Clustering beamforming method, communication and sensing computing integrated system and related device

Abstract

Embodiments of the present application provide a clustering beamforming method, a communication and sensing computing integrated system and related devices, and relate to the technical field of communication. The method comprises: constructing a first result constraint condition related to a reception result, a first sensing matrix constraint condition related to sensing performance, and a first power constraint condition related to transmission power; constructing a first constraint condition set by using the first result constraint condition, the first sensing matrix constraint condition and the first power constraint condition, and then solving the first constraint condition set to obtain the optimized values of a receiving end beamformer and a transmitting end beamformer. Embodiments of the present application design the beamforming of the transmitting end to guarantee the radar sensing performance, design the beamforming of the receiving end to improve the air computing performance, and adjust the antennas of the transmitting and receiving ends at the same time, thereby further improving the resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a clustered beamforming method, an integrated communication sensing and computing system, and related devices. Background Technology

[0002] With the development of the Internet of Things (IoT), massive amounts of data need to be collected from the environment by sensing devices and transmitted to servers for further processing. In data processing solutions, the data sensing, transmission, and computing stages are designed independently. This mechanism leads to the data sensing and transmission stages competing for spectrum resources, while the computing stage competes with the other two for time resources.

[0003] In related technologies, to improve spectral efficiency, radar communication and sensing multiplexed signals are designed, and communication-sensing integration technology is used to achieve simultaneous data sensing and transmission at the physical layer. The computational performance of the communication-sensing integration system is limited by the parameter design of the beamformer, but the design performance of the beamformer in related technologies has low resource utilization efficiency. Summary of the Invention

[0004] The main objective of this application is to propose a clustered beamforming method, a communication-sensing-computing integrated system, and related devices to improve the optimization performance and efficiency of the beamforming matrix.

[0005] To achieve the above objectives, a first aspect of this application proposes an antenna clustering beamforming method applied to a communication-sensing-computing integrated system. The communication-sensing-computing integrated system includes: a transmitting beamformer, a receiving beamformer, a radar sensing beamformer, and at least one sensing device. The transmitting signal of the sensing device includes: beamforming an initial data transmission signal using the transmitting beamformer to obtain a data transmission signal, and beamforming an initial radar sensing signal using the radar sensing beamformer to obtain a radar sensing signal. The sensing device is also used to receive a target reflection signal obtained by sensing a target reflecting the transmitted signal. The method includes:

[0006] The target reflection signal received by the sensing device is obtained to obtain a processed signal. A statistical result matrix is ​​calculated based on the processed signal. The root mean square error of the target reflection matrix of each sensing device is obtained based on the statistical result matrix. A first sensing matrix constraint condition is constructed based on the error tolerance value.

[0007] Based on the receiver beamformer, a receiving vector is obtained according to the data transmission signal and the radar sensing signal, and the mean square error between the receiving vector and the real data value is calculated. The first result constraint condition is constructed by minimizing the mean square error.

[0008] Obtain the transmit power of the sensing device to construct a first power constraint condition;

[0009] A first set of constraints is constructed based on the first result constraint, the first sensing matrix constraint, and the first power constraint. The first set of constraints is solved to obtain the first optimized beamforming weight value of the receiver beamformer and the second optimized beamforming weight value of the transmitter beamformer.

[0010] In some embodiments, the step of obtaining a received vector based on the received beamformer, according to the data transmission signal and the radar sensing signal, and calculating the mean square error between the received vector and the actual data value, and minimizing the mean square error to construct a first result constraint, includes:

[0011] The actual data value is calculated based on the initial data transmission signal of each of the aforementioned sensing devices;

[0012] The total transmission signal is calculated based on the transmission signal of each of the aforementioned sensing devices. The transmission signal consists of a first transmission signal and a second transmission signal. The first transmission signal is calculated based on the data transmission signal and the data transmission channel matrix, and the second transmission signal is calculated based on the radar sensing signal and the target reflection signal channel matrix.

[0013] The receiving vector is obtained based on the receiving beamformer and the total transmitted signal.

[0014] Calculate the mean square error between the received vector and the actual data value to obtain the result mean square error, and apply a minimization constraint to the result mean square error to obtain the first result constraint condition.

[0015] In some embodiments, the process of obtaining a processed signal from the target reflection signal received by the sensing device, calculating a statistical result matrix based on the processed signal, obtaining the root mean square error of the target reflection matrix for each sensing device based on the statistical result matrix, and constructing a first sensing matrix constraint condition based on the error tolerance value includes:

[0016] The target reflection signal is calculated based on the target reflection matrix of the sensing device, the radar sensing signal, and the interference signal.

[0017] The target reflection signals of all the sensing devices are acquired to obtain the processed signal, and the processed signal is optimized according to the law of large numbers to obtain the statistical result matrix;

[0018] The estimated value of the target reflection matrix is ​​obtained based on the statistical result matrix;

[0019] Calculate the mean squared error between the target reflection matrix and the estimated value to obtain the mean squared error of the target reflection matrix, and make the mean squared error of the target reflection matrix less than the error tolerance value to construct the first perception matrix constraint condition.

[0020] In some embodiments, obtaining the transmit power of the sensing device to construct a first power constraint includes:

[0021] The first power information is obtained by calculating the trace of the data transmission signal, and the second power information is obtained by calculating the trace of the radar sensing signal;

[0022] Calculate power information based on the first power information and the second power information, and make the power information less than or equal to the transmission power to construct the first power constraint condition.

[0023] In some embodiments, solving the first set of constraints to obtain the optimized first beamforming weight value of the receiver beamformer and the optimized second beamforming weight value of the transmitter beamformer includes:

[0024] The first transformation relationship between the transmitting beamformer and the receiving beamformer is obtained by using zero-forcing design;

[0025] Based on the first transformation relationship, the first set of constraints is transformed into the second set of constraints.

[0026] Using the principle of orthogonal matrices, the result update formula of the radar sensing beamformer is obtained based on the unitary matrix and the scaling factor, and the second set of constraints is transformed into the third set of constraints based on the result update formula.

[0027] The receiver beamformer is represented as a positive semi-definite matrix using positive semi-definite scaling, and the third set of constraints is transformed into a fourth set of constraints based on the positive semi-definite matrix.

[0028] The fourth set of constraints is solved by convex optimization to obtain the first beamforming weight optimization value of the receiver beamformer, and the second beamforming weight optimization value of the transmitter beamformer is obtained based on the first transformation relationship.

[0029] In some embodiments, transforming the first set of constraints into a second set of constraints based on the first transformation relationship includes:

[0030] Based on the first transformation relationship, the receiver beamformer is used to replace the transmitter beamformer in the first result constraint to obtain the second result constraint.

[0031] Based on the first conversion relationship, the receiver beamformer is used to replace the transmitter beamformer in the first power constraint condition to obtain the second power constraint condition;

[0032] The second set of constraints is generated based on the second result constraint, the first perception matrix constraint, and the second power constraint.

[0033] In some embodiments, the step of using the orthogonal matrix principle to obtain the result update formula of the radar sensing beamformer based on the unitary matrix and scaling factor, and transforming the second constraint set into a third constraint set based on the result update formula, includes:

[0034] The radar sensing beamformer in the second result constraint is replaced with the result update formula to obtain the third result constraint.

[0035] The radar sensing beamformer in the first sensing matrix constraint is replaced with the result update formula to obtain the second sensing matrix constraint.

[0036] The radar sensing beamformer in the second power constraint condition is replaced with the updated formula to obtain the third power constraint condition.

[0037] The third set of constraints is generated based on the third result constraint, the second perception matrix constraint, and the third power constraint.

[0038] In some embodiments, representing the receiver beamformer as a positive semi-definite matrix using positive semi-definite scaling, and transforming the third set of constraints into a fourth set of constraints based on the positive semi-definite matrix, includes:

[0039] Obtain the minimum factor value of the scaling factor;

[0040] The third result constraint is converted into a fourth result constraint based on the minimum factor value and the positive semidefinite matrix.

[0041] The third power constraint is converted into a fourth power constraint based on the minimum factor value and the positive semidefinite matrix.

[0042] The fourth set of constraints is generated based on the fourth result constraint, the fourth power constraint, and the semi-positive definite matrix.

[0043] In some embodiments, the step of performing convex optimization on the fourth set of constraints to obtain the optimized value of the first beamforming weight of the receiver beamformer includes:

[0044] The positive semi-definite matrix is ​​obtained by performing convex optimization on the fourth set of constraints.

[0045] The positive semi-definite matrix is ​​solved by Gaussian cyclic solution to obtain the first beamforming weight optimization value of the receiver beamformer.

[0046] To achieve the above objectives, a second aspect of this application provides an antenna clustering beamforming device applied to a communication-sensing-computing integrated system. The communication-sensing-computing integrated system includes: a transmitting beamformer, a receiving beamformer, a radar sensing beamformer, and at least one sensing device. The transmitting signal of the sensing device includes: beamforming an initial data transmission signal using the transmitting beamformer to obtain a data transmission signal, and beamforming an initial radar sensing signal using the radar sensing beamformer to obtain a radar sensing signal. The sensing device is also used to receive a target reflection signal obtained by sensing a target reflecting the transmitted signal. The device includes:

[0047] The perception matrix constraint construction module is used to obtain the target reflection signal received by the sensing device to obtain a processed signal, calculate a statistical result matrix based on the processed signal, obtain the root mean square error of the target reflection matrix of each sensing device based on the statistical result matrix, and construct a first perception matrix constraint based on the error tolerance value.

[0048] The result constraint construction module is used to obtain the receiving vector based on the receiving beamformer, the data transmission signal and the radar sensing signal, calculate the result mean square error between the receiving vector and the real data value, and minimize the result mean square error to construct the first result constraint.

[0049] A power constraint construction module is used to obtain the transmission power of the sensing device and construct a first power constraint condition.

[0050] The weight optimization value calculation module is used to construct a first set of constraints based on the first result constraint, the first sensing matrix constraint, and the first power constraint, and solve the first set of constraints to obtain the first beamforming weight optimization value of the receiver beamformer and the second beamforming weight optimization value of the transmitter beamformer.

[0051] To achieve the above objectives, a third aspect of the present application proposes a communication sensing and computing integrated system, the system including a transmitter beamformer and a receiver beamformer, wherein a first beamforming weight optimization value of the receiver beamformer and a second beamforming weight optimization value of the transmitter beamformer are calculated according to the antenna clustering beamforming method described in any one of the first aspects.

[0052] To achieve the above objectives, a fourth aspect of the present application provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in the first aspect.

[0053] To achieve the above objectives, a fifth aspect of the present application provides a storage medium, which is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0054] The clustered beamforming method, integrated communication-sensing-computing system, and related devices proposed in this application construct first result constraints related to the received results, first sensing matrix constraints related to sensing performance, and first power constraints related to the transmitted power. A first constraint set is constructed using these constraints, and then the first constraint set is solved to obtain optimized values ​​for the receiver and transmitter beamformers. This application designs the transmitter beamforming to ensure radar sensing performance and the receiver beamforming to improve airborne computing performance, while simultaneously adjusting the antennas at both the transmitter and receiver ends, thereby further improving resource utilization efficiency. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the integrated communication-sensing-computing system provided in an embodiment of the present invention.

[0056] Figure 2 This is a flowchart of an antenna clustering beamforming method provided in another embodiment of the present invention.

[0057] Figure 3 yes Figure 2 The flowchart of step S110.

[0058] Figure 4 yes Figure 2 The flowchart for step S120.

[0059] Figure 5 yes Figure 2 The flowchart of step S130.

[0060] Figure 6 yes Figure 2 The flowchart for step S140.

[0061] Figure 7 yes Figure 6 The flowchart for step S142 in the process.

[0062] Figure 8 yes Figure 6 The flowchart for step S143.

[0063] Figure 9 yes Figure 6 The flowchart for step S144.

[0064] Figure 10 yes Figure 6 The flowchart for step S145.

[0065] Figure 11 This is a signal processing flowchart of an integrated communication, sensing, and computing system provided in another embodiment of the present invention.

[0066] Figure 12 This is a schematic diagram illustrating the variation of the root mean square error of the uniformized aerial computation result with the number of antennas on the server in an application scenario of the antenna clustering beamforming method provided in another embodiment of the present invention.

[0067] Figure 13 This is a schematic diagram showing the variation curve of the root mean square error of the uniformized aerial calculation result with the number of antennas of the sensing device in an application scenario of the antenna clustering beamforming method provided in another embodiment of the present invention.

[0068] Figure 14 This is a structural block diagram of an antenna clustering beamforming device provided in another embodiment of the present invention.

[0069] Figure 15 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be 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 illustrative and not intended to limit the invention.

[0071] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may 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 one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0073] First, let's clarify some of the terms used in this invention:

[0074] Beamforming is a signal processing technique that improves signal transmission quality by adjusting the direction of transmitted (or received) signals. It can reduce interference during transmission, improve signal coverage and reliability, and more. In communication systems, beamforming typically refers to using multiple antennas or arrays to control the direction and shape of signals in a specific way, allowing the signal to be transmitted more concentratedly to the target location, thereby improving communication quality. Unlike traditional omnidirectional transmission or reception, beamforming can focus signal energy on the area that needs coverage, reducing signal transmission in areas that do not need coverage, resulting in higher efficiency and capacity. Beamforming is widely used in next-generation wireless communication technologies such as 5G and millimeter-wave communication. Besides communication systems, beamforming can also be used in radar, sonar, medical imaging, and other fields to improve signal detection range and accuracy.

[0075] With the development of the Internet of Things (IoT), massive amounts of data need to be collected from the environment by sensing devices and transmitted to servers for further processing. In data processing solutions, the data sensing, transmission, and computing stages are designed independently. This mechanism leads to the data sensing and transmission stages competing for spectrum resources, while the computing stage competes with the other two for time resources.

[0076] To achieve simultaneous communication and sensing, the target reflected signal is projected into a transmission space orthogonal to the communication signal. To further improve communication and sensing efficiency, multi-antenna systems have been developed to achieve multiple-transmit, multiple-receive radar sensing and communication. Such systems require real-time feedback from both the sensing and communication transceivers, resulting in a significant information exchange burden. Therefore, to improve spectral efficiency, related technologies design radar communication and sensing multiplexed signals, utilizing integrated communication and sensing technology to achieve simultaneous data sensing and transmission at the physical layer. This involves designing dual-function signals that can be used for both target sensing and data transmission simultaneously. In practical applications, the dual-function waveform design for simultaneous target sensing and data transmission has been further extended to multi-antenna multiple-transmit, multiple-receive systems, where data information is embedded in the sidelobes of the target reflected signal.

[0077] However, since computation often occurs at the network or application layer, it is difficult to integrate with physical layer communication-sensing technologies. Over-the-air computing makes physical layer data computation possible. By utilizing the superposition property of analog signals propagating in multiple access channels, over-the-air computing can perform function calculations during signal propagation. Unlike traditional multiple access schemes, over-the-air computing aims to reduce the error between collected statistical information and true values. Based on over-the-air computing, integrated communication-sensing-computing technology can be implemented at the physical layer air interface. The over-the-air computing performance of integrated communication-sensing-computing systems is limited by the parameter design of beamformers. However, the design performance of beamformers in related technologies does not fully consider the computational stage, resulting in low resource utilization efficiency and poor computational performance in over-the-air computing.

[0078] Based on this, embodiments of the present invention provide a clustered beamforming method, an integrated communication sensing and computing system, and related devices. The beamforming at the transmitting end is designed to ensure radar sensing performance, and the beamforming at the receiving end is designed to improve airborne computing performance. The antennas at both the transmitting and receiving ends are adjusted simultaneously, thereby further improving resource utilization efficiency.

[0079] This application provides an antenna beamforming method and an integrated communication sensing and computing system, which are specifically described through the following embodiments. First, the antenna beamforming method in this application embodiment is described.

[0080] First, the communication-aware computing integrated system in the embodiments of this application is described.

[0081] Reference Figure 1 The integrated communication, sensing, and computing system 10 includes: one sensing target 110 and M sensing devices 120 each equipped with Ns antennas. The M sensing devices 120 constitute a device cluster. And a server 130 for performing over-the-air computing, the server 130 having Na antennas. In one embodiment, the server 130 may be a wireless router with data processing capabilities.

[0082] The entire signal transmission and reception time is divided into T time periods. Within each time period, each sensor device 120 can simultaneously transmit radar sensing signals for sensing the target 110 and data transmission signals for data communication. The radar sensing signals, after being reflected by the target 110, become target reflection signals, which are received by the corresponding sensor device 120. The data transmission signals, after being processed in the air, are received by the server 130. In the radar sensing phase, the target reflection matrix of each sensor device 120 to the target 110 is denoted as Gi, and in the data communication phase, the data transmission channel matrix of each sensor device 120 to the server 130 is denoted as Hi, where 1 ≤ i ≤ M.

[0083] In one embodiment, the antenna of each sensing device 120 is divided into two parts: one part is a sensing antenna for radar sensing, which has Nr antennas, and the other part is a data transmission antenna, which has Nc antennas. The number of antennas satisfies: Ns = Nr + Nc.

[0084] During radar sensing, in the m-th sensing device, Ntx antennas out of Nr sensing antennas are used to transmit radar sensing signals, and Nrx antennas are used to receive target reflected signals y. m (t), the number of antennas satisfies: Nr=Ntx+Nrx.

[0085] The initial data transmission signal sent by the m-th sensor device 120 in the t-th time period can be represented as a K-dimensional vector d. m (t), where K represents the number of functions that need to be computed in the air, which can be obtained in the actual computing scenario.

[0086] For different sensing devices 120, the initial data transmission signal d m (t) needs to satisfy the condition that the mean is 0 and the variance is 0.

[0087] Similarly, the initial radar sensing signal generated by the m-th sensor 120 in the t-th time period can also be represented as a K-dimensional vector s. m (t), the radar sensing signal needs to satisfy the condition that the mean is 0 and the variance is 1.

[0088] Furthermore, the data transmission signal and the radar sensing signal are orthogonal and statistically independent, meaning that for all i and m, the following condition is satisfied:

[0089] In one embodiment, the integrated communication sensing and computing system 10 further includes a beamformer. A beamformer is a device that uses an antenna sensor array to achieve beamforming and spatial filtering. It is a signal processing technique used for directional transmission or reception, implemented by combining elements in an antenna array. Beamforming is achieved by utilizing the principle that signals at specific angles are subject to relevant interference, while other signals are subject to interference cancellation, thus achieving spatial selectivity. The beamformer in this embodiment includes: a transmitter beamformer, a receiver beamformer, and a radar sensing beamformer. It is understood that the beamformer can be in matrix form. This application embodiment utilizes beamforming to improve the signal-to-noise ratio of the received signal, eliminate unwanted interference sources, and focus the transmitted signal to a specific location.

[0090] The antenna clustering beamforming method in the embodiments of the present invention is described below.

[0091] Figure 2This is an optional flowchart of the antenna clustering beamforming method provided in the embodiments of the present invention. Figure 2 The method may include, but is not limited to, steps S110 to S140. It is also understood that this embodiment... Figure 2 The order of steps S110 to S140 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0092] Step S110: Obtain the target reflection signals received by all sensing devices to obtain the processed signals, calculate the statistical result matrix based on the processed signals, obtain the root mean square error of the target reflection matrix of each sensing device based on the statistical result matrix, and construct the first sensing matrix constraint condition based on the error tolerance value.

[0093] In one embodiment, reference is made to Figure 3 The process of constructing the first perception matrix constraint conditions in step S110 includes the following steps:

[0094] Step S111: Calculate the target reflection signal based on the target reflection matrix of the sensing device, the radar sensing signal, and the interference signal.

[0095] In one embodiment, for the m-th sensing device, a transmitter beamformer W is used. m For the initial data transmission signal d m (t) Beamforming is performed to obtain the data transmission signal, represented as: W m d m (t), using radar sensing beamformer F m For the initial radar sensing signal s m (t) Beamforming is performed to obtain the radar sensing signal, denoted as F. m s m (t), therefore the transmitted signal x of the m-th sensing device in the t-th time period. m (t) is represented as:

[0096]

[0097] Among them, the transmitter beamformer W m For N c A matrix of order ×K, radar sensing beamformer F m For N tx A matrix of order ×K.

[0098] For the m-th sensing device, the target reflected signal y is obtained after the target reflects the radar sensing signal. m (t), represented as:

[0099] y m (t)=G mmF m s m (t)+Ω m (t)+n r (t)

[0100]

[0101] Among them, G mmm Let G represent the target reflection matrix of the Mth sensor. For the mth and ith sensors, G... imm Let Q represent the target reflection matrix of order Nrx×Ntx. im Let C represent the radar signal direct-path channel matrix of order Nrx×Ntx. im This represents the Nrx×Nc order data signal reflection channel matrix, O imm This represents the Nrx×Nc order data signal direct channel matrix, where n r (t) is an Nrx-dimensional additive white Gaussian noise vector that follows a Rayleigh distribution. Understandably, Q im and o im Obtained from the parameters of the actual communication system.

[0102] The above Ω m (t) represents the interference signal, which is calculated based on the transmitter beamformer, the data signal reflection channel matrix, the data signal direct channel matrix, the initial data transmission signal, and the initial radar sensing signal.

[0103] Step S112: Obtain the target reflection signal from the sensing device to obtain the processed signal, and optimize the processed signal according to the law of large numbers to obtain a statistical result matrix.

[0104] In one embodiment, by analyzing the target reflection signal y over T time periods... m By performing matched filtering on (t), the signal statistics of the m-th sensor can be obtained, denoted as the processed signal. This is an Nrx×K order matrix, specifically represented as:

[0105]

[0106] In one embodiment, based on the law of large numbers, the following approximate expression holds when the time period T is sufficiently long:

[0107]

[0108]

[0109]

[0110] Based on the above approximate expression, the processed signal will be... The results were optimized to obtain a statistical result matrix. Represented as:

[0111]

[0112]

[0113] Where, N m It is an Nrx×K matrix that follows a Rayleigh distribution:

[0114] Step S113: Obtain the estimated value of the target reflection matrix based on the statistical result matrix.

[0115] In one embodiment, a statistical result matrix is ​​calculated. probability density function Represented as:

[0116]

[0117]

[0118] The above probability density function Used to describe the statistical results matrix G at the target reflection matrix mm The probability density.

[0119] Next, G is found by minimizing the log-likelihood function. mm Maximum likelihood Represented as:

[0120]

[0121] The derivative of the maximum likelihood is expressed as:

[0122]

[0123] Setting the maximum likelihood value and its corresponding derivative to zero, we obtain an estimate of the target reflection matrix. Represented as:

[0124]

[0125] Step S114: Calculate the mean square error between the target reflection matrix and the estimated value to obtain the mean square error of the target reflection matrix. Make the mean square error of the target reflection matrix less than the error tolerance value and construct the first perception matrix constraint condition.

[0126] In one embodiment, the root mean square error of the target reflection matrix is ​​expressed as:

[0127]

[0128] Given the error tolerance value η of the m-th sensor. m Then the constraint condition of the first perception matrix is ​​expressed as:

[0129]

[0130] This yields the first perception matrix constraint condition.

[0131] Step S120: Based on the receiver beamformer, obtain the received vector according to the data transmission signal and the radar sensing signal, calculate the mean square error between the received vector and the real data value, and minimize the mean square error to construct the first result constraint condition.

[0132] In one embodiment, reference is made to Figure 4 The process of constructing the first result constraint in step S120 includes the following steps:

[0133] Step S121: Calculate the actual data value based on the initial data transmission signal of each sensing device.

[0134] In one embodiment, the actual data value is the initial data transmission signal of each sensing device, represented as:

[0135] Step S122: Calculate the total transmitted signal based on the transmitted signal of each sensing device.

[0136] In one embodiment, the transmission signal consists of a first transmission signal and a second transmission signal, wherein the first transmission signal is based on the data transmission signal W. m d m (t) and data transmission channel matrix H m The calculated value is represented as: H m W m d m (t). The second transmitted signal is based on the radar sensing signal F. m s m (t) and the target reflection signal channel matrix R m The calculated value is represented as: R m F m s m (t).

[0137] The transmission signal of the m-th sub-sensor obtained from the first and second transmission signals described above is expressed as: H m W m d m (t)+R m F m s m (t), the total transmitted signal is calculated from the transmitted signal of each sensing device and expressed as:

[0138]

[0139] Step S123: Obtain the receiving vector based on the beamformer at the receiving end and the total transmitted signal.

[0140] In one embodiment, the signal received by the server is the total transmitted signal resulting from the over-the-air superposition of the transmitted signals from each sensor device. Simultaneously, after reception, it undergoes beamforming by a beamformer at the receiving end to obtain the received vector. Received vector Let K be a K-dimensional vector, represented as:

[0141]

[0142] Where A represents the receiver beamformer, H m N represents the m-th sensor. a ×N c The data transmission channel matrix of order R m N represents the m-th sensor. a ×N tx The radar sensing signal channel matrix of order n c (t) is an N a A dimensional additive white Gaussian noise vector, which follows a distribution And with d m (t) and s m (t) Statistically independent. This is understandable given that R... m and H m Obtained from the parameters of the actual communication system.

[0143] Step S124: Calculate the mean square error between the received vector and the actual data value, obtain the result mean square error, and apply a minimization constraint to the result mean square error to obtain the first result constraint condition.

[0144] In one embodiment, the mean square error of the result obtained from the received vector and the actual data value is expressed as:

[0145]

[0146] In one embodiment, minimizing the mean squared error of the results, the first result constraint is expressed as:

[0147]

[0148] This leads to the first result constraint condition.

[0149] Step S130: Obtain the transmit power of the sensing device and construct the first power constraint condition.

[0150] In one embodiment, reference is made to Figure 5 The process of constructing the first perception matrix constraint conditions in step S130 includes the following steps:

[0151] Step S131: Calculate the trace of the data transmission signal to obtain the first power information, and calculate the trace of the radar sensing signal to obtain the second power information.

[0152] In one embodiment, the first power information is represented as: The second power information is represented as follows:

[0153] Step S132: Calculate the power information based on the first power information and the second power information, and make the power information less than or equal to the transmission power to construct the first power constraint condition.

[0154] In one embodiment, since the transmit power P of each sensing device is limited, the beamforming design must satisfy a first power constraint condition, expressed as:

[0155]

[0156] This leads to the first power constraint condition.

[0157] Step S140: Construct a first set of constraints based on the first result constraints, the first sensing matrix constraints, and the first power constraints; solve the first set of constraints to obtain the first optimized beamforming weight value of the receiver beamformer and the second optimized beamforming weight value of the transmitter beamformer.

[0158] In one embodiment, the first set of constraints is represented as:

[0159]

[0160]

[0161] In one embodiment, due to the coupling relationship between the transmitter beamformer, the receiver beamformer, and the radar sensing beamformer, the first set of constraints is a non-convex optimization problem, thus requiring transformation. (Refer to...) Figure 6 Step S140 includes the following steps:

[0162] Step S141: Use zero-forcing design to obtain the first transformation relationship between the transmitter beamformer and the receiver beamformer.

[0163] In one embodiment, nulling design is a technique for suppressing signal interference. By introducing specific structures and parameters into the system, the response of the interfering signal to a specific output is made zero, thereby suppressing the interfering signal. In this embodiment, nulling design is used to remove the coupling relationship between the transmitter beamformer and the receiver beamformer. The first transformation relationship between the transmitter beamformer and the receiver beamformer is expressed as:

[0164]

[0165] Step S142: Transform the first set of constraints into the second set of constraints based on the first transformation relationship.

[0166] In one embodiment, reference is made to Figure 7 Step S142 includes the following steps:

[0167] Step S1421: Based on the first transformation relationship, replace the transmitter beamformer with the receiver beamformer in the first result constraint to obtain the second result constraint.

[0168] In one embodiment, substituting the first transformation relationship into the first result constraint yields the second result constraint, expressed as:

[0169]

[0170] Step S1422: Based on the first transformation relationship, replace the transmitter beamformer with the receiver beamformer in the first power constraint condition to obtain the second power constraint condition.

[0171] In one embodiment, the second power constraint condition is expressed as:

[0172]

[0173] Step S1423: Generate a second set of constraints based on the second result constraints, the first perception matrix constraints, and the second power constraints.

[0174] In one embodiment, the second set of constraints is represented as:

[0175]

[0176]

[0177]

[0178] After the above process, the coupling relationship between the transmitting beamformer and the receiving beamformer is removed. The variables in the second constraint set include the receiving beamformer A and the radar sensing beamformer F. m Receiver beamformer A and radar sensing beamformer F m The coupling relationship still exists, so the second set of constraints is still a non-convex optimization problem.

[0179] Step S143: Using the principle of orthogonal matrices, obtain the result update formula of the radar sensing beamformer based on the unitary matrix and scaling factor, and transform the second set of constraints into the third set of constraints based on the result update formula.

[0180] In one embodiment, a unitary matrix in linear algebra refers to a complex square matrix whose conjugate transpose is equal to its inverse. Simply put, a unitary matrix is ​​a complex matrix that satisfies specific conditions. If the domain of the matrix is ​​limited to the real number domain, then a unitary matrix is ​​called a real orthogonal matrix, and in this case, a unitary matrix is ​​equivalent to an orthogonal matrix. In this embodiment, to further decouple the radar sensing beamformer F, a design is employed in a multi-antenna system. m Restricting it to an orthogonal matrix, let D m Indicates satisfaction From the unitary matrix, we can obtain the result update formula, expressed as: F m =α m D m , where α m It is a positive proportionality factor.

[0181] In one embodiment, reference is made to Figure 8 Step S143 includes the following steps:

[0182] Step S1431: Replace the radar sensing beamformer in the second result constraint with the result update formula to obtain the third result constraint.

[0183] In one embodiment, the third result constraint is expressed as:

[0184]

[0185] Step S1432: Replace the radar sensing beamformer in the first sensing matrix constraint with the result update formula to obtain the second sensing matrix constraint.

[0186] In one embodiment, the result update formula is substituted into the first perception matrix constraint to obtain the second perception matrix constraint, which is expressed as follows:

[0187]

[0188] Step S1433: Replace the radar sensing beamformer in the second power constraint with the result update formula to obtain the third power constraint.

[0189] In one embodiment, the third power constraint condition is expressed as:

[0190]

[0191] Step S1434: Generate a third set of constraints based on the third result constraints, the second perception matrix constraints, and the third power constraints.

[0192] In one embodiment, the third set of constraints is represented as:

[0193]

[0194]

[0195]

[0196] As can be seen from the above, the third set of constraints has already included the receiver beamformer A and the radar sensing beamformer F. m The coupling relationship is removed.

[0197] Step S144: Represent the receiver beamformer as a positive semi-definite matrix using positive semi-definite scaling. The third set of constraints is transformed into the fourth set of constraints based on the positive semi-definite matrix.

[0198] In one embodiment, the receiver beamformer is represented as a positive semi-definite matrix using positive semi-definite scaling. A positive semidefinite matrix is ​​represented as: Reference Figure 9 Step S144 includes the following steps:

[0199] Step S1441: Obtain the minimum factor value of the scaling factor.

[0200] From the constraints of the second perception matrix mentioned above, it can be seen that increasing α m This will increase the error in aerial calculations. Therefore, in this embodiment, to minimize the aerial calculation error, we take α. m To find the minimum value, we can make the left and right sides of the second perception matrix constraint equal, thus obtaining the minimum factor value. Represented as:

[0201]

[0202] Step S1442: Convert the third result constraint into the fourth result constraint based on the minimum factor value and the positive semidefinite matrix.

[0203] In one embodiment, the fourth result constraint is expressed as:

[0204]

[0205] Step S1443: Convert the third power constraint into the fourth power constraint based on the minimum factor value and the positive semidefinite matrix.

[0206] In one embodiment, the fourth power constraint condition is expressed as:

[0207]

[0208] Step S1444: Generate the fourth set of constraints based on the fourth result constraint, the fourth power constraint, and the positive semi-definite matrix.

[0209] In one embodiment, the fourth set of constraints is represented as:

[0210]

[0211]

[0212]

[0213] Here, ≥ indicates that the matrix is ​​positive semi-definite, that is, every element in the matrix is ​​not less than zero.

[0214] At this point, the transmitter beamformer W... m The optimized transformation of the receiver beamformer A into a semi-positive definite matrix. The optimization process, i.e., the objective function of the fourth constraint set is a positive semi-definite matrix. Solve for it.

[0215] Step S145: Perform convex optimization on the fourth constraint set to obtain the first beamforming weight optimization value of the receiver beamformer, and obtain the second beamforming weight optimization value of the transmitter beamformer based on the first transformation relationship.

[0216] In one embodiment, reference is made to Figure 10 Step S145 includes the following steps:

[0217] Step S1451: Perform convex optimization on the fourth constraint set to obtain a positive semi-definite matrix.

[0218] In one embodiment, since the objective function in the fourth constraint set is linear and all constraints are convex, the fourth constraint set is transformed into a convex problem. Solving the fourth constraint set using convex optimization yields the positive semi-definite matrix. The value of .

[0219] In one embodiment, convex optimization is solved using a convex optimization toolkit (e.g., Matlab CVX toolkit, etc.). This embodiment does not specifically limit the solution method.

[0220] Step S1452: Perform Gaussian cyclic solving on the positive semi-definite matrix to obtain the first optimized beamforming weight value of the receiver beamformer.

[0221] In one embodiment, due to The first beamforming weight optimization value of receiver beamformer A is obtained by using the Gaussian cyclic algorithm, and then the first beamforming weight optimization value of receiver beamformer A is obtained according to the first transformation relationship.

[0222] In one embodiment, the Gaussian loop solver is used to solve systems of linear equations. It can transform an arbitrarily complex system of linear equations into a row echelon form matrix, thereby simplifying the solution process. The basic idea of ​​the Gaussian loop solver is to simplify the system of equations through a series of linear transformations, ultimately converting it into a row echelon form matrix such that the coefficient of each unknown appears only on the main diagonal of its corresponding row. Specifically, the algorithm consists of the following steps:

[0223] Perform elementary row operations on the coefficient matrix to transform it into an upper triangular matrix. This involves eliminating elements from the lower triangular region of the coefficient matrix. This can be achieved by multiplying the first row by the reciprocal of the coefficient of the first term in the first column of the coefficient matrix and then adding it to the subsequent rows (i.e., Gaussian elimination), or by using elementary row operations on the matrix (e.g., swapping two rows or adding a multiple of one row to another).

[0224] Then, starting from the last row, solve for the corresponding unknowns one by one. For the i-th unknown, the value of the i-th unknown can be obtained by multiplying all the coefficients on the left side of the i-th row (i-1 rows and above) by their corresponding unknown values, and then subtracting them from the right-hand side of the equation.

[0225] Repeat the above process until all unknowns have been solved.

[0226] It is important to note that when performing Gaussian loop solutions, if all elements in a column of the coefficient matrix are 0, that column cannot be used as the main diagonal. Instead, a non-zero element needs to be moved to the main diagonal of that column by swapping rows.

[0227] Thus, this embodiment of the application obtains the first beamforming weight optimization value of the receiver beamformer A and the transmitter beamformer W of the M sensing devices. m The second beamforming weight optimization value enables simultaneous adjustment of the antennas at both the transmitting and receiving ends.

[0228] In one embodiment, reference is made to Figure 11 This is a flowchart of the signal processing of a communication-sensing-computing integrated system.

[0229] Reference Figure 11 For the m-th sensing device, the transmitting beamformer W is first used. m For the initial data transmission signal d m (t) Beamforming is performed to obtain the data transmission signal W m d m (t), using radar sensing beamformer F m For the initial radar sensing signal s m (t) Beamforming is performed to obtain the radar sensing signal F m s m (t). Then the radar sensing signal and the data transmission signal propagate in the channel, utilizing the data transmission channel matrix H. m Gain is applied to obtain the transmitted signal of the m-th sensor. Simultaneously, the radar sensing signal F... m s m (t) Passing through the target reflection matrix G mm After adjustment, Ω is then added. m (t) represents the interference signal and the additive white Gaussian noise vector n. r (t) Obtain the target reflection signal y m (t), based on the target reflection signal y m (t) yields an estimate of the target reflection matrix. This yields the mean square error of the target reflection matrix (MSE, Gmm). Then, the server's receive vector is calculated. For the server, the received signal is the total transmitted signal resulting from the overlay of the signals from each sensor after in-flight computation. After reception, the signal is beamformed by the receiver's beamformer to obtain the receive vector.

[0230] It is understood that, after the joint design of beamforming for the antennas at the transceiver ends of radar sensing and communication signals in the embodiments of this application, the computing performance of airborne computing is improved.

[0231] In integrated communication-sensing-computing applications, multiple multi-antenna sensing devices simultaneously transmit radar sensing signals for target detection and communication signals for data transmission. The radar sensing signals are received by the sensing devices after being reflected from the target, while the communication signals are received by the server after in-flight computation. The sensors extract target information based on the received radar sensing signals, while the server infers statistical information from the received in-flight computation results. In related technologies, antenna selection schemes superimpose the channel gains of all users and select the receiving antennas with the highest channel gains from the superimposed results as the receiving ends. However, the antenna clustering beamforming technology in this application's integrated communication-sensing-computing system uses the target reflection matrix mean square error as the standard for measuring radar sensing performance and the mean square error between the received vector and the actual data value as the standard for measuring in-flight computation performance. Since these two are in competition, to maximize in-flight computation performance while ensuring radar sensing performance, this application's embodiment employs a joint beamforming design for the antennas at both the radar sensing and communication signal transceiver ends, thereby improving communication performance.

[0232] Figure 12 The curve shows the variation of the mean square error (MSE) of the over-the-air computation results with the number of server antennas. It can be seen that the MSE gradually decreases as the number of server antennas increases. This is because more receiving antennas increase the optimization dimensionality of the receiver beamformer, thereby reducing errors through graded gain. Compared with antenna selection schemes in related technologies, the antenna clustering beamforming method of this application can significantly reduce the MSE of the over-the-air computation results.

[0233] Figure 13 The curve showing the variation of the mean square error of the airborne computation results with the number of antennas of the sensing device is presented to normalize the results. It can be seen that the mean square error of the airborne computation gradually increases with the increase of the number of antennas of the sensing device. This is because increasing the number of antennas of the sensing device leads to an increase in the dimension of the target reflection matrix of the sensed target, thus making the radar sensing performance more strictly limited. Compared with antenna selection schemes in related technologies, the antenna clustering beamforming method of this application can significantly reduce the mean square error of the airborne computation results.

[0234] The technical solution provided by this invention constructs a first result constraint condition related to the received result, a first perception matrix constraint condition related to the perception performance, and a first power constraint condition related to the transmitted power. A first constraint condition set is constructed using these constraints, and then the first constraint condition set is solved to obtain the optimized values ​​of the receiver beamformer and the transmitter beamformer. This application's embodiment designs the transmitter beamformer to ensure radar perception performance and the receiver beamformer to improve airborne computing performance, while simultaneously adjusting the antennas at both the transmitter and receiver ends, thereby further improving resource utilization efficiency.

[0235] This invention also provides an antenna clustering beamforming device, which can implement the above-described antenna clustering beamforming method, see reference. Figure 14 Applied to, for example Figure 1 The communication-sensing-computing integrated system includes: a transmitter beamformer, a receiver beamformer, a radar sensing beamformer, and at least one sensing device. The sensing device transmits signals by: beamforming an initial data transmission signal using the transmitter beamformer to obtain a data transmission signal, and beamforming an initial radar sensing signal using the radar sensing beamformer to obtain a radar sensing signal. The sensing device also receives target reflection signals obtained from the perceived target reflection transmission signal. The device includes:

[0236] The perception matrix constraint construction module 1410 is used to obtain the target reflection signal received by the sensing device to obtain the processed signal, calculate the statistical result matrix based on the processed signal, obtain the root mean square error of the target reflection matrix of each sensing device based on the statistical result matrix, and construct the first perception matrix constraint based on the error tolerance value.

[0237] The result constraint construction module 1420 is used to obtain the received vector based on the data transmission signal and the radar sensing signal from the receiver beamformer, calculate the result mean square error between the received vector and the real data value, and construct the first result constraint by minimizing the result mean square error.

[0238] The power constraint construction module 1430 is used to obtain the transmission power of the sensing device and construct the first power constraint.

[0239] The weight optimization value calculation module 1440 is used to construct a first set of constraints based on the first result constraints, the first sensing matrix constraints, and the first power constraints, and to solve the first set of constraints to obtain the first beamforming weight optimization value of the receiver beamformer and the second beamforming weight optimization value of the transmitter beamformer.

[0240] The specific implementation of the antenna clustering beamforming device in this embodiment is basically the same as the specific implementation of the antenna clustering beamforming method described above, and will not be repeated here.

[0241] This invention also provides an electronic device, comprising:

[0242] At least one memory;

[0243] At least one processor;

[0244] At least one program;

[0245] The program is stored in a memory, and the processor executes the at least one program to implement the antenna clustering beamforming method described above in this invention. The electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0246] Please see Figure 15 , Figure 15 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0247] The processor 1501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, 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 invention.

[0248] The memory 1502 can be implemented in the form of ROM (Read-Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1502 can store the 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 1502 and is called and executed by the processor 1501 to execute the antenna clustering beamforming method of the embodiments of this invention.

[0249] The input / output interface 1503 is used to implement information input and output;

[0250] Communication interface 1504 is used to enable communication and interaction between this device and other devices. Communication can be achieved via wired means (e.g., USB, Ethernet cable) or wireless means (e.g., mobile network, Wi-Fi, Bluetooth).

[0251] Bus 1505 transmits information between various components of the device (e.g., processor 1501, memory 1502, input / output interface 1503, and communication interface 1504);

[0252] The processor 1501, memory 1502, input / output interface 1503 and communication interface 1504 are connected to each other within the device via bus 1505.

[0253] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described antenna clustering beamforming method.

[0254] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0255] The clustered beamforming method, integrated communication-sensing-computing system, and related devices proposed in this invention construct first result constraints related to the received results, first sensing matrix constraints related to sensing performance, and first power constraints related to the transmitted power. A first constraint set is constructed using these constraints, and then the first constraint set is solved to obtain optimized values ​​for the receiver and transmitter beamformers. This application's embodiments design the transmitter beamforming to ensure radar sensing performance and the receiver beamforming to improve airborne computing performance, while simultaneously adjusting the antennas at both the transmitter and receiver ends, thereby further improving resource utilization efficiency.

[0256] The embodiments described in this application are for the purpose of more clearly illustrating 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. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0257] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0258] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0259] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0260] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0261] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0262] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0263] The units described above as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0264] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0265] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0266] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for antenna clustering beamforming, characterized in that, An integrated communication-sensing-computing system is applied, comprising: a transmitting beamformer, a receiving beamformer, a radar sensing beamformer, and at least one sensing device. The transmitting signal of the sensing device includes: beamforming an initial data transmission signal using the transmitting beamformer to obtain a data transmission signal, and beamforming an initial radar sensing signal using the radar sensing beamformer to obtain a radar sensing signal. The sensing device is also used to receive a target reflection signal obtained by sensing a target reflecting the transmitted signal. The method includes: The target reflection signal received by the sensing device is obtained to obtain a processed signal. A statistical result matrix is ​​calculated based on the processed signal. The root mean square error of the target reflection matrix of each sensing device is obtained based on the statistical result matrix. A first sensing matrix constraint condition is constructed based on the error tolerance value. Based on the receiver beamformer, a receiving vector is obtained according to the data transmission signal and the radar sensing signal, and the mean square error between the receiving vector and the real data value is calculated. The first result constraint condition is constructed by minimizing the mean square error. Obtain the transmit power of the sensing device to construct a first power constraint condition; A first set of constraints is constructed based on the first result constraint, the first sensing matrix constraint, and the first power constraint. The first set of constraints is solved to obtain the first optimized beamforming weight value of the receiver beamformer and the second optimized beamforming weight value of the transmitter beamformer.

2. The antenna clustering beamforming method according to claim 1, characterized in that, The receiver beamformer obtains a received vector based on the data transmission signal and the radar sensing signal, calculates the mean square error between the received vector and the actual data value, and minimizes the mean square error to construct a first result constraint, including: The actual data value is calculated based on the initial data transmission signal of each of the aforementioned sensing devices; The total transmission signal is calculated based on the transmission signal of each of the aforementioned sensing devices. The transmission signal consists of a first transmission signal and a second transmission signal. The first transmission signal is calculated based on the data transmission signal and the data transmission channel matrix, and the second transmission signal is calculated based on the radar sensing signal and the target reflection signal channel matrix. The receiving vector is obtained based on the receiving beamformer and the total transmitted signal. Calculate the mean square error between the received vector and the actual data value to obtain the result mean square error, and apply a minimization constraint to the result mean square error to obtain the first result constraint condition.

3. The antenna clustering beamforming method according to claim 1, characterized in that, The process involves acquiring the target reflection signal received by the sensing device to obtain a processed signal, calculating a statistical result matrix based on the processed signal, obtaining the root mean square error of the target reflection matrix for each sensing device based on the statistical result matrix, and constructing a first sensing matrix constraint condition based on the error tolerance value, including: The target reflection signal is calculated based on the target reflection matrix of the sensing device, the radar sensing signal, and the interference signal. The target reflection signals of all the sensing devices are acquired to obtain the processed signal, and the processed signal is optimized according to the law of large numbers to obtain the statistical result matrix; The estimated value of the target reflection matrix is ​​obtained based on the statistical result matrix; Calculate the mean squared error between the target reflection matrix and the estimated value to obtain the mean squared error of the target reflection matrix, and make the mean squared error of the target reflection matrix less than the error tolerance value to construct the first perception matrix constraint condition.

4. The antenna clustering beamforming method according to claim 1, characterized in that, The step of obtaining the transmit power of the sensing device to construct the first power constraint condition includes: The first power information is obtained by calculating the trace of the data transmission signal, and the second power information is obtained by calculating the trace of the radar sensing signal; Calculate power information based on the first power information and the second power information, and make the power information less than or equal to the transmission power to construct the first power constraint condition.

5. The antenna clustering beamforming method according to claim 1, characterized in that, Solving the first set of constraints to obtain the optimized first beamforming weight value of the receiver beamformer and the optimized second beamforming weight value of the transmitter beamformer includes: The first transformation relationship between the transmitting beamformer and the receiving beamformer is obtained by using zero-forcing design; Based on the first transformation relationship, the first set of constraints is transformed into the second set of constraints. Using the principle of orthogonal matrices, the result update formula of the radar sensing beamformer is obtained based on the unitary matrix and the scaling factor, and the second set of constraints is transformed into the third set of constraints based on the result update formula. The receiver beamformer is represented as a positive semi-definite matrix using positive semi-definite scaling, and the third set of constraints is transformed into a fourth set of constraints based on the positive semi-definite matrix. The fourth set of constraints is solved by convex optimization to obtain the first beamforming weight optimization value of the receiver beamformer, and the second beamforming weight optimization value of the transmitter beamformer is obtained based on the first transformation relationship.

6. The antenna clustering beamforming method according to claim 5, characterized in that, The step of transforming the first set of constraints into the second set of constraints based on the first transformation relationship includes: Based on the first transformation relationship, the receiver beamformer is used to replace the transmitter beamformer in the first result constraint to obtain the second result constraint. Based on the first conversion relationship, the receiver beamformer is used to replace the transmitter beamformer in the first power constraint condition to obtain the second power constraint condition; The second set of constraints is generated based on the second result constraint, the first perception matrix constraint, and the second power constraint.

7. The antenna clustering beamforming method according to claim 6, characterized in that, The process of using the orthogonal matrix principle to obtain the result update formula of the radar sensing beamformer based on the unitary matrix and scaling factor, and transforming the second set of constraints into a third set of constraints based on the result update formula, includes: The radar sensing beamformer in the second result constraint is replaced with the result update formula to obtain the third result constraint. The radar sensing beamformer in the first sensing matrix constraint is replaced with the result update formula to obtain the second sensing matrix constraint. The radar sensing beamformer in the second power constraint condition is replaced with the updated formula to obtain the third power constraint condition. The third set of constraints is generated based on the third result constraint, the second perception matrix constraint, and the third power constraint.

8. The antenna clustering beamforming method according to claim 7, characterized in that, The step of representing the receiver beamformer as a positive semi-definite matrix using positive semi-definite scaling, and then transforming the third set of constraints into a fourth set of constraints based on the positive semi-definite matrix, includes: Obtain the minimum factor value of the scaling factor; The third result constraint is converted into a fourth result constraint based on the minimum factor value and the positive semidefinite matrix. The third power constraint is converted into a fourth power constraint based on the minimum factor value and the positive semidefinite matrix. The fourth set of constraints is generated based on the fourth result constraint, the fourth power constraint, and the semi-positive definite matrix.

9. The antenna clustering beamforming method according to claim 8, characterized in that, The step of performing convex optimization on the fourth set of constraints to obtain the optimized value of the first beamforming weight of the receiver beamformer includes: The positive semi-definite matrix is ​​obtained by performing convex optimization on the fourth set of constraints. The positive semi-definite matrix is ​​solved by Gaussian cyclic solution to obtain the first beamforming weight optimization value of the receiver beamformer.

10. An antenna clustering beamforming device, characterized in that, An integrated communication-sensing-computing system is applied, comprising: a transmitter beamformer, a receiver beamformer, a radar sensing beamformer, and at least one sensing device. The transmitting signal of the sensing device includes: beamforming an initial data transmission signal using the transmitter beamformer to obtain a data transmission signal, and beamforming an initial radar sensing signal using the radar sensing beamformer to obtain a radar sensing signal. The sensing device is also used to receive a target reflection signal obtained by sensing a target reflecting the transmitted signal. The device includes: The perception matrix constraint construction module is used to obtain the target reflection signal received by the sensing device to obtain a processed signal, calculate a statistical result matrix based on the processed signal, obtain the root mean square error of the target reflection matrix of each sensing device based on the statistical result matrix, and construct a first perception matrix constraint based on the error tolerance value. The result constraint construction module is used to obtain the receiving vector based on the receiving beamformer, the data transmission signal and the radar sensing signal, calculate the result mean square error between the receiving vector and the real data value, and minimize the result mean square error to construct the first result constraint. A power constraint construction module is used to obtain the transmission power of the sensing device and construct a first power constraint condition. The weight optimization value calculation module is used to construct a first set of constraints based on the first result constraint, the first sensing matrix constraint, and the first power constraint, and solve the first set of constraints to obtain the first beamforming weight optimization value of the receiver beamformer and the second beamforming weight optimization value of the transmitter beamformer.

11. A communication-sensing-computing integrated system, characterized in that, The system includes a transmitter beamformer and a receiver beamformer, wherein the first beamforming weight optimization value of the receiver beamformer and the second beamforming weight optimization value of the transmitter beamformer are calculated according to the antenna clustering beamforming method according to any one of claims 1 to 9.

12. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the antenna clustering beamforming method according to any one of claims 1 to 9.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the antenna clustering beamforming method according to any one of claims 1 to 9.

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