A spectrum and energy limited connected cooperative driving communication system resource allocation method

Abstract

The present application relates to a kind of spectrum and energy limited network connection cooperative driving communication system resource allocation method, belong to the field of wireless short distance communication of Internet of Vehicles.The present application is directed to the problem of communication and computing resource limited in network connection cooperative driving communication system and link interruption under complex communication scenario, considering the maximum transmit power of vehicle end, maximum bandwidth, vehicle-to-base station communication maximum energy consumption, vehicle-to-vehicle communication minimum rate and other constraints, construct the task computing total delay minimization resource allocation model based on partial task offloading, use variable relaxation, continuous convex approximation, alternate optimization and variable substitution and other methods to convert mixed integer nonlinear programming resource allocation model into convex optimization model to solve.The present application can effectively reduce the vehicle-to-base station communication delay, while meeting the user service quality demand of vehicle-to-vehicle communication.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless short-distance communication of Internet of Vehicles, and relates to a resource allocation method for a spectrum and energy limited network cooperative driving communication system. BACKGROUND

[0002] Resource allocation is one of the key technologies in the communication network of Internet of Vehicles, involving power allocation, spectrum allocation and other aspects. Effective implementation of resource allocation can significantly improve the efficiency and performance of the vehicle communication system, thereby guaranteeing the quality of service of vehicle users.

[0003] Due to the rapid development of intelligent networked automobile technology, the data volume increases sharply, and the computing power resources and energy resources of electric vehicle end devices are limited. The vehicle end faces multiple challenges when processing massive computing tasks. In order to cope with the problem of limited computing and energy resources at the vehicle end, with the rich edge computing capability of the roadside unit, the vehicle device can offload its tasks to the edge server for processing. In addition, when facing dense vehicle access, the limited spectrum resources of the network cooperative driving communication system will intensify the competition of vehicle access, which may cause communication interruption and cannot guarantee the exchange of safety information of the network cooperative driving communication system. However, in the existing resource allocation methods, few studies have researched the communication mode selection and resource allocation problem in the network cooperative driving communication system under the constraint of limited spectrum and energy, which makes it difficult to meet the quality of service demand of vehicle users and the low delay demand of computing tasks when facing complex communication environment and high data volume computing tasks, and also seriously affects the endurance mileage of electric vehicles. Therefore, a resource allocation method for a network cooperative driving communication system under the constraint of limited spectrum and energy is needed to solve the above problems. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a resource allocation method for a network cooperative driving communication system under the constraint of limited spectrum and energy. In view of the problems of high computing delay and high energy consumption of the vehicle end caused by massive computing tasks and the problem of unstable communication link caused by complex communication scenarios, the constraints of maximum transmit power of the vehicle end, maximum bandwidth, maximum energy consumption of vehicle-to-base station communication, minimum rate of vehicle-to-vehicle communication, communication mode selection factor, user and roadside unit association factor are considered, and the network model of the network cooperative driving communication system is established with the optimization objective of minimizing the total task computing delay. The mixed integer nonlinear programming resource allocation model is converted into a convex optimization model for solving by using variable relaxation, continuous convex approximation, alternating optimization and variable substitution.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A resource allocation method for a network cooperative driving communication system under the constraint of limited spectrum and energy, comprising the following steps:

[0007] S1. Initialize the parameters of the connected cooperative driving communication system, including: one macro base station, N roadside units equipped with Mobile Edge Computing (MEC) servers, K vehicle-to-vehicle communication user pairs, and M vehicle-to-infrastructure communication users; each vehicle-to-vehicle communication user pair consists of a vehicle-to-vehicle communication transmitter and a vehicle-to-vehicle communication receiver. The transmitter sends safety information to the receiver. Each vehicle-to-vehicle communication user pair can choose direct communication or roadside unit relay communication according to channel quality; each vehicle-to-infrastructure communication user has computing tasks and offloads some computing tasks to the roadside units; all communication devices adopt frequency division multiple access mode, modulate their information onto the incident signal and transmit it to the information receiver. The roadside units and the macro base station are connected through optical fiber. The macro base station centrally allocates subcarriers for vehicles accessing the roadside units.

[0008] S2. Based on the constraints of the vehicle's maximum transmit power, maximum bandwidth, maximum energy consumption of vehicle-to-base station communication, minimum vehicle-to-vehicle communication rate, communication mode selection factor, and user-roadside unit association factor, the optimization objective is to minimize the total computational latency of the connected cooperative driving communication system. Based on this objective, a resource allocation model is constructed based on partial task offloading, mode selection, and user association.

[0009] S3. By using methods such as variable relaxation, continuous convex approximation, alternating optimization, and variable substitution, the mixed integer nonlinear programming resource allocation model is transformed into a convex optimization model for solution, thereby obtaining the optimal total task computation delay.

[0010] Furthermore, in S1: the parameters of the connected cooperative driving communication system also include: the number of roadside units N, the number of vehicle-to-vehicle communication user pairs K, the number of vehicle-to-infrastructure communication users M, and the noise variance at the roadside units. Vehicle-to-vehicle user-to-receiver noise variance Vehicle-to-vehicle user sends security information size L k Vehicle-to-vehicle user's maximum tolerable latency T max Maximum transmit power for vehicle-to-vehicle users Maximum transmit power P from vehicle to infrastructure user max Maximum bandwidth usage for vehicle-to-vehicle users Vehicles consume a maximum of B bandwidth for infrastructure users. max Total computation time T and maximum number of iterations D max The convergence accuracy ω and the number of iterations d.

[0011] Furthermore, in S2: the construction of a resource allocation model based on partial task unloading, mode selection, and user association specifically includes:

[0012]

[0013] Among them, s k Indicates the mode selection for the k-th vehicle-to-vehicle communication user pair, a m,n This represents the association status between the m-th vehicle-to-infrastructure communication user and the n-th roadside unit. P represents the association status between the vehicle and the roadside unit in relay mode. m This represents the transmit power of the m-th vehicle to the infrastructure communication user. Let ξ represent the rate of the k-th vehicle-to-vehicle communication user pair. m C represents the percentage of tasks offloaded from infrastructure communication users by the m-th vehicle. m f represents the number of CPU loops required to execute the task. local V represents the vehicle's local computing power. m R represents the amount of data used in the computation task. m f represents the communication rate between the m-th vehicle and the infrastructure user. rsu This indicates the computational capability of the roadside unit. This represents the local computing power consumption of the m-th vehicle in relation to the infrastructure communication user. E represents the local offloading computational energy consumption of the m-th vehicle to the infrastructure communication user. max This indicates the maximum energy consumption for task calculation. P represents the transmit power of the k-th vehicle-to-vehicle communication user pair. max Indicates the vehicle's maximum transmission power to infrastructure users, B m This represents the bandwidth of the m-th vehicle to the infrastructure communication user. This represents the bandwidth of the k-th vehicle-to-vehicle communication user pair in relay mode. This represents the achievable data rate for user k in vehicle-to-vehicle communication under direct communication mode. This represents the bandwidth of the k-th vehicle-to-vehicle communication user pair in direct connection mode. This indicates the maximum bandwidth for vehicle-to-vehicle communication. This indicates the data rate that vehicle-to-vehicle communication pair k can achieve in relay mode.

[0014] Furthermore, S3 also includes the following steps:

[0015] By using variable relaxation, the non-convex objective function is transformed into a linear function, and the problem is transformed into:

[0016]

[0017] Among them, C 10 C 11 It is obtained by relaxing the auxiliary variable μ. The objective function and constraints of this optimization problem still contain coupled variables and binary variables, and it is still a non-convex optimization problem.

[0018] Furthermore, S3 also includes the following steps:

[0019] S31. Fixed pattern selection factor s k Vehicle transmission power Partial unloading factor ξ m The mixed-integer nonlinear programming problem model is transformed into a convex optimization problem model using the variable substitution method, variable relaxation method, and alternating optimization method, and the vehicle bandwidth is calculated. User and Roadside Unit Selection Factors The slack variable μ;

[0020] S32. Fixed The mixed-integer nonlinear programming problem model is transformed into a convex optimization problem model using the variable substitution method, continuous convex approximation method, variable relaxation method, and alternating optimization method, and the partial unloading factor ξ is calculated. m Vehicle power The slack variable μ;

[0021] S33. Based on the slack variable μ, update the total task calculation delay T of the connected cooperative driving communication system based on partial task offloading;

[0022] S34. Determine whether the total task computation delay of the connected cooperative driving communication system based on partial task offloading has converged; if so, output the optimal total task computation delay T of the connected cooperative driving communication system based on partial task offloading. * Then end; otherwise, proceed to S35;

[0023] S35. Determine if the current iteration count is greater than the maximum iteration count; if so, output T. * If the iteration ends, then proceed to the next iteration and return to S31.

[0024] Furthermore, in S31, the optimal mode selection strategy is specifically as follows:

[0025] For the k-th vehicle-to-vehicle communication user pair, if Then the k-th vehicle-to-vehicle communication user pair operates in relay mode; otherwise, it selects direct communication mode. Constraint C7 is equivalently transformed into... binary variable Relaxed to a continuous variable on the interval [0,1], and defined and The following joint bandwidth allocation and user and roadside unit selection subproblems are obtained:

[0026]

[0027] The joint bandwidth allocation and user and roadside unit selection subproblems are convex optimization problems, which are solved using the CVX Toolbox.

[0028] Furthermore, in S32: a slack variable θ is introduced. m and satisfy Introducing variable λ m Replace θ m P m Using the continuous convex approximation technique to Approximately a convex function

[0029] Based on the above transformation, we obtain the following joint task offloading and power allocation subproblem:

[0030]

[0031] in,

[0032]

[0033] The joint task unloading and power allocation subproblem is a convex optimization problem, which can be solved using the CVX toolbox.

[0034] Furthermore, in S33, the updated connected cooperative driving communication system calculates the total task delay T = μ based on partial task unloading.

[0035] Furthermore, in S34, the determination of whether the total task calculation delay of the connected cooperative driving communication system based on partial task offloading converges is specifically as follows: when the total task calculation delay of the connected cooperative driving communication system based on partial task offloading in the d-th iteration satisfies |T(d)-T(d-1)|≤ω, then it converges; otherwise, it does not converge.

[0036] The beneficial effects of this invention are as follows:

[0037] This invention effectively reduces the total latency of computational tasks, meeting the quality of service requirements of vehicle-to-vehicle communication users within limited spectrum and energy resources. Compared with methods such as equal bandwidth allocation, equal power transmission, no task offloading, full task offloading, and no mode selection, the proposed solution has lower latency and improves the performance and flexibility of connected cooperative driving communication systems.

[0038] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0040] Figure 1 This is a model diagram of the China Internet-connected cooperative driving communication system according to an embodiment of the present invention;

[0041] Figure 2 This is a flowchart illustrating a resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to an embodiment of the present invention.

[0042] Figure 3 This diagram illustrates a comparison between a spectrum- and energy-constrained resource allocation method for a networked cooperative driving communication system according to an embodiment of the present invention and a traditional method. Detailed Implementation

[0043] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0044] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0045] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0046] Please see Figure 1This is a model diagram of a network-connected cooperative driving communication system according to an embodiment of the present invention. The network-connected cooperative driving communication system includes a macro base station, N roadside units equipped with Mobile Edge Computing (MEC) servers, K vehicle-to-vehicle communication user pairs, and M vehicle-to-infrastructure communication users. Each vehicle-to-vehicle communication user pair consists of a vehicle-to-vehicle communication transmitter and a vehicle-to-vehicle communication receiver. The transmitter sends safety information to the receiver. Each vehicle-to-vehicle communication user pair can choose direct communication or roadside unit relay communication according to channel quality. Each vehicle-to-infrastructure communication user has computing tasks and offloads some computing tasks to the roadside units. All communication devices adopt frequency division multiple access mode, modulate their information onto the incident signal and transmit it to the information receiver. The roadside units and the macro base station are connected through optical fibers. The macro base station centrally allocates subcarriers for vehicles accessing the roadside units.

[0047] Please see Figure 2 The above is a flowchart of a resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to an embodiment of the present invention. The method specifically includes the following steps:

[0048] S1. Initialize the parameters of the connected cooperative driving communication system, including:

[0049] Number of roadside units N, number of vehicle-to-vehicle communication users K, number of vehicle-to-infrastructure communication users M, noise variance at roadside units Vehicle-to-vehicle user-to-receiver noise variance Vehicle-to-vehicle user sends security information size L k Vehicle-to-vehicle user's maximum tolerable latency T max Maximum transmit power for vehicle-to-vehicle users Maximum transmit power P from vehicle to infrastructure user max Maximum bandwidth usage for vehicle-to-vehicle users Vehicles consume a maximum of B bandwidth for infrastructure users. max Total computation time T, maximum number of iterations D max The convergence accuracy ω and the number of iterations d.

[0050] S2. Based on the constraints of the vehicle's maximum transmit power, maximum bandwidth, maximum energy consumption of vehicle-to-base station communication, minimum vehicle-to-vehicle communication rate, communication mode selection factor, and user-roadside unit association factor, the optimization objective is to minimize the total computational latency of the connected cooperative driving communication system. Based on this objective, a resource allocation model is constructed based on partial task offloading, mode selection, and user association.

[0051] The uplink signal (Signal to Interference plus Noise Ratio, SINR) of the m-th vehicle-to-infrastructure communication user is represented as:

[0052]

[0053] in, P represents the SINR of the m-th vehicle-to-infrastructure communication user. m B represents the transmit power of the m-th vehicle to the infrastructure communication user. m This represents the bandwidth of the m-th vehicle to the infrastructure communication user. This represents the channel gain from the m-th vehicle-to-infrastructure communication user to the roadside unit. This represents the noise variance at the roadside unit.

[0054] The achievable data rate R for vehicle-to-infrastructure communication user m m , is represented as:

[0055]

[0056] Among them, a m,n This represents the association status between the m-th vehicle-to-infrastructure communication user and the n-th roadside unit.

[0057] In direct communication mode, the SINR of the vehicle-to-vehicle communication receiver k is expressed as:

[0058]

[0059] in, This represents the SINR of the vehicle-to-vehicle communication receiver k in direct communication mode. The transmit power of the k-th vehicle-to-vehicle communication user pair, h k The channel gain between user k and vehicle-to-vehicle communication is expressed as: This represents the bandwidth of the k-th vehicle-to-vehicle communication user pair in direct connection mode. Let V represent the noise variance at the receiver of the k-th vehicle-to-vehicle communication user.

[0060] Data rate achievable by user k in vehicle-to-vehicle communication under direct communication mode Represented as:

[0061]

[0062] The SINR of the vehicle-to-vehicle communication pair receiver k in relay mode is expressed as:

[0063]

[0064] in, This indicates the SINR of the vehicle-to-vehicle communication receiver in relay mode. This represents the channel gain from the transmitter to the roadside unit in the k-th vehicle-to-vehicle communication pair. This represents the bandwidth of the k-th vehicle-to-vehicle communication user pair in relay mode.

[0065] Data rate achievable by vehicle-to-vehicle communication in relay mode (k) Represented as:

[0066]

[0067] in, This indicates the association status between the vehicle and the roadside unit in relay mode.

[0068] Therefore, the achievable rate of vehicle-to-vehicle communication user k Represented as:

[0069]

[0070] Among them, s k This indicates the mode selection for the k-th vehicle-to-vehicle communication user pair.

[0071] Local computational latency of vehicle-to-infrastructure communication user vehicle m With energy consumption Represented as:

[0072]

[0073] Where, ξ m C represents the percentage of tasks offloaded from infrastructure communication users by the m-th vehicle. m f represents the number of CPU loops required to execute the task. local k represents the vehicle's local computing power. V This represents the computational energy consumption coefficient related to chip performance.

[0074] Unloading delay when the task vehicle unloads the task to the roadside unit and energy consumption Represented as:

[0075]

[0076] Among them, V m R represents the amount of data used in the computation task. m P represents the achievable data rate for vehicle-to-infrastructure communication user m. m P represents the transmit power of the m-th vehicle to the infrastructure communication user. rsu f represents the calculated power of the roadside unit. rsuThis indicates the computational capability of the roadside unit.

[0077] The resource allocation model constructed based on partial task unloading, mode selection, and user association is represented as follows:

[0078]

[0079]

[0080] Among them, s k Indicates the mode selection for the k-th vehicle-to-vehicle communication user pair, a m,n This represents the association status between the m-th vehicle-to-infrastructure communication user and the n-th roadside unit. P represents the association status between the vehicle and the roadside unit in relay mode. m This represents the transmit power of the m-th vehicle to the infrastructure communication user. Let ξ represent the rate of the k-th vehicle-to-vehicle communication user pair. m C represents the percentage of tasks offloaded from infrastructure communication users by the m-th vehicle. m f represents the number of CPU loops required to execute the task. local V represents the vehicle's local computing power. m R represents the amount of data used in the computation task. m f represents the communication rate between the m-th vehicle and the infrastructure user. rsu This indicates the computational capability of the roadside unit. This represents the local computing power consumption of the m-th vehicle in relation to the infrastructure communication user. E represents the local offloading computational energy consumption of the m-th vehicle to the infrastructure communication user. max This indicates the maximum energy consumption for task calculation. P represents the transmit power of the k-th vehicle-to-vehicle communication user pair. max Indicates the vehicle's maximum transmission power to infrastructure users, B m This represents the bandwidth of the m-th vehicle to the infrastructure communication user. This represents the bandwidth of the k-th vehicle-to-vehicle communication user pair in relay mode. This represents the achievable data rate for user k in vehicle-to-vehicle communication under direct communication mode. This represents the bandwidth of the k-th vehicle-to-vehicle communication user pair in direct connection mode. This indicates the maximum bandwidth for vehicle-to-vehicle communication. This indicates the data rate that vehicle-to-vehicle communication pair k can achieve in relay mode.

[0081] S3. Transform the mixed-integer nonlinear programming resource allocation model into a convex optimization model using methods such as variable relaxation, continuous convex approximation, alternating optimization, and variable substitution.

[0082] Based on the variable relaxation method, the objective function is replaced as follows:

[0083]

[0084] Based on the above transformation, the problem becomes:

[0085]

[0086] Among them, C 10 C 11 It is obtained by relaxing the auxiliary variable μ. The objective function and constraints of this optimization problem still contain coupled variables and binary variables, and it is still a non-convex optimization problem.

[0087] Optimal mode selection s k It can be determined as follows: for the k-th vehicle-to-vehicle communication user pair, if... The k-th vehicle-to-vehicle communication user pair operates in relay mode; otherwise, it selects direct communication mode.

[0088] Based on the alternating optimization method, for a given mode selection, power, and partial unloading factor, The joint bandwidth allocation and user and roadside unit selection sub-problems are as follows:

[0089]

[0090] stC2,C4-C9,C 11

[0091] Due to the tight coupling between variables and the existence of binary variables This problem is non-convex. To make it easier to handle, based on the variable relaxation and variable substitution methods, constraint C7 can be equivalently transformed as follows:

[0092]

[0093] binary variable Relaxed to a continuous variable on the interval [0,1], and defined as follows:

[0094]

[0095] The following joint bandwidth allocation and user and roadside unit selection subproblems are obtained:

[0096]

[0097] The joint bandwidth allocation and user / roadside unit selection subproblem is a convex optimization problem, which can be solved directly using the CVX toolbox. Based on the alternating optimization method, given the mode selection factor, user / roadside unit selection factor, and vehicle bandwidth...

[0098] The joint offloading and power control subproblem is:

[0099]

[0100] stC1-C3,C9-C 11 ,

[0101] Introducing slack variable θ m And satisfy:

[0102]

[0103] Introducing variable λ m Replace θ m P m Using the continuous convex approximation technique to Approximately a convex function Based on the above transformation, we obtain the following joint task offloading and power allocation subproblem:

[0104]

[0105] in:

[0106]

[0107] The joint task offloading and power allocation subproblem is a convex optimization problem, which can be solved directly using the CVX Toolbox.

[0108] S4, Fixed Mode Selection, Power and Partial Unloading Factor Using the CVX toolbox to calculate the joint bandwidth allocation and user and roadside unit selection convex problem:

[0109]

[0110] get

[0111] S5, Fixed Calculating the Joint Task Offloading and Power Allocation Bump Problem using the CVX Toolbox:

[0112]

[0113] Among them, by We obtain {μ}, {ξ m};

[0114] S6. Based on T = μ, update the total task calculation delay T of the networked cooperative driving communication system based on partial task offloading;

[0115] S7. Determine whether the total task computation delay of the connected cooperative driving communication system based on partial task offloading has converged. Specifically, if the total task computation delay of the connected cooperative driving communication system based on partial task offloading in the d-th iteration satisfies |T(d)-T(d-1)|≤ω, then it has converged; otherwise, it has not converged and proceeds to S8.

[0116] S8. Determine if the current iteration count is greater than the maximum iteration count; if so, output the optimal total task computation delay T. * If the iteration ends, then proceed to the next iteration and return to S4.

[0117] The application effects of this invention are described in detail using simulation, as follows:

[0118] Simulation conditions: Assume the vehicle-to-roadside unit user path loss model is as follows: in, This represents the distance between the vehicle and the roadside unit. The user path loss model for vehicle-to-vehicle communication is as follows: in This refers to the distance between vehicles.

[0119] Other simulation parameters are given in Table 1:

[0120] Table 1

[0121]

[0122] Please see Figure 3 This diagram compares a spectrum- and energy-constrained resource allocation method for a connected cooperative driving communication system according to an embodiment of the present invention with a traditional method. In this simulation experiment, from... Figure 3 It can be seen that, compared with equal bandwidth allocation, equal power transmission, no task offloading, full task offloading, and no mode selection methods, the method of the present invention has a lower total computational latency for vehicle-to-roadside unit users, thus confirming that the method of the present invention can effectively reduce the total computational latency for vehicle-to-roadside unit users under the constraints of limited spectrum and energy.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system, characterized in that, Includes the following steps: S1. Initialize the parameters of the connected cooperative driving communication system, including: one macro base station, N roadside units equipped with Mobile Edge Computing (MEC) servers, K vehicle-to-vehicle communication user pairs, and M vehicle-to-infrastructure communication users; each vehicle-to-vehicle communication user pair consists of a vehicle-to-vehicle communication transmitter and a vehicle-to-vehicle communication receiver. The transmitter sends safety information to the receiver. Each vehicle-to-vehicle communication user pair can choose direct communication or roadside unit relay communication according to channel quality; each vehicle-to-infrastructure communication user has computing tasks and offloads some computing tasks to the roadside units; all communication devices adopt frequency division multiple access, modulate their information onto the incident signal and transmit it to the information receiver. The roadside units and the macro base station are connected through optical fiber. The macro base station centrally allocates subcarriers for vehicles accessing the roadside units. S2. Based on the constraints of the vehicle's maximum transmit power, maximum bandwidth, maximum energy consumption of vehicle-to-base station communication, minimum vehicle-to-vehicle communication rate, communication mode selection factor, and user-roadside unit association factor, the optimization objective is to minimize the total computational latency of the connected cooperative driving communication system. Based on this objective, a resource allocation model is constructed based on partial task offloading, mode selection, and user association. S3. By using methods such as variable relaxation, continuous convex approximation, alternating optimization, and variable substitution, the mixed integer nonlinear programming resource allocation model is transformed into a convex optimization model for solution, thereby obtaining the optimal total task computation delay.

2. The resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to claim 1, characterized in that, In S1, the parameters of the connected cooperative driving communication system further include: the number of roadside units N, the number of vehicle-to-vehicle communication user pairs K, the number of vehicle-to-infrastructure communication users M, and the noise variance at the roadside units. Vehicle-to-vehicle user-to-receiver noise variance Vehicle-to-vehicle user sends security information size L k Vehicle-to-vehicle user's maximum tolerable latency T max Maximum transmit power for vehicle-to-vehicle users Maximum transmit power P from vehicle to infrastructure user max Maximum bandwidth usage for vehicle-to-vehicle users Vehicles consume a maximum of B bandwidth for infrastructure users. max Total computation time T and maximum number of iterations D max The convergence accuracy ω and the number of iterations d.

3. The resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to claim 2, characterized in that, In S2: the construction of a resource allocation model based on partial task unloading, mode selection, and user association specifically includes: Among them, s k Indicates the mode selection for the k-th vehicle-to-vehicle communication user pair, a m,n This represents the association status between the m-th vehicle-to-infrastructure communication user and the n-th roadside unit. P represents the association status between the vehicle and the roadside unit in relay mode. m This represents the transmit power of the m-th vehicle to the infrastructure communication user. Let ξ represent the rate of the k-th vehicle-to-vehicle communication user pair. m C represents the percentage of tasks offloaded from infrastructure communication users by the m-th vehicle. m f represents the number of CPU loops required to execute the task. local V represents the vehicle's local computing power. m R represents the amount of data used in the computation task. m f represents the communication rate between the m-th vehicle and the infrastructure user. rsu This indicates the computational capability of the roadside unit. This represents the local computing power consumption of the m-th vehicle in relation to the infrastructure communication user. E represents the local offloading computational energy consumption of the m-th vehicle to the infrastructure communication user. max This indicates the maximum energy consumption for task calculation. P represents the transmit power of the k-th vehicle-to-vehicle communication user pair. max Indicates the vehicle's maximum transmission power to infrastructure users, B m This represents the bandwidth of the m-th vehicle to the infrastructure communication user. This represents the bandwidth of the k-th vehicle-to-vehicle communication user pair in relay mode. This represents the achievable data rate for user k in vehicle-to-vehicle communication under direct communication mode. This represents the bandwidth of the k-th vehicle-to-vehicle communication user pair in direct connection mode. This indicates the maximum bandwidth for vehicle-to-vehicle communication. This indicates the data rate that vehicle-to-vehicle communication pair k can achieve in relay mode.

4. The resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to claim 3, characterized in that, S3 further includes the following steps: By using variable relaxation, the non-convex objective function is transformed into a linear function, and the problem is transformed into: Among them, C 10 C 11 It is obtained by relaxing the auxiliary variable μ. The objective function and constraints of this optimization problem still contain coupled variables and binary variables, and it is still a non-convex optimization problem.

5. The resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to claim 4, characterized in that, S3 further includes the following steps: S31. Fixed pattern selection factor s k Vehicle transmission power Partial unloading factor ξ m The mixed-integer nonlinear programming problem model is transformed into a convex optimization problem model using the variable substitution method, variable relaxation method, and alternating optimization method, and the vehicle bandwidth is calculated. User and Roadside Unit Selection Factors The slack variable μ; S32. Fixed The mixed-integer nonlinear programming problem model is transformed into a convex optimization problem model using the variable substitution method, continuous convex approximation method, variable relaxation method, and alternating optimization method, and the partial unloading factor ξ is calculated. m Vehicle power The slack variable μ; S33. Based on the slack variable μ, update the total task calculation delay T of the connected cooperative driving communication system based on partial task offloading; S34. Determine whether the total task computation delay of the connected cooperative driving communication system based on partial task offloading has converged; if so, output the optimal total task computation delay T of the connected cooperative driving communication system based on partial task offloading. * Then end; otherwise, proceed to S35; S35. Determine if the current iteration count is greater than the maximum iteration count; if so, output T. * If the iteration ends, then proceed to the next iteration and return to S31.

6. The resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to claim 5, characterized in that: In S31, the optimal mode selection strategy is specifically as follows: For the k-th vehicle-to-vehicle communication user pair, if Then the k-th vehicle-to-vehicle communication user pair operates in relay mode; otherwise, it selects direct communication mode. Constraint C7 is equivalently transformed into... binary variable Relaxed to a continuous variable on the interval [0,1], and defined and The following joint bandwidth allocation and user and roadside unit selection subproblems are obtained: The joint bandwidth allocation and user and roadside unit selection subproblems are convex optimization problems, which are solved using the CVX Toolbox.

7. A resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to claim 5, characterized in that, In S32: Introducing slack variable θ m and satisfy Introducing variable λ m Replace θ m P m Using the continuous convex approximation technique to Approximately a convex function Based on the above transformation, we obtain the following joint task offloading and power allocation subproblem: in, The joint task unloading and power allocation subproblem is a convex optimization problem, which can be solved using the CVX toolbox.

8. A resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to claim 5, characterized in that, In step S33, the updated connected cooperative driving communication system calculates the total task delay T = μ based on partial task unloading.

9. A resource allocation method for a spectrum- and energy-constrained connected cooperative driving communication system according to claim 5, characterized in that, In step S34, determining whether the total task calculation delay of the connected cooperative driving communication system based on partial task offloading converges is specifically as follows: when the total task calculation delay of the connected cooperative driving communication system based on partial task offloading in the d-th iteration satisfies |T(d)-T(d-1)|≤ω, then it converges; otherwise, it does not converge.

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