Beam forming method, device and system in communication and induction integrated system

Abstract

The invention discloses a beam forming method, a beam forming device and a beam forming system in a communication and induction integrated system, and belongs to the technical field of wireless communication. Taking Pr (SINRk < = gamma k) < = pk as a first constraint, taking a row sparse constraint of a beamforming precoding matrix W as a second constraint, and calculating the beamforming precoding matrix by maximizing the sum of sensing signal echo powers in all sensing device directions; wherein the first constraint relates to a channel vector hk of the kth communication equipment, comprises an estimation error and is a constraint constructed after the channel estimation error is considered, and a beam forming method with robustness is designed for channel state information; meanwhile, the second constraint ensures the use of an irregular sparse array of the sensing communication base station in the sensing integrated system; and the sensing capability with higher precision is realized. On the basis of the method, the error during channel estimation based on the irregular sparse array is reduced, and a high-precision sensing function and a communication achievable rate can be well compatible.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and more specifically, relates to a beamforming method, device and system in a synaesthesia integrated system. Background Art

[0002] As the number of wireless terminals and data rate demands increase, spectrum resources are becoming scarce. The Integrated Interawareness Control (ISAC) system significantly conserves spectrum resources by transmitting signals that simultaneously perform both sensing and communication functions using a single physical platform. However, since the signal must simultaneously fulfill both communication and signaling functions, the ISAC system also faces new challenges. For example, when precoding the signal to implement beamforming, how can the performance requirements for both communication and sensing be met simultaneously?

[0003] Existing ISAC system beamforming methods often derive precoding matrices based on the assumption of perfect channel state information to implement ISAC system beamforming. However, in order to achieve higher-precision sensing capabilities, some ISAC systems use irregular sparse arrays at the sensing communication base station to expand the antenna aperture. Compared to the same number of uniformly distributed arrays, sparse arrays can reduce array redundancy, achieve larger antenna apertures, obtain better beam shapes, and have higher antenna spatial degrees of freedom. However, there will be large errors when performing channel estimation based on irregular sparse arrays. The above assumption based on perfect channel state information will reduce the communication achievable rate in the ISAC system in this scenario. Therefore, existing methods are not compatible with high-precision sensing capabilities and communication achievable rates. Summary of the Invention

[0004] In response to the above-mentioned defects or improvement needs of the existing technology, the present invention provides a beamforming method, device and system in a synaesthesia integrated system, which is used to solve the technical problem that the existing methods are not compatible with high-precision perception functions and communication achievable rates.

[0005] In order to achieve the above-mentioned object, in a first aspect, the present invention provides a beamforming method in a synaesthesia integrated system, which is applied to a perception communication base station in the synaesthesia integrated system; the perception communication base station is a time division duplex single static multi-antenna dual-function radar perception communication base station, comprising N antennas; N ≥ 2;

[0006] The above-mentioned sparse beamforming method includes:

[0007] The objective function is constructed with the goal of maximizing the sum of the echo power of the sensing signals in the directions of all sensing devices;

[0008] Under the constraints, the objective function is solved to obtain the beamforming precoding matrix W = [w1,…,w K ]∈C N×K, thereby realizing beamforming; w k is the beamforming precoding vector of the kth communication device; K is the number of communication devices; C represents a complex number set;

[0009] Among them, the objective function is:

[0010]

[0011] L is the number of sensing devices; the direction of the sensing device indexed as l is θ l The perceived signal echo power on α is the positive proportional coefficient; the direction of the sensing device θ with index l l Steering vector on j is the imaginary number sign; d0 is the antenna spacing; λ0 is the wavelength of the sensing signal;

[0012] Constraints include: first constraint and second constraint;

[0013] The first constraint is: Pr(SINR k ≤γ k )≤p k ;Pr(SINR k ≤γ k ) is the link interruption probability of the kth communication device; p k is the preset threshold of the link interruption probability of the kth communication device; the signal to interference noise ratio of the kth communication device h k is the channel vector of the kth communication device; is the estimated value of the channel vector of the kth communication device; is the variance of the channel estimation error vector of the kth communication device; represents a complex Gaussian distribution; is the noise variance; k=1,2,…,K;

[0014] The second constraint is: the row sparsity constraint of the beamforming precoding matrix W.

[0015] Further preferably, the second constraint is: ‖W‖ 2,0 =S; ‖W‖ 2,0 is the beamforming precoding matrix W 2,0 norm; S is the preset number of antennas selected.

[0016] Further preferably, the above constraints further include: a third constraint;

[0017] The third constraint is: P(θ s,i )≤d;P(θ s,i) is the discrete direction of the i-th sidelobe of the beam reflected by the sensing device received by the base station; d is the preset sidelobe beam receiving power threshold.

[0018] Further preferably, solving the objective function includes:

[0019] Relax the first constraint and transform it into: is the variance of the channel estimation error vector;

[0020]

[0021] ν k ≥0;ν k I N +A k is a positive semidefinite matrix;

[0022] Relax the second constraint and convert it into a penalty term ξ is the weight coefficient, z n is the adjustment coefficient corresponding to the nth antenna; W k,nn W k The element at row n and column n in ;

[0023] The objective function is transformed and the penalty term and the preset sidelobe beam receiving power threshold d are added. The transformed objective function is:

[0024]

[0025] Where Ψ is the omnidirectional steering matrix; Ψ = [a(0°), a(Δθ), a(2Δθ), …, a(180°)]; Δθ is the preset angle step size; represents the ideal beam shape; A Θ =[a(θ0),…,a(θ L-1 )] is the guidance matrix of the target direction;

[0026] Under the constraints, solve the converted objective function and get W k , thus obtaining w k , and then obtain the beamforming precoding matrix W; k = 1, 2, …, K.

[0027] Further preferably, the above constraints also include: a fourth constraint and a fifth constraint;

[0028] The fourth constraint is: W k is a positive semidefinite matrix;

[0029] The fifth constraint is: W kThe sum of the elements on the diagonal of ; P0 is the preset power.

[0030] In a second aspect, the present invention provides a perception communication base station, which is a time division duplex single static multi-antenna dual-function radar perception communication base station, including N antennas and a beamforming module; N≥2;

[0031] The beamforming module is used to execute the beamforming method provided by the first aspect of the present invention.

[0032] In a third aspect, the present invention provides a synaesthesia integration system, comprising: a perception device, a communication device, and the perception communication base station provided in the second aspect of the present invention.

[0033] In a fourth aspect, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the beamforming method provided in the first aspect of the present invention when executing the computer program.

[0034] In a fifth aspect, the present invention further provides a computer-readable storage medium, comprising a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the beamforming method provided in the first aspect of the present invention.

[0035] In a sixth aspect, the invention further provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the beamforming method provided in the first aspect of the invention.

[0036] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0037] 1. The present invention provides a beamforming method in a synaesthesia integrated system, using Pr(SINR k ≤γ k )≤p k As the first constraint, the row sparsity constraint of the beamforming precoding matrix W is used as the second constraint, and the beamforming precoding matrix is calculated by maximizing the sum of the sensing signal echo power in the direction of all sensing devices; wherein the first constraint involves the channel vector h of the kth communication device k , which includes estimation error, is a constraint constructed after considering channel estimation error, designing a robust beamforming method for channel state information. Simultaneously, the second constraint ensures the use of irregular sparse arrays of perceptual communication base stations in the integrated synaesthesia system, resulting in higher-precision perception capabilities. Based on this, the present invention reduces the error in channel estimation based on irregular sparse arrays, achieving better compatibility between high-precision perception capabilities and achievable communication rates.

[0038] 2. Furthermore, the beamforming method provided by the present invention relaxes and transforms the first constraint and the second constraint respectively when solving the objective function. At the same time, the objective function is also transformed, which can transform a non-convex optimization problem into a convex optimization problem, making it easier to solve. By transforming the objective function, the optimization weight imbalance problem caused by multiple sensing devices is avoided.

[0039] 3. Furthermore, in the beamforming method provided by the present invention, the constraint conditions also include a third constraint, in which the discrete direction of each sidelobe of the beam reflected back by the sensing device received by the base station is constrained within a preset sidelobe beam receiving power threshold, thereby suppressing the sensing beam reflection power in the non-target direction and reducing non-target interference, thereby further increasing the sensing accuracy.

[0040] 4. Furthermore, in the beamforming method provided by the present invention, the constraints further include: a fourth constraint and a fifth constraint; by setting W k Restricted to a semi-positive matrix, and W k The sum of the elements on the diagonal of is limited to the preset power, further ensuring the system performance under the rated power and increasing the system energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of beamforming in a synaesthesia integration system provided by an embodiment of the present invention;

[0042] Figure 2 The beamforming beam pattern provided in an embodiment of the present invention; wherein, (a) is the beam pattern of 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the beamforming method provided by the present invention when the outage probability threshold p = 0.1, under an SINR threshold γ of 1 dB; (b) is the beam pattern of 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the beamforming method provided by the present invention when the outage probability threshold p = 0.1, under an SINR threshold γ of 2 dB; (c) is the beam pattern of 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the beamforming method provided by the present invention when the outage probability threshold p = 0.1, under an SINR threshold γ of 3 dB; (d) is the beam pattern of 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the beamforming method provided by the present invention when the outage probability threshold p = 0.1, under an SINR threshold γ of 4 dB;

[0043] Figure 3Provided are graphs of the achievable rate of an integrated synaesthesia system after adopting the beamforming method provided by the present invention, provided in an embodiment of the present invention; wherein, (a) is a graph showing the change in the achievable rate of the communication system relative to the SINR threshold when the interruption probability threshold p is 0.1 using 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the sparse beamforming method of the present invention; (b) is a graph showing the change in the achievable rate of the communication system relative to the SINR threshold when the interruption probability threshold p is 0.3 using 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the sparse beamforming method of the present invention; and (c) is a graph showing the change in the achievable rate of the communication system relative to the SINR threshold when the interruption probability threshold p is 0.5 using 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the sparse beamforming method of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0045] In order to achieve the above-mentioned object, in a first aspect, the present invention provides a beamforming method in a synaesthesia integrated system, which is applied to a perception communication base station in the synaesthesia integrated system; the perception communication base station is a time division duplex single static multi-antenna dual-function radar perception communication base station, comprising N antennas; N ≥ 2;

[0046] The above-mentioned sparse beamforming method includes:

[0047] The objective function is constructed with the goal of maximizing the sum of the echo power of the sensing signals in the directions of all sensing devices;

[0048] Under the constraints, the objective function is solved to obtain the beamforming precoding matrix W = [w1,…,w K ]∈C N×K , thereby realizing beamforming; w k is the beamforming precoding vector of the kth communication device; K is the number of communication devices; C represents a complex number set;

[0049] Among them, the objective function is:

[0050]

[0051] L is the number of sensing devices; the direction of the sensing device indexed as l is θ l The perceived signal echo power P(θ l ) is proportional to Expressed as α is the positive proportional coefficient; the direction of the sensing device θ with index l l Steering vector on j is the imaginary number sign; d0 is the antenna spacing; λ0 is the wavelength of the sensing signal;

[0052] Constraints include: first constraint and second constraint;

[0053] The first constraint is: Pr(SINR k ≤γ k )≤p k ;Pr(SINR k ≤γ k ) is the link interruption probability of the kth communication device; p k is the preset threshold of the link interruption probability of the kth communication device; the signal to interference noise ratio of the kth communication device h k is the channel vector of the kth communication device; is the estimated value of the channel vector of the kth communication device; is the variance of the channel estimation error vector of the kth communication device; represents a complex Gaussian distribution; is the noise variance; k=1,2,…,K;

[0054] The second constraint is: the row sparsity constraint of the beamforming precoding matrix W.

[0055] In an optional implementation, the second constraint is: ‖W‖ 2,0 =S; ‖W‖ 2,0 is the beamforming precoding matrix W 2,0 norm; S is the preset number of antennas selected.

[0056] It should be noted that there are many ways to express the above row sparse constraints. In addition to the above representations, you can also use ‖W‖ 2,1 =S and other forms of expression are not limited here.

[0057] In an optional implementation manner, the above constraints further include: a third constraint;

[0058] The third constraint is: P(θ s,i )≤d;P(θ s,i ) is the discrete direction of the i-th sidelobe of the beam reflected by the sensing device received by the base station; d is the preset sidelobe beam receiving power threshold.

[0059] In an optional implementation, solving the objective function includes:

[0060] Relax the first constraint and transform it into: is the variance of the channel estimation error vector; ∈ k =-ln(p k ); ν k ≥0;ν k I N +A k is a positive semidefinite matrix;

[0061] Relax the second constraint and convert it into a penalty term ξ is the weight coefficient, z n is the adjustment coefficient corresponding to the nth antenna; W k,nn W k The element at row n and column n in ;

[0062] The objective function is transformed and the penalty term and the preset sidelobe beam receiving power threshold d are added. The transformed objective function is:

[0063]

[0064] Where Ψ is the omnidirectional steering matrix; Ψ = [a(0°), a(Δθ), a(2Δθ), …, a(180°)]; Δθ is the preset angle step size; represents the ideal beam shape; A Θ =[a(θ0),…,a(θ L-1 )] is the guidance matrix of the target direction;

[0065] Under the constraints, solve the converted objective function and get W k , thus obtaining w k , and then obtain the beamforming precoding matrix W; k = 1, 2, …, K.

[0066] In an optional implementation manner, the above constraints further include: a fourth constraint and a fifth constraint;

[0067] The fourth constraint is: W k is a positive semidefinite matrix;

[0068] The fifth constraint is: W k The sum of the elements on the diagonal of ; P0 is the preset power.

[0069] In order to further illustrate the beamforming method in the synaesthesia integrated system provided by the present invention, a specific embodiment is described below in detail:

[0070] This embodiment takes radar as an example of a sensing device (it should be noted that the sensing device can be any existing sensing device and is not limited to radar, and radar is used as an example here). Figure 1 The channel model and radar model of the multi-antenna integrated interaceptive system (ISAC system) single-static dual-function radar communication base station shown: The ISAC system includes: a time-division duplex single-static multi-antenna dual-function radar communication base station with M radio frequency chains and N antennas (M<N), L radar targets and K single-antenna communication users.

[0071] The specific implementation steps of the beamforming method are as follows:

[0072] Step 1: Establish a communication model: The downlink channel model of the communication user is:

[0073] y=Hx+n

[0074] Where y∈C K×1 is the received signal of the communication user, n∈C K×1 is additive noise, is the channel matrix. x∈C N×1 is the precoded transmitted signal, which can be expressed as x=Ws, where W=[w1,…,w K ] is the beamforming precoding matrix, s∈C K×1 is a modulation symbol of unit power. C represents a set of complex numbers.

[0075] Establish radar model: The radar model can be expressed as

[0076]

[0077] in, θ represents the steering vector, d0 represents the antenna spacing, and λ0 is the signal wavelength. l is the arrival angle of the echo signal, γ l is the echo signal path loss coefficient, n r is the additive white noise of the radar received signal.

[0078] Step 2: Determine the performance metrics for communication and radar functions. Considering the uncertainty of channel estimation and the hardware overhead of the number of base station RF chains, formulate an optimization problem to maximize the radar signal echo power in the target direction. Specifically, the problem includes:

[0079] Determine the performance metric of the communication function: take the link interruption probability of the communication user as the performance metric of the communication function Where SINR is the signal-to-interference-and-noise ratio of the user, γ k is the threshold of user signal-to-interference-noise ratio, p k is the threshold of the probability of user communication link interruption. The signal-to-interference-noise ratio is expressed as in is the noise variance. The channel vector h k Including estimation error, it can be expressed as in represents an estimated value, and

[0080] Determine the performance metrics of radar function: target direction The radar echo power is a performance indicator of the radar function. l The radar echo power in the direction can be expressed as:

[0081]

[0082] because Further simplification yields

[0083]

[0084] Considering the uncertainty of channel estimation and the hardware overhead of the number of base station RF chains, we construct an optimization problem P1 to maximize the radar signal echo power in the target direction:

[0085] Constraints:

[0086]

[0087] ‖W‖ 2,0 =S

[0088] in, Yes 2,0 Mixed norm, is the nth row vector of W; Meaning is defined as; Indicates the number of row vectors in W that are zero.

[0089] Step 3: Relax and transform the optimization problem and solve it using a solver to obtain the optimal sparse beamforming matrix. This includes:

[0090] Decomposition of the signal-to-interference-noise ratio

[0091] definition and Then the inequality SINR k ≤γ k It can be expressed as:

[0092]

[0093] Further definition and The above formula can be further expressed as

[0094] A generalized Bernstein-type inequality is introduced to relax the interruption probability constraint.

[0095] The generalized Bernstein-type inequality is: If the random variable X = z H Az+2Re{z H b}, where A is a Hermitian matrix and z is complex and follows a complex Gaussian distribution. Then for any ∈≥0 we have:

[0096]

[0097] Among them, s - (A) = max(-λ max (A),0) and λ max (A) represents the largest eigenvalue of A.

[0098] Introducing the slack variable set {μ k} and {ν k}, to satisfy and ν k I N s - (A k )I N -A k , then the outage probability constraint {Pr(SINR k ≤γ k )<p k} can be relaxed to in:

[0099]

[0100] and ∈ k =-ln(p k ).

[0101] Relax the sparse constraint: Set the sparse constraint ‖W‖ 2,0 =S is transformed into a penalty term ξ(‖W‖ 2,0 -S), where ξ is the weight coefficient. In order to make the penalty term differentiable, it is further relaxed to where z n is the adjustment coefficient, W k,nn It's W k The n,nth element of . At this time, the objective function becomes In order to avoid the optimization weight imbalance problem caused by multiple radar targets, the objective function Need to be converted to:

[0102]

[0103] in, represents the ideal beam shape; Ψ is the omnidirectional steering matrix; Ψ = [a(0°), a(Δθ), a(2Δθ), …, a(180°)]; Δθ is the preset angle step size, which is 2° in this embodiment; A Θ =[a(θ0),…,a(θ L-1 )] is the steering matrix in the target direction.

[0104] Adding the sidelobe beam receiving power control constraint, we get the final optimization problem: set the predetermined sidelobe beam receiving power threshold as d, and set the receiving power constraint of the sidelobe in the non-target direction Among them, {θ s,i} represents the discrete sidelobe direction. Adding d to the objective function to minimize the sidelobe received power, we get the objective function:

[0105]

[0106] Constraints

[0107]

[0108] rank(W k )=1

[0109]

[0110] Step 4: Solve the optimization problem, including:

[0111] Step 4-1, initialize ξ∈[ξ min ,ξ max ],{z n =1},δ=10 -6 , determine the target direction {θ l} and the constraint threshold {γ k ,p k}.

[0112] Step 4-2, remove the constraint rank (W k )=1, the convex optimization solver CVX is used to solve the optimization problem and extract the optimal {W k}The eigenvector corresponding to the maximum eigenvalue {w k}.renew

[0113] Step 4-3, if {w k}Satisfy the sparse constraint ‖W‖ 2,0 =S, then the final beamforming vector {w k}; If the sparse constraint is not satisfied, update ξ∈[ξ min ,ξ max ], and repeat step 4-2. In this embodiment, the value of S is 3.

[0114] The following is a further detailed description of the effects of the present invention with the help of simulation experiments. Specifically, the ISAC base station is equipped with 10 uniformly distributed linear antenna arrays and has 7 radio frequency chains. At each transmission time, the base station selects 7 antennas from the 10 antennas for transmission. Set the channel estimation error The interruption probability threshold p and SINR threshold γ are set to be the same for each communication user. The channel matrix H is a randomly generated Rayleigh channel. All simulation results are based on 500 Monte Carlo experiments.

[0115] Figure 2 The figure shows the beam patterns for 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the beamforming method provided by the present invention at different SINR thresholds, when the outage probability threshold p = 0.1. The red circles indicate the beam energy leakage caused by the communication user. Compared with the case using the same 7-antenna configuration, the beamforming method provided by the present invention achieves better sidelobe control and lower energy leakage in the direction of the communication user.

[0116] Figure 3 The following graph shows the variation of the achievable communication rate relative to the SINR threshold γ for 10 uniformly distributed antennas, 7 uniformly distributed antennas, and the beamforming method provided by the present invention at different outage probability thresholds. It can be seen that the beamforming method proposed by the present invention can achieve the highest achievable communication rate.

[0117] In a second aspect, the present invention provides a perception communication base station, which is a time division duplex single static multi-antenna dual-function radar perception communication base station, including N antennas and a beamforming module; N≥2;

[0118] The beamforming module is used to execute the beamforming method provided by the first aspect of the present invention.

[0119] The related technical solution is the same as the beamforming method provided in the first aspect of the present invention, and will not be described in detail here.

[0120] In a third aspect, the present invention provides a synaesthesia integration system, comprising: a perception device, a communication device, and the perception communication base station provided in the second aspect of the present invention.

[0121] The relevant technical solution is the same as the perception communication base station provided in the second aspect of the present invention, and will not be repeated here.

[0122] In a fourth aspect, the present invention provides an electronic device comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the beamforming method provided in the first aspect of the present invention when executing the computer program.

[0123] The related technical solution is the same as the beamforming method provided in the first aspect of the present invention, and will not be described in detail here.

[0124] In a fifth aspect, the present invention further provides a computer-readable storage medium, comprising a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the beamforming method provided in the first aspect of the present invention.

[0125] The related technical solution is the same as the beamforming method provided in the first aspect of the present invention, and will not be described in detail here.

[0126] In a sixth aspect, the invention further provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the beamforming method provided in the first aspect of the invention.

[0127] The related technical solution is the same as the beamforming method provided in the first aspect of the present invention, and will not be described in detail here.

[0128] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A beamforming method in a synaesthesia integrated system, characterized in that: A perception communication base station used in the synaesthesia integrated system; the perception communication base station is a time division duplex single static multi-antenna dual-function radar perception communication base station, including N antennas; N≥2; the beamforming method includes: constructing an objective function with the goal of maximizing the sum of the echo powers of the sensing signals in the directions of all sensing devices; Under the constraints, the objective function is solved to obtain the beamforming precoding matrix W = [w1,…,w K ]∈C N×K , thereby realizing beamforming; w k is the beamforming precoding vector of the kth communication device; K is the number of communication devices; C represents a complex number set; The objective function is: L is the number of sensing devices; the direction of the sensing device indexed as l is θ l The perceived signal echo power on α is the positive proportional coefficient; the direction of the sensing device θ with index l l Steering vector on j is the imaginary number sign; d0 is the antenna spacing; λ0 is the wavelength of the sensing signal; The constraint conditions include: a first constraint and a second constraint; The first constraint is: Pr(SINR k ≤γ k )≤p k ;Pr(SINR k ≤γ k ) is the link interruption probability of the kth communication device; p k is the preset threshold of the link interruption probability of the kth communication device; the signal to interference noise ratio of the kth communication device h k is the channel vector of the kth communication device; is the estimated value of the channel vector of the kth communication device; is the variance of the channel estimation error vector of the kth communication device; represents a complex Gaussian distribution; is the noise variance; k=1,2,…,K; The second constraint is: a row sparse constraint of the beamforming precoding matrix W.

2. The beamforming method according to claim 1, wherein: The second constraint is: ‖W‖ 2,0 =S; ‖W‖ 2,0 is the beamforming precoding matrix W 2,0 norm; S is the preset number of antennas selected.

3. The beamforming method according to claim 1, wherein: The constraints also include: a third constraint; The third constraint is: P(θ s,i )≤d;P(θ s,i ) is the discrete direction of the i-th sidelobe of the beam reflected by the sensing device received by the base station; d is the preset sidelobe beam receiving power threshold.

4. The beamforming method according to claim 3, wherein: Solving the objective function includes: Relax the first constraint and transform it into: ∈ k =-ln(p k ); ν k ≥0;ν k I N +A k is a positive semidefinite matrix; Relax the second constraint and convert it into a penalty term ξ is the weight coefficient, z n is the adjustment coefficient corresponding to the nth antenna; W k,nn W k The element at row n and column n in ; The objective function is transformed and a penalty term and a preset sidelobe beam receiving power threshold d are added to obtain the transformed objective function: Where Ψ is the omnidirectional steering matrix; Ψ = [a(0°), a(Δθ), a(2Δθ), …, a(180°)]; Δθ is the preset angle step size; represents the ideal beam shape; A Θ =[a(θ0),…,a(θ L-1 )] is the guidance matrix of the target direction; Under the constraints, solve the converted objective function and get W k , thus obtaining w k , and then obtain the beamforming precoding matrix W; k = 1, 2, …, K.

5. The beamforming method according to any one of claims 1 to 4, characterized in that: The constraints also include: a fourth constraint and a fifth constraint; The fourth constraint is: W k is a positive semidefinite matrix; The fifth constraint is: W k The sum of the elements on the diagonal of ; P0 is the preset power.

6. A perception communication base station, characterized in that: The sensing communication base station is a time division duplex single static multi-antenna dual-function radar sensing communication base station, including N antennas and a beamforming module; N ≥ 2; The beamforming module is used to execute the beamforming method according to any one of claims 1 to 5.

7. A synaesthesia integrated system, characterized in that: include: A perception device, a communication device, and the perception communication base station as claimed in claim 6.

8. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the beamforming method according to any one of claims 1 to 5 when executing the computer program.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the processor controls the device where the storage medium is located to execute the beamforming method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program / instruction, which implements the beamforming method according to any one of claims 1 to 5 when executed by a processor.

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