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

Abstract

The embodiment of the application provides a multiplex beamforming method, a communication and sensing and computing integrated system and related devices, and relates to the technical field of communication. The method comprises the following steps: constructing a first result constraint condition related to a receiving result, a first sensing matrix constraint condition related to sensing performance and a first power constraint condition related to transmitting power by using a multiplex signal capable of simultaneously performing communication, sensing and computing; 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, so as to obtain the optimized values of a receiving end beamformer and a transmitting end beamformer. The beamforming of the transmitting end and the beamforming of the receiving end are designed, and the antennas of the transmitting and receiving ends are simultaneously adjusted, the air computing error is minimized under the premise of ensuring the sensing accuracy, the air computing performance and the data processing efficiency are improved, and therefore the resource utilization efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to multiplexed beamforming methods, integrated communication sensing and computing systems, 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 radar 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 sensing signals and communication signals competing for wireless spectrum resources, increasing the burden on wireless channels and causing communication links to become more congested.

[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-computing integrated 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 multiplexed 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 multiplexing beamforming method applied to a communication-sensing-computing integrated system. The communication-sensing-computing integrated system includes: a transmitting beamformer, a receiving beamformer, and at least one radar device. The radar device transmits a multiplexed signal obtained by beamforming an initial multiplexed signal using the transmitting beamformer. The radar 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 radar 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 radar device is obtained based on the statistical result matrix. A first perception 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 multiplexed signal, and the mean square error between the receiving vector and the actual data value is calculated. The first result constraint condition is constructed by minimizing the mean square error.

[0008] Obtain the transmit power of the radar device and 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 one embodiment, the step of obtaining a received vector based on the multiplexed signal using the receiver beamformer, 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 multiplexed signal of each of the radar devices;

[0012] The total transmitted signal is calculated based on the transmitted signal of each of the radar devices.

[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 one embodiment, the process of acquiring the target reflection signal received by the radar 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 radar 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 radar equipment, the multiplexed signal, and the interference signal; the interference signal is calculated based on the transmitter beamformer.

[0017] The target reflection signals of all the radar 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 one embodiment, obtaining the transmit power of the radar device to construct a first power constraint includes:

[0021] Power information is obtained from the transmitting beamformer;

[0022] The power information is set to be less than or equal to the transmission power, and the first power constraint condition is constructed.

[0023] In one embodiment, solving 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 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] The receiver beamformer is represented as a positive semi-definite matrix using positive semi-definite scaling, and the second set of constraints is transformed into a third set of constraints based on the positive semi-definite matrix.

[0027] The third 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.

[0028] In one embodiment, transforming the first set of constraints into a second set of constraints based on the first transformation relationship includes:

[0029] 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.

[0030] 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;

[0031] Based on the first transformation relationship, the receiving beamformer is used to replace the transmitting beamformer in the first sensing matrix constraint condition to obtain the second sensing matrix constraint condition.

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

[0033] In one embodiment, representing the receiver beamformer as a positive semi-definite matrix using positive semi-definite scaling, and transforming the second set of constraints into a third set of constraints based on the positive semi-definite matrix, includes:

[0034] The second result constraint is converted into a third result constraint based on the positive semi-definite matrix.

[0035] The second power constraint is converted into a third power constraint based on the positive semidefinite matrix.

[0036] The second perception matrix constraint is converted into a third perception matrix constraint based on the positive semi-definite matrix.

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

[0038] In one embodiment, the step of performing convex optimization on the third set of constraints to obtain the optimized first beamforming weight value of the receiver beamformer includes:

[0039] The positive semi-definite matrix is ​​obtained by performing convex optimization on the third set of constraints.

[0040] The optimization problem of the semi-positive definite matrix is ​​solved to obtain the optimized value of the first beamforming weight of the receiver beamformer.

[0041] To achieve the above objectives, a second aspect of this application provides an antenna multiplexing beamforming device applied to a communication sensing and computing integrated system. The communication sensing and computing integrated system includes: a transmitting beamformer, a receiving beamformer, and at least one radar device. The radar device transmits a multiplexed signal obtained by beamforming an initial multiplexed signal using the transmitting beamformer. The radar device is also used to receive a target reflection signal obtained by sensing a target reflecting the transmitted signal. The device includes:

[0042] The perception matrix constraint construction module is used to obtain the target reflection signal received by the radar 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 radar device based on the statistical result matrix, and construct a first perception matrix constraint based on the error tolerance value.

[0043] The result constraint construction module is used to obtain the receiving vector based on the multiplexed signal using the receiving beamformer, 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.

[0044] A power constraint construction module is used to obtain the transmit power of the radar device and construct a first power constraint condition.

[0045] 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.

[0046] To achieve the above objectives, a third aspect of the present application proposes a communication-sensing-computing integrated system, the system comprising 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 multiplexing beamforming method described in any one of the first aspects.

[0047] 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.

[0048] 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.

[0049] The multiplexed beamforming method, integrated communication-sensing-computing system, and related devices proposed in this application utilize multiplexed signals capable of simultaneous communication, sensing, and computation to 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 design incorporates beamforming at both the transmitter and receiver ends, simultaneously adjusting the antennas at both ends. While ensuring sensing accuracy, it minimizes in-flight computation errors, improves in-flight computation performance and data processing efficiency, thereby enhancing resource utilization efficiency. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the integrated communication-sensing-computing system provided in an embodiment of the present invention.

[0051] Figure 2 This is a flowchart of an antenna multiplexing beamforming method provided in another embodiment of the present invention.

[0052] Figure 3 yes Figure 2 The flowchart of step S110.

[0053] Figure 4 yes Figure 2 The flowchart of step S120.

[0054] Figure 5 yes Figure 2 The flowchart of step S130.

[0055] Figure 6 yes Figure 2 The flowchart for step S140.

[0056] Figure 7 yes Figure 6 The flowchart for step S142 in the process.

[0057] Figure 8 yes Figure 6 The flowchart for step S144.

[0058] Figure 9 yes Figure 6 The flowchart for step S145.

[0059] Figure 10 This is a signal processing flowchart of an integrated communication, sensing, and computing system provided in another embodiment of the present invention.

[0060] Figure 11 This is a schematic diagram of a target positioning application scenario in the application scenario of the antenna multiplexing beamforming method provided in another embodiment of the present invention.

[0061] Figure 12 yes Figure 11 A schematic diagram of the calculation results.

[0062] Figure 13 This is a structural block diagram of an antenna multiplexing beamforming device provided in another embodiment of the present invention.

[0063] Figure 14 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0064] 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.

[0065] 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.

[0066] 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.

[0067] First, let's clarify some of the terms used in this invention:

[0068] 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.

[0069] With the development of the Internet of Things (IoT), massive amounts of data need to be collected from the environment by radar 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.

[0070] 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.

[0071] 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.

[0072] Based on this, embodiments of the present invention provide a multiplexed beamforming method, an integrated communication sensing and computing system, and related devices. The method designs beamforming at the transmitting end and beamforming at the receiving end, and simultaneously adjusts the antennas at the transmitting and receiving ends. Under the premise of ensuring sensing accuracy, it minimizes in-flight computing errors, improves in-flight computing performance and data processing efficiency, thereby improving resource utilization efficiency.

[0073] 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.

[0074] First, the communication-aware computing integrated system in the embodiments of this application is described.

[0075] Reference Figure 1 The integrated communication, sensing, and computing system 10 includes: one sensing target 110 and M radar devices 120 each equipped with Ns antennas. The M radar devices 120 constitute a device cluster. The system also includes a server 130 for in-flight computation, which has Na antennas. In one embodiment, each radar device 120 uses Ns antennas to simultaneously sense one target and transmits the obtained multi-type sensing data to the server 130. The data undergoes waveform superposition during signal transmission to achieve in-flight computation, and the server 130 ultimately demodulates the required computation results. In one embodiment, the server 130 may be a wireless router with data processing capabilities.

[0076] The entire signal transmission and reception time is divided into T time periods. Within each time period, each radar device 120 transmits a multiplexed signal. Each multiplexed signal carries data for in-flight computation and also serves as a radar sensing pulse for sensing the target 110 and for data communication. The multiplexed signal can perform sensing, communication, and computation simultaneously. The multiplexed signal, after being reflected by the target 110, becomes the target reflection signal, which is received by the corresponding radar device 120. Simultaneously, the multiplexed signal, after in-flight computation, is received by the server 130. In the radar sensing phase, the target reflection matrix of each radar device 120 to the target 110 is Gii, and in the data communication phase, the data transmission channel matrix of each radar device 120 to the server 130 is Hi, where 1 ≤ i ≤ M.

[0077] In one embodiment, N on each radar device 120 tx One antenna is used for transmitting multiplexed signals, N rx The root is used for receiving the target's reflected signal, where N tx +N rx =N s The transmitted signals of each device follow an independent identical distribution with a mean of 0 and a variance of 1.

[0078] In one embodiment, the initial multiplexed signal transmitted by the m-th radar device 120 in the t-th time period can be represented as a K-dimensional vector s. m [t], where K represents the number of functions requiring in-flight computation, which can be obtained in the actual computation scenario. In one embodiment, the initial multiplexed signal can be either a sensing signal or a data transmission signal. The antenna multiplexing beamforming method of this application embodiment enables the multiplexed signal obtained from the initial multiplexed signal to simultaneously possess radar sensing and data communication functions.

[0079] For different radar devices 120, the initial multiplexed signal s m [t] needs to satisfy the condition of being independent and identically distributed with a mean of 0 and a variance of 1, i.e. In addition, the initial multiplexed signals of all radar devices 120 with i ≠ m must meet the following conditions:

[0080] 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 and a receiver beamformer, wherein the transmitter beamformer is used to beamform multiplexed signals. 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.

[0081] The antenna multiplexing beamforming method in the embodiments of the present invention is described below.

[0082] Figure 2 This is an optional flowchart of the antenna multiplexing 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.

[0083] Step S110: Obtain the target reflection signal received by the radar 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 radar device based on the statistical result matrix, and construct the first perception matrix constraint condition based on the error tolerance value.

[0084] 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:

[0085] Step S111: Calculate the target reflection signal based on the target reflection matrix, multiplexed signal, and interference signal of the radar equipment.

[0086] In one embodiment, for the m-th radar device, the transmitting beamformer W is used. m For the initial multiplexed signal s m [t] Beamforming is performed to obtain a multiplexed signal, which is the transmitted signal x. m (t), represented as:

[0087] x m [t] = W m s m [t]

[0088] Among them, the transmitter beamformer W m For N tx A matrix of order ×K.

[0089] For the m-th radar device, the target reflected signal y is obtained after sensing the multiplexed signal reflection from the target. m (t), represented as:

[0090] y m [t] = G mm W m s m [t]+Ω m [t]+n r [t]

[0091]

[0092] Among them, G mn Let G represent the target reflection matrix of the Mth radar device. For the mth and ith radar devices, G... im Let Q represent the target reflection matrix of order Nrx×Ntx. im Let n represent the direct interference matrix of order Nrx×Ntx. r (t) is an additive white Gaussian noise vector of order Nrx, with a mean of 0 and a variance of . Understandably, Q im Obtained from the parameters of the actual communication system.

[0093] The above Ω m (t) represents the interference signal, which is calculated based on the target reflection matrix, the transmitter beamformer, the direct interference matrix, and the initial multiplexed signal.

[0094] Step S112: Obtain the target reflection signal from the radar equipment to obtain the processed signal, and optimize the processed signal according to the law of large numbers to obtain a statistical result matrix.

[0095] In one embodiment, to minimize interference during radar sensing, matched filtering is used to obtain the statistical result matrix. This is achieved by analyzing the target reflection signals y over T time periods. m By performing matched filtering on (t), the signal statistics of the m-th radar device can be obtained, denoted as the processed signal. This is an Nrx×K order matrix, specifically represented as:

[0096]

[0097] In one embodiment, based on the law of large numbers, the following approximate expression holds when the time period T is sufficiently long:

[0098]

[0099]

[0100] Based on the above approximate expression, the processed signal will be... The results were optimized to obtain a statistical result matrix. Represented as:

[0101]

[0102]

[0103] Where, N m It is an Nrx×K order statistical noise matrix.

[0104] Step S113: Obtain the estimated value of the target reflection matrix based on the statistical result matrix.

[0105] In one embodiment, the statistical noise matrix N is calculated. m The probability density function is expressed as:

[0106]

[0107] Based on the above probability density function p(N) m By performing maximum likelihood estimation on the target reflection matrix, we can obtain...

[0108] Estimated value of the target reflection matrix Represented as:

[0109]

[0110] 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.

[0111] In one embodiment, the root mean square error of the target reflection matrix is ​​expressed as:

[0112]

[0113] Given the error tolerance value η of the m-th radar device m Then the constraint condition of the first perception matrix is ​​expressed as:

[0114]

[0115] This yields the first perception matrix constraint condition.

[0116] Step S120: Based on the receiver beamformer, obtain the received vector according to the multiplexed 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.

[0117] In one embodiment, reference is made to Figure 4 The process of constructing the first result constraint in step S120 includes the following steps:

[0118] Step S121: Calculate the actual data value based on the initial multiplexed signal of each radar device.

[0119] In one embodiment, the actual data value is the initial multiplexed signal of each radar device, denoted as:

[0120]

[0121] Step S122: Calculate the total transmitted signal based on the multiplexed signal of each radar device.

[0122] In one embodiment, the transmission signal is based on the multiplexed signal W m s m [t] and data transmission channel matrix H m The calculated value is represented as: H m W m s m [t].

[0123] The total transmitted signal is calculated by superimposing the signal waveforms of each radar device during the in-flight calculation process, and is represented as follows:

[0124]

[0125] Step S123: Obtain the receiving vector based on the beamformer at the receiving end and the total transmitted signal.

[0126] In one embodiment, the signal received by the server is the total transmitted signal obtained by superimposing the transmitted signals from each radar device after in-flight calculation. 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:

[0127]

[0128] Where A represents the receiver beamformer, H m N represents the distance between the m-th radar device and the server. a ×N tx n-order data transmission channel matrix c (t) is an Na A vector of additive white Gaussian noise of dimension 1, with a mean of 0 and a variance of 0. H is understandable m Obtained from the parameters of the actual communication system.

[0129] 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.

[0130] In one embodiment, the error in over-the-air computation is generally measured by the mean square error between the received signal and the actual data value, expressed as:

[0131]

[0132] In one embodiment, minimizing the mean squared error of the results, the first result constraint is expressed as:

[0133]

[0134] This leads to the first result constraint condition.

[0135] Step S130: Obtain the transmit power of the radar equipment and construct the first power constraint condition.

[0136] 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:

[0137] Step S131: Obtain power information based on the transmitter beamformer.

[0138] In one embodiment, the power information is represented as:

[0139] Step S132: Set the power information to be less than or equal to the transmission power, and construct the first power constraint condition.

[0140] In one embodiment, since the transmit power P of each radar device is limited, the beamforming design must satisfy a first power constraint condition, expressed as:

[0141]

[0142] This leads to the first power constraint condition.

[0143] 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.

[0144] In one embodiment, the first set of constraints is represented as:

[0145]

[0146]

[0147]

[0148] In one embodiment, due to the coupling relationship between the transmitter beamformers and the receiver beamformers, the first set of constraints is a non-convex optimization problem, thus requiring transformation. (Refer to...) Figure 6 Step S140 includes the following steps:

[0149] Step S141: Use zero-forcing design to obtain the first transformation relationship between the transmitter beamformer and the receiver beamformer.

[0150] 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, to ensure that the signal amplitudes of the same type of data transmitted by various devices are aligned as much as possible, the transmitter beamformer of the radar equipment will employ nulling design. Nulling design removes 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:

[0151]

[0152] Step S142: Transform the first set of constraints into the second set of constraints based on the first transformation relationship.

[0153] In one embodiment, reference is made to Figure 7 Step S142 includes the following steps:

[0154] 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.

[0155] In one embodiment, the first transformation relationship is substituted into the first result constraint to obtain the second result constraint, which is expressed as:

[0156]

[0157] 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.

[0158] In one embodiment, the second power constraint condition is expressed as:

[0159]

[0160] Step S1423: Based on the first transformation relationship, replace the transmitter beamformer with the receiver beamformer in the first sensing matrix constraint condition to obtain the second sensing matrix constraint condition.

[0161] In one embodiment, the constraint condition of the second perception matrix is ​​expressed as:

[0162]

[0163] Step S1424: Generate a second set of constraints based on the second result constraints, the second perception matrix constraints, and the second power constraints.

[0164] In one embodiment, the second set of constraints is represented as:

[0165]

[0166]

[0167]

[0168] After the above process, the coupling relationship between the transmitting beamformer and the receiving beamformer is removed, and the variables in the second constraint set include the receiving beamformer A.

[0169] Step S143: Use positive semi-definite scaling to represent the receiver beamformer as a positive semi-definite matrix, and transform the second set of constraints into the third set of constraints based on the positive semi-definite matrix.

[0170] 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 8 Step S143 includes the following steps:

[0171] Step S1431: Transform the second result constraint into a third result constraint based on the positive semi-definite matrix. In one embodiment, the third result constraint only contains noise-related terms. The third result constraint is expressed as follows:

[0172]

[0173] Step S1432: Convert the second power constraint into a third power constraint based on the positive semi-definite matrix. In one embodiment, the third power constraint is expressed as:

[0174]

[0175] Step S1433: Convert the second perception matrix constraint condition into a third perception matrix constraint condition based on the positive semi-definite matrix. In one embodiment, the third perception matrix constraint condition is expressed as:

[0176]

[0177] Step S1434: Generate the third constraint set based on the third result constraint, the third perception matrix constraint, the third power constraint, and the positive semi-definite matrix.

[0178] In one embodiment, the third set of constraints is represented as:

[0179]

[0180]

[0181]

[0182]

[0183] Here, ≥ indicates that the matrix is ​​positive semi-definite, that is, every element in the matrix is ​​not less than zero.

[0184] 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 third constraint set is a positive semi-definite matrix. Solve for it.

[0185] Step S144: Perform convex optimization on the third 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.

[0186] In one embodiment, reference is made to Figure 9 Step S144 includes the following steps:

[0187] Step S1441: Perform convex optimization on the third constraint set to obtain a positive semi-definite matrix.

[0188] In one embodiment, since the objective function in the third constraint set is linear and all constraints are convex, the third 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 .

[0189] 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.

[0190] Step S1442: Solve the optimization problem of the positive semi-definite matrix to obtain the optimized value of the first beamforming weight of the receiver beamformer.

[0191] In one embodiment, due to The first beamforming weight optimization value of receiver beamformer A can be obtained by using the Gaussian cyclic algorithm, and then the first beamforming weight optimization value of receiver beamformer A can be obtained according to the first transformation relationship.

[0192] 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:

[0193] 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).

[0194] 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.

[0195] Repeat the above process until all unknowns have been solved.

[0196] 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.

[0197] In one embodiment, Gaussian stochastic solution can generate the first beamforming weight optimization value through the following steps: drawing a random number from a Gaussian distribution with a mean of 0 and a variance of 1; transforming the random number into the required mean and variance through linear transformation and translation operations; repeating the above steps until the total number of required random variables has been generated.

[0198] Thus, this embodiment of the application obtains the first optimized beamforming weight value of the receiver beamformer A and the transmitter beamformers W of the M radar devices. m The second beamforming weight optimization value enables simultaneous adjustment of the antennas at both the transmitting and receiving ends.

[0199] In one embodiment, reference is made to Figure 10 This is a flowchart of the signal processing of a communication-sensing-computing integrated system.

[0200] Reference Figure 10 For the m-th radar device, the transmitting beamformer W is first used. m For the initial multiplexed signal s m [t] Beamforming is performed to obtain the multiplexed signal W m s m [t]. The multiplexed signal then propagates in the channel, utilizing the data transmission channel matrix H. m Gain is applied to obtain the transmitted signal of the m-th radar device. Simultaneously, the initial multiplexed signal 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 leads to the mean square error (MSE) of the target reflection matrix. mm Then, the server's receive vector is calculated. For the server, the received signal is the total transmitted signal after the signals from each radar device are superimposed and processed in the air. After reception, the signal is beamformed by the receiver beamformer A to obtain the receive vector.

[0201] 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.

[0202] In integrated communication-sensing-computing applications, multiple multi-antenna radar devices simultaneously transmit sensing signals for target detection and communication signals for data transmission. The sensing signals are received by the radar devices after being reflected by the target, while the communication signals are received by a server after in-flight computation. Sensors extract target information based on the received signals, while the server infers statistical information from the data of each radar device based on the received in-flight computation results. In related technologies, sensing, communication, and computation are often treated as independent processes, lacking a holistic consideration, leading to limited data processing efficiency. For example, the design of the data sensing stage may not take into account the carrying capacity of the communication channel and the computing power of the server, resulting in excessive sensing data being wasted because it cannot be subsequently transmitted and computed. Furthermore, for radar devices using radar sensing, the radar sensing signals and data transmission signals compete for wireless spectrum resources, increasing the burden on the wireless channel and causing further congestion in the communication link. Therefore, this application aims to combine in-flight computation and communication-sensing fusion technologies to minimize in-flight computation errors while ensuring sensing accuracy, thereby improving in-flight computation performance and data processing efficiency, and ultimately enhancing resource utilization efficiency.

[0203] The communication-sensing-computing integrated antenna multiplexing beamforming technology of this application embodiment can be used for target localization. For example... Figure 10 As shown, in a target localization application scenario, multiple radar devices will detect the same target. From the received target reflection signal, the relative angle and distance of the target are estimated. Then, based on their own coordinates, the position of the target is estimated, and the target's position information is sent to the server. (Refer to...) Figure 10 In the middle, radar device i measures the target position as: The target position measured by radar device m is: The information sent by all radar devices is calculated in the air by superimposing waveforms during transmission, and finally the server receives the average value of the target position estimated by all radar devices.

[0204] Reference Figure 10 In one embodiment, assuming the actual target location is at (5, 30) meters, 10 radar devices are placed at equal intervals along the y-axis from 0 to 20 meters to estimate the target position and transmit the estimation results to the server. It can be seen that the target position estimated by each radar device deviates from the actual position, but the estimated target position is more accurate after averaging through aerial calculations. This reflects the design philosophy of this embodiment, which minimizes aerial calculation errors while ensuring perception accuracy. A performance comparison was conducted with the angle of arrival method used in related target positioning techniques, showing that the effect of this embodiment is superior to the angle of arrival method.

[0205] The technical solution provided by this invention utilizes a multiplexed signal capable of simultaneous communication sensing computation to construct a first result constraint related to the received result, a first sensing matrix constraint related to sensing performance, and a first power constraint 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 embodiment designs the beamforming at both the transmitter and receiver ends, simultaneously adjusting the antennas at both ends. While ensuring sensing accuracy, it minimizes in-flight computation errors, improves in-flight computation performance and data processing efficiency, thereby enhancing resource utilization efficiency.

[0206] This invention also provides an antenna multiplexing beamforming apparatus, which can implement the above-described antenna multiplexing beamforming method, see reference. Figure 13 Applied to, for example Figure 1 The communication-sensing-computing integrated system includes: a transmitter beamformer, a receiver beamformer, and at least one radar device. The radar device transmits a signal obtained by beamforming an initial multiplexed signal using transmitter beamforming. The multiplexed signal is a data communication signal or a multiplexed signal. The radar device is also used to receive target reflection signals obtained by sensing the target reflection transmission signal. The device includes:

[0207] The perception matrix constraint construction module 1310 is used to obtain the target reflection signal received by the radar equipment, process the signal, calculate the statistical result matrix based on the processed signal, obtain the mean square error of the target reflection matrix of each radar equipment based on the statistical result matrix, and construct the first perception matrix constraint based on the error tolerance value.

[0208] The result constraint construction module 1320 is used to obtain the received vector based on the multiplexed signal using 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.

[0209] The power constraint construction module 1330 is used to obtain the transmit power of the radar equipment and construct the first power constraint.

[0210] The weight optimization value calculation module 1340 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.

[0211] The specific implementation of the antenna multiplexing beamforming device in this embodiment is basically the same as the specific implementation of the antenna multiplexing beamforming method described above, and will not be repeated here.

[0212] This invention also provides an electronic device, comprising:

[0213] At least one memory;

[0214] At least one processor;

[0215] At least one program;

[0216] The program is stored in a memory, and the processor executes the at least one program to implement the antenna multiplexing beamforming method described above. The electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0217] Please see Figure 14 , Figure 14 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0218] The processor 1401 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.

[0219] The memory 1402 can be implemented in the form of ROM (Read-Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1402 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 1402 and is called and executed by the processor 1401 to execute the antenna multiplexing beamforming method of the embodiments of this invention.

[0220] The input / output interface 1403 is used to implement information input and output;

[0221] Communication interface 1404 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).

[0222] Bus 1405 transmits information between various components of the device (e.g., processor 1401, memory 1402, input / output interface 1403, and communication interface 1404);

[0223] The processor 1401, memory 1402, input / output interface 1403 and communication interface 1404 are connected to each other within the device via bus 1405.

[0224] 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 multiplexing beamforming method.

[0225] 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.

[0226] The multiplexed beamforming method, integrated communication sensing and computing system, and related devices proposed in this invention utilize multiplexed signals capable of simultaneous communication sensing and computing to construct a first result constraint related to the received result, a first sensing matrix constraint related to sensing performance, and a first power constraint related to the transmitted power. A first constraint set is constructed using these constraints, and then the first constraint set is solved to obtain the optimized values ​​of the receiver beamformer and the transmitter beamformer. This application's embodiments design the beamforming at both the transmitter and receiver ends, simultaneously adjusting the antennas at both ends. While ensuring sensing accuracy, it minimizes in-flight computing errors, improves in-flight computing performance and data processing efficiency, thereby enhancing resource utilization efficiency.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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 multiplexing beamforming, characterized in that, An integrated communication sensing and computing system is applied, comprising: a transmitting beamformer, a receiving beamformer, and at least one radar device. The transmitting signal of the radar device is a multiplexed signal obtained by beamforming an initial multiplexed signal using the transmitting beamformer. The radar 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 radar 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 radar device is obtained based on the statistical result matrix. A first perception matrix constraint condition is constructed based on the error tolerance value. Based on the receiver beamformer, a receiving vector is obtained according to the multiplexed signal. 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 radar device and construct the 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 multiplexing beamforming method according to claim 1, characterized in that, The step of obtaining a received vector based on the multiplexed signal using the receiver beamformer, 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 condition includes: The actual data value is calculated based on the initial multiplexed signal of each of the radar devices; The total transmitted signal is calculated based on the transmitted signal of each of the radar devices. 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 multiplexing beamforming method according to claim 1, characterized in that, The process involves acquiring the target reflection signal received by the radar 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 radar 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 radar equipment, the multiplexed signal, and the interference signal. The target reflection signals of all the radar 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 multiplexing beamforming method according to claim 1, characterized in that, The step of obtaining the transmit power of the radar device to construct the first power constraint condition includes: Power information is obtained from the transmitting beamformer; The power information is set to be less than or equal to the transmission power, and the first power constraint condition is constructed.

5. The antenna multiplexing 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. The receiver beamformer is represented as a positive semi-definite matrix using positive semi-definite scaling, and the second set of constraints is transformed into a third set of constraints based on the positive semi-definite matrix. The third 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 multiplexing 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; Based on the first transformation relationship, the receiving beamformer is used to replace the transmitting beamformer in the first sensing matrix constraint condition to obtain the second sensing matrix constraint condition. The second set of constraints is generated based on the second result constraint, the second perception matrix constraint, and the second power constraint.

7. The antenna multiplexing beamforming method according to claim 6, characterized in that, The step of representing the receiver beamformer as a positive semi-definite matrix using positive semi-definite scaling, and transforming the second set of constraints into a third set of constraints based on the positive semi-definite matrix, includes: The second result constraint is converted into a third result constraint based on the positive semi-definite matrix. The second power constraint is converted into a third power constraint based on the positive semidefinite matrix. The second perception matrix constraint is converted into a third perception matrix constraint based on the positive semi-definite matrix. The third set of constraints is generated based on the third result constraint, the third perception matrix constraint, the third power constraint, and the semi-positive definite matrix.

8. The antenna multiplexing beamforming method according to claim 7, characterized in that, The step of performing convex optimization on the third 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 third set of constraints. The optimization problem of the semi-positive definite matrix is ​​solved to obtain the optimized value of the first beamforming weight of the receiver beamformer.

9. An antenna multiplexing beamforming device, characterized in that, An integrated communication sensing and computing system is applied, comprising: a transmitting beamformer, a receiving beamformer, and at least one radar device. The transmitting signal of the radar device is a multiplexed signal obtained by beamforming an initial multiplexed signal using the transmitting beamformer. The radar 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 radar 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 radar 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 a receiving vector based on the multiplexed signal using the receiving beamformer, 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 transmit power of the radar 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.

10. 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 multiplexing beamforming method according to any one of claims 1 to 8.

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

12. 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 multiplexing beamforming method according to any one of claims 1 to 8.

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