix optimization process by reducing the rank, thereby improving the optimization performance of the beamforming matrix. At the same time, there is no need to use manual experience to adjust parameters, thereby improving the optimization efficiency of the beamforming matrix, and can improve the communication perception and energy transmission performance of the energy-carrying communication perception integrated system including multiple energy receiving ends and multiple communication receiving ends.

[0277] The embodiment of the present application also provides an antenna beam forming device, which can implement the above antenna beam forming method, referring to Figure 12 , applied to an integrated energy-carrying communication and perception system, which includes a beamformer and multiple transmitting antennas. The integrated energy-carrying communication and perception system uses the transmitting antennas to send communication information to a communication receiving end, supply energy to an energy receiving end, and perceive a target. The integrated energy-carrying communication and perception system includes a communication channel corresponding to the communication receiving end and an energy channel corresponding to the energy receiving end. The device includes:

[0278] The total transmit power acquisition module 1210 is used to obtain the total transmit power of the system; the total transmit power is calculated based on the initial value of the beamforming vector of the beamformer at each communication receiving end, where the initial value of the beamforming vector is the component value of the beamforming matrix of the beamformer in the direction of each communication receiving end.

[0279] The signal-to-interference-and-noise ratio acquisition module 1220 is configured to acquire an initial value of a communication channel matrix for each communication channel, and calculate a received signal-to-interference-and-noise ratio of each communication receiving end based on the initial value of the communication channel matrix and the initial value of the beamforming vector.

[0280] The energy collection module 1230 is used to obtain the initial value of the channel gain of each energy channel, and calculate the collection power of the energy receiving end according to the energy collection coefficient, the initial value of the energy channel matrix and the initial value of the beamforming vector.

[0281] The first constraint condition construction module 1240 is configured to construct a first constraint condition set according to the total transmit power, the received signal interference-to-noise ratio, and the acquisition power. The first optimization objective of the first constraint condition set is to minimize the Cramer-Rao bound parameter of the target azimuth.

[0282] The rank reduction optimization module 1250 is configured to reduce the rank of the initial value of the beamforming vector based on the first constraint condition set to obtain a linear equation system including the initial value of the beamforming vector, and obtain the optimized value of the beamforming vector according to the linear equation system.

[0283] The specific implementation of the antenna beamforming device of this embodiment is basically the same as the specific implementation of the above-mentioned antenna beamforming method, and will not be repeated here.

[0284] An embodiment of the present application also provides an integrated energy-carrying communication and perception system, which includes a beamformer, and an optimized value of a beamforming vector of the beamformer is calculated according to the antenna beamforming method described in any one of the above embodiments.

[0285] An embodiment of the present application further provides an electronic device, including:

[0286] at least one memory;

[0287] at least one processor;

[0288] at least one program;

[0289] The program is stored in the memory, and the processor executes the at least one program to implement the antenna beamforming method described above. The electronic device can be any smart terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0290] See also Figure 13 , Figure 13 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0291] The processor 1301 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0292] The memory 1302 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1302 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1302 and is called by the processor 1301 to execute the antenna beamforming method of the embodiments of the present application.

[0293] Input / output interface 1303, used to implement information input and output;

[0294] Communication interface 1304, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); and

[0295] Bus 1305 , which transmits information between various components of the device (e.g., processor 1301 , memory 1302 , input / output interface 1303 , and communication interface 1304 );

[0296] The processor 1301 , the memory 1302 , the input / output interface 1303 and the communication interface 1304 are connected to each other in communication within the device via a bus 1305 .

[0297] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned antenna beamforming method is implemented.

[0298] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0299] The antenna beamforming method, antenna beamforming device, electronic device, and storage medium proposed in the embodiments of the present application obtain the total transmission power of the system, the received signal interference-noise ratio of each communication receiving end, and the collection power of the energy receiving end through the initial value of the beamforming vector, and then construct a first constraint condition set based on the total transmission power, the received signal interference-noise ratio, and the collection power. The initial value of the beamforming vector is then reduced in rank based on the first constraint condition set to obtain a linear equation group containing the initial value of the beamforming vector, and the optimized value of the beamforming vector is obtained based on the linear equation group. The embodiments of the present application improve the convergence of the beamforming matrix optimization process by reducing the rank, thereby improving the optimization performance of the beamforming matrix. At the same time, there is no need to use manual experience to adjust parameters, thereby improving the optimization efficiency of the beamforming matrix, and can improve the communication energy transmission performance of the energy-carrying communication and perception integrated system including multiple energy receiving ends and multiple communication receiving ends.

[0300] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0301] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0302] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0303] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0304] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0305] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0306] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0307] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0308] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0309] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. An antenna beamforming method, characterized in that: Applied to an integrated energy-carrying communication and perception system, the integrated energy-carrying communication and perception system includes a beamformer and multiple transmitting antennas. The integrated energy-carrying communication and perception system uses the transmitting antennas to perceive a perception target, send communication information to a communication receiving end, and supply energy to an energy receiving end; The energy-carrying, communication, and perception integrated system includes a communication channel corresponding to the communication receiving end and an energy channel corresponding to the energy receiving end; the method includes: Obtaining a total transmit power of the system; the total transmit power is calculated based on an initial value of a beamforming vector of the beamformer at each of the communication receiving ends, where the initial value of the beamforming vector is a component value of a beamforming matrix of the beamformer in the direction of each of the communication receiving ends; Obtaining an initial value of a communication channel matrix of each of the communication channels, and calculating a received signal interference-to-noise ratio of each of the communication receiving ends based on the initial value of the communication channel matrix and the initial value of the beamforming vector; Obtaining an initial value of the channel gain of each of the energy channels, and calculating the collection power of the energy receiving end according to the energy collection coefficient, the initial value of the energy channel matrix, and the initial value of the beamforming vector; A first constraint condition set is constructed according to the total transmit power, the received signal interference-to-noise ratio, and the acquisition power, wherein a first optimization objective of the first constraint condition set is to minimize a Cramer-Rao bound parameter of a target azimuth angle; The initial value of the beamforming vector is rank-reduced based on the first constraint condition set to obtain a linear equation system including the initial value of the beamforming vector, and an optimized value of the beamforming vector is obtained according to the linear equation system.

2. The antenna beamforming method according to claim 1, wherein: The step of reducing the rank of the initial value of the beamforming vector based on the first constraint condition set to obtain a linear equation system containing the initial value of the beamforming vector includes: transforming the first constraint condition set into a second constraint condition set according to the Schur complement condition; Using the semi-definite relaxation principle, the second set of constraints is relaxed to obtain a third set of constraints; Obtaining the dual variable of the third constraint condition set to obtain a complementary condition set of the third constraint condition set; The initial value of the beamforming vector is decomposed, and the linear equation group is obtained according to the complementary condition set.

3. The antenna beamforming method according to claim 2, wherein: The integrated energy-carrying communication and sensing system includes multiple receiving antennas; and constructing a first constraint condition set according to the total transmit power, the received signal interference-to-noise ratio, and the collection power includes: Acquire a transmitting antenna steering vector of the transmitting antenna at the target azimuth angle, and a receiving antenna steering vector of the receiving antenna at the target azimuth angle; Calculating the Cramer-Rao bound parameter of the target azimuth angle according to the transmitting antenna steering vector, the receiving antenna steering vector, and the initial value of the beamforming vector; Establishing a first transmit power constraint condition based on the preset transmit power and the total transmit power; Establishing a first signal-to-interference-plus-noise ratio constraint condition based on a signal-to-interference-plus-noise ratio threshold and the received signal-to-interference-plus-noise ratio; A first collection power constraint condition is constructed based on a preset collection power and the collection power.

4. The antenna beamforming method according to claim 3, wherein: The converting the first constraint condition set into the second constraint condition set according to the Schur complement condition includes: According to the Schur complement condition, the first optimization objective is changed to a second optimization objective, where the second optimization objective includes: all elements in a first optimization matrix are not less than zero, the first optimization matrix is generated according to the transmit antenna steering vector, the receive antenna steering vector, and the initial value of the beamforming vector; The second constraint condition set is obtained according to the second optimization objective and the first constraint condition set.

5. The antenna beamforming method according to claim 4, wherein: The method of relaxing the second set of constraints by using the semi-definite relaxation principle to obtain a third set of constraints includes: The second constraint condition set is updated using the communication channel matrix parameter, the beamforming vector parameter, and the channel gain parameter to obtain a second constraint condition on transmit power, a second constraint condition on signal-to-interference-and-noise ratio, and a second constraint condition on acquisition power; Updating the first optimization matrix using communication channel matrix parameters, beamforming vector parameters, and channel gain parameters to obtain a second optimization matrix; Generating a third optimization objective according to the second optimization matrix, the third optimization objective including: the rank of the beamforming vector parameter is a preset value, and all elements in the second optimization matrix are not less than zero; The third constraint condition set is generated according to the third optimization objective, the second transmit power constraint condition, the second signal to interference plus noise ratio constraint condition, and the second collection power constraint condition.

6. The antenna beamforming method according to claim 5, characterized in that: The complementary condition set includes: a first expression, a second expression, a third expression, a fourth expression, and a fifth expression; obtaining the dual variable of the third constraint condition set to obtain the complementary condition set of the third constraint condition set includes: Acquire a first number of the communication receiving terminals and a second number of the energy receiving terminals, and generate a first dual variable, a second dual variable, and a third dual variable according to the first number and the second number; Obtain a first expression according to the first dual variable and the second signal to interference noise ratio constraint; Obtain a second expression according to the second dual variable and the second constraint condition of the collection power; Obtain a third expression according to the first dual variable and the second constraint condition of the transmit power; generating a fourth expression according to the beamforming vector parameter and the third dual variable; A fifth expression is generated according to the second optimization matrix and the third dual variable.

7. The antenna beamforming method according to claim 6, wherein: Decomposing the initial value of the beamforming vector to obtain the linear equations according to the complementary condition set includes: Obtaining a rank vector of the beamforming vector parameters, generating a Hermitian matrix of the rank vector, and decomposing the beamforming vector parameters according to the rank vector to obtain a third optimization matrix; constructing a first linear equation based on the third optimization matrix, the Hermitian matrix, and the first expression; constructing a second linear equation based on the third optimization matrix, the Hermitian matrix, and the second expression; constructing a third system of linear equations based on the third optimization matrix, the Hermitian matrix, the third expression, the fourth expression, and the fifth expression; The linear equation group is obtained based on the first linear equation, the second linear equation and the third linear equation group.

8. The antenna beamforming method according to claim 7, wherein: Obtaining an optimized value of a beamforming vector according to the linear equation group includes: Calculating the eigenvalues of the Hermitian matrix, and calculating the maximum eigenvalue based on the eigenvalues; reducing the rank of the beamforming matrix of the beamformer based on the maximum eigenvalue, the third optimization matrix, and the Hermitian matrix to obtain a reduced rank matrix; Solving the reduced rank matrix to obtain the optimized value of the beamforming vector.

9. An antenna beamforming device, characterized in that: Applied to an integrated energy-carrying communication and perception system, the integrated energy-carrying communication and perception system includes a beamformer and multiple transmitting antennas. The integrated energy-carrying communication and perception system uses the transmitting antennas to perceive the perception target, send communication information to the communication receiving end, and supply energy to the energy receiving end; The energy-carrying, communication, and perception integrated system includes a communication channel corresponding to the communication receiving end and an energy channel corresponding to the energy receiving end; the device includes: a total transmit power acquisition module, configured to acquire the total transmit power of the system; the total transmit power is calculated based on an initial value of a beamforming vector of the beamformer at each of the communication receiving ends, where the initial value of the beamforming vector is a component value of a beamforming matrix of the beamformer in the direction of each of the communication receiving ends; a signal-to-interference-and-noise ratio acquisition module, configured to acquire an initial value of a communication channel matrix of each communication channel, and calculate a received signal-to-interference-and-noise ratio of each communication receiving end based on the initial value of the communication channel matrix and the initial value of the beamforming vector; an energy collection module, configured to obtain an initial value of a channel gain of each of the energy channels, and calculate the collection power of the energy receiving end based on an energy collection coefficient, an initial value of the energy channel matrix, and an initial value of the beamforming vector; A first constraint condition construction module is configured to construct a first constraint condition set according to the total transmit power, the received signal interference-to-noise ratio, and the acquisition power, wherein a first optimization objective of the first constraint condition set is to minimize a Cramer-Rao bound parameter of a target azimuth angle; A rank reduction optimization module is used to reduce the rank of the initial value of the beamforming vector based on the first set of constraints to obtain a linear equation system including the initial value of the beamforming vector, and obtain an optimized value of the beamforming vector according to the linear equation system.

10. An integrated system for carrying energy, communication and perception, characterized in that: The system includes a beamformer, and an optimized value of a beamforming vector of the beamformer is calculated according to the antenna beamforming method according to any one of claims 1 to 8.

11. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the antenna beamforming method according to any one of claims 1 to 8 when executing the computer program.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the antenna beamforming method according to any one of claims 1 to 8 is implemented.

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