Communication perception integrated waveform design method for Internet of Vehicles

Abstract

The invention provides a communication perception integrated waveform design method for the Internet of Vehicles, which belongs to the technical field of wireless communication, and can realize peak performance, enhance radar capability and ensure efficient communication by minimizing the weight sum of multi-user interference, the similarity between a design waveform and a reference waveform, a Cramer-Rao bound and an ISAC waveform. According to the method, optimization with the total transmission power and the peak-to-average power ratio as constraints is adopted, a non-convex problem is converted into a convex problem, an alternating direction multiplier method algorithm is adopted for solving, and a simulation result shows that the ISAC waveform has good communication and target detection capability, and the V2X spectrum utilization rate can be improved. According to the method, a target can be detected during communication with a downlink user, the sum of MUI, CRB and waveform similarity is minimized, weighing parameters are introduced, flexible weighing between communication and sensing is achieved under the constraint of the total power and the peak-to-average power ratio, the method better adapts to a V2X high-dynamic scene, and mutual interference caused by separation of traditional sensing and communication functions is reduced.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a communication-perception integrated waveform design method for Internet of Vehicles. Background Art

[0002] Due to the rapid deployment of 5G networks, the sharp increase in the number of vehicles, and the continuous advancement of vehicle-to-everything (V2X) technology, spectrum resources are facing unprecedented pressure. At the same time, V2X applications have strict requirements on latency, but the transmission of a large amount of perception and communication data hinders real-time communication between vehicles and the surrounding environment, which exacerbates the contradiction between the limited communication and perception resources of connected vehicle devices. In this case, Integrated Sensing and Communication (ISAC) has become a key solution in V2X applications. ISAC is a technology that combines perception with communication. It can perceive details such as direction, distance, speed, etc. while transmitting information. It has the advantages of improving spectrum utilization, reducing hardware deployment costs, and alleviating electromagnetic interference between communication and perception, thereby achieving the complementarity of communication and perception functions. On the one hand, perception can enhance communication through more accurate channel modeling, channel estimation, beam management, and joint management of multi-vehicle resources; on the other hand, communication can also enhance perception. ISAC not only meets the communication needs between vehicles, but also uses its perception capabilities to collect accurate environmental information. By efficiently utilizing limited spectrum resources, ISAC improves communication and perception performance, providing strong support for smarter traffic management and vehicle safety control.

[0003] At present, ISAC and based on V2X have been deeply studied in many aspects, but the research on ISAC waveform design in V2X is still blank. Waveform design is a key aspect of ISAC and determines the performance boundary that the ISAC system can achieve. V2X has more stringent requirements on communication and perception capabilities. The existing ISAC waveform design method only considers the weighted optimization of MUI and waveform similarity, and does not take into account the perception and communication difficulties caused by high dynamic changes in V2X, so it cannot adapt well to the V2X environment. The ISAC collaborative waveform design method involves creating a new waveform that can seamlessly integrate communication and perception functions. This requires the waveform to be able to perform perception tasks such as parameter estimation and target detection, while supporting communication through information data transmission. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a communication perception integrated waveform design method for vehicle networking, and a V2X MIMO-ISAC system waveform design scheme is proposed. By minimizing the weight sum of multi-user interference (MUI), the similarity between the designed waveform and the reference waveform, and the Cramér Rao bound (CRB), the ISAC waveform can achieve peak performance, while enhancing radar capabilities and ensuring efficient communication. An optimization scheme with total transmission power and peak-to-average power ratio (PAPR) as constraints is designed. The waveform designed in this way has good communication and sensing performance at the same time, transforming the non-convex problem into a convex problem, and using the alternating direction method of multipliers (ADMM) algorithm to solve it. The simulation results show that the designed ISAC waveform has good communication and target detection capabilities and can improve the spectrum utilization of V2X.

[0005] In order to solve the above technical problems, the technical solution provided by the present invention is: A communication perception integrated waveform design method for Internet of Vehicles includes the following steps: S1. Construct a multi-input multi-output integrated communication and perception system model for the Internet of Vehicles by defining the communication and perception capabilities of the ISAC base station, the transmitted waveform, the Internet of Vehicles scenario, and the target of interest; S2. Based on the communication perception integrated system model constructed in S1, a communication model is established by defining the indicators MUI and PAPR of the user receiving signal and the waveform communication performance that affects the transmission; S3. Based on the communication perception integrated system model constructed in S1, a perception model is established by defining the received signal of the target of interest received by the ISAC base station and the waveform similarity and CRB, which are indicators affecting the waveform perception performance of the transmission; S4. Combining the communication model established in S2 and the perception model established in S3, construct an optimization problem that minimizes the weighted sum of the MUI, CRB, and waveform similarity, and takes the total transmission power and PAPR as constraints, and converts the optimization problem into a matrix form, making it a QCQP problem with quadratic equation constraints and quadratic inequality constraints; S5. Based on the QCQP problem described in S4, auxiliary variables are introduced to transform the non-convex problem into a convex problem. The ADMM algorithm is used to fix different parameters and alternately iterate each parameter variable to finally solve the optimal waveform X.

[0006] Furthermore, the ISAC base station in S1 is composed of a uniform linear array, including Root transmitting antenna and Root receiving antenna; The ISAC base station communicates with mobile users such as vehicles and mobile terminals, and the targets of interest detected are surrounding buildings, roadside pedestrians, and vehicles.

[0007] Furthermore, S2 comprises the following steps: S2.1 Signal matrix received by communication users , ; in, is a complex matrix, where Represents the length of the communication frame; represents the downlink communication channel matrix, assuming undergoes flat Rayleigh fading and can be perfectly estimated; represents the transmission signal matrix; The additive white Gaussian noise (AWGN) represents the downlink user; S2.2 rewrites the received signal matrix described in S2.1 as: ; MUI is expressed as: ; To maximize the sum rate, the MUI is minimized; the signal-to-noise ratio and achievable sum rate of each user in each frame are calculated; ; in, is the ensemble average over the time index, yes The Item, indicating that From The signal transmitted between the communication users Indicated in The signal transmitted in time slots; The achievable sum rate for a downlink mobile user is expressed as: ; S2.3 Considering the impact of PAPR on the effectiveness of the communication system, the PAPR is controlled to provide sufficient transmission distance and reliability. Based on the signal matrix received by the communication user described in S2.1 , the calculation formula of PAPR is: ; in, is a vector No. elements, , which means that the matrix The vector obtained after vectorization is, The PAPR threshold represents the total transmit power of the ISAC waveform. for: ; The right half of equation (7) above can be rewritten as: ; in .

[0008] Furthermore, S3 comprises the following steps: S3.1 For radar perception, assuming that the propagation path of the transmitted waveform is a line-of-sight path, the direction The signal matrix received by the far-field point target It is expressed as: ; in, Indicates The reflection coefficient of the target; Indicates the angle of the radar detecting the target relative to the ISAC base station. and are the steering vectors for the transmit and receive antennas, respectively; a half-wavelength ULA is used between adjacent elements; It is the AWGN of MIMO radar; The transmission direction and arrival direction of a single-base radar are the same, assuming that DOA and DOD are and It is expressed as: ; ; S3.2 introduces waveform similarity constraints in ISAC waveform design to affect its perceptual performance; waveform similarity is expressed as: ; in, is the reference waveform matrix; without imposing equality constraints on the waveform covariance matrix, minimize The expected radar beam pattern can be approximated; S3.3 CRB is used to measure perception performance, provide a lower bound on the mean square error, and has a closed-form expression; for radar point target detection, CRB is expressed as: ; in , yes The derivative of represents the reflection coefficient; Formula (13) is further equivalent to: ; For the convenience of calculation, assume ,get ;CRB can be expressed as: .

[0009] Furthermore, the S4 comprises the following steps: S4.1 The optimization problem is expressed as: ; in, is a trade-off parameter that can be adjusted to affect the characteristics of the waveform; it can be further expressed as: ; S4.2 further simplifies the optimization problem; S4.2.1 Order: , in is an all-zero matrix, and the optimization problem is transformed into: ; S4.2.2 Convert the objective function into matrix form: ; make , then the objective equation can be expressed as: ; make , and at the same time Vectorize to a diagonal matrix ,Will Vectorized to , and order ; Then the objective equation can be rewritten as: ; in .

[0010] Furthermore, the S5 comprises the following steps: S5.1 By introducing variables and , the original optimization problem is expressed as ; From the above optimization problem, the augmented Lagrangian expression is obtained, which is expressed as: ; in is the penalty factor. The augmented Lagrangian function can also be regarded as the non-augmented Lagrangian function of the following optimization problem: ; S5.2 Update ; In the iterative process of the ADMM algorithm, The variable update expression for the iteration is as follows: (25); By taking the derivative of equation (25) and setting its gradient to 0, we can obtain The update expression of The gradient is obtained by differentiating the augmented Lagrangian function: (26); Set the gradient equal to zero and sort out The update equation is: ; S5.3 Update ; Update completed Afterwards, update the auxiliary variables , No. The variable update expression for the iteration is as follows: (28); It is expressed as the following optimization problem: ; Similarly, we get its Lagrangian expression as follows; (30); Update Similarly, get the next group for finding Lagrangian expression for the gradient; (31); And make get The update equation is: (32); S5.4 Update ; Update auxiliary variables Similarly, The variable update expression for the iteration is: (twenty three); The optimization problem can be expressed as: ; If the constraints are met, the Lagrangian function and gradient are calculated, expressed as: ; ; make ,get The update expression is: (37); If the constraints are not satisfied, Project to an area that satisfies the constraints; set a scaling factor , so that ,get: ; S5.5 Update auxiliary variables: By iterating the auxiliary variables, the optimal solution is obtained, which is expressed as: (39); .

[0011] The beneficial effects of the present invention are: The present invention provides a MIMO-ISAC transmission waveform design method for V2X scenarios, which can detect targets when communicating with downlink users, minimize the sum of MUI, CRB and waveform similarity, and introduce trade-off parameters to achieve flexible trade-offs between communication and sensing under the constraints of total power and peak-to-average power ratio. This waveform design scheme can better adapt to the high-dynamic scenarios of V2X, reduce the mutual interference caused by the separation of traditional sensing and communication functions, and achieve seamless integration of communication and sensing. The simulation results verify the performance of the designed ISAC waveform, significantly improve the information transmission rate and accuracy in communication, achieve superior target detection performance in sensing, and maximize the sensing performance of V2X on the basis of ensuring the effective transmission of vehicle sensor information. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 Flowchart of the communication perception integrated waveform design system for the Internet of Vehicles of this invention Figure 2 Schematic diagram of the multi-input multi-output integrated perception and communication system model of the present invention Figure 3 ISAC Base Station Figure 4is the average reachable sum rate for different number of users Figure 5 Radar pulse compression performance under different ISAC waveform designs. DETAILED DESCRIPTION

[0013] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the following embodiments are provided only for the purpose of illustration and are not intended to limit the scope of the present invention. Those skilled in the art may make various modifications and substitutions to the present invention without departing from the purpose and spirit of the present invention.

[0014] The purpose of the present invention is to provide a communication-sensing integrated waveform design method for vehicle networking, which weighs and optimizes the ISAC waveform. It is a MIMO-ISAC transmission waveform design method for V2X scenarios, which can detect targets when communicating with downlink users and minimize the sum of MUI, CRB and waveform similarity; it also introduces a trade-off parameter to achieve a flexible trade-off between communication and sensing under the constraints of total power and PAPR. Among them, MUI represents the communication function, and the similarity between the reference and design waveforms and CRB represents the sensing function. By modifying the trade-off parameter, the relative importance of communication and sensing performance is changed.

[0015] The optimization problem is a non-convex quadratic constrained quadratic programming problem, and the ADMM algorithm is used to solve the optimization problem. The waveform design method can better adapt to the high dynamic scenarios of V2X, reduce the mutual interference caused by the separation of traditional sensing and communication functions, and achieve seamless integration of communication and sensing. The simulation results verify the performance of the designed ISAC waveform, significantly improve the information transmission rate and accuracy in communication, achieve superior target detection performance in sensing, and maximize the sensing performance of V2X on the basis of ensuring the effective transmission of vehicle sensing information.

[0016] The present invention provides a communication perception integrated waveform design method for vehicle networking, comprising the following steps: S1. Construct a multi-input multi-output integrated communication and perception system model for the Internet of Vehicles by defining the communication and perception capabilities of the ISAC base station, the transmitted waveform, the Internet of Vehicles scenario, and the target of interest; S2. Based on the communication perception integrated system model constructed in S1, a communication model is established by defining the indicators MUI and PAPR of the user receiving signal and the waveform communication performance that affects the transmission; S3. Based on the communication perception integrated system model constructed in S1, a perception model is established by defining the received signal of the target of interest received by the ISAC base station and the waveform similarity and CRB, which are indicators affecting the waveform perception performance of the transmission; S4. Combining the communication model established in S2 and the perception model established in S3, construct an optimization problem that minimizes the weighted sum of the MUI, CRB, and waveform similarity, and takes the total transmission power and the PAPR as constraints, and converts the optimization problem into a matrix form, making it a QCQP problem with quadratic equation constraints and quadratic inequality constraints; S5. Based on the QCQP problem described in S4, auxiliary variables are introduced to transform the non-convex problem into a convex problem. The ADMM algorithm is used to fix different parameters and alternately iterate each parameter variable to finally solve the optimal waveform X.

[0017] Specifically: S1. Design of MIMO-ISAC system model for V2X.

[0018] The present invention designs a multi-input multi-output integrated sensing and communication (MIMO-ISAC) system model for V2X. The system model transmits ISAC waveforms, which can not only communicate with mobile users such as vehicles and mobile terminals, but also detect targets of interest such as surrounding buildings, roadside pedestrians, and vehicles. The ISAC base station is composed of a uniform linear array (ULA), including Root transmitting antenna and The schematic diagram of the system model is as follows Figure 2 The schematic diagram of the ISAC base station is shown in Figure 3 shown.

[0019] S2. Building a communication model S2.1 Signal matrix received by communication users , ; in, is a complex matrix, where Represents the length of the communication frame; represents the downlink communication channel matrix, assuming undergoes flat Rayleigh fading and can be perfectly estimated; represents the transmission signal matrix; Represents the Additive White Gaussian Noise (AWGN) of downlink users.

[0020] S2.2 In order to maximize the sum rate, it is necessary to minimize MUI. By rewriting the received signal matrix as: ; MUI can be expressed as: ; Based on this, the signal-to-noise ratio and achievable sum rate of each user in each frame are calculated.

[0021] ; in, is the ensemble average over the time index, yes The Item, indicating that From The signal transmitted between the communication users Indicated in Therefore, the achievable sum rate of the downlink mobile user can be expressed as follows: ; S2.3 Considering the impact of PAPR on the effectiveness of the communication system, PAPR must be properly controlled to provide sufficient transmission distance and reliability.

[0022] The formula for calculating PAPR is; ; in, is a vector No. elements, , which means that the matrix The vector obtained after vectorization is, The total transmit power of the ISAC waveform is for: ; Then, the right half of the above equation can be rewritten as: ; in .

[0023] S3. Building a perception model S3.1 For radar perception, assuming that the propagation path of the transmitted waveform is a line-of-sight path, then the direction The signal matrix received by the far-field point target It can be expressed as: ; in, Indicates The reflection coefficient of a target. Indicates the angle of the radar detecting the target relative to the ISAC base station. and are the steering vectors for the transmit and receive antennas, respectively. In addition, a half-wavelength ULA is used between adjacent elements. It is the AWGN of MIMO radar.

[0024] Since a monostatic radar is used, the direction of departure (DOD) and the direction of arrival (DOA) are the same. Assume that both DOA and DOD are .

[0025] So, and It can be expressed as: ; ; S3.2 introduces waveform similarity constraints in ISAC waveform design to affect its perceptual performance. The waveform similarity constraint represents the difference between the designed waveform and the reference waveform. The waveform similarity is expressed as: ; in, is the reference waveform matrix. Without imposing equality constraints on the waveform covariance matrix, we minimize The expected radar beam pattern can be approximated.

[0026] S3.3 CRB can be used to measure perceptual performance, provides a lower bound on the mean square error, and has a closed-form expression.

[0027] For radar point target detection, CRB can be expressed as: ; in yes The derivative of represents the reflection coefficient. Therefore, (13) can be further equivalent to: ; For the convenience of calculation, assume , from which we can get . Therefore, CRB can be expressed as: .

[0028] S4. Establishing the optimization problem Combining the above communication and perception models, an optimization problem with total transmission power and PAPR as constraints is constructed. The objective function of the optimization problem includes the weighted sum of MUI, CRB and waveform similarity, and the relative importance of each item is adjusted by introducing a trade-off parameter.

[0029] S4.1 The optimization problem is expressed as: ; in, It is a trade-off parameter that can be adjusted to affect the characteristics of the waveform.

[0030] It can be further expressed as: ; Obviously, this optimization problem is a non-convex optimization problem and is difficult to solve.

[0031] S4.2 In order to further solve the optimization problem, it is necessary to further simplify the optimization problem.

[0032] First, let: ; in is an all-zero matrix, and the optimization problem is transformed into: ; Then, the objective function needs to be converted into matrix form; ; make , then the objective equation can be expressed as: ; make , and at the same time Vectorize to a diagonal matrix ,Will Vectorized to , and order .

[0033] Then the objective equation can be rewritten as: ; in .

[0034] The above optimization problem is a QCQP problem consisting of quadratic equation constraints and quadratic inequality constraints, and the ADMM algorithm is used to solve it. This algorithm is an effective iterative method for dealing with non-convex optimization problems. It can decompose the original problem into multiple sub-problems and update the solutions of each sub-problem through alternating iterations, and finally converge to the optimal solution or approximate optimal solution of the original problem.

[0035] S5. Solve using ADMM algorithm S5.1 By introducing variables and , the original optimization problem can be expressed as: ; From the above optimization problem, we can get an augmented Lagrangian expression, which is expressed as: ; in is the penalty factor. The augmented Lagrangian function can also be viewed as the non-augmented Lagrangian function of the following optimization problem, ; S5.2 Update , In the iterative process of the ADMM algorithm, The variable update expression for the iteration is as follows: (25); By taking the derivative of equation (25) and setting its gradient to 0, we can obtain Update expression. First, the gradient is obtained by differentiating the augmented Lagrangian function, (26), Set the gradient equal to zero and sort out The update equation is: ; S5.3 Update , Now, after updating After that, you can then update the auxiliary variables , No. The variable update expression for the iteration is as follows: (28), It can be expressed as the following optimization problem: , Similarly, its Lagrangian expression can be obtained as follows: (30); Update Similarly, we can get the next set for Lagrangian expression for the gradient: (31), And make Can get The update equation is: (32), S5.4 Update , Next, update the auxiliary variables Similarly, The variable update expression for the iteration is: (twenty three), The optimization problem can be expressed as: ; If the constraints are satisfied, the Lagrangian function and gradient can be calculated as: , , So, let , we can get The update expression is: (37); If this constraint is not satisfied, the usual approach is to Project to a region that satisfies the constraints. Set a scaling factor , so that , so that we can get .

[0036] S5.5 Update auxiliary variables By iterating the auxiliary variables, the optimal solution can be obtained.

[0037] It can be expressed as: (39), ; Simulation parameters:

[0038] The performance of the proposed method is demonstrated by numerical simulation. Assuming that the transmitting antenna and receiving antenna The number is the same and is 9, the communication frame length , total transmission power , the carrier frequency is 32 GHz and the communication symbols use QPSK. In addition, we set the sum of the trade-off parameters to 1, usually ,and .

[0039] like Figure 3 As shown, it is an ISAC base station, which is composed of a uniform linear array, including Root transmitting antenna and The receiving antenna can send ISAC waveforms to vehicles, mobile terminals, etc. It can communicate with mobile users and detect surrounding buildings, pedestrians and vehicles. Target of interest, Indicates a downlink communication channel.

[0040] like Figure 4As shown in the figure, the horizontal axis is the number of communication users, and the vertical axis is the communication rate. The blue curve in the figure represents the ideal radar waveform, the orange curve represents the traditional ISAC waveform, and the purple curve represents the ISAC waveform we proposed. The results show that the communication rate increases with the increase in the number of downlink mobile communication users. In addition, the communication performance of the ISAC waveform we proposed is higher than that of the ideal radar waveform, but lower than that of the traditional ISAC waveform, indicating that increasing the degree of freedom and the number of communication users can effectively reduce MUI and further optimize the communication performance.

[0041] like Figure 5 As shown, the horizontal axis is the index of each output sample in the IFFT transform process, and the vertical axis is the pulse compression gain. The blue curve in the figure represents the ISAC waveform we proposed, and the yellow curve represents the ideal radar waveform. The results show that the ISAC waveform we designed exhibits a pulse compression result that is highly similar to the ideal radar waveform, and the pulse compression gains are almost overlapping, which indicates that it has robust pulse compression performance.

[0042] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0043] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A communication perception integrated waveform design method for Internet of Vehicles, characterized in that: The steps include: S1. Construct a multi-input multi-output integrated communication and perception system model for the Internet of Vehicles by defining the communication and perception capabilities of the ISAC base station, the transmitted waveform, the Internet of Vehicles scenario, and the target of interest; S2. Based on the communication perception integrated system model constructed in S1, a communication model is established by defining the indicators MUI and PAPR of the user receiving signal and the waveform communication performance that affects the transmission; S3. Based on the communication perception integrated system model constructed in S1, a perception model is established by defining the received signal of the target of interest received by the ISAC base station and the waveform similarity and CRB, which are indicators affecting the waveform perception performance of the transmission; S4. Combining the communication model established in S2 and the perception model established in S3, construct an optimization problem that minimizes the weighted sum of the MUI, CRB, and waveform similarity, and takes the total transmission power and the PAPR as constraints, and converts the optimization problem into a matrix form, making it a QCQP problem with quadratic equation constraints and quadratic inequality constraints; S5. Based on the QCQP problem described in S4, auxiliary variables are introduced to transform the non-convex problem into a convex problem. The ADMM algorithm is used to fix different parameters and alternately iterate each parameter variable to finally solve the optimal waveform X.

2. A communication-perception integrated waveform design method for Internet of Vehicles according to claim 1, characterized in that: The ISAC base station in S1 is composed of a uniform linear array, including Root transmitting antenna and Root receiving antenna; The ISAC base station communicates with mobile users such as vehicles and mobile terminals, and the targets of interest detected are surrounding buildings, roadside pedestrians, and vehicles.

3. The communication-perception integrated waveform design method for Internet of Vehicles according to claim 1 is characterized in that: The S2 comprises the following steps: S2.1 Signal matrix received by communication users ; ; in, is a complex matrix, where Represents the length of the communication frame; represents the downlink communication channel matrix, assuming undergoes flat Rayleigh fading and can be perfectly estimated; represents the transmission signal matrix; The additive white Gaussian noise (AWGN) represents the downlink user; S2.2 rewrites the received signal matrix described in S2.1 as: ； MUI is represented by: ； To maximize the sum rate, the MUI is minimized; the signal-to-noise ratio and achievable sum rate of each user in each frame are calculated; ； in, is the ensemble average over the time index, yes The Item, indicating that From The signal transmitted between the communication users Indicated in The signal transmitted in time slots; The achievable sum rate for a downlink mobile user is expressed as: ； S2.3 Considering the impact of PAPR on the effectiveness of the communication system, the PAPR is controlled to provide sufficient transmission distance and reliability. Based on the signal matrix received by the communication user described in S2.1 , the calculation formula of PAPR is: ； in, is a vector No. elements, , which means that the matrix The vector obtained after vectorization is, The PAPR threshold represents the total transmit power of the ISAC waveform. for: ； The right half of equation (7) above can be rewritten as: ； in .

4. The method for designing a communication-aware integrated waveform for Internet of Vehicles according to claim 1, characterized in that: The S3 comprises the following steps: S3.1 For radar perception, assuming that the propagation path of the transmitted waveform is a line-of-sight path, the direction The signal matrix received by the far-field point target It is expressed as: ； in, Indicates The reflection coefficient of the target; Indicates the angle of the radar detecting the target relative to the ISAC base station. and are the steering vectors of the transmitting antenna and the receiving antenna respectively; Use half wavelength between adjacent elements It is the AWGN of MIMO radar; The transmission direction and arrival direction of a single-base radar are the same, assuming that DOA and DOD are and It is expressed as: ； ； S3.2 introduces waveform similarity constraints in ISAC waveform design to affect its perceptual performance; waveform similarity is expressed as: ； in, is the reference waveform matrix; without imposing equality constraints on the waveform covariance matrix, minimize The expected radar beam pattern can be approximated; S3.3 CRB is used to measure perception performance, provide a lower bound on the mean square error, and has a closed-form expression; for radar point target detection, CRB is expressed as: ； in yes The derivative of represents the reflection coefficient; Equation (13) is further equivalent to; ； For the convenience of calculation, assume ,get ;CRB can be expressed as: 。 5. The method for designing a communication-aware integrated waveform for Internet of Vehicles according to claim 4, characterized in that: The S4 comprises the following steps: S4.1 The optimization problem is expressed as: ； in, is a trade-off parameter that can be adjusted to affect the characteristics of the waveform; it can be further expressed as: ； S4.2 further simplifies the optimization problem; S4.2.1 Order; , in is an all-zero matrix, and the optimization problem is transformed into: ; S4.2.2 Convert the objective function into matrix form: ; make , then the objective equation can be expressed as: ; make , and at the same time Vectorize to a diagonal matrix ,Will Vectorized to , and order ; Then the objective equation can be rewritten as: ; in .

6. A communication-perception integrated waveform design method for Internet of Vehicles according to claim 5, characterized in that: The S5 comprises the following steps: S5.1 By introducing variables , the original optimization problem is expressed as: ; From the above optimization problem, the augmented Lagrangian expression is obtained, which is expressed as; ； in is the penalty factor. The augmented Lagrangian function can also be regarded as the non-augmented Lagrangian function of the following optimization problem; ; S5.2 Update , In the iterative process of the ADMM algorithm, The variable update expression for the iteration is as follows: （25）； By taking the derivative of equation (25) and setting its gradient to 0, we can obtain The update expression of The gradient is obtained by differentiating the augmented Lagrangian function: （26）； Set the gradient equal to zero and sort out The update equation is: ； S5.3 Update : Update completed Afterwards, update the auxiliary variables , No. The variable update expression for the iteration is as follows: （28）; It is expressed as the following optimization problem; ； Similarly, we get its Lagrangian expression as follows: （30）； Get the next set for Lagrangian expression for the gradient: （31）； And make get The update equation of ; （32）; S5.4 Update , Update auxiliary variables Similarly, The variable update expression for the iteration is （23）； The optimization problem can be expressed as: ； If the constraints are satisfied, then the Lagrangian function and gradient are calculated, expressed as; ; ; make ,get The update expression is: （37）; If the constraints are not satisfied, Project to a region that satisfies the constraints; Set a scaling factor , so that ,get ; S5.5 Update auxiliary variables, By iterating the auxiliary variables, the optimal solution is obtained, which is expressed as: （39）； 。

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