Communication sensing symbiotic digital-analog hybrid beam generation method and device

Abstract

The invention discloses a communication perception symbiotic digital analog hybrid beam generation method and device, and relates to the field of wireless communication or other related technical fields, and the method comprises the steps: building a communication signal model and a perception signal model; for the communication signal model and the sensing signal model, adopting a Cramer-Rao lower bound of target angle estimation as a measurement parameter of sensing performance, and constructing a beam forming matrix optimization problem under the constraint of the measurement parameter; the beam forming matrix optimization problem is simplified by adopting a fractional programming algorithm and a Shuerr complement condition, and the beam forming matrix optimization problem is iteratively solved by adopting a penalty dual decomposition algorithm; and based on the optimized digital beam forming matrix and analog beam forming matrix, generating a digital-analog hybrid beam in an Internet of Vehicles communication sensing scene. According to the invention, the technical problem that a communication structure obtained by an all-digital array cannot simultaneously support efficient communication and high-precision remote sensing in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communications or other related technical fields. Specifically, it relates to a digital-analog hybrid beam generation method and apparatus for communication and sensing symbiosis. Background Art

[0002] With the rapid development of intelligent applications, the demand for communication technologies in fields such as the Internet of Things, autonomous driving, and intelligent manufacturing has gradually shifted from simple "data transmission" to more complex "intelligent perception and interaction". Although 5G networks can provide higher data transmission rates and lower latency, they are still difficult to meet the application requirements of full perception, full connection, and full intelligence. The diversified communication services make the limited spectrum resources in short supply. The operation of sensing and communication subsystems on independent physical devices exacerbates problems such as spectrum resource waste and interference between multiple devices. The contradiction between the continuously growing demand and the shortage of resources is increasing day by day. 6G communication technology has proposed the integrated sensing and communication (ISAC) technology, which realizes the dual functions of sensing and communication on the same time-frequency resource to meet the requirements of high-precision sensing, real-time response, and intelligent decision-making in the future intelligent society. Using the current wireless communication network to achieve a comprehensive perception of the environment, user, and device status, and realizing the integrated communication and sensing technology has become the focus of research by scholars from all walks of life.

[0003] In related technologies, in order to implement the communication technology of integrated communication and sensing, researchers have proposed to make the obtained beam pattern close to the desired radar beam pattern through a beamformer, while satisfying the signal-to-noise ratio constraint of users, minimizing the power of multi-user interference, and considering the beam pattern mismatch level to achieve a trade-off between communication and sensing performance. However, most of the research focuses on the full-digital array architecture, which requires the same number of radio frequency chains as the number of antennas. Each radio frequency chain includes a signal mixer and a digital-to-analog converter, bringing huge hardware costs. At the same time, it faces the problem of self-interference, that is, the mutual interference between communication signals and sensing signals, which not only causes waste of energy but also seriously affects sensing performance and communication quality, making it difficult to simultaneously meet the requirements of high-throughput communication and high-precision remote sensing.

[0004] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present invention provide a digital-analog hybrid beam generation method and apparatus for communication and sensing symbiosis, so as to at least solve the technical problem that the communication structure obtained by a full-digital array in related technologies cannot support both efficient communication and high-precision remote sensing simultaneously.

[0006] According to one aspect of the embodiments of the present invention, a digital-analog hybrid beamforming generation method for communication-aware coexistence is provided, including: collecting the scenario information of the vehicle-to-everything (V2X) communication-aware scenario, and establishing a communication signal model and a sensing signal model based on the scenario information, where the V2X communication-aware scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node; for the communication signal model and the sensing signal model, using the Cramér-Rao lower bound of target angle estimation as a metric parameter for sensing performance, constructing a beamforming matrix optimization problem under the constraint of the metric parameter, where the beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem; using the fractional programming algorithm and the Schur complement condition to simplify the beamforming matrix optimization problem, obtaining the simplified beamforming matrix optimization problem, and using the penalty dual decomposition algorithm to iteratively solve the simplified beamforming matrix optimization problem, obtaining the optimized digital beamforming matrix and analog beamforming matrix; generating a digital-analog hybrid beam in the V2X communication-aware scenario based on the optimized digital beamforming matrix and the analog beamforming matrix.

[0007] According to another aspect of the embodiments of the present invention, a digital-analog hybrid beamforming generation device for communication-aware coexistence is further provided, including: a collection unit, configured to collect the scenario information of the V2X communication-aware scenario, and establish a communication signal model and a sensing signal model based on the scenario information, where the V2X communication-aware scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node; a construction unit, configured to, for the communication signal model and the sensing signal model, use the Cramér-Rao lower bound of target angle estimation as a metric parameter for sensing performance, establish a beamforming matrix optimization problem under the constraint of the metric parameter, where the beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem; an iteration unit, configured to use the fractional programming algorithm and the Schur complement condition to simplify the beamforming matrix optimization problem, obtain the simplified beamforming matrix optimization problem, and use the penalty dual decomposition algorithm to iteratively solve the simplified beamforming matrix optimization problem, obtaining the optimized digital beamforming matrix and analog beamforming matrix; a generation unit, configured to generate a digital-analog hybrid beam in the V2X communication-aware scenario based on the optimized digital beamforming matrix and the analog beamforming matrix.

[0008] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above-mentioned digital-analog hybrid beamforming generation method for communication-aware coexistence.

[0009] According to another aspect of the embodiments of the present invention, an electronic device is further provided, including one or more processors and a memory. The memory is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the digital-analog hybrid beam generation method for communication-aware symbiosis described in any one of the above.

[0010] In this application, through the following steps: acquiring the scenario information of the vehicle-to-everything (V2X) communication-aware scenario, and establishing a communication signal model and a sensing signal model based on the scenario information. The V2X communication-aware scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node. Then, for the communication signal model and the sensing signal model, the Cramer-Rao lower bound of the target angle estimation is used as a metric parameter for the sensing performance, and a beamforming matrix optimization problem under the constraint of the metric parameter is constructed. The beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem. The fractional programming algorithm and the Schur complement condition are used to simplify the beamforming matrix optimization problem, and the simplified beamforming matrix optimization problem is obtained. The penalty dual decomposition algorithm is used to iteratively solve the simplified beamforming matrix optimization problem, and the optimized digital beamforming matrix and analog beamforming matrix are obtained. Finally, a digital-analog hybrid beam in the V2X communication-aware scenario is generated based on the optimized digital beamforming matrix and analog beamforming matrix.

[0011] In this application, a communication signal model and a sensing signal model are established according to the real-time V2X communication-aware scenario, and thus a beamforming matrix optimization problem under the Cramer-Rao constraint of the target angle estimation is constructed, enabling the model to take into account both communication and sensing performance. The digital beamforming matrix and the analog beamforming matrix are solved through iterative optimization. The hybrid digital-analog array is used to continuously iterate to maximize the communication rate under the condition of satisfying the sensing performance constraint, and can simultaneously meet the requirements of high-precision sensing and high-throughput communication, achieving the technical effect of simultaneously improving the sensing accuracy and communication efficiency, and further solving the technical problem in the related art that the communication structure obtained by the all-digital array cannot support both efficient communication and high-precision long-distance sensing at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0013] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing the digital-analog hybrid beam generation method for communication-aware symbiosis is shown;

[0014] Figure 2 It is a flowchart of an optional digital - analog hybrid beamforming method for communication - sensing symbiosis according to an embodiment of the present invention;

[0015] Figure 3 It is a schematic diagram of an optional communication - sensing scenario in a vehicle - to - everything (V2X) communication according to an embodiment of the present invention;

[0016] Figure 4 It is a schematic diagram of an optional digital - analog hybrid beamforming process according to an embodiment of the present invention;

[0017] Figure 5 It is a schematic diagram of an optional digital - analog hybrid beamforming device for communication - sensing symbiosis according to an embodiment of the present invention;

[0018] Figure 6 It is a hardware structure block diagram of an electronic device (or mobile device) that executes the digital - analog hybrid beamforming method for communication - sensing symbiosis according to an embodiment of the present invention. Detailed implementation manners

[0019] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above - mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non - exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0021] It should be noted that the digital - analog hybrid beam generation method and its device for communication - sensing symbiosis in the present application can be used in the field of mobile communication when generating digital - analog hybrid beams to achieve communication - sensing symbiosis. It can also be used in any field other than the field of mobile communication when generating digital - analog hybrid beams to achieve communication - sensing symbiosis. The application field of the digital - analog hybrid beam generation method and its device for communication - sensing symbiosis in the present application is not limited.

[0022] It should be noted that the information collected (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in the present application are information and data authorized by users or fully authorized by all parties. And the processing of relevant data, such as collection, storage, use, processing, transmission, provision, disclosure, and application, all comply with relevant laws, regulations, and standards, take necessary processing measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse. For example, there is an interface between the present system and relevant users or institutions to provide corresponding operation entrances for users to choose to agree or refuse the results of automated decision - making; if the user chooses to refuse, the expert decision - making process will be entered.

[0023] The following embodiments of the present invention can be applied to various communication - sensing symbiotic digital - analog hybrid beam generation systems / applications / devices. The present invention constructs a communication signal model and a sensing signal model under a hybrid beam based on the vehicle - to - everything (V2X) communication - sensing integration scenario. Then, the Cramer - Rao lower bound (CRLB) is used as a measure of sensing performance, and an optimization problem of the hybrid beamforming matrix under the CRLB constraint of target angle estimation is constructed. The hybrid beamforming matrix is optimized by iterative solution, so that the communication rate between the base station and the communication object is maximized while meeting the requirements of sensing accuracy, thereby effectively improving the sensing accuracy of the system for the target and the communication efficiency. It can also balance the communication performance and sensing performance by changing the thresholds of relevant parameters to adapt to different scenarios.

[0024] The present invention will be described in detail below in conjunction with each embodiment.

[0025] Embodiment 1

[0026] According to an embodiment of the present invention, an embodiment of a digital - analog hybrid beam generation method for communication - sensing symbiosis is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer - executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0027] The method embodiment provided by the first embodiment of the present application can be executed on a mobile terminal, a computer terminal, or a similar computing device.Figure 1 The hardware block diagram of a computer terminal (or mobile device) for implementing a digital-analog hybrid beamforming method for communication-aware coexistence is shown. As Figure 1 shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (illustrated as 102a, 102b, ……, 102n in the figure) (the processor 102 may include, but is not limited to, processing devices such as a microprocessor MCU or a field-programmable gate array FPGA), a storage device 104 for storing data, and a transmission device 106 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only illustrative and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0028] It should be noted that the above one or more processors 102 and / or other data processing circuits are generally referred to as "data processing circuits" herein. The data processing circuit may be embodied in whole or in part as software, hardware, firmware, or any combination thereof. In addition, the data processing circuit may be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the computer terminal 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is a kind of processor control (such as the selection of a variable resistor terminal path connected to an interface).

[0029] The storage device 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the digital-analog hybrid beamforming method for communication-aware coexistence in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the storage device 104, that is, implements the above-mentioned digital-analog hybrid beamforming method for communication-aware coexistence. The storage device 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the storage device 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0031] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables users to interact with the user interface of the computer terminal 10 (or mobile device).

[0032] Under the above operating environment, the present application provides a Figure 2 communication-aware coexisting digital-analog hybrid beamforming method as shown, and the implementation entity of this method is a communication-aware coexisting digital-analog hybrid beamforming system.

[0033] Figure 2 is a flowchart of an optional communication-aware coexisting digital-analog hybrid beamforming method according to an embodiment of the present invention, as Figure 2 shown, and this method includes the following steps:

[0034] Step S201, collect the scene information of the vehicle-to-everything (V2X) communication-aware scene, and establish a communication signal model and a sensing signal model based on the scene information.

[0035] In the above step S201, a communication signal model and a sensing signal model are established through the real-time scene information in the V2X communication-aware scene. In the embodiments of the present invention, a bistatic communication-aware integrated system is adopted to avoid strong self-interference in a monostatic communication-aware system by using a pair of physically separated sensing transceivers, while maintaining co-design of radar sensing and communication in shared spectrum and hardware.

[0036] In the embodiments of the present invention, the V2X communication-aware scene at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node. Figure 3 is a schematic diagram of an optional V2X communication-aware scene according to an embodiment of the present invention, as Figure 3 shown. The V2X communication-aware scene includes a communication base station BS, multiple vehicle user terminals VUEs, a sensing node, and a passive sensing target. The communication base station is used to communicate with the vehicle user terminals, and the sensing node is used to capture the reflection or scattering of wireless signals to detect, locate, and identify surrounding objects or environmental changes. By constructing a Cartesian coordinate system, the position coordinates of all objects in the scene can be obtained (for exampleFigure 3 The position coordinates of the communication base station are (0, 0, 20), the coordinates of the VUEs are (0, 200, 0), (20, 200, 0), (40, 200, 0), (60, 200, 0) respectively, the position coordinates of the sensing node are (1, 210, 20), and the position coordinates of the passive sensing target are (-40, 160, 0). The passive sensing target can be various types of sensing objects, such as obstacles, etc. In the communication sensing scenario of the vehicle network, the communication base station and the sensing node are the keys to realizing bistatic communication sensing. Separating the deployment of the communication base station and the sensing node can well avoid self-interference and lay a foundation for high-precision sensing and efficient communication.

[0037] Specifically, based on the communication sensing scenario of the vehicle network, determine the number of transmitting antennas M of the base station, the number K of vehicle user terminals for a single receiving antenna, and the number of receiving antennas of the sensing node planar array and , and respectively represent scenario information such as the number of receiving antennas in the horizontal and vertical directions of the sensing node.

[0038] In the embodiment of the present invention, establishing a communication signal model based on the scenario information includes: The communication base station adopts a hybrid digital-analog array, including transmitting antennas, receiving antennas and radio frequency chains, and , ensuring that each radio frequency chain is connected to all antennas, then the transmitted signal of the base station is expressed as:

[0039]

[0040] Wherein, is the digital beamforming matrix, is the analog beamforming matrix, each vehicle user terminal is assigned a beam, and the received signal of the th vehicle user terminal is expressed as:

[0041] Wherein, is the data sent to the vehicle user terminal at time slot , represents the baseband equivalent channel from the communication base station to the th vehicle user terminal, represents the additive white Gaussian noise in the baseband equivalent channel.

[0042] Based on the above transmitted signal of the base station and the received signal of the vehicle user terminal, a communication signal model is obtained.

[0043] ​In the embodiments of the present invention, establishing a sensing signal model based on scenario information includes: establishing an echo signal model of the reflection of the sensing target, thereby obtaining the sensing signal model, and the sensing signal model is expressed as:

[0044]

[0045] Wherein, and respectively represent the echo signals from the vehicle user side and the passive sensing target, represents the range-Doppler signal transmitted by the communication base station, represents the transmission signal length, represents the reflection link between the sensing node and the vehicle user side, represents the reflection link between the sensing node and the passive sensing target, represents the noise in the radar echo, and the noise of each echo beam follows Gaussian white noise , represents the mean value of , and the variance is complex Gaussian distribution. By establishing a communication and sensing integration model between the communication base station and the vehicle user side, and between the sensing node and the sensing target in the vehicle-to-everything (V2X) communication scenario as shown in formulas (1), (2), and (3), while performing high-rate communication, precise sensing and positioning of the sensing target are achieved.

[0046] Step S202, for the communication signal model and the sensing signal model, use the Cramér-Rao lower bound of target angle estimation as a metric parameter for sensing performance, and construct a beamforming matrix optimization problem under the constraint of the metric parameter.

[0047] In step S202 above, based on the communication signal model and the sensing signal model constructed in step S201, an optimization objective is further formulated for the beamforming matrix design. The beamforming matrix is a key signal processing component in the communication and sensing integrated system. It adjusts the direction and intensity of the transmitted signal to enhance the signal reception in the target area while suppressing interference in other directions, thereby improving the communication quality and sensing performance. To ensure that the sensing performance reaches the optimal level, we use the Cramer-Rao Lower Bound (CRLB) of target angle estimation as a metric parameter for sensing performance. The target angle (such as the azimuth angle and elevation angle of the sensing target) is a key parameter in the sensing task. By measuring and analyzing the angle of arrival of the signal, the target can be located and tracked; the CRLB provides a theoretically minimum error bound, that is, in a given signal and noise environment, the variance of any unbiased estimator cannot be lower than this bound. Taking the CRLB of target angle estimation as a constraint condition for sensing performance ensures that the system can estimate the target angle with an error level not higher than the CRLB, thereby ensuring that the sensing accuracy of the system can meet the preset requirements. On this basis, the communication rate is increased to achieve the dual performance of high-precision sensing and high-efficiency communication.

[0048] In this application, the base station uses a hybrid digital and analog array. Therefore, the beamforming matrix optimization problem includes: the digital beamforming matrix optimization problem and the analog beamforming matrix optimization problem. The digital beamforming matrix is closely related to the digital signal processing system of the base station and is responsible for signal encoding, demodulation, and complex beamforming control. The analog beamforming matrix is related to the analog components of the antenna array, such as phase shifters, which are used for beam control in the radio frequency stage.

[0049] Specifically, for the sensing signal model established in step S201, after spatial filtering, it is vectorized into , and it can be known that the received signal follows a Gaussian distribution, that is . According to the CRLB theorem, the Fisher information matrix of the vector to be estimated can be obtained:

[0050]

[0051] In this formula, is the parameter value to be estimated, which includes represents the angle of the sensing target relative to the sensing node, represent the horizontal angle and the vertical angle respectively, represents the real part and the imaginary part of the complex reflection coefficient; in the formula represents taking the real part of the complex number, represents taking the trace of the matrix, represents is a 5×5 matrix, which is divided into four blocks in the form of 2 rows and 2 columns, 3 rows and 3 columns, that is, we get , , ;

[0052] The CRLB matrix for estimating the target angle of the sensing target according to Equation (4) is:[[]]

[0053]

[0054] Next, construct the beamforming matrix optimization problem under the CRLB constraint of the target angle to maximize the communication rate between the communication base station and the vehicle user terminal. This optimization problem is expressed as:[[]]

[0055]

[0056] (6a)

[0057] (6b)

[0058] (6c)

[0059] Among them, represents the received signal-to-interference-plus-noise ratio at the vehicle receiving end, The subsequent polynomials (6a, 6b, 6c) represent the constraint conditions, represents the F-norm of the matrix, represents the maximum communication sum rate under this beamforming matrix, then represents the CRLB constraint threshold for target angle estimation;

[0060] Step S203: Use the fractional programming algorithm and the Schur complement condition to simplify the beamforming matrix optimization problem, obtain the simplified beamforming matrix optimization problem, and use the penalty dual decomposition algorithm to iteratively solve the simplified beamforming matrix optimization problem to obtain the optimized digital beamforming matrix and analog beamforming matrix.[[]]

[0061] In the above step S203, the constructed beamforming matrix optimization problem is further simplified to accelerate the optimization calculation efficiency and find the optimal digital beamforming matrix and analog beamforming matrix. Fractional programming can transform the problem into a series of sub-problems that are easier to handle, and each sub-problem is a standard maximization or minimization problem. By this method, the optimal solution of the original problem can be gradually approximated without directly facing its complexity. The Schur complement condition is applied under the CRLB constraint of the target angle estimation, making the optimization problem become a more intuitive form, which is convenient for the subsequent application of the solution algorithm. Applying the fractional programming algorithm and the Schur complement condition can transform the originally complex beamforming matrix optimization problem into a series of simpler sub-problems. This simplification not only reduces the difficulty of problem solving but also accelerates the convergence speed of the algorithm, making the optimization process more efficient.

[0062] Furthermore, the penalty dual decomposition algorithm is used to solve the simplified beamforming matrix optimization problem. By iteratively optimizing the augmented Lagrangian function, the optimal beamforming matrix can be gradually approximated while ensuring that all constraint conditions are satisfied. Each step of the algorithm is optimized based on the current digital beamforming matrix and analog beamforming matrix and finally converges to the digital beamforming matrix and analog beamforming matrix that satisfy the maximum communication rate and the CRLB constraint condition of the target angle estimation.

[0063] Specifically, first, fractional programming is applied to transform the initialization problem, and the transformed optimization problem is:

[0064] (7)

[0065] (7a)

[0066] (7b)

[0067] (7c)

[0068] Among them, represents the maximum transmit power of the base station, and are auxiliary variables; represents the set of all vehicle users.

[0069] Furthermore, the simplified beamforming matrix optimization problem is expressed as:

[0070] ;

[0071] Among them, is the number of transmit antennas of the communication base station and the number of receive antennas of the sensing node, The number of radio frequency chains configured for the communication base station, and , represents the number of vehicle user terminals, represents the set of all optimization variables, and are auxiliary variables; is the digital beamforming matrix, is the analog beamforming matrix, and the feasible region is a complex circular manifold; is the penalty parameter, represents the dual variable related to the equality constraint, is the parameter value to be estimated, represents the relative angle of the sensing target, represent the horizontal angle and the vertical angle respectively, represent the real part and the imaginary part of the complex reflection coefficient respectively; represents is a 5×5 matrix, which is divided into four blocks in the form of 2 rows and 2 columns, 3 rows and 3 columns, that is, , , ; then represents the two-dimensional angle of arrival threshold under the metric parameter constraint, represents the constraint condition of the polynomial, represents the maximum transmit power of the communication base station, represents the set of all vehicle user terminals, represents taking the trace of the matrix, represents the Frobenius norm of the matrix.

[0072] Specifically, in order to improve the convergence speed of the algorithm and make the optimization process more efficient, the embodiment of the present invention also introduces an auxiliary positive semi-definite matrix and an auxiliary matrix , simplifies the transformed optimization problem according to the Schur complement condition, transforms the originally complex beamforming matrix optimization problem into a series of simpler sub-problems, and obtains the simplified optimization problem as:

[0073]

[0074] (8a)

[0075] (8b)

[0076] (8c)

[0077] (8d)

[0078] (8e)

[0079] (8f)

[0080] Among them, represents taking the inverse of the matrix, represents substituting and into formula (4) to obtain the vector to be estimated, and the Fisher information matrix of

[0081] Furthermore, according to the penalty dual decomposition algorithm framework, the augmented Lagrangian function is defined as:

[0082]

[0083]

[0084] Among them, represents the set of all optimization variables, is the penalty parameter, represents the dual variable related to the equality constraint.

[0085] Through the augmented Lagrangian function, the objective function under the optimization problem can be constructed. The original optimization problem contains a complex objective function and multiple constraint conditions. The augmented Lagrangian function incorporates the constraint conditions of a single optimization problem into the objective function, forming an objective function corresponding to this optimization problem, thereby making the solution of the optimization problem more intuitive and simplifying the solution algorithm.

[0086] Furthermore, the steps of using the penalty dual decomposition algorithm to iteratively solve the simplified beamforming matrix optimization problem include: Step 1, using the penalty dual decomposition algorithm to calculate the optimal solutions of the auxiliary variables and in the beamforming matrix optimization problem and the optimal solutions of the auxiliary matrices and ; Step 2, based on the optimal solutions of the auxiliary variables and and the optimal solutions of the auxiliary matrices and The construction of the optimal solution involves constructing an augmented Lagrangian function for the hybrid beamforming matrix; Step 3, fix the analog beamforming matrix, construct the objective function of the digital beamforming matrix based on the augmented Lagrangian function to obtain the first objective function, calculate the gradient value of the first objective function, and optimize the digital beamforming matrix based on the gradient value; Step 4, fix the digital beamforming matrix based on the optimized digital beamforming matrix in Step 3 above, and construct the objective function of the analog beamforming matrix based on the augmented Lagrangian function to obtain the second objective function, calculate the Euclidean gradient of the second objective function, and optimize the analog beamforming matrix based on the Euclidean gradient; Step 5, repeat the above Steps 1 to 4 to iteratively optimize the digital beamforming matrix and the analog beamforming matrix.

[0087] Specifically, when iteratively solving the beamforming matrix optimization problem, it specifically includes: First, fix the variable , and solve for the optimal solutions of the auxiliary variables and ; After obtaining the optimal solutions of the auxiliary variables and , substitute the auxiliary variables into the original optimization problem, then fix the variable , and use the interior point method to solve for the auxiliary matrices and .

[0088] Then fix , establish the objective function of the digital beamforming matrix according to the Lagrangian function with the hybrid beamforming matrix, calculate the gradient of the objective function, and then update the digital beamforming matrix through the gradient ascent method. After removing the terms unrelated to , ensure that it satisfies the constraint conditions and project it into the feasible region to complete one optimization iteration.

[0089] Utilize the optimized , fix , and establish the objective function of the analog beamforming matrix according to the Lagrangian function with the hybrid beamforming matrix. Then add the power constraint as a penalty term to the objective function, and finally calculate the Euclidean gradient of the objective function value, project it on the tangent space to obtain the Riemannian gradient, and select the Riemannian gradient as the ascent direction to complete one optimization of the analog beamforming matrix .

[0090] Repeat the above steps to iteratively optimize the digital beamforming matrix and the analog beamforming matrix until the maximum number of iterations is reached.

[0091] Furthermore, the penalty dual decomposition algorithm is adopted to calculate the optimal solutions of the auxiliary variables and in the beamforming matrix optimization problem, as well as the optimal solutions of the auxiliary matrices and . The steps are as follows: Fix the variable in the beamforming matrix optimization problem, and calculate the optimal solutions of the auxiliary variables and in the beamforming matrix optimization problem; Based on the optimal solutions of the auxiliary variables and , fix the variable in the beamforming matrix optimization problem, and calculate the optimal solutions of the auxiliary matrices and in the beamforming matrix optimization problem.

[0092] Specifically, when calculating the optimal solution of the auxiliary variable, fix the variable , and solve the optimal solutions of the auxiliary variables and . Take the derivative of the objective function in Equation (7) with respect to the auxiliary variables and , that is , and the optimal solutions of the auxiliary variables and are expressed as:

[0093]

[0094]

[0095] Based on the obtained optimal solutions of the auxiliary variables and , fix the variable , and further simplify the simplified optimization problem into a semidefinite programming problem:

[0096]

[0097]

[0098] Then use the interior point method to solve the auxiliary matrices and .

[0099] Based on the obtained optimal auxiliary variables and as well as the optimal auxiliary matrices and , substitute it into Equation (9) and simplify the augmented Lagrangian function to obtain the simplified Lagrangian function:

[0100]

[0101] Furthermore, the steps of calculating the gradient value of the first objective function and optimizing the digital beamforming matrix based on the gradient value include: calculating the gradient value of the first objective function based on the gradient formula of the first objective function, and calculating a new digital beamforming matrix based on the iteration step size and the gradient value of the first objective function; projecting the calculated new digital beamforming matrix into the feasible region that satisfies the power constraint condition to obtain the optimized digital beamforming matrix.

[0102] Specifically, fix the analog beamforming matrix, construct the first objective function according to the augmented Lagrangian function with the hybrid beamforming matrix. When solving the optimal solution of the digital beamforming matrix, the gradient ascent projection algorithm can be used for solution, and calculate the simplified function The gradient of is:

[0103] where , The symbol represents taking the gradient of the matrix.

[0104] Update the digital beamforming matrix according to the calculated gradient value. The at the (n + 1)-th iteration is updated as:

[0105]

[0106] where represents the digital beamforming matrix at the n-th iteration , represents the iteration step size.

[0107] Since in a communication system, the transmission power of the base station is strictly limited, excessive transmission power will lead to increased energy consumption, interference problems, and other potential system stability problems. Therefore, after each update of the digital beamforming matrix, it is necessary to ensure that the transmission power of the digital beamforming matrix does not exceed the allowed maximum value. Specifically, calculate the transmission power corresponding to the updated digital beamforming matrix and compare whether it exceeds the pre-set maximum power limit. If the calculated transmission power meets the maximum power constraint condition (i.e., it does not exceed the allowed maximum transmission power), the new digital beamforming matrix can be directly used as the optimized result without adjustment. If the transmission power caused by the updated matrix exceeds the limit, a projection operation is required at this time. That is, it is necessary to adjust the value of the digital beamforming matrix so that its corresponding transmission power drops below the maximum allowed power, while maintaining the original performance of the matrix as much as possible, such as communication rate and sensing accuracy.

[0108] Specifically, the process of projecting the obtained digital beamforming matrix into the feasible region can be expressed as:

[0109]

[0110] where is the projection operation, and the calculation method is:

[0111]

[0112] Furthermore, based on the optimized digital beamforming matrix in step 3 above, the digital beamforming matrix is fixed, and an objective function for the analog beamforming matrix is constructed based on the augmented Lagrangian function to obtain a second objective function. The steps of calculating the Euclidean gradient of the second objective function and optimizing the analog beamforming matrix based on the Euclidean gradient include: substituting the fixed digital beamforming matrix into the augmented Lagrangian function, and adding the power constraint as a penalty term to the augmented Lagrangian function to obtain the second objective function; calculating the Euclidean gradient value of the second objective function based on the Euclidean gradient formula of the second objective function; projecting the Euclidean gradient value onto the tangent space to calculate the Riemannian gradient value of the second objective function, and calculating a new analog beamforming matrix according to the Riemannian gradient value.

[0113] Specifically, according to the digital beamforming matrix solved in each iteration, this digital beamforming matrix is fixed. Since the analog beamforming matrix is restricted by the power constraint and the constant modulus constraint, the power constraint is added as a penalty term to the augmented Lagrangian function, and thus the second objective function is obtained:

[0114]

[0115] (18a)

[0116] where represents the square of the penalty term, represents , represents the weight of the penalty term, which decreases with iteration.

[0117] Based on the analysis of the second objective function, the feasible region is a complex circular manifold, and (18) is an unconstrained problem in the manifold problem. The Euclidean gradient of the objective function is obtained as:

[0118]

[0119]

[0120] Among them, .

[0121] After calculating the Euclidean gradient of the second objective function, the Riemannian gradient is calculated by projecting the Euclidean gradient onto the tangent space Calculate the Riemannian gradient, Define the manifold At The tangent space at is the set of all tangent vectors at Denoted as:

[0122]

[0123] Among them, Represents the tangent vector at Denotes the tangent vector at, Denotes A matrix with all elements of dimension being 0, Represents the element-wise product of two matrices.

[0124] Thus, calculate the Riemannian gradient:

[0125]

[0126] Based on the calculated Riemannian gradient, the ascent direction is selected as the Riemannian gradient. Given , the analog beamforming matrix is optimized. The analog beamforming matrix obtained by the (n + 1)-th iteration optimization is denoted as:

[0127]

[0128]

[0129] Furthermore, the digital-analog hybrid beam generation method for communication-sensing coexistence further includes: after optimizing the digital beamforming matrix and the analog beamforming matrix each time, calculating the degree of constraint violation of the beamforming matrix optimization problem; comparing the degree of constraint violation with a preset constraint violation degree threshold to obtain a comparison result; updating the penalty parameter and the dual variable in the beamforming matrix optimization problem based on the comparison result.

[0130] Specifically, the digital beamforming matrix and the analog beamforming matrix are obtained by continuously alternating iterative optimization to find the optimal solution. After optimizing the digital beamforming matrix and the analog beamforming matrix each time, calculate the degree of constraint violation:

[0131]

[0132] Among them, is the infinity norm, which judges the degree of constraint violation to determine whether it is less than the set threshold. If it is less than or equal to, update the variables and :

[0133]

[0134]

[0135] Otherwise, update , .

[0136] Based on the updated variables and perform the next optimization on the digital beamforming matrix and the analog beamforming matrix.

[0137] Step S204, generate a digital-analog hybrid beam in the vehicle-to-everything (V2X) communication and sensing scenario based on the optimized digital beamforming matrix and analog beamforming matrix.

[0138] Furthermore, the digital beamforming matrix represents the control of the beam direction and power by the base station at the digital signal processing level. By adjusting the phase and amplitude of each antenna element, the signal strength in a specific direction is enhanced while minimizing interference. The analog beamforming matrix is responsible for beam control at the radio frequency (RF) level, mainly by adjusting the phase of each element in the antenna array to achieve directional management of the transmitted signal and optimization of the power distribution. Based on the optimized digital beamforming matrix and analog beamforming matrix, control the antenna array to output a specific digital-analog hybrid beam to achieve the fusion of communication and sensing functions.

[0139] Through the above steps, the scenario information of the vehicle-to-everything (V2X) communication and sensing scenario is collected, and a communication signal model and a sensing signal model are established based on the scenario information. The V2X communication and sensing scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node. Then, for the communication signal model and the sensing signal model, the Cramer-Rao lower bound (CRLB) of the target angle estimation is used as a metric parameter for the sensing performance, and a beamforming matrix optimization problem under the constraint of the metric parameter is constructed. The beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem. The fractional programming algorithm and the Schur complement condition are used to simplify the beamforming matrix optimization problem, and the simplified beamforming matrix optimization problem is obtained. The penalty dual decomposition algorithm is used to iteratively solve the simplified beamforming matrix optimization problem, and the optimized digital beamforming matrix and analog beamforming matrix are obtained. Finally, a digital-analog hybrid beam in the V2X communication and sensing scenario is generated based on the optimized digital beamforming matrix and analog beamforming matrix.

[0140] In this embodiment, a communication signal model and a sensing signal model are established according to the real-time V2X communication and sensing scenario, and thus a beamforming matrix optimization problem under the Cramer-Rao constraint of the target angle estimation is constructed, so that the model can take into account both communication and sensing performance. The digital beamforming matrix and the analog beamforming matrix are solved by iterative optimization. The hybrid digital-analog array is used to continuously iterate to maximize the communication rate under the condition of satisfying the sensing performance constraint, which can simultaneously meet the requirements of high-precision sensing and high-throughput communication, achieving the technical effect of simultaneously improving the sensing accuracy and communication efficiency, and further solving the technical problem in the related art that the communication structure obtained by the all-digital array cannot support both efficient communication and high-precision remote sensing at the same time.

[0141] A detailed description will be given below in combination with another optional specific implementation manner.

[0142] Figure 4 is a schematic diagram of an optional digital-analog hybrid beam generation process according to an embodiment of the present invention. As Figure 4 shown, the digital-analog hybrid beam generation process includes:

[0143] Step 1, construct an integrated model of the V2X scenario, including: a communication signal model and a sensing signal model. The V2X scenario includes a base station, multiple vehicle user terminals with only receiving antennas, sensing nodes, and passive sensing targets.

[0144] Step 2, construct a beamforming matrix optimization problem under the CRLB constraint;

[0145] Based on the communication signal model and sensing signal model established in Step 1, using the Cramer-Rao lower bound as a measure of sensing performance, an optimization problem of the beamforming matrix is constructed under the CRLB constraint of target angle estimation, so as to maximize the communication rate between the base station and the vehicle user while meeting the requirements of sensing accuracy.

[0146] Step 3, simplify the optimization problem constructed in Step 2;

[0147] Use the Fractional Programming (FP) method to transform the optimization problem, and simplify the transformed optimization problem according to the Schur complement condition to accelerate the algorithm iteration efficiency and efficiently find the optimal solution of the beamforming matrix.

[0148] Step 4, introduce the Lagrangian function and auxiliary variables using the penalty dual decomposition algorithm;

[0149] Step 5, initialize the parameters, set the iteration number n = 1, and enter the iterative optimization process;

[0150] Step 6, update the auxiliary variables, fix the analog beamforming matrix and the digital beamforming matrix , and solve the optimal solutions of the auxiliary variables and based on the alternating optimization method, and update the auxiliary variables and , as well as the auxiliary matrices and .

[0151] Step 7, iteratively update the digital beamforming matrix and the analog beamforming matrix according to the updated auxiliary variables;

[0152] Fix the analog beamforming matrix, construct the objective function of the digital beamforming matrix based on the augmented Lagrangian function with the hybrid beamforming matrix, calculate the gradient of the objective function, update the digital beamforming matrix through the gradient ascent method, remove the terms irrelevant to , and ensure that it satisfies the transmit power constraint condition. If it does not satisfy the transmit power constraint condition, project it into the feasible region to obtain the optimized digital beamforming matrix.

[0153] Based on the optimized digital beamforming matrix fix , add the power constraint as a penalty term to the augmented Lagrangian function, construct the objective function of the analog beamforming matrix, calculate the Euclidean gradient of the objective function, project it onto the tangent space to obtain the Riemannian gradient, select the Riemannian gradient as the ascending direction, and update the analog beamforming matrix according to the Riemannian gradient 。

[0154] Step 8, calculate whether the constraint violation value is less than or equal to the preset threshold. If not, update the penalty function and dual variables based on Rule 1. If so, update the penalty parameter and dual variables based on Rule 2;

[0155] Rule 1: , ;

[0156] Rule 2: , 。

[0157] Step 9, based on the updated parameters, let n = n + 1;

[0158] Step 10, determine whether the communication rate reaches the maximum or the number of iterations reaches N, that is, n = N. If not, repeat Steps 6 to 10. If so, execute Step 11;

[0159] Step 11; Output the digital beamforming matrix and the analog beamforming matrix 。

[0160] Based on the output optimal digital beamforming matrix and the analog beamforming matrix Control the antenna array to output a specific digital - analog hybrid beam to achieve the integration of communication and sensing functions.

[0161] In the embodiment of the present invention, a communication signal model and a sensing signal model under a hybrid beam in an integrated communication - sensing scenario based on the vehicle - to - everything network are constructed. Then, the Cramer - Rao lower bound (CRLB) is used as a measure of sensing performance to construct an optimization problem of the hybrid beamforming matrix under the CRLB constraint of target angle estimation. The hybrid beamforming matrix is optimized by iterative solution, maximizing the communication rate between the base station and the communication object while meeting the requirements of sensing accuracy, thereby effectively improving the system's sensing accuracy and communication efficiency for the target. Also, by changing the thresholds of relevant parameters, the communication performance and sensing performance can be balanced to adapt to different scenarios.

[0162] The following is a detailed description in combination with another embodiment.

[0163] Embodiment 2

[0164] A digital - analog hybrid beam generation device for communication - sensing symbiosis provided in this embodiment includes multiple implementation units. Each implementation unit corresponds to each implementation step in Embodiment 1 above. Its specific implementation manner and beneficial effects can be referred to the foregoing method embodiment and will not be elaborated here.

[0165] Figure 5Schematic diagram of an optional digital-analog hybrid beam generation device for communication and sensing coexistence according to an embodiment of the present invention, as Figure 5 shown, the digital-analog hybrid beam generation device for communication and sensing coexistence may include: an acquisition unit 51, a construction unit 52, an iteration unit 53, and a generation unit 54, where

[0166] The acquisition unit 51 is configured to acquire the scene information of the vehicle-to-everything (V2X) communication and sensing scenario, and establish a communication signal model and a sensing signal model based on the scene information. Among them, the V2X communication and sensing scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node;

[0167] The construction unit 52 is configured to use the Cramér-Rao lower bound of target angle estimation as a metric parameter for sensing performance for the communication signal model and the sensing signal model, and establish a beamforming matrix optimization problem under the constraint of the metric parameter. Among them, the beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem;

[0168] The iteration unit 53 is configured to simplify the beamforming matrix optimization problem by using the fractional programming algorithm and the Schur complement condition, obtain the simplified beamforming matrix optimization problem, and use the penalty dual decomposition algorithm to iteratively solve the simplified beamforming matrix optimization problem to obtain the optimized digital beamforming matrix and analog beamforming matrix;

[0169] The generation unit 54 is configured to generate a digital-analog hybrid beam in the V2X communication and sensing scenario based on the optimized digital beamforming matrix and analog beamforming matrix.

[0170] The above digital-analog hybrid beamforming device for communication-perception symbiosis collects the scene information of the vehicle-to-everything (V2X) communication-perception scenario through the acquisition unit 51, and establishes a communication signal model and a perception signal model based on the scene information. Among them, the V2X communication-perception scenario at least includes: a communication base station, a vehicle user terminal, a perception target, and a perception node. Through the construction unit 52, for the communication signal model and the perception signal model, the Cramer-Rao lower bound of target angle estimation is used as a metric parameter for perception performance, and a beamforming matrix optimization problem under the constraint of the metric parameter is established. Among them, the beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem. Through the iteration unit 53, the fractional programming algorithm and the Schur complement condition are used to simplify the beamforming matrix optimization problem, and the simplified beamforming matrix optimization problem is obtained. Then, the penalty dual decomposition algorithm is used to iteratively solve the simplified beamforming matrix optimization problem, and the optimized digital beamforming matrix and analog beamforming matrix are obtained. Through the generation unit 54, a digital-analog hybrid beam is generated for the V2X communication-perception scenario based on the optimized digital beamforming matrix and analog beamforming matrix.

[0171] In this embodiment, a communication signal model and a perception signal model are established according to the real-time V2X communication-perception scenario, and thus a beamforming matrix optimization problem under the Cramer-Rao constraint of target angle estimation is constructed, so that the model can take into account both communication and perception performance. By iteratively optimizing, the digital beamforming matrix and the analog beamforming matrix are solved. Using the hybrid digital-analog array, the communication rate is maximized by continuously iterating under the condition of meeting the perception performance constraint, which can simultaneously meet the requirements of high-precision perception and high-throughput communication, achieving the technical effect of simultaneously improving perception accuracy and communication efficiency, and thus solving the technical problem in the related art that the communication structure obtained by the all-digital array cannot support both efficient communication and high-precision long-distance perception at the same time.

[0172] Further, the simplified beamforming matrix optimization problem is expressed as:

[0173] ;

[0174] Among them, is the number of transmitting antennas of the communication base station and the number of receiving antennas of the perception node, is the number of radio frequency chains configured by the communication base station, and , represents the number of vehicle user terminals, represents the set of all optimization variables, and are auxiliary variables; is the digital beamforming matrix, is the analog beamforming matrix, and the feasible region is a complex circular manifold; is the penalty parameter, represents the dual variable related to the equation constraint, is the parameter value to be estimated, represents the relative angle of the sensing target, represent the horizontal angle and the vertical angle respectively, represent the real part and the imaginary part of the complex reflection coefficient respectively; represents is a 5×5 matrix, which is divided into four blocks in the form of 2 rows and 2 columns, 3 rows and 3 columns, that is, , , ; then represents the two-dimensional angle of arrival threshold under the metric parameter constraint, represents the constraint condition of the polynomial, represents the maximum transmit power of the communication base station, represents the set of all vehicle user terminals, represents taking the trace of the matrix, represents the Frobenius norm of the matrix.

[0175] Furthermore, the iterative unit includes: a first calculation module, for step one, using the penalty dual decomposition algorithm to calculate the optimal solutions of the auxiliary variables and in the beamforming matrix optimization problem and the optimal solutions of the auxiliary matrices and ; a first construction module, for step two, based on the optimal solutions of the auxiliary variables and and the optimal solutions of the auxiliary matrices and to construct an augmented Lagrangian function including the hybrid beamforming matrix; a first optimization module, for step three, fixing the analog beamforming matrix, constructing the objective function of the digital beamforming matrix based on the augmented Lagrangian function to obtain the first objective function, calculating the gradient value of the first objective function, and optimizing the digital beamforming matrix based on the gradient value; a second optimization module, for step four, fixing the digital beamforming matrix based on the optimized digital beamforming matrix in the above step three, and constructing the objective function of the analog beamforming matrix based on the augmented Lagrangian function to obtain the second objective function, calculating the Euclidean gradient of the second objective function, and optimizing the analog beamforming matrix based on the Euclidean gradient; a first iteration module, for step five, repeating the above steps one to four to iteratively optimize the digital beamforming matrix and the analog beamforming matrix.

[0176] Furthermore, the first calculation module includes: a first calculation sub-module, for fixing the variable , calculating the auxiliary variables in the beamforming matrix optimization problem and ; a second calculation sub-module, configured to calculate the optimal solutions of and based on the optimal solutions of the auxiliary variables; fixing the variables in the beamforming matrix optimization problem, and calculating the optimal solutions of the auxiliary matrices and in the beamforming matrix optimization problem.

[0177] Further, the first optimization module includes: a third calculation sub-module, configured to calculate the gradient value of the first objective function based on the gradient formula of the first objective function, and calculate a new digital beamforming matrix based on the iteration step and the gradient value of the first objective function; a first projection sub-module, configured to project the calculated new digital beamforming matrix into the feasible region that satisfies the power constraint condition to obtain an optimized digital beamforming matrix.

[0178] Further, the second optimization module includes: a first substitution sub-module, configured to substitute the fixed digital beamforming matrix into the augmented Lagrangian function, and add the power constraint as a penalty term to the augmented Lagrangian function to obtain a second objective function; a fourth calculation sub-module, configured to calculate the Euclidean gradient value of the second objective function based on the Euclidean gradient formula of the second objective function; a fifth calculation sub-module, configured to project the Euclidean gradient value into the tangent space to calculate the Riemannian gradient value of the second objective function, and calculate a new analog beamforming matrix according to the Riemannian gradient value.

[0179] Further, the digital-analog hybrid beam generation device for communication and sensing coexistence further includes: a second calculation module, configured to calculate the constraint violation degree of the beamforming matrix optimization problem after each optimization of the digital beamforming matrix and the analog beamforming matrix; a first comparison module, configured to compare the constraint violation degree with a preset constraint violation degree threshold to obtain a comparison result; a first update module, configured to update the penalty parameter and the dual variable in the beamforming matrix optimization problem based on the comparison result.

[0180] It should be noted here that the above-mentioned acquisition unit 51, construction unit 52, iteration unit 53, and generation unit 54 correspond to steps S201 to S204 in Embodiment 1. The above-mentioned units and the corresponding steps have the same implemented examples and application scenarios, but are not limited to the content disclosed in the above-mentioned Embodiment 1. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a storage device (for example, storage device 104) and processed by one or more processors (for example, processors 102a, 102b,..., 102n). The above-mentioned modules or units may also be part of the device and can run in the computer terminal 10 provided in Embodiment 1.

[0181] The present invention will be described below in connection with another optional embodiment.

[0182] Embodiment 3

[0183] An embodiment of the present invention may further provide an electronic device, Figure 6 which is a hardware structure block diagram of an electronic device (or mobile device) that optionally implements a communication-aware co-existing digital-analog hybrid beamforming method according to an embodiment of the present invention, as Figure 6 shown. The electronic device may include: one or more ( Figure 6 only one is shown in the figure) processors 602, a memory 604, a storage controller, and a peripheral interface, where the peripheral interface is connected to a radio frequency module, an audio module, and a display.

[0184] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and devices in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the above methods. The memory may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory may further include a memory remotely provided with respect to the processor, and these remote memories may be connected to the terminal through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0185] The processor can call the information and application programs stored in the memory through a transmission device to perform the following steps: collecting scenario information of a vehicle-to-everything (V2X) communication-aware scenario, and establishing a communication signal model and a sensing signal model based on the scenario information, where the V2X communication-aware scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node; for the communication signal model and the sensing signal model, using the Cramér-Rao lower bound of target angle estimation as a metric parameter for sensing performance, constructing a beamforming matrix optimization problem under the constraint of the metric parameter, where the beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem; using a fractional programming algorithm and a Schur complement condition to simplify the beamforming matrix optimization problem, obtaining a simplified beamforming matrix optimization problem, and using a penalty dual decomposition algorithm to iteratively solve the simplified beamforming matrix optimization problem to obtain an optimized digital beamforming matrix and an analog beamforming matrix; generating a digital-analog hybrid beam in the V2X communication-aware scenario based on the optimized digital beamforming matrix and the analog beamforming matrix.

[0186] An embodiment of the present invention provides a digital-analog hybrid beamforming generation scheme for communication and sensing symbiosis. A communication signal model and a sensing signal model are established according to the real-time vehicle-to-everything (V2X) communication and sensing scenario, and thus an optimization problem of the beamforming matrix under the Cramer-Rao bound of target angle estimation is constructed, enabling the model to take into account both communication and sensing performance. The digital beamforming matrix and the analog beamforming matrix are solved through iterative optimization. By using a hybrid digital-analog array, the communication rate is maximized through continuous iteration under the condition of meeting the sensing performance constraint, which can simultaneously meet the requirements of high-precision sensing and high-throughput communication, achieving the technical effect of simultaneously improving sensing accuracy and communication efficiency, and further solving the technical problem in the related art that the communication structure obtained by a fully digital array cannot support both efficient communication and high-precision long-range sensing.

[0187] Those of ordinary skill in the art can understand that Figure 6 the structure shown is only schematic, and the electronic device can also be a terminal device such as a smart phone, a tablet computer, a personal digital assistant, and mobile Internet devices (MIDs), PADs, etc. Figure 6 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, a display device, etc.) than those shown Figure 6 in the figure, or have a different configuration from that shown Figure 6 in the figure.

[0188] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware of the terminal device through a program, and the program can be stored in a computer-readable storage medium. The storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0189] The present invention will be described below in conjunction with another optional embodiment.

[0190] Embodiment 4

[0191] The embodiment of the present invention further provides a computer-readable storage medium. Optionally, in the embodiment of the present invention, the above computer-readable storage medium can be used to store the program code executed by the digital-analog hybrid beamforming generation method for communication and sensing symbiosis provided in the first embodiment above.

[0192] Optionally, in the embodiment of the present invention, the above storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0193] An embodiment of the present invention also provides a computer program product. When executed on a data processing device, it is a program suitable for executing the steps of a digital-analog hybrid beamforming method for communication-aware coexistence: collecting scene information of a vehicle-to-everything (V2X) communication-aware scenario, and establishing a communication signal model and a sensing signal model based on the scene information. Among them, the V2X communication-aware scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node; for the communication signal model and the sensing signal model, the Cramér-Rao lower bound of target angle estimation is used as a metric parameter for sensing performance, and a beamforming matrix optimization problem under the constraint of the metric parameter is constructed. Among them, the beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem; the fractional programming algorithm and the Schur complement condition are used to simplify the beamforming matrix optimization problem, and the simplified beamforming matrix optimization problem is obtained. Then, the penalty dual decomposition algorithm is used to iteratively solve the simplified beamforming matrix optimization problem to obtain the optimized digital beamforming matrix and analog beamforming matrix; based on the optimized digital beamforming matrix and analog beamforming matrix, a digital-analog hybrid beam is generated in the V2X communication-aware scenario.

[0194] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0195] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0196] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0197] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] In addition, in each embodiment of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0199] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, 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 several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0200] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A digital-analog hybrid beamforming generation method for communication and sensing coexistence, characterized in that Including: Collecting the scenario information of the vehicle-to-everything (V2X) communication sensing scenario, and establishing a communication signal model and a sensing signal model based on the scenario information, where the V2X communication sensing scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node; For the communication signal model and the sensing signal model, using the Cramer-Rao lower bound of target angle estimation as a metric parameter for sensing performance, and constructing a beamforming matrix optimization problem under the constraint of the metric parameter, where the beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem; Simplifying the beamforming matrix optimization problem by using the fractional programming algorithm and the Schur complement condition to obtain the simplified beamforming matrix optimization problem, and iteratively solving the simplified beamforming matrix optimization problem by using the penalty dual decomposition algorithm to obtain the optimized digital beamforming matrix and analog beamforming matrix; Generating a digital-analog hybrid beam in the V2X communication sensing scenario based on the optimized digital beamforming matrix and analog beamforming matrix.

2. The method according to claim 1, characterized in that, The simplified beamforming matrix optimization problem is expressed as: ； wherein, is the number of transmit antennas of the communication base station and the number of receive antennas of the sensing node, is the number of radio frequency chains configured for the communication base station, and , represents the number of vehicle user terminals, represents the set of all optimization variables, and are auxiliary variables; is the digital beamforming matrix, is the analog beamforming matrix, and the feasible region is a complex circular manifold; is the penalty parameter, represents the dual variable related to the constraint of equation , is the parameter value to be estimated, represents the relative angle of the sensing target, represent the horizontal angle and the vertical angle respectively, represent the real part and the imaginary part of the complex reflection coefficient respectively; represents is a 5×5 matrix, which is divided into four blocks in the form of 2 rows and 2 columns, 3 rows and 3 columns, that is, we get , , ; then represents the two-dimensional angle of arrival threshold under the constraint of the metric parameter, represents the constraint condition of the polynomial, represents the maximum transmit power of the communication base station, represents the set of all vehicle user terminals, represents taking the trace of the matrix, represents the F-norm of the matrix.

3. The method according to claim 2, wherein The steps of iteratively solving the simplified beamforming matrix optimization problem by using the penalty dual decomposition algorithm include: Step 1: Use the penalty dual decomposition algorithm to calculate the optimal solutions of the auxiliary variables and in the beamforming matrix optimization problem, as well as the optimal solutions of the auxiliary matrices and ; Step 2, based on the optimal solutions of the auxiliary variables and and the optimal solutions of the auxiliary matrices and construct an augmented Lagrangian function containing the hybrid beamforming matrix; Step 3, fixing the analog beamforming matrix, constructing an objective function of the digital beamforming matrix based on the augmented Lagrangian function to obtain a first objective function, calculating the gradient value of the first objective function, and optimizing the digital beamforming matrix based on the gradient value; Step 4, fixing the digital beamforming matrix based on the optimized digital beamforming matrix in the above step 3, constructing an objective function of the analog beamforming matrix based on the augmented Lagrangian function to obtain a second objective function, calculating the Euclidean gradient of the second objective function, and optimizing the analog beamforming matrix based on the Euclidean gradient; Step 5, repeating the above steps 1 to 4 to iteratively optimize the digital beamforming matrix and the analog beamforming matrix.

4. The method according to claim 3, characterized in that, The penalty dual decomposition algorithm is used to calculate the auxiliary variables in the beamforming matrix optimization problem and of the optimal solution, as well as the auxiliary matrix and The steps of the optimal solution include: Fix the variables in the beamforming matrix optimization problem , and calculate the auxiliary variables in the beamforming matrix optimization problem and to obtain the optimal solution; Based on the auxiliary variable and the optimal solution, fix the variables in the beamforming matrix optimization problem and calculate the auxiliary matrices and in the beamforming matrix optimization problem for their optimal solutions.

5. The method according to claim 3, wherein The steps of calculating the gradient value of the first objective function and optimizing the digital beamforming matrix based on the gradient value include: Calculating the gradient value of the first objective function based on the gradient formula of the first objective function, and calculating a new digital beamforming matrix based on the iteration step size and the gradient value of the first objective function; Projecting the calculated new digital beamforming matrix into the feasible region that satisfies the power constraint condition to obtain the optimized digital beamforming matrix.

6. The method according to claim 3, characterized in that, The steps of fixing the digital beamforming matrix based on the optimized digital beamforming matrix in the above step 3, constructing an objective function of the analog beamforming matrix based on the augmented Lagrangian function to obtain a second objective function, calculating the Euclidean gradient of the second objective function, and optimizing the analog beamforming matrix based on the Euclidean gradient include: Substitute the fixed digital beamforming matrix into the augmented Lagrangian function, and add the power constraint as a penalty term to the augmented Lagrangian function to obtain the second objective function; Calculate the Euclidean gradient value of the second objective function based on the Euclidean gradient formula of the second objective function; Project the Euclidean gradient value onto the tangent space to calculate the Riemannian gradient value of the second objective function, and calculate the new analog beamforming matrix according to the Riemannian gradient value.

7. The method according to claim 3, characterized in that, Further includes: After optimizing the digital beamforming matrix and the analog beamforming matrix each time, calculate the constraint violation degree of the beamforming matrix optimization problem; Compare the constraint violation degree with a preset constraint violation degree threshold to obtain a comparison result; Update the penalty parameter and the dual variable in the beamforming matrix optimization problem based on the comparison result.

8. A digital-analog hybrid beamforming device for communication and sensing coexistence, characterized in that Includes: An acquisition unit, configured to acquire the scenario information of the vehicle-to-everything (V2X) communication sensing scenario, and establish a communication signal model and a sensing signal model based on the scenario information, where the V2X communication sensing scenario at least includes: a communication base station, a vehicle user terminal, a sensing target, and a sensing node; A construction unit, configured to use the Cramér-Rao lower bound of target angle estimation as a metric parameter for sensing performance for the communication signal model and the sensing signal model, and establish a beamforming matrix optimization problem under the constraint of the metric parameter, where the beamforming matrix optimization problem includes: a digital beamforming matrix optimization problem and an analog beamforming matrix optimization problem; An iteration unit, configured to simplify the beamforming matrix optimization problem by using the fractional programming algorithm and the Schur complement condition to obtain the simplified beamforming matrix optimization problem, and iteratively solve the simplified beamforming matrix optimization problem by using the penalty dual decomposition algorithm to obtain the optimized digital beamforming matrix and analog beamforming matrix; A generation unit, configured to generate a digital-analog hybrid beam in the V2X communication sensing scenario based on the optimized digital beamforming matrix and the analog beamforming matrix.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the digital-analog hybrid beam generation method for communication sensing coexistence according to any one of claims 1 to 7.

10. An electronic device, characterized in that, Includes one or more processors and a memory, the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the digital-analog hybrid beam generation method for communication sensing coexistence according to any one of claims 1 to 7.

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