Waveform generation method and device, equipment, storage medium and computer program product

Abstract

The invention relates to the technical field of communication, and discloses a waveform generation method, device and equipment, a storage medium and a computer program product, and the method comprises the steps: determining an optimal covariance matrix according to a given expected radiation pattern, generating an optimal radar waveform through a least square method based on the optimal covariance matrix, and generating a current time slot sensing waveform, the current time slot sensing waveform is a waveform which is generated based on an application demand, the phase after transmission is completely aligned with the phase of the communication symbol or the phase after transmission falls in a columnar neighborhood of the phase of the communication symbol, and a transmitted waveform which is most close to an optimal radar waveform is determined through constraint conditions of the waveform optimization model. According to the method, the optimal covariance matrix is determined according to the expected radiation pattern, the optimal radar waveform meeting the constant modulus and transmitting power constraint is generated accordingly, the current time slot sensing waveform is generated at the same time, and finally the transmitting waveform is determined in combination with multiple constraints, so that sensing and communication waveform cooperative gain is realized, and the system performance is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a waveform generation method, apparatus, device, storage medium, and computer program product. Background Art

[0002] As communications pursue ultra-high speeds, ultra-low latency, and massive connections, Integrated Sensing and Communication (ISAC) is a key technology that integrates communication and sensing functions to improve spectrum efficiency and system performance. The Multiple Input Multiple Output (MIMO) architecture, due to its advantages in communication and radar sensing, is widely considered in ISAC system design, with transmit waveform design being a key consideration.

[0003] Current waveform design schemes for ISAC-MIMO systems often treat perception waveforms as communication interference sources, which leads to mutual constraints between perception and communication. As communication performance requirements increase, the freedom of perception waveform design is limited, making it impossible to achieve synergistic gain between communication and perception waveforms, making it difficult to improve overall system performance. Summary of the Invention

[0004] The main purpose of this application is to provide a waveform generation method, device, equipment, storage medium and computer program product, aiming to solve the technical problem that the existing technology cannot achieve collaborative gain of perception waveforms and communication waveforms.

[0005] To achieve the above objectives, the present application proposes a waveform generation method, which includes:

[0006] Determine the optimal covariance matrix based on the given desired radiation pattern;

[0007] Based on the optimal covariance matrix, generating an optimal radar waveform by a least square method, wherein the optimal radar waveform is a radar waveform that satisfies a constant modulus constraint and a transmit power;

[0008] Generate a current time slot sensing waveform based on the current time slot communication symbol and current channel information, wherein the current time slot sensing waveform is a waveform generated based on application requirements, and the phase after transmission is completely aligned with the phase of the communication symbol or the phase after transmission falls within a columnar neighborhood of the phase of the communication symbol;

[0009] The transmit waveform that is closest to the optimal radar waveform is determined through the constraints of the waveform optimization model, wherein the constraints include the communication signal to interference plus noise ratio, the transmit power, and the current time slot sensing waveform.

[0010] In one embodiment, the step of determining the optimal covariance matrix according to a given desired radiation pattern includes:

[0011] Receiving a desired radiation pattern input by a user, analyzing the desired radiation pattern, and obtaining a desired radiation intensity value corresponding to each angle;

[0012] Determine the array steering vector at each angle based on the physical arrangement parameters of the transmitting antenna array;

[0013] The desired radiation intensity value and the array steering vector are input into a convex optimization solver, and an optimal covariance matrix that satisfies all constraints is output.

[0014] In one embodiment, the step of generating an optimal radar waveform by least squares method based on the optimal covariance matrix includes:

[0015] Discretizing a preset complex circle with equal intervals to obtain a discrete complex number set satisfying a constant modulus constraint, wherein the discrete complex number set is a candidate value set for each antenna transmit phase;

[0016] Determining a waveform configuration set according to the discrete complex number set and the total number of antennas, and determining a covariance matrix set of the waveform configuration set;

[0017] The discrete probability density function is defined as the probability that the waveform corresponding to each element in the covariance matrix set is selected as the transmission waveform in each time slot;

[0018] Determining a probability distribution of a waveform configuration set by a least squares method based on the covariance matrix set, the discrete probability density function, and the optimal covariance matrix;

[0019] An optimal radar waveform is determined according to the probability distribution.

[0020] In one embodiment, the step of determining the probability distribution of the waveform configuration set by the least squares method based on the covariance matrix set, the discrete probability density function and the optimal covariance matrix includes:

[0021] Vectorizing all covariance matrices in the covariance matrix set to obtain a vectorized matrix;

[0022] Stacking the vectorized matrices row by row to obtain a transformed matrix, and determining a product result of the transformed matrix and a column vector formed by stacking elements in the discrete probability density function;

[0023] The norm of the difference between the product result and the optimal covariance matrix is ​​minimized by the least squares method to obtain the probability distribution of the waveform configuration set.

[0024] In one embodiment, the step of determining a transmit waveform that is closest to the optimal radar waveform through the constraints of the waveform optimization model includes:

[0025] By introducing auxiliary variables, the objective function and constraints in the waveform optimization model are reformulated to obtain a second-order cone programming problem, wherein the auxiliary variables are composed of the communication waveform and the current time slot sensing waveform.

[0026] Solve the second-order cone programming problem to obtain a transmission waveform.

[0027] In one embodiment, before the step of determining the transmit waveform closest to the optimal radar waveform based on the constraints of the waveform optimization model, the method further includes:

[0028] Determine the communication signal to interference plus noise ratio of each user based on the expected received communication symbol power, user interference power and received noise power;

[0029] A waveform optimization model is constructed with minimizing the Euclidean distance between the transmitted waveform and the optimal radar waveform as the objective function and with the communication signal to interference plus noise ratio, the transmit power and the current time slot sensing waveform as constraints.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a waveform generating device, which includes:

[0031] A matrix optimization module is used to determine the optimal covariance matrix based on a given desired radiation pattern;

[0032] a reference waveform determination module, configured to generate an optimal radar waveform by a least squares method based on the optimal covariance matrix, wherein the optimal radar waveform is a radar waveform that satisfies a constant modulus constraint and a transmit power;

[0033] A perception waveform adjustment module is configured to generate a perception waveform for the current time slot based on the communication symbol of the current time slot and the current channel information, wherein the perception waveform for the current time slot is a waveform generated based on application requirements, wherein the phase after transmission is completely aligned with the phase of the communication symbol or the phase after transmission falls within a columnar neighborhood of the phase of the communication symbol;

[0034] A target waveform determination module is configured to determine a transmit waveform that is closest to the optimal radar waveform through constraints of a waveform optimization model, wherein the constraints include a communication signal to interference plus noise ratio, the transmit power, and the current time slot sensing waveform.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a waveform generation device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the waveform generation method described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the waveform generation method described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the waveform generation method described above.

[0038] The technical solution proposed in this application includes: determining the optimal covariance matrix based on a given expected radiation pattern, generating the optimal radar waveform through the least squares method based on the optimal covariance matrix, the optimal radar waveform is a radar waveform that satisfies the constant modulus constraint and transmit power, generating the current time slot perception waveform based on the current time slot communication symbol and the current channel information, the current time slot perception waveform is a waveform generated based on application requirements, the phase after transmission is completely aligned with the communication symbol phase or the phase after transmission falls within the columnar neighborhood of the communication symbol phase, and determining the transmission waveform that is closest to the optimal radar waveform through the constraints of the waveform optimization model, wherein the constraints include the communication signal to interference plus noise ratio, transmit power, and the current time slot perception waveform. This application first determines the optimal covariance matrix based on the expected radiation pattern, and then generates the optimal radar waveform that satisfies the constant modulus and transmit power constraints based on it, and simultaneously generates the current time slot perception waveform to ensure compatibility with the communication waveform. Finally, combining multiple constraints to determine the transmission waveform that is closest to the optimal radar waveform, achieves synergistic gain between perception and communication waveforms, and improves system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 A schematic diagram of a flow chart provided for the first embodiment of the waveform generation method of the present application;

[0042] Figure 2 A schematic diagram of a flow chart provided for the second embodiment of the waveform generation method of the present application;

[0043] Figure 3 A flowchart of the third embodiment of the waveform generation method of the present application is provided;

[0044] Figure 4 Schematic diagram of the ISAC-MIMO system model;

[0045] Figure 5 Schematic diagram of communication-aware collaborative gain waveform generation scheme;

[0046] Figure 6 This is a schematic diagram of an example technical route of the waveform generation method of this application;

[0047] Figure 7 This is a schematic diagram of the module structure of the waveform generating device according to an embodiment of the present application;

[0048] Figure 8 Schematic diagram of the device structure of the hardware operating environment involved in the waveform generation method in the embodiment of the present application.

[0049] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0051] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0052] Current waveform design schemes for ISAC-MIMO systems often treat perception waveforms as communication interference sources, which leads to mutual constraints between perception and communication. As communication performance requirements increase, the freedom of perception waveform design is limited, making it impossible to achieve synergistic gain between communication and perception waveforms, making it difficult to improve overall system performance.

[0053] Therefore, in order to overcome the above-mentioned defects, the present application provides a solution by first determining the optimal covariance matrix based on the expected radiation pattern, and then generating the optimal radar waveform that meets the constant modulus and transmit power constraints based on this. At the same time, the current time slot perception waveform is generated to ensure compatibility with the communication waveform. Finally, the transmit waveform that is closest to the optimal radar waveform is determined by combining multiple constraints, thereby achieving synergistic gain between perception and communication waveforms and improving system performance.

[0054] It should be noted that the execution entity of each embodiment of the present application can be a computing service system with data processing, network communication, and program execution functions, such as an electronic system or waveform generation system capable of implementing the above functions. The following embodiments are described using a waveform generation system as an example (hereinafter referred to as the "system").

[0055] Based on this, the embodiment of the present application provides a waveform generation method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the waveform generation method of the present application.

[0056] In this embodiment, the waveform generation method includes steps S10 to S40:

[0057] Step S10: determining an optimal covariance matrix according to a given expected radiation pattern.

[0058] With the rapid development of 6G (sixth-generation mobile communication standard) communication technology, the global communications sector is striving for higher performance targets, including ultra-high speeds, ultra-low latency, and massive connections to meet the demands of emerging applications such as intelligent transportation, the Industrial Internet of Things, and augmented reality. Against this backdrop, ISAC has emerged as a key 6G technology. By integrating communication and perception functions, it leverages wireless signals for simultaneous data transmission and environmental awareness, thereby improving spectrum efficiency and system performance, injecting new impetus into the intelligent development of 6G. Furthermore, MIMO technology, leveraging spatial diversity and multiplexing gain, enables efficient data transmission in wireless communications and high-precision target detection and positioning in radar sensing. Therefore, MIMO architectures are widely considered in ISAC system design. Transmit waveform design is a key technology in ISAC-MIMO system design. In terms of communication, high data rates and low bit error rates are required, while in terms of perception, high-precision target detection probability is required. The transmit waveform needs to strike a balance between these two requirements, and innovative signal design can achieve coordinated performance optimization.

[0059] Currently, there are two waveform design schemes for ISAC-MIMO systems. The first scheme only designs the communication waveform and uses the communication waveform to simultaneously complete communication and perception functions. The advantage of this scheme is that the model is simple, but the disadvantage is that it limits the system's perception performance, especially when the number of targets to be detected is greater than the number of communicating users, the perception performance is severely limited. The second scheme designs both communication and perception waveforms, uses the communication waveform to implement the communication function, and uses the perception and communication waveforms to collaboratively complete the perception function. This scheme effectively overcomes the shortcomings of the first scheme, but at the same time increases the complexity of system design. Because the perception waveform often does not carry communication information, how to deal with its interference with communication is a key technology.

[0060] For ISAC systems that require the simultaneous design of both sensing and communication waveforms, current research often treats the sensing waveform as a source of interference to communication during waveform design. This design approach inevitably leads to a mutual constraint between sensing and communication. As communication performance requirements increase, the design freedom of the sensing waveform is severely limited. Therefore, if the transmit waveform is designed so that the sensing waveform benefits communication, rather than treating it as a source of interference to communication, a certain degree of sensing-communication synergy gain can be achieved, improving overall system performance.

[0061] Symbol-level precoding technology allows for the design of the transmitted signal in each time slot, greatly improving the design freedom compared to traditional waveform design techniques. Therefore, for ISAC-MIMO systems that simultaneously have perception waveforms and communication waveforms, this application proposes a waveform generation method for an integrated interawareness system based on symbol-level precoding technology. Specifically, it is a method for generating transmit waveforms in a multi-antenna ISAC system to achieve communication perception collaborative gain.

[0062] In this step, it should be noted that the expected radiation pattern refers to the expected distribution of radiation energy of the radar system in space, reflecting the radar's detection capability requirements for targets in different directions; the covariance matrix in radar waveform design describes the statistical characteristics of the radar transmitted signal in different dimensions, such as the power distribution of the signal and the correlation between different signal components.

[0063] In order to obtain the optimal covariance matrix, it is necessary to base the target detection requirement information contained in the expected radiation pattern on a series of mathematical operations and optimization algorithms, comprehensively consider parameters such as the main lobe width, side lobe level, and beam pointing in the pattern, and convert the expected radiation characteristics into element values ​​of the covariance matrix, thereby determining the optimal covariance matrix that can meet specific radiation requirements.

[0064] As an implementation method, the above-mentioned step S10 in this embodiment may include: receiving the expected radiation pattern input by the user, parsing the expected radiation pattern to obtain the expected radiation intensity value corresponding to each angle; determining the array steering vector at each angle based on the physical arrangement parameters of the transmitting antenna array; inputting the expected radiation intensity value and the array steering vector into a convex optimization solver, and outputting the optimal covariance matrix that meets all constraints.

[0065] In its implementation, the system first receives the desired radiation pattern, which represents the user's expectations for the radar system's radiation characteristics at different spatial angles. Using specific algorithms and mathematical processing, the system extracts the expected radiation intensity values ​​corresponding to each angle from the pattern. This value describes the amount of radiation energy the radar is expected to achieve at each angle. Then, considering the actual hardware structure of the radar system, the array steering vector at each angle must be determined based on the physical layout parameters of the transmitting antenna array, such as the number of antennas, the spacing between antennas, and the antenna arrangement (linear, planar, etc.). The array steering vector is a mathematical quantity related to the antenna array and the signal incident angle. It characterizes the antenna array's response to signals at different angles and is crucial for accurately controlling the radar's radiation direction.

[0066] Furthermore, the expected radiation intensity value obtained from the previous analysis and the determined array steering vector are used as input and sent to the convex optimization solver. The convex optimization solver is a mathematical tool that can find an optimal solution under given constraints through a series of complex mathematical operations and optimization algorithms. In this step, the convex optimization solver outputs the optimal covariance matrix that satisfies all constraints.

[0067] Step S20 : ​​generating an optimal radar waveform based on the optimal covariance matrix by a least square method, wherein the optimal radar waveform is a radar waveform that satisfies a constant modulus constraint and a transmit power.

[0068] In this step, the optimal radar waveform is generated using the optimal covariance matrix as the basic reference information. This process uses the least squares method to construct an optimization problem. The core idea of ​​the least squares method is to find the best function matching the data by minimizing the sum of squared errors. The goal of the optimization problem is to find a set of radar waveform parameters that minimizes the sum of squared errors between the generated radar waveform and the desired waveform based on the optimal covariance matrix, while satisfying constant modulus constraints and transmit power. By solving this optimization problem, an optimal radar waveform that meets specific conditions can be generated.

[0069] It should be noted that the constant modulus constraint means that the amplitude of the radar waveform remains constant to meet the hardware characteristics requirements of the radar transmitter; the transmit power constraint ensures that the radar waveform has an appropriate energy level when transmitted.

[0070] Step S30, generating a current time slot perception waveform based on the current time slot communication symbol and the current channel information, wherein the current time slot perception waveform is a waveform generated based on application requirements, and the phase after transmission is completely aligned with the communication symbol phase or the phase after transmission falls within the columnar neighborhood of the communication symbol phase.

[0071] It should be understood that when generating the current-slot sensing waveform, it is necessary to comprehensively consider the current-slot communication symbol, the current channel information, and specific application requirements. These requirements may include communication reliability, data transmission rate, and the impact on radar sensing capabilities. Based on these factors, a specific algorithm and design method are used to generate a current-slot sensing waveform so that, after transmission, its phase is either fully aligned with the phase of the communication symbol or falls within a columnar neighborhood of the communication symbol phase. This design ensures accurate transmission of the communication signal while, to a certain extent, meeting the waveform requirements of the radar sensing function.

[0072] Step S40: determining a transmission waveform that is closest to the optimal radar waveform through the constraints of the waveform optimization model, wherein the constraints include the communication signal to interference plus noise ratio, the transmission power, and the current time slot sensing waveform.

[0073] It should be noted that the waveform optimization model is a mathematical framework for finding the optimal transmit waveform, and its constraints serve to limit and guide waveform design. The signal-to-interference plus noise ratio (SINR) is an important metric for measuring communication signal quality, reflecting the reliability of the communication signal after being affected by interference and noise. When determining the transmit waveform, it is necessary to ensure that the SINR meets certain requirements to ensure the normal implementation of communication functions. The transmit power constraint controls the energy level of the radar waveform to prevent excessive power from damaging the system or violating relevant regulations. The current time slot sensing waveform requires that the transmit waveform be as close as possible to the current time slot sensing waveform while meeting communication and power requirements. By solving the waveform optimization model under these constraints, the transmit waveform that is closest to the optimal radar waveform can be found, enabling the coordinated operation of radar and communication functions.

[0074] This embodiment first determines the optimal covariance matrix based on the desired radiation pattern. Based on this, it generates an optimal radar waveform that satisfies constant modulus and transmit power constraints. It also generates a sensing waveform for the current time slot, ensuring compatibility with the communication waveform. Finally, combining multiple constraints, it determines a transmit waveform that most closely approximates the optimal radar waveform. This achieves synergistic gain between the sensing and communication waveforms, improving system performance. Furthermore, by fully considering actual needs and antenna characteristics, the matrix is ​​precisely determined based on user needs and antenna characteristics, improving the accuracy and effectiveness of transmit waveform generation.

[0075] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , the step S20 may include steps S201 to S205:

[0076] Step S201 : discretizing a preset complex circle with equal intervals to obtain a discrete complex number set satisfying a constant modulus constraint, wherein the discrete complex number set is a candidate value set for each antenna transmit phase.

[0077] It should be understood that the constant modulus constraint is a crucial requirement in radar waveform design. It requires that the amplitude of the radar waveform remain constant, with only the phase being variable. The preset complex circle represents the set of complex numbers with fixed moduli (satisfying the constant modulus requirement) on the complex plane. Discretizing the preset complex circle at equal intervals means selecting a series of discrete points on the complex circle at certain angular intervals. The complex numbers corresponding to these discrete points have the same modulus (satisfying the constant modulus constraint) but different phases. The resulting set of discrete complex numbers constitutes the candidate value set for each antenna's transmit phase, providing a limited range of options for the subsequent determination of the antenna's transmit phase.

[0078] Step S202: determining a waveform configuration set according to the discrete complex number set and the total number of antennas, and determining a covariance matrix set of the waveform configuration set.

[0079] For each antenna, a complex number is selected from a discrete set of complex numbers as the phase component of its transmitted signal. The complex numbers selected by all antennas are combined to form a waveform configuration. Taking all possible combinations into account, a waveform configuration set is obtained. For each waveform configuration in the waveform configuration set, the corresponding covariance matrix can be determined based on the definition and calculation method of the covariance matrix, thereby obtaining a set of covariance matrices.

[0080] Step S203 : defining a discrete probability density function as the probability that the waveform corresponding to each element in the covariance matrix set is selected as the transmission waveform in each time slot.

[0081] It should be noted that discrete probability density function is used to describe the probability distribution of discrete random variables on each possible value. In this step, discrete probability density function is defined as the probability that the waveform corresponding to each element in the covariance matrix set is selected as the transmission waveform in each time slot, that is, for each covariance matrix (corresponding to a specific waveform configuration) in the covariance matrix set, give it the probability value selected as the transmission waveform in each time slot, these probability values ​​constitute discrete probability density function, reflect the possibility size of different waveform configurations in the transmission process.

[0082] Step S204 : determining the probability distribution of the waveform configuration set by a least squares method based on the covariance matrix set, the discrete probability density function, and the optimal covariance matrix.

[0083] Based on a set of covariance matrices, a discrete probability density function, and a predetermined optimal covariance matrix, an optimization problem is constructed. This optimization problem adjusts the probability distribution of the waveform configuration set (that is, adjusts the individual probability values ​​in the discrete probability density function) so that the sum of squared errors between the covariance matrix calculated based on the current probability distribution and the optimal covariance matrix is ​​minimized. By solving this optimization problem, the probability distribution of the waveform configuration set can be determined.

[0084] As an implementation method, the above-mentioned step S204 in this embodiment may include: vectorizing all covariance matrices in the covariance matrix set to obtain a vectorized matrix; stacking the vectorized matrices by rows to obtain a converted matrix, and determining the product result of the converted matrix and the column vector formed by stacking each element in the discrete probability density function; minimizing the norm of the difference between the product result and the optimal covariance matrix through the least squares method to obtain the probability distribution of the waveform configuration set.

[0085] In practice, the covariance matrix is ​​typically in the form of a two-dimensional square matrix. Vectorizing each covariance matrix in a covariance matrix set involves converting the two-dimensional matrix into a one-dimensional vector according to certain rules (such as column-first or row-first order). This converts the original multiple two-dimensional covariance matrices into multiple one-dimensional vectors, which form the vectorized matrix. This facilitates subsequent mathematical operations and processing of matrices, as vector and matrix operations have more mature and convenient methods in mathematical theory, simplifying complex computations.

[0086] Stacking the vectorized matrix in rows involves arranging multiple one-dimensional vectors (i.e., the vectorized covariance matrix) vertically to form a new, larger matrix, the transformed matrix. Simultaneously, each element in the discrete probability density function represents the probability that the waveform corresponding to each element in the covariance matrix set will be selected as the transmitted waveform in each time slot. These elements are stacked into a column vector. The product of the transformed matrix and this column vector is then calculated. This product reflects the comprehensive statistical characteristics of different waveform configurations (corresponding to different covariance matrices) under a given probability distribution.

[0087] Further, by least square method, find the probability value (i.e. the probability distribution of waveform configuration set) in discrete probability density function, make certain objective function reach minimum value.In this step, objective function is the norm of product result and optimal covariance matrix difference, and norm can be understood as a kind of metric measuring difference size between two matrices.By constantly adjusting the probability value in discrete probability density function, make product result as close as possible to optimal covariance matrix, namely allow the norm of their difference minimize.When this objective function reaches minimum value, the probability value combination in corresponding discrete probability density function is exactly the optimal probability distribution that satisfies condition, i.e. the probability distribution of waveform configuration set, and this distribution can make the statistical characteristic obtained according to current waveform configuration combination be closest to the characteristic represented by pre-set optimal covariance matrix.

[0088] Step S205: determining an optimal radar waveform according to the probability distribution.

[0089] After obtaining the probability distribution of the waveform configuration set, the most likely overall waveform configuration can be determined based on this distribution. Since each element in the waveform configuration set corresponds to a specific radar waveform, the waveform configuration with the highest probability is selected based on the probability distribution, or a weighted combination of all waveform configurations is performed based on the probability distribution to ultimately determine the optimal radar waveform that meets the design requirements. This optimal radar waveform is determined by considering factors such as constant modulus constraints, the optimal covariance matrix, and the likelihood of various waveform configurations.

[0090] This embodiment starts from a discrete perspective and combines the least squares method to effectively generate an optimal radar waveform that meets multiple constraints, thereby enhancing the rationality of waveform generation.

[0091] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction and will not be described in detail later. Figure 3 , the step S40 may include steps S401 to S402:

[0092] Step S401: Re-express the objective function and constraints in the waveform optimization model by introducing auxiliary variables to obtain a second-order cone programming problem, wherein the auxiliary variables are composed of the communication waveform and the current time slot sensing waveform.

[0093] It should be noted that in waveform optimization models, the original objective function and constraints are relatively complex, making direct solutions often difficult. Introducing auxiliary variables is a key step in transforming the problem into a more manageable form.

[0094] The auxiliary variable introduced in this step is composed of a concatenated communication waveform and the current-slot sensing waveform. The communication waveform carries communication information and is essential for ensuring the proper functioning of radar-communication systems. The current-slot sensing waveform is generated based on the communication symbols and channel information for the current time slot, reflecting the radar sensing requirements at that specific moment. Combining these two waveforms to form the auxiliary variable comprehensively accounts for both communication and radar sensing.

[0095] By introducing auxiliary variables, the objective function and constraints in the waveform optimization model are reformulated. The objective function typically measures the proximity of the transmitted waveform to the optimal radar waveform. This reformulation uses the auxiliary variables as independent variables, resulting in a more concise and mathematically compliant representation. Constraints are also rewritten around the auxiliary variables. These constraints can include requirements for signal-to-noise ratio (SINR) and transmit power limits. This process transforms the entire waveform optimization problem into a second-order cone programming problem.

[0096] Step S402: Solve the second-order cone programming problem to obtain a transmission waveform.

[0097] It's important to note that second-order cone programming falls under the category of convex optimization problems, meaning that its local optimal solution is also the global optimal solution. Solving second-order cone programming problems can utilize a variety of mature algorithms and software tools, such as optimization algorithms like the interior point method, as well as specialized mathematical optimization software like the CVX convex optimization solver.

[0098] During the solution process, the algorithm uses the reformulated objective function and constraints to find the value of the auxiliary variable that optimizes the objective function (i.e., the transmit waveform most closely matches the optimal radar waveform) while satisfying all constraints. Since the auxiliary variable is composed of the communication waveform and the current time slot sensing waveform, once the optimal auxiliary variable value is obtained, the corresponding transmit waveform can be extracted. This transmit waveform is the solution that most closely approximates the optimal radar waveform, taking into account the constraints of both communication and radar sensing, and can meet the requirements of the integrated radar-communication system at a specific moment.

[0099] As an implementation method, before step S40, the following may also be included: determining the communication signal to interference plus noise ratio of each user based on the expected received communication symbol power, user interference power, and received noise power; and constructing a waveform optimization model with minimizing the Euclidean distance between the transmitted waveform and the optimal radar waveform as the objective function and with the communication signal to interference plus noise ratio, the transmit power, and the current time slot perception waveform as constraints.

[0100] In specific implementations, the expected received communication symbol power represents the strength of the useful communication signal that the receiver expects to obtain under ideal conditions, which reflects the amount of energy carried by the communication information itself; the user interference power refers to the interference energy generated by other users or external interference sources on the current communication signal. These interferences will destroy the integrity of the communication signal and affect the accuracy of communication; the received noise power is the random noise energy generated by thermal noise and other factors inside the receiving device, which will also interfere with the communication signal.

[0101] By dividing the expected received communication symbol power by the sum of the user's interference power and the received noise power, we can calculate each user's signal-to-interference-plus-noise ratio (SINR). A larger ratio indicates a higher proportion of the desired communication signal to the interference and noise, and thus better communication quality. Conversely, a smaller ratio indicates poorer communication quality. Accurately determining each user's SINR is crucial for building waveform optimization models. It directly impacts the constraints set for communication functions within the model, ensuring that communication quality requirements are met when optimizing the transmit waveform.

[0102] This embodiment simplifies the solution process by transforming complex optimization problems into solvable second-order cone programming problems, improving the efficiency of determining the transmit waveform. Furthermore, by constructing a waveform optimization model that comprehensively considers multiple constraints, it can more scientifically guide the determination of the transmit waveform and improve overall system performance.

[0103] For easier understanding, refer to Figure 4-Figure 6 ( Figure 4 Schematic diagram of the ISAC-MIMO system model; Figure 5 Schematic diagram of communication-aware collaborative gain waveform generation scheme; Figure 6 This is a diagram of an example technical route of the waveform generation method of the present application) for illustration, but does not serve as a limitation on the waveform generation method of the present application.

[0104] like Figure 4 As shown, the transmit waveform consists of an independent sensing waveform (similar to the current time slot sensing waveform described above, and referred to as the sensing waveform in the following text) and a communication waveform. The communication waveform carries communication information and is sent to the communication user side via the wireless channel to complete the communication function. The system sensing function is achieved by combining the sensing waveform and the communication waveform. This is because the transmitter has all the information of the communication waveform, so the communication waveform can also be used for sensing.

[0105] against Figure 4For the ISAC-MIMO system shown, current research has shown that the perception waveform is a source of interference to communication. Therefore, when designing the transmitting waveform, the perception waveform should be minimized in terms of interference to communication. For example, the most typical design scheme is to completely eliminate the interference of the perception waveform on communication. Such a design pattern severely restricts the design freedom of the perception waveform and cannot achieve collaborative gain between communication and perception. This example designs a perception waveform and a communication waveform for the ISAC-MIMO system transmitter in each time slot based on symbol-level precoding technology, so that the phase of the perception waveform after transmission through the communication channel is completely aligned or partially aligned with the phase of the communication symbol to be received, which is beneficial to communication demodulation and thus enhances communication. At the same time, such a design also reduces the constraints on the design freedom of the perception waveform, further improves the system perception performance, and ultimately achieves communication perception collaborative gain. The core ideas and innovations of this technical solution are as follows: Figure 5 shown.

[0106] Figure 5 The whole is a complex plane, s k Indicates the communication symbol using phase modulation mode that the kth user expects to receive in the current time slot ( Figure 5 It is assumed to be located at the current position in the first quadrant for ease of explanation. In actual systems, it can be located at other positions in the complex plane). LZF and CZF are two waveform design strategies designed for this example, namely Line Zero-Forcing (LZF) and Column Zero-Forcing (CZF). The LZF strategy makes the phase of the perceived waveform after channel transmission completely aligned with the communication symbol, that is, the phase of the perceived waveform after channel transmission falls on the yellow ray in the figure; the CZF strategy makes the phase of the perceived waveform after channel transmission fall within the columnar neighborhood of the communication symbol phase, that is, the phase of the perceived waveform after channel transmission falls in the blue columnar area in the figure.

[0107] Since communication uses a phase modulation mode, during the demodulation process, the perception waveform designed by the LZF strategy is completely beneficial to communication and will not interfere with communication. The perception waveform designed by the CZF strategy is beneficial to communication to a certain extent. Both strategies can achieve perception-enhanced communication. At the same time, compared with the current traditional design scheme (which treats the perception waveform as a source of interference to communication and eliminates it, that is, the perception waveform after channel transmission falls on the red dot in the above figure), the LZF strategy and the CZF strategy increase the freedom of perception waveform design (which can be understood as the design freedom increasing from a point to a ray or a columnar area), suggesting system perception performance. Therefore, compared with existing research, this example scheme can simultaneously improve system perception and communication performance, and achieve synergistic gains in communication and perception.

[0108] The following is a detailed description of the specific implementation methods and processes of each part, mainly including optimal radar waveform design, transmit waveform design strategy and system parameter selection, and ISAC-MIMO system transmit waveform generation.

[0109] (1) Optimal radar waveform design

[0110] Research has shown that constant modulus radar waveforms help maximize power amplifier efficiency, thereby achieving optimal perception performance. Therefore, this application considers designing a constant modulus optimal radar waveform. Radar target detection performance is directly determined by the radiation pattern, and existing research shows that the radiation pattern is directly determined by the covariance matrix of the transmitted waveform. Based on this idea, optimal radar waveform design consists of two steps: designing the optimal covariance matrix based on a given radiation pattern (the desired radiation pattern); and designing the constant modulus radar waveform (the optimal radar waveform) so that its average covariance matrix is ​​closest to the optimal covariance matrix.

[0111] 1) Design the optimal covariance matrix: The radiation pattern can be represented by the covariance matrix of the transmitted waveform and the antenna array steering vector through appropriate mathematical operations. The design idea of ​​the optimal covariance matrix is ​​to design the optimal covariance matrix based on the given expected radiation pattern so that the expected radiation pattern is closest to the pattern calculated by the covariance matrix and the steering vector. The specific implementation method is explained below through mathematical expressions. Under the premise of a given expected radiation pattern, the optimal covariance matrix is ​​designed under constant modulus and power constraints, as shown in the following formula (1), where a represents the array steering vector of the ISAC-MIMO system, R represents the expected optimal covariance matrix, and P d (θ) is the expected radiation pattern intensity at angle θ, P t is the total power of the system transmitter, M is the number of transmitter antennas, represents an M×M positive semidefinite matrix, and diag(·) represents taking the main diagonal elements of the matrix.

[0112]

[0113] The above problem is a typical convex problem, which can be solved in polynomial time using the interior point method. The relevant algorithms are integrated into toolboxes such as CVX for easy calling and solving.

[0114] 2) Designing the Optimal Radar Transmit Waveform: After obtaining the optimal covariance matrix R, a constant modulus radar waveform is designed such that its covariance matrix is ​​closest to R. This radar waveform is considered the optimal radar waveform. To satisfy the constant modulus constraint, each component of the radar waveform must take values ​​on a certain complex circle. Furthermore, the transmit power constraint must be met. Define the following complex circle U, where each element of the radar waveform vector takes values ​​from this complex circle. N represents the number of values, and these N values ​​are evenly spaced on the complex circle.

[0115]

[0116] For a transmitter with M antennas, there are N M A waveform configuration scheme is proposed. In each time slot, one of the waveform configurations is selected to be sent so that the generated average transmission waveform covariance matrix is ​​closest to the designed optimal covariance matrix, that is, the optimal radar waveform design is completed. The core of completing this operation is how to select the waveform configuration for each time slot.

[0117] Assuming that the above N M Waveform configuration set U M , the corresponding covariance matrix set is represented as A, and the discrete probability density function P is defined A Indicates the probability that the waveform corresponding to each element in A is selected as the transmitted waveform in each time slot. To make the covariance matrix of the average transmitted waveform closest to the optimal covariance matrix, the least squares problem shown in formula (3) naturally arises, where R A It represents the matrix formed by vectorizing all elements in A and stacking them in rows, and p represents P A The column vector formed by stacking the elements in , r represents the vectorized result of the optimal covariance matrix, and the two constraints represent the sum of probabilities to be one and non-negative constraints. This is a typical fully constrained least squares problem:

[0118]

[0119] After the probability distribution p is obtained, the corresponding element is selected from the set A as the transmission waveform x for each time slot according to the corresponding probability. d , thus completing the optimal radar waveform design.

[0120] (2) ISAC-MIMO system transmission waveform design strategy and system parameter selection

[0121] This example proposes two transmit waveform design strategies, LZF and CZF, both of which include custom parameters that can adjust the system's communication and perception functions. The following mathematical formulas describe the specific implementation methods of the two waveform design strategies and related parameter selection recommendations:

[0122] 1) LZF strategy

[0123] The LZF strategy aims to completely align the phase of the perceived waveform transmitted through the communication channel with the communication symbol. For the sake of convenience, the communication symbol of the kth user in the current time slot is defined as s k , is the channel information between the current ISAC-MIMO transmitter and the kth user, x sIndicates the current time slot sensing waveform. α is a system parameter greater than 0 that can be customized. The larger its value, the greater the sensing waveform’s communication gain. However, when its value is too large, there is a probability that no x that meets this condition will be met. s According to the previous simulation experiments, it is preferred that the value be between 0.3 and 0.8, and the mathematical expression is shown in the following formula (4):

[0124]

[0125] 2) CZF Strategy

[0126] The CZF strategy constrains the perception waveform transmitted through the communication channel to a columnar neighborhood of the communication symbol phase. The mathematical expression is shown in the following formula (5), where the same parameters have the same meaning as LZF. ε is a system parameter greater than 0 that can be customized. The larger its value, the greater the freedom of perception waveform design, the smaller the gain of the communication demodulation process, and even causes interference. According to previous simulation experiments, it is preferred that this value be between 0 and 0.6.

[0127]

[0128] Analysis shows that the LZF strategy focuses on improving system communication performance, while the CZF strategy is more effective in increasing the freedom of system perception waveform design and improving system perception performance. Compared to existing designs that completely eliminate the impact of the perception waveform on communication, both strategies improve both communication and perception performance. When designing a system, the specific transmit waveform design strategy can be selected based on actual application requirements, while appropriate system parameters can be selected to optimize system performance.

[0129] (3) ISAC-MIMO system transmit waveform generation

[0130] Based on the selected waveform design strategy and system parameters, a transmit waveform is generated. It is expected that the generated transmit waveform will have the smallest difference from the optimal radar waveform while satisfying constraints such as system power, waveform design strategy, and communication SINR.

[0131] For the sake of clarity, we first define the communication SINR. The communication SINR is expressed by the following equation (6), where x c represents the communication waveform, the numerator is the desired received communication symbol power, the first term in the denominator is the multi-user interference power, and the second term is the received noise power. Equation (6) does not consider the impact of the sensing signal on the communication SINR. In this application, the sensing waveform is carefully designed to benefit communication to a certain extent. The two specific design strategies have been introduced above.

[0132]

[0133] Next, a waveform optimization model is designed to generate the transmission waveform, including the perception waveform and the communication waveform. The objective function is to minimize the difference between the total transmission waveform and the optimal radar waveform. Mathematically, it is expressed by the Euclidean distance. The constraints include the transmission power constraint, the communication SINR constraint, and the LZF or CZF design strategy. The mathematical expression is shown in the following formula (7), where x d represents the optimal radar waveform, γ k represents the communication SINR of the kth user, Γ k Indicates the corresponding threshold.

[0134]

[0135] This waveform optimization model is not a typical optimization problem that can be solved directly by the solver. By introducing auxiliary variables, it is converted into a classic optimization problem form and then solved. The following auxiliary constants or variables are defined:

[0136]

[0137] Then formulas (4) and (5) can be converted into the following formulas (9) and (10):

[0138]

[0139] Substitute it into formula (7) and simplify the communication SINR constraint condition as shown in the following formula (11), where, is the expected communication symbol power. Obviously, this problem is a typical second-order cone programming problem, which can be solved by calling relevant toolkits, such as the CVX toolbox. After the solution is completed, take The first half of the elements of x s , the second half of the elements constitute x c , that is, the ISAC-MIMO system transmission waveform design is completed.

[0140]

[0141] At the same time, for ease of understanding, a more detailed algorithm implementation process is given in pseudo code. The pseudo code of the waveform generation method of the ISAC-MIMO system based on symbol-level precoding technology proposed in this application is as follows:

[0142] Input: Transmitter power P t , the number of transmitter antennas M, the expected radiation pattern P d (θ), the covariance matrix R corresponding to the waveform configuration set A , waveform design strategy LZF (or CZF) and corresponding system parameters a (or a, ε);

[0143] 1: Solve the optimal covariance matrix R according to formula (1);

[0144] 2: Given R, solve equation (3) to obtain the probability distribution p of the waveform configuration scheme set, and select the optimal radar waveform x for each time slot accordingly d ;

[0145] 3: Construct the waveform optimization model according to equations (7) to (11);

[0146] 4: Solve (11) and generate the optimal waveform x s and x c ;

[0147] Output: x s , x c .

[0148] In short, if Figure 6 As shown, this example first designs the optimal radar waveform based on equations (1) and (3), considering only perception performance. This waveform serves as a reference and benchmark for subsequent designs. The transmit waveform design strategy and related system parameters are then selected. When the system communication performance is expected to be stronger, the LZF strategy is recommended, while the CZF strategy is recommended. Both design strategies allow designers to optimize system performance by adjusting system parameters. Finally, using the aforementioned results as input, the optimal waveform for the ISAC system, i.e., the final transmit waveform, is generated according to equation (11).

[0149] The innovation of the waveform generation method of the present application lies in: unlike the existing technical solutions that regard the perception signal as a communication interference source, the waveform design freedom is improved through symbol-level precoding technology, so that the perception waveform is beneficial to the communication demodulation process to a certain extent; the above-mentioned constant modulus optimal radar waveform (considering only the perception function) design method proposes a least squares-based optimal radar waveform design method, which can be used not only for ISAC system waveform design, but also for pure radar system waveform design; the above-mentioned LZF and CZF waveform design strategies; the above-mentioned waveform optimization model and its solution method are specifically shown in Equations (7) to (11).

[0150] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the waveform generation method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0151] This application also provides a waveform generating device, please refer to Figure 7 , the waveform generating device comprises:

[0152] The matrix optimization module 10 is used to determine the optimal covariance matrix according to a given desired radiation pattern;

[0153] a reference waveform determination module 20 for generating an optimal radar waveform by a least square method based on the optimal covariance matrix, wherein the optimal radar waveform is a radar waveform that satisfies a constant modulus constraint and a transmit power;

[0154] A sensing waveform adjustment module 30 is configured to generate a sensing waveform for the current time slot based on the communication symbol of the current time slot and the current channel information, wherein the sensing waveform for the current time slot is generated based on application requirements and has a phase after transmission that is completely aligned with the phase of the communication symbol or falls within a columnar neighborhood of the phase of the communication symbol;

[0155] The target waveform determination module 40 is used to determine the transmission waveform that is closest to the optimal radar waveform through the constraints of the waveform optimization model, wherein the constraints include the communication signal to interference plus noise ratio, the transmission power, and the current time slot sensing waveform.

[0156] The waveform generation device provided in this application, which employs the waveform generation method described in the aforementioned embodiment, can resolve the technical problem of the prior art in being unable to achieve synergistic gain between the perception waveform and the communication waveform. Compared to the prior art, the beneficial effects of the waveform generation device provided in this application are the same as those of the waveform generation method described in the aforementioned embodiment, and the other technical features of the waveform generation device are the same as those disclosed in the aforementioned embodiment, and are not further elaborated here.

[0157] The present application provides a waveform generation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the waveform generation method in the above-mentioned embodiment one.

[0158] Reference below Figure 8 , which shows a schematic diagram of the structure of a waveform generating device suitable for implementing the embodiments of the present application. The waveform generating device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Devices), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The waveform generating device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0159] like Figure 8As shown, the waveform generation device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of the waveform generation device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication device 1009. Communication device 1009 can allow the waveform generation device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a waveform generation device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0160] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0161] The waveform generation device provided in this application, using the waveform generation method in the above-mentioned embodiment, can solve the technical problem that the existing technology cannot achieve the synergistic gain of the perception waveform and the communication waveform. Compared with the existing technology, the beneficial effects of the waveform generation device provided in this application are the same as those of the waveform generation method provided in the above-mentioned embodiment, and the other technical features of the waveform generation device are the same as those disclosed in the method of the previous embodiment, and are not further described here.

[0162] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0163] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0164] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the waveform generation method in the above-mentioned embodiment.

[0165] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0166] The computer-readable storage medium may be included in the waveform generating device; or it may exist independently without being assembled into the waveform generating device.

[0167] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the waveform generating device, the waveform generating device is caused to: determine the optimal covariance matrix according to a given expected radiation pattern, generate the optimal radar waveform through the least squares method based on the optimal covariance matrix, and the optimal radar waveform is a radar waveform that satisfies the constant modulus constraint and the transmit power; generate the current time slot perception waveform according to the current time slot communication symbol and the current channel information; the current time slot perception waveform is a waveform generated based on application requirements, and the phase after transmission is completely aligned with the phase of the communication symbol or the phase after transmission falls within the columnar neighborhood of the communication symbol phase; determine the transmission waveform that is closest to the optimal radar waveform through the constraints of the waveform optimization model, wherein the constraints include the communication signal to interference plus noise ratio, the transmit power and the current time slot perception waveform.

[0168] The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, entirely on a remote computer or server, or on an ARM (Advanced RISC Machines) development board. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0169] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0170] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0171] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., a computer program) for executing the above-mentioned waveform generation method. This computer-readable storage medium can solve the technical problem that the prior art cannot achieve synergistic gain between the perception waveform and the communication waveform. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the waveform generation method provided in the above-mentioned embodiment, and will not be elaborated here.

[0172] The present application also provides a computer program product, comprising a computer program, which implements the steps of the waveform generation method as described above when executed by a processor.

[0173] The above descriptions are only some embodiments of the present application and do not limit the scope of protection of the present application. All equivalent structural transformations made based on the technical concept of the present application and the contents of the present application description and drawings, or direct / indirect applications in other related technical fields are included in the scope of protection of the present application.

Claims

1. A waveform generation method, characterized in that: The method comprises the following steps: Determine the optimal covariance matrix based on the given desired radiation pattern; Based on the optimal covariance matrix, generating an optimal radar waveform by a least square method, wherein the optimal radar waveform is a radar waveform that satisfies a constant modulus constraint and a transmit power; Generate a current time slot sensing waveform based on the current time slot communication symbol and current channel information, wherein the current time slot sensing waveform is a waveform generated based on application requirements, and the phase after transmission is completely aligned with the phase of the communication symbol or the phase after transmission falls within a columnar neighborhood of the phase of the communication symbol; The transmit waveform that is closest to the optimal radar waveform is determined through the constraints of the waveform optimization model, wherein the constraints include the communication signal to interference plus noise ratio, the transmit power, and the current time slot sensing waveform.

2. The waveform generation method according to claim 1, wherein: The step of determining the optimal covariance matrix according to a given expected radiation pattern includes: Receiving a desired radiation pattern input by a user, analyzing the desired radiation pattern, and obtaining a desired radiation intensity value corresponding to each angle; Determine the array steering vector at each angle based on the physical arrangement parameters of the transmitting antenna array; The desired radiation intensity value and the array steering vector are input into a convex optimization solver, and an optimal covariance matrix that satisfies all constraints is output.

3. The waveform generation method according to claim 1, wherein: The step of generating an optimal radar waveform by least squares method based on the optimal covariance matrix includes: Discretizing a preset complex circle with equal intervals to obtain a discrete complex number set satisfying a constant modulus constraint, wherein the discrete complex number set is a candidate value set for each antenna transmit phase; Determining a waveform configuration set according to the discrete complex number set and the total number of antennas, and determining a covariance matrix set of the waveform configuration set; The discrete probability density function is defined as the probability that the waveform corresponding to each element in the covariance matrix set is selected as the transmission waveform in each time slot; Determining a probability distribution of a waveform configuration set by a least squares method based on the covariance matrix set, the discrete probability density function, and the optimal covariance matrix; An optimal radar waveform is determined according to the probability distribution.

4. The waveform generation method according to claim 3, wherein: The step of determining the probability distribution of the waveform configuration set by the least squares method based on the covariance matrix set, the discrete probability density function and the optimal covariance matrix includes: Vectorizing all covariance matrices in the covariance matrix set to obtain a vectorized matrix; Stacking the vectorized matrices row by row to obtain a transformed matrix, and determining a product result of the transformed matrix and a column vector formed by stacking elements in the discrete probability density function; The norm of the difference between the product result and the optimal covariance matrix is ​​minimized by the least squares method to obtain the probability distribution of the waveform configuration set.

5. The waveform generation method according to any one of claims 1 to 3, characterized in that: The step of determining the transmission waveform closest to the optimal radar waveform through the constraints of the waveform optimization model includes: By introducing auxiliary variables, the objective function and constraints in the waveform optimization model are reformulated to obtain a second-order cone programming problem, wherein the auxiliary variables are composed of the communication waveform and the current time slot sensing waveform. Solve the second-order cone programming problem to obtain a transmission waveform.

6. The waveform generation method according to any one of claims 1 to 3, wherein: Before the step of determining the transmission waveform closest to the optimal radar waveform through the constraints of the waveform optimization model, the method further includes: Determine the communication signal to interference plus noise ratio of each user based on the expected received communication symbol power, user interference power and received noise power; A waveform optimization model is constructed with minimizing the Euclidean distance between the transmitted waveform and the optimal radar waveform as the objective function and with the communication signal to interference plus noise ratio, the transmit power and the current time slot sensing waveform as constraints.

7. A waveform generating device, characterized in that: The waveform generating device comprises: A matrix optimization module is used to determine the optimal covariance matrix based on a given desired radiation pattern; a reference waveform determination module, configured to generate an optimal radar waveform by a least squares method based on the optimal covariance matrix, wherein the optimal radar waveform is a radar waveform that satisfies a constant modulus constraint and a transmit power; A perception waveform adjustment module is configured to generate a current time slot perception waveform based on the current time slot communication symbol and current channel information, wherein the current time slot perception waveform is a waveform generated based on application requirements, wherein the phase after transmission is completely aligned with the phase of the communication symbol or the phase after transmission falls within a columnar neighborhood of the phase of the communication symbol; A target waveform determination module is configured to determine a transmit waveform that is closest to the optimal radar waveform through constraints of a waveform optimization model, wherein the constraints include a communication signal to interference plus noise ratio, the transmit power, and the current time slot sensing waveform.

8. A waveform generating device, characterized in that: The waveform generating device includes: a memory, a processor, and a waveform generating program stored in the memory and executable on the processor. When the waveform generating program is executed by the processor, the waveform generating method according to any one of claims 1 to 6 is implemented.

9. A storage medium, characterized in that: The storage medium stores a waveform generation program, which, when executed by a processor, implements the waveform generation method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the waveform generation method according to any one of claims 1 to 6 are implemented.

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