Frequency spectrum and energy limited networked cooperative driving communication system resource allocation method

Abstract

The invention relates to a resource allocation method for a frequency spectrum and energy limited networked cooperative driving communication system, and belongs to the technical field of wireless short-distance communication of the Internet of Vehicles. Aiming at the problems of limited communication and computing resources in a network-connected cooperative driving communication system and link interruption in a complex communication scene, constraints such as the maximum transmitting power and the maximum bandwidth of a vehicle end, the maximum energy consumption of vehicle-to-base station communication, the minimum rate of vehicle-to-vehicle communication and the like are considered; a task calculation total time delay minimization resource allocation model based on partial task unloading is constructed, and a mixed integer nonlinear programming resource allocation model is converted into a convex optimization model for solving by using methods such as variable relaxation, continuous convex approximation, alternate optimization and variable replacement. According to the invention, the time delay of vehicle-to-base station communication can be effectively reduced, and the user service quality requirement of vehicle-to-vehicle communication can be met at the same time.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless short-range communications in vehicle networks, and relates to a resource allocation method for a networked cooperative driving communication system with limited spectrum and energy. Background Art

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

[0003] Due to the rapid development of intelligent connected vehicle technology, the amount of data has increased dramatically. The computing power and energy resources of electric vehicle-side equipment are limited, and the vehicle-side faces multiple challenges when processing massive computing tasks. In order to cope with the problem of limited vehicle-side computing and energy resources, with the help of the rich edge computing capabilities of the roadside unit, the vehicle equipment can offload its tasks to the edge server for processing. In addition, when the networked cooperative driving communication system faces dense vehicle access, the limited spectrum resources will lead to intensified competition for vehicle access, which may cause communication interruption and fail to guarantee the secure information exchange of the networked cooperative driving communication system. However, in the existing resource allocation methods, there is little research on the communication mode selection and resource allocation problems in the networked cooperative driving communication system under the constraints of spectrum and energy constraints, which makes it difficult to meet the service quality requirements of vehicle users and the low latency requirements of computing tasks when facing complex communication environments and high-data-volume computing tasks, and will also seriously affect the cruising range of electric vehicles. Therefore, a resource allocation method for a networked cooperative driving communication system under the constraints of spectrum and energy constraints is needed to solve the above problems. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a resource allocation method for a networked cooperative driving communication system with limited spectrum and energy, aiming at the problem of high computing delay and high energy consumption on the vehicle side caused by massive computing tasks and the problem of unstable communication link caused by complex communication scenarios, considering the constraints such as the maximum transmission power, maximum bandwidth, maximum energy consumption of vehicle-to-base station communication, minimum rate of vehicle-to-vehicle communication, communication mode selection factor, user-roadside unit association factor on the vehicle side, and taking minimizing the total delay of task calculation as the optimization goal, a network model is established for the networked cooperative driving communication system. The mixed integer nonlinear programming resource allocation model is converted into a convex optimization model for solution by using methods such as variable relaxation, continuous convex approximation, alternating optimization and variable substitution.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A spectrum and energy limited networked cooperative driving communication system resource allocation method comprises the following steps:

[0007] S1. Initialize the parameters of the networked cooperative driving communication system, including: a macro base station, N roadside units equipped with 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 a computing task, and some of the computing tasks are offloaded to the roadside unit; all communication devices use frequency division multiple access to modulate their information onto the incident signal and transmit it to the information receiver. The roadside unit is connected to the macro base station through optical fiber, and the macro base station centrally allocates subcarriers for vehicles accessing the roadside unit;

[0008] S2. Based on the constraints of the vehicle-side maximum transmission 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 total delay of task calculation in the networked cooperative driving communication system is minimized as the optimization goal. Based on this goal, a resource allocation model based on partial task offloading, mode selection, and user association is constructed;

[0009] S3. The mixed integer nonlinear programming resource allocation model is transformed into a convex optimization model by using methods such as variable relaxation, continuous convex approximation, alternating optimization and variable substitution, so as to obtain the optimal total task calculation delay.

[0010] Furthermore, in S1: the parameters of the networked 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, the noise variance at the roadside unit Noise variance at the receiver of a vehicle-to-vehicle user pair The size of the safety information sent by the car-to-car user pair is L k , the maximum tolerable delay T for a vehicle-to-vehicle user max , maximum transmission power of vehicle-to-vehicle users Maximum transmission power P of vehicle to infrastructure users max , the maximum bandwidth occupied by car-to-car users The maximum bandwidth occupied by vehicles to infrastructure users is B max , total task calculation delay T, maximum number of iterations D max , convergence accuracy ω and number of iterations d.

[0011] Further, in S2: the resource allocation model based on partial task offloading, mode selection, and user association is constructed, specifically:

[0012]

[0013] Among them, s k represents the mode selection of the kth vehicle-to-vehicle communication user pair, a m,n represents the association status of the mth vehicle-to-infrastructure communication user and the nth roadside unit, Indicates the association status between the vehicle and the roadside unit in the relay mode, P m represents the transmission power of the mth vehicle to the infrastructure communication user, represents the rate of the kth car-to-car communication user pair, ξ m represents the task offloading ratio of the mth vehicle to the infrastructure communication user, C m Indicates the number of CPU cycles required to execute the task, f local Represents the local computing power of the vehicle, V m represents the amount of computing task data, R m represents the rate of the mth vehicle-to-infrastructure communication user, f rsu Indicates the computing power of the roadside unit, represents the local computing energy consumption of the mth vehicle-to-infrastructure communication user, represents the local offloading computational energy consumption of the mth vehicle-to-infrastructure communication user, E max Indicates the maximum energy consumption of task calculation, represents the transmission power of the kth vehicle-to-vehicle communication user pair, P max represents the maximum transmission power of the vehicle to the infrastructure user, B m represents the bandwidth of the mth vehicle-to-infrastructure communication user, represents the bandwidth of the kth vehicle-to-vehicle communication user pair in relay mode, represents the data rate that can be achieved by k users in vehicle-to-vehicle communication in direct communication mode, represents the bandwidth of the kth vehicle-to-vehicle communication user pair in direct connection mode, represents the maximum bandwidth of vehicle-to-vehicle communication, represents the data rate achievable for vehicle-to-vehicle communication in relay mode.

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

[0015] The variable relaxation method is used to transform the non-convex objective function 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. There are still coupling variables and binary variables in the objective function and constraints of this optimization problem, and it is still a non-convex optimization problem.

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

[0019] S31. Fixed mode selection factor s k , Vehicle transmission power Partial unloading factor ξ m , using variable substitution method, variable relaxation method and alternating optimization method to transform the mixed integer nonlinear programming problem model into a convex optimization problem model, and calculate the vehicle bandwidth User and RSU selection factors Slack variable μ;

[0020] S32.Fixed The mixed integer nonlinear programming problem model is transformed into a convex optimization problem model by 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 Slack variable μ;

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

[0022] S34. Determine whether the total delay of task calculation based on partial task offloading of the networked cooperative driving communication system converges; if so, output the optimal total delay of task calculation based on partial task offloading of the networked cooperative driving communication system T * , then end; otherwise, enter S35;

[0023] S35. Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output T * , then end, otherwise, update the current iteration number d, then enter the next iteration, and return to S31.

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

[0025] For the kth car-to-car communication user pair, if Then the kth car-to-car communication user pair works in relay mode, otherwise it chooses direct communication mode; constraint C 7 The equivalent transformation is Binary variables is relaxed to a continuous variable on the interval [0,1] and defined and The following joint bandwidth allocation and user and roadside unit selection subproblem is obtained:

[0026]

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

[0028] Furthermore, in S32: introduce the slack variable θ m and meet Introducing variable λ m Replace θ m P m , using continuous convex approximation technology to Approximately a convex function

[0029] Based on the above transformation, the following joint task offloading and power allocation sub-problem is obtained:

[0030]

[0031] in,

[0032]

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

[0034] Further, in S33, the updated networked cooperative driving communication system calculates a total delay T=μ based on the task of partial task offloading.

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

[0036] The beneficial effects of the present invention are:

[0037] It can effectively reduce the total latency of computing tasks and meet the service quality requirements of vehicle-to-vehicle communication users under limited spectrum and energy resources. Compared with equal bandwidth allocation, equal power transmission, no task offloading, full task offloading, and no mode selection methods, the scheme of the present invention has lower latency and improves the performance and flexibility of the networked cooperative driving communication system.

[0038] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0040] Figure 1 A model diagram of the ChinaNetLink collaborative driving communication system according to an embodiment of the present invention;

[0041] Figure 2 A flow chart of a method for allocating resources in a networked cooperative driving communication system with limited spectrum and energy according to an embodiment of the present invention;

[0042] Figure 3 A diagram comparing a spectrum and energy limited networked cooperative driving communication system resource allocation method according to an embodiment of the present invention and a traditional method. DETAILED DESCRIPTION

[0043] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways 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 only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0044] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0045] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0046] See also Figure 1, is a model diagram of the networked cooperative driving communication system of an embodiment of the present invention. The networked cooperative driving communication system includes a macro base station, N roadside units equipped with edge computing (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 is composed 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 a computing task, and offloads part of the computing task to the roadside unit; all communication devices use frequency division multiple access to modulate their information onto the incident signal and transmit it to the information receiver. The roadside unit is connected to the macro base station through optical fiber, and the macro base station centrally allocates subcarriers for vehicles accessing the roadside unit.

[0047] See also Figure 2 , is a flow chart of a method for allocating resources in a networked cooperative driving communication system with limited spectrum and energy according to an embodiment of the present invention. The method specifically includes the following steps:

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

[0049] 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 unit Noise variance at the receiver of a vehicle-to-vehicle user pair The size of the safety information sent by the car-to-car user pair is L k , the maximum tolerable delay T for a vehicle-to-vehicle user max , maximum transmission power of vehicle-to-vehicle users Maximum transmission power P of vehicle to infrastructure users max , the maximum bandwidth occupied by car-to-car users The maximum bandwidth occupied by vehicles to infrastructure users is B max , total task calculation delay T, maximum number of iterations D max , convergence accuracy ω and number of iterations d.

[0050] S2. Based on the constraints of the maximum vehicle-side transmission 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 total delay of task calculation in the networked cooperative driving communication system is minimized as the optimization goal. With this goal, a resource allocation model based on partial task offloading, mode selection and user association is constructed.

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

[0052]

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

[0054] Achievable data rate R for vehicle-to-infrastructure communication user m m , expressed as:

[0055]

[0056] Among them, a m,n Indicates the association status of the mth vehicle-to-infrastructure communication user and the nth roadside unit.

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

[0058]

[0059] in, represents the SINR of vehicle-to-vehicle communication receiver k in direct communication mode, The transmission power of the kth vehicle-to-vehicle communication user pair, h k The channel gain between the vehicle-to-vehicle communication pair k is expressed as, represents the bandwidth of the kth vehicle-to-vehicle communication user pair in direct connection mode, represents the noise variance at the receiver of the kth pair of vehicle-to-vehicle communication users.

[0060] The data rate that can be achieved by k users in vehicle-to-vehicle communication under direct communication mode It is expressed as:

[0061]

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

[0063]

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

[0065] Achievable data rate for car-to-car communication in relay mode It is expressed as:

[0066]

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

[0068] Therefore, the achievable rate of k for car-to-car communication users is It is expressed as:

[0069]

[0070] Among them, s k Represents the mode selection of the kth vehicle-to-vehicle communication user pair.

[0071] Local computation delay of vehicle m in vehicle-to-infrastructure communication and energy consumption It is expressed as:

[0072]

[0073] Among them, ξ m represents the task offloading ratio of the mth vehicle to the infrastructure communication user, C m Indicates the number of CPU cycles required to execute the task, f local represents the local computing capacity of the vehicle, k V Represents the computing energy consumption coefficient related to chip performance.

[0074] When the task vehicle unloads the task to the roadside unit, the unloading delay and energy consumption It is expressed as:

[0075]

[0076] Among them, V m represents the amount of computing task data, R m represents the achievable data rate for vehicle-to-infrastructure communication user m, P m represents the transmission power of the mth vehicle to the infrastructure communication user, P rsu represents the computing power of the roadside unit, f rsuIndicates the computing capability of the roadside unit.

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

[0078]

[0079]

[0080] Among them, s k represents the mode selection of the kth vehicle-to-vehicle communication user pair, a m,n represents the association status of the mth vehicle-to-infrastructure communication user and the nth roadside unit, Indicates the association status between the vehicle and the roadside unit in the relay mode, P m represents the transmission power of the mth vehicle to the infrastructure communication user, represents the rate of the kth car-to-car communication user pair, ξ m represents the task offloading ratio of the mth vehicle to the infrastructure communication user, C m Indicates the number of CPU cycles required to execute the task, f local Represents the local computing power of the vehicle, V m represents the amount of computing task data, R m represents the rate of the mth vehicle-to-infrastructure communication user, f rsu Indicates the computing power of the roadside unit, represents the local computing energy consumption of the mth vehicle-to-infrastructure communication user, represents the local offloading computational energy consumption of the mth vehicle-to-infrastructure communication user, E max Indicates the maximum energy consumption of task calculation, represents the transmission power of the kth vehicle-to-vehicle communication user pair, P max represents the maximum transmission power of the vehicle to the infrastructure user, B m represents the bandwidth of the mth vehicle-to-infrastructure communication user, represents the bandwidth of the kth vehicle-to-vehicle communication user pair in relay mode, represents the data rate that can be achieved by k users in vehicle-to-vehicle communication in direct communication mode, represents the bandwidth of the kth vehicle-to-vehicle communication user pair in direct connection mode, represents the maximum bandwidth of vehicle-to-vehicle communication, represents the data rate achievable for vehicle-to-vehicle communication in relay mode.

[0081] S3. Use variable relaxation, continuous convex approximation, alternating optimization and variable substitution to transform the mixed integer nonlinear programming resource allocation model into a convex optimization model:

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

[0083]

[0084] Based on the above transformation, the problem is transformed into:

[0085]

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

[0087] Optimal mode selections k It can be determined as follows: for the kth vehicle-to-vehicle communication user pair, if Then the kth vehicle-to-vehicle communication user pair works in relay mode, otherwise it chooses 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 RSU selection sub-problem is:

[0089]

[0090] sC 2 ,C 4 -C 9 ,C 11

[0091] Due to the tight coupling between variables and the presence of binary variables This problem is non-convex. To make it easier to handle, based on the variable relaxation method and variable substitution method, the constraint C 7 It can be equivalently transformed into:

[0092]

[0093] Binary variables is relaxed to a continuous variable on the interval [0,1] and defined as:

[0094]

[0095] The following joint bandwidth allocation and user and roadside unit selection subproblem is obtained:

[0096]

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

[0098] The joint unloading and power control sub-problem is:

[0099]

[0100] sC 1 -C 3 ,C 9 -C 11 ,

[0101] Introducing slack variables θ m And meet:

[0102]

[0103] Introducing variable λ m Replace θ m P m , using continuous convex approximation technology to Approximately a convex function Based on the above transformation, the following joint task offloading and power allocation sub-problem is obtained:

[0104]

[0105] in:

[0106]

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

[0108] S4, Fixed mode selection, power and partial unloading factor The CVX toolbox is used to calculate the joint bandwidth allocation and user and roadside unit selection convex subproblem:

[0109]

[0110] get

[0111] S5, Fixed The convex subproblem of joint task offloading and power allocation is calculated using the CVX toolbox:

[0112]

[0113] Among them, Get {μ}, {ξ m};

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

[0115] S7, judging whether the total delay of task calculation based on partial task offloading of the networked cooperative driving communication system converges, specifically: when the total delay of task calculation based on partial task offloading of the networked cooperative driving communication system in the d-th iteration satisfies |T(d)-T(d-1)|≤ω, it converges; otherwise, it does not converge, and enters S8;

[0116] S8. Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output the optimal task calculation total delay T * , then end, otherwise, update the current iteration number d, then enter the next iteration and return to S4.

[0117] The application effect of the present invention is described in detail in combination with simulation, as follows:

[0118] Simulation conditions: Assume that the vehicle-to-roadside unit user path loss model is in, Represents the distance between the vehicle and the roadside unit. The vehicle-to-vehicle communication user path loss model is: in is the distance between vehicles.

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

[0120] Table 1

[0121]

[0122] See also Figure 3 , which is a comparison diagram of a spectrum and energy limited networked cooperative driving communication system resource allocation method according to an embodiment of the present invention and a traditional method. Figure 3 It can be seen that compared with the 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 vehicle-to-roadside unit user computing delay, which confirms that the method of the present invention can effectively reduce the total vehicle-to-roadside unit user computing delay 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 solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A spectrum and energy limited network cooperative driving communication system resource allocation method, characterized in that: The following steps are involved: S1. Initialize the parameters of the networked cooperative driving communication system, including: a macro base station, N roadside units equipped with 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 a computing task, and some of the computing tasks are offloaded to the roadside unit; all communication devices use frequency division multiple access to modulate their information onto the incident signal and transmit it to the information receiver. The roadside unit is connected to the macro base station through optical fiber, and the macro base station centrally allocates subcarriers for vehicles connected to the roadside unit; S2. Based on the constraints of the vehicle-side maximum transmission 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 total delay of task calculation in the networked cooperative driving communication system is minimized as the optimization goal. Based on this goal, a resource allocation model based on partial task offloading, mode selection, and user association is constructed; S3. The mixed integer nonlinear programming resource allocation model is transformed into a convex optimization model by using methods such as variable relaxation, continuous convex approximation, alternating optimization and variable substitution, so as to obtain the optimal total task calculation delay.

2. The method for allocating resources of a spectrum and energy limited networked cooperative driving communication system according to claim 1, characterized in that: In S1: the parameters of the networked 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, the noise variance at the roadside unit Noise variance at the receiver of a vehicle-to-vehicle user pair The size of the safety information sent by the car-to-car user pair is L k , the maximum tolerable delay T for a vehicle-to-vehicle user max , maximum transmission power of vehicle-to-vehicle users Maximum transmission power P of vehicle to infrastructure users max , the maximum bandwidth occupied by car-to-car users The maximum bandwidth occupied by vehicles to infrastructure users is B max , total task calculation delay T, maximum number of iterations D max , convergence accuracy ω and number of iterations d.

3. The method for allocating resources in a spectrum and energy limited networked cooperative driving communication system according to claim 2, characterized in that: In S2: the resource allocation model based on partial task offloading, mode selection, and user association is constructed, specifically: Among them, s k represents the mode selection of the kth vehicle-to-vehicle communication user pair, a m,n represents the association status of the mth vehicle-to-infrastructure communication user and the nth roadside unit, Indicates the association status between the vehicle and the roadside unit in the relay mode, P m represents the transmission power of the mth vehicle to the infrastructure communication user, represents the rate of the kth car-to-car communication user pair, ξ m represents the task offloading ratio of the mth vehicle to the infrastructure communication user, C m It is the number of CPU cycles required to execute the task, f local Represents the local computing power of the vehicle, V m represents the amount of computing task data, R m represents the rate of the mth vehicle-to-infrastructure communication user, f rsu Indicates the computing power of the roadside unit, represents the local computing energy consumption of the mth vehicle-to-infrastructure communication user, represents the local offloading computational energy consumption of the mth vehicle-to-infrastructure communication user, E max Indicates the maximum energy consumption of task calculation, represents the transmission power of the kth vehicle-to-vehicle communication user pair, P max represents the maximum transmission power of the vehicle to the infrastructure user, B m represents the bandwidth of the mth vehicle-to-infrastructure communication user, represents the bandwidth of the kth vehicle-to-vehicle communication user pair in relay mode, represents the data rate that can be achieved by k users in vehicle-to-vehicle communication in direct communication mode, represents the bandwidth of the kth vehicle-to-vehicle communication user pair in direct connection mode, represents the maximum bandwidth of vehicle-to-vehicle communication, represents the data rate achievable for vehicle-to-vehicle communication in relay mode.

4. The method for allocating resources in a spectrum and energy limited networked cooperative driving communication system according to claim 3, characterized in that: The S3 further includes the following steps: The variable relaxation method is used to transform the non-convex objective function 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. There are still coupling variables and binary variables in the objective function and constraints of this optimization problem, and it is still a non-convex optimization problem.

5. The method for allocating resources of a spectrum and energy limited networked cooperative driving communication system according to claim 4, characterized in that: The S3 further includes the following steps: S31. Fixed mode selection factor s k , Vehicle transmission power Partial unloading factor ξ m , using variable substitution method, variable relaxation method and alternating optimization method to transform the mixed integer nonlinear programming problem model into a convex optimization problem model, and calculate the vehicle bandwidth User and RSU selection factors Slack variable μ; S32.Fixed The mixed integer nonlinear programming problem model is transformed into a convex optimization problem model by 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 Slack variable μ; S33. Based on the relaxation variable μ, update the total delay T of the task calculation based on partial task offloading of the networked cooperative driving communication system; S34. Determine whether the total delay of task calculation based on partial task offloading of the networked cooperative driving communication system converges; if so, output the optimal total delay of task calculation based on partial task offloading of the networked cooperative driving communication system T * , then end; otherwise, enter S35; S35. Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output T * , then end, otherwise, update the current iteration number d, then enter the next iteration, and return to S31.

6. The method for allocating resources in a spectrum and energy limited networked cooperative driving communication system according to claim 5, characterized in that: In S31, the optimal mode selection strategy is specifically: For the kth car-to-car communication user pair, if Then the kth car-to-car communication user pair works in relay mode, otherwise it chooses direct communication mode; constraint C7 is equivalently transformed to Binary variables is relaxed to a continuous variable on the interval [0,1] and defines and The following joint bandwidth allocation and user and roadside unit selection subproblem is obtained: The joint bandwidth allocation and user and RSU selection subproblems are convex optimization problems, which are solved using the CVX Toolbox.

7. The method for allocating resources in a spectrum and energy limited networked cooperative driving communication system according to claim 5, characterized in that: In the S32: Introducing slack variables θ m and meet Introducing variable λ m Replace θ m P m , using continuous convex approximation technology to Approximately convex function Based on the above transformation, the following joint task offloading and power allocation sub-problem is obtained: in, The joint task offloading and power allocation subproblem is a convex optimization problem, which is solved using the CVX toolbox.

8. The method for allocating resources in a spectrum and energy limited networked cooperative driving communication system according to claim 5, characterized in that: In S33, the updated networked cooperative driving communication system calculates the total delay T=μ based on the task of partial task offloading.

9. The method for allocating resources in a spectrum and energy limited networked cooperative driving communication system according to claim 5, characterized in that: In S34, the judgment on whether the total delay of task calculation based on partial task offloading of the networked cooperative driving communication system converges is specifically as follows: when the total delay of task calculation based on partial task offloading of the d-th iteration networked cooperative driving communication system satisfies |T(d)-T(d-1)|≤ω, it converges, otherwise it does not converge.

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