Energy efficiency optimization method for power-limited networked cooperative driving communication system

Abstract

The invention relates to a power-limited networked cooperative driving communication system energy efficiency optimization method, and belongs to the technical field of Internet of Vehicles. Aiming at the problems of scarcity of spectrum resources of the Internet of Vehicles, serious co-frequency interference, insufficient guarantee of vehicle-to-vehicle communication security requirements and channel uncertainty, a resource allocation scheme based on rate segmentation multiple access is provided. The method comprises the following steps: initializing system parameters; constructing an optimization model which takes vehicle-road communication energy efficiency maximization as a target and fuses vehicle end power constraint, a base station power threshold value, a vehicle-vehicle communication minimum rate and channel uncertainty; a worst criterion is adopted to process channel errors, variable replacement, logarithm approximation and relaxation methods are combined to convert mixed integer nonlinear programming into convex optimization problem solving, and the transmitting power, the rate segmentation proportion and sub-channel allocation are dynamically optimized. According to the method, the spectrum utilization rate and the interference management capability are remarkably improved, the vehicle-to-vehicle communication safety requirement is ensured, and breakthrough optimization of the system energy efficiency in a complex scene is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle networking, and relates to an energy efficiency optimization method for a power-constrained networked cooperative driving communication system. Background Art

[0002] With the rapid development of intelligent transportation systems, vehicle networking communication technology has become a key support for achieving efficient cooperation between vehicles and between vehicles and infrastructure. This technology significantly improves road safety and traffic efficiency by real-time exchanging safety information and entertainment data. However, the large-scale deployment of vehicle networking faces severe challenges: the spectrum resources are highly scarce and it is difficult to meet the communication needs of a large number of devices. Traditional orthogonal multiple access technologies adopt static resource allocation strategies, with low spectrum utilization rate, which easily leads to communication congestion and deterioration of service quality; while non-orthogonal multiple access technologies can improve spectrum efficiency, but their interference management capabilities are limited and it is difficult to adapt to the dynamic topology environment of vehicle networking.

[0003] In recent years, rate splitting multiple access technology has attracted attention due to its flexible interference processing mechanism. This technology designs a precoding rate splitting strategy at the transmitter and adopts a successive interference cancellation mechanism at the receiver, which can theoretically significantly improve the system capacity. However, existing vehicle networking solutions based on rate splitting multiple access have two major defects: firstly, they only focus on co-frequency access within a single frequency band and do not consider the scenario of multi-subchannel multiplexing, resulting in increased cross-channel interference; secondly, they ignore the security information transmission requirements of vehicle-to-vehicle communication links and do not establish a vehicle-to-vehicle communication service quality guarantee mechanism, which may endanger driving safety.

[0004] In addition, the high-speed mobile characteristics of vehicle networking lead to inaccurate acquisition of channel state information, and the performance of traditional resource allocation methods degrades sharply in an environment of channel uncertainty. At the same time, the power limitation of vehicle terminals and the base station transmission power constraint further restrict the improvement of system energy efficiency. Existing energy efficiency optimization solutions lack joint consideration of power constraints, channel uncertainty, and vehicle-to-vehicle communication service quality requirements, and it is difficult to achieve a balance between energy efficiency and reliability in a complex vehicle networking environment.

[0005] Therefore, there is an urgent need to develop a robust resource allocation method for power-constrained scenarios to maximize the energy efficiency of vehicle-road communication while ensuring the safety requirements of vehicle-to-vehicle communication. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide an energy efficiency optimization method for a power-constrained connected cooperative driving communication system. Aiming at the problem of power constraint in the vehicle-to-everything (V2X) network, considering constraints such as vehicle transmit power threshold, base station transmit power threshold, vehicle-to-vehicle (V2V) communication minimum rate, rate splitting, and channel uncertainty, a network model of the V2X communication system is established with the maximization of vehicle-road communication energy efficiency as the optimization objective. The non-convex optimization allocation model is transformed into a convex optimization model for solution by using methods such as the worst-case criterion, variable relaxation, logarithmic approximation, and variable substitution.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An energy efficiency optimization method for a power-constrained connected cooperative driving communication system, the method specifically includes the following steps:

[0009] S1: Initialize the parameters of the V2X communication system;

[0010] The V2X communication system includes a base station, N orthogonal sub-channels, K V2V communication user pairs, and M vehicle-road communication users; M n represents the number of vehicle-road communication users on sub-channel n, and M n ≤M, The base station uses rate splitting multiple access technology to serve vehicle-road communication users on each sub-channel; each V2V communication user pair consists of a V2V communication transmitter and a V2V communication receiver, and the transmitter sends safety information to the receiver. Each vehicle-to-vehicle communication user reuses the sub-channel of the vehicle-road user for communication;

[0011] S2: Construct a resource allocation model based on rate splitting multiple access with the maximization of vehicle-road communication energy efficiency as the optimization objective according to constraints such as vehicle transmit power threshold, base station transmit power threshold, vehicle-to-vehicle communication minimum rate, rate splitting, and channel uncertainty;

[0012] S3: Use methods such as the worst-case criterion, variable relaxation, logarithmic approximation, and variable substitution to transform the mixed-integer non-linear programming resource allocation model into a convex optimization model for solution, so as to obtain the optimal vehicle-road communication energy efficiency.

[0013] Further, in S1, the parameters of the V2X communication system include: the transmit power of the private stream of the vehicle-road communication user the transmit power p of the public stream of the vehicle-road communication user n,c the transmit power of the vehicle-to-vehicle communication user the circuit power consumption P c the rate splitting ratio sub-channel selection the channel of vehicle-road communication user m the channel of vehicle-to-vehicle communication user k Interference channel from vehicle-to-vehicle communication user k to vehicle-to-road communication user m Interference channel from base station to vehicle-to-vehicle communication user k Vehicle transmit power threshold Base station transmit power threshold P max , Noise variance at the receiver of vehicle-to-road communication user Noise variance at the receiver of vehicle-to-vehicle communication user Total available system bandwidth B total , Minimum throughput of vehicle-to-vehicle user Maximum number of iterations L max , Convergence accuracy ε and number of iterations l.

[0014] Furthermore, the resource allocation model based on rate splitting multiple access constructed in S2 is as follows:

[0015]

[0016] Wherein, Represents the data rate of vehicle-to-road communication user m on sub-channel n, Is the splitting ratio of the common rate; Represents the total power consumption; Represents the private rate at which vehicle-to-road communication user m decodes the private signal on sub-channel n, and Is the equivalent channel, Is the equivalent noise, B = B total / N is the bandwidth of each sub-channel; Is the upper bound of the rate at which the base station transmits the common signal; Represents the common rate at which vehicle-to-road communication user m decodes the common signal on sub-channel n, and Represents the data rate of the kth vehicle-to-vehicle communication user, and The channel uncertainty set is And Respectively represent the estimated channel gains, And Represents the corresponding estimation error; And Respectively represent the upper bounds of these estimation errors.

[0017] Furthermore, in S3, the mixed integer non-linear programming problem is transformed into the following convex problem by using the worst-case criterion method, variable substitution method, variable relaxation method, and logarithmic approximation method:

[0018]

[0019] Wherein, And Are slack variables,​

[0020] Furthermore, S3 specifically includes the following steps:

[0021] S31: Convert the mixed-integer non-linear programming problem model into a deterministic optimization problem model using the worst-case criterion method;

[0022] S32: Convert the non-convex optimization problem model into a convex optimization problem using variable substitution method, variable relaxation method, and logarithmic approximation method, and calculate the transmission power p n,c , rate splitting ratio sub-channel selection factor

[0023] S33: Update the total energy efficiency η of the vehicle-road users in the vehicle-to-everything communication system based on the obtained optimal variables;

[0024] S34: Determine whether the total energy efficiency of the vehicle-road users in the vehicle-to-everything communication system converges; if so, output the optimal total energy efficiency η of the vehicle-road users in the vehicle-to-everything communication system * , and then end; otherwise, enter S35;

[0025] S35: Determine whether the current iteration number is greater than the maximum iteration number; if so, output η * , and then end, otherwise, update the current iteration number l, and then enter the next iteration, and return to S31.

[0026] Furthermore, in S31, based on the worst-case criterion and the Dinkelbach method, the objective function is transformed into where Constraint C4 is transformed into where Constraint C5 is transformed into where Based on the above transformation, the following deterministic optimization problem is obtained:

[0027]

[0028] Furthermore, in S32, use the variable substitution method, variable relaxation method, and logarithmic approximation method to transform the non-convex optimization problem model into a convex optimization problem; The lower bound of where Similarly, The lower bound of where The lower bound of where Based on the above transformation, the following convex problem is obtained:

[0029]

[0030] Solve directly using the CVX toolbox.

[0031] Furthermore, in S33, update the total energy efficiency of the vehicle-road users in the vehicle networking communication system as:

[0032] Furthermore, in S34, determine whether the total energy efficiency of the vehicle-road users in the vehicle networking communication system converges. Specifically: when the total energy efficiency of the vehicle-road users in the vehicle networking communication system in the l-th iteration satisfies |η(l) - η(l - 1)| ≤ ε, it converges; otherwise, it does not converge.

[0033] The beneficial effects of the present invention are as follows:

[0034] (1) Adopt the rate splitting multiple access technology to perform intelligent precoding design on the common stream and private stream of vehicle-road communication users at the base station side, and hierarchically decode signals through the serial interference cancellation mechanism at the receiving end. Combine the multi-subchannel dynamic multiplexing architecture to allow multiple vehicle-to-vehicle communication users to share the vehicle-road subchannel resources, break through the spectral efficiency bottleneck of traditional orthogonal access, and significantly alleviate the spectrum scarcity problem in vehicle networking.

[0035] (2) By jointly optimizing the common stream power allocation, private stream power splitting ratio, and subchannel selection factor, precisely coordinate the co-frequency and cross-channel interference between the vehicle-road communication and vehicle-to-vehicle communication links. The rate splitting mechanism converts the interference signal into a decodable information stream, turning passive suppression into active utilization, and realizing flexible control of interference in a complex dynamic topology environment.

[0036] (3) For the first time, incorporate the minimum rate requirement of vehicle-to-vehicle communication users as a hard constraint into the optimization model to ensure the quality of service for secure information transmission. Deal with channel uncertainty through robust modeling, and use the worst-case criterion method to construct the equivalent channel gain error boundary, so that the resource allocation scheme can still meet the reliability threshold of vehicle-to-vehicle communication under channel estimation deviation.

[0037] (4) Aim at maximizing the total energy efficiency of vehicle-road communication, and jointly optimize the transmit power, rate splitting ratio, and subchannel allocation. Use variable substitution, logarithmic approximation, and relaxation methods to transform the non-convex mixed integer programming into a convex optimization problem that can be efficiently solved, and achieve global energy efficiency optimization under strict power constraints, extend the battery life of vehicle terminals, and reduce the energy consumption of the base station.

[0038] (5) The designed robust algorithm can be compatible with the channel time-varying characteristics caused by the high-speed mobility of vehicle networking, and dynamically track the network state changes through the iterative update mechanism. The scheme remains stable under multiple challenges of channel uncertainty, power constraints, and multi-service quality of service requirements, providing universal communication support for large-scale networked cooperative driving..

[0039] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0041] Figure 1 is a model diagram of the vehicle networking communication system in the present invention;

[0042] Figure 2 is a flowchart of an energy efficiency optimization method for a power-constrained vehicle networking communication system in the present invention;

[0043] Figure 3 is a comparison diagram of an energy efficiency optimization method for a power-constrained vehicle networking communication system in the present invention and a traditional method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following specific examples illustrate the embodiments 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 also 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 diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0045] Among them, the accompanying drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation to the present invention; in order to better illustrate the embodiments of the present invention, some components in the accompanying drawings will be omitted, enlarged, or reduced, and do not represent the dimensions of actual products; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the accompanying drawings may be omitted.

[0046] 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 there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the accompanying drawings. This 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 positional relationship in the accompanying drawings are only for illustrative purposes and should not be construed as a limitation of the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0047] Please refer to Figures 1 to 3 , the present invention provides an energy efficiency optimization method based on rate-splitting multiple access for a vehicle-to-everything (V2X) communication system. As shown in Figure 1 , the number of orthogonal sub-channels is N, the number of vehicle-to-vehicle (V2V) communication user pairs is K, and the number of vehicle-to-roadside (V2I) communication users is M. There are M n V2I communication users on sub-channel n; the base station uses rate-splitting multiple access technology to serve V2I communication users on each sub-channel; each V2V communication user pair consists of a V2V communication transmitter and a V2V communication receiver. The transmitter sends security information to the receiver, and each V2V communication user reuses the sub-channel of the V2I user for communication.

[0048] As shown in Figure 2 , the method specifically includes the following steps:

[0049] S1: Initialize the parameters of the V2X communication system;

[0050] Among them, the parameters of the V2X communication system include: the transmit power of the private stream of the V2I communication user the transmit power of the public stream of the V2I communication user p n,c , the transmit power of the V2V communication user the circuit power consumption P c , the rate-splitting ratio sub-channel selection the channel of the V2I communication user m the channel of the V2V communication user k the interference channel from the V2V communication user k to the V2I communication user m the interference channel from the base station to the V2V communication user k the transmit power threshold of the vehicle terminal the transmit power threshold of the base station P max , the noise variance at the receiver of the V2I communication user the noise variance at the receiver of the V2V communication user the total available bandwidth B of the system total the minimum throughput of the vehicle-to-vehicle user The maximum number of iterations L max , the convergence accuracy ε, and the number of iterations l.

[0051] S2: According to the constraints such as the vehicle - end transmission power threshold, the base - station transmission power threshold, the minimum vehicle - to - vehicle communication rate, rate splitting, and channel uncertainty, with the maximization of vehicle - to - road communication energy efficiency as the optimization objective, a resource allocation model based on rate - splitting multiple access is constructed. In step S2, the downlink signal of the m - th vehicle - to - road communication user is expressed as where p n,c and represent the transmission signals of the common rate and the private rate on sub - channel n respectively, represents the channel coefficient from the base station to the m - th vehicle - to - road communication user, represents the channel coefficient from the k - th vehicle - to - vehicle communication user to the m - th vehicle - to - road communication user, and s k represent the transmission power of the k - th vehicle - to - vehicle communication user on sub - channel n and the secure message it sends respectively. Let represent the sub - channel selection factor of the k - th vehicle - to - vehicle communication user. When , it means that the n - th sub - channel is reused by the k - th vehicle - to - vehicle communication user; otherwise, represents the additive white Gaussian noise received by the m - th vehicle - to - road communication user, with a mean of 0 and a variance of The common rate at which the m - th vehicle - to - road communication user decodes the common signal s n,c is where B = B total / N. After decoding the common signal s n,c , it is removed from the received signal using serial interference cancellation technology, and then decoded under the condition of treating other private signals as noise Therefore, the private rate at which the m - th vehicle - to - road communication user decodes its private signal is In addition, to ensure that all vehicle - to - road communication users on sub - channel n can successfully decode the target information in the common signal s n,c , there is an upper bound for the common rate Let represent the share of the common information rate allocated to the m - th vehicle - to - road communication user, then there is The received signal of the k - th vehicle - to - vehicle communication user is where, represents the additive white Gaussian noise received by the k - th vehicle - to - vehicle communication user, with a mean of 0 and a variance of The data rate of the k - th vehicle - to - vehicle communication user on sub - channel n is Therefore, the data rate of the k - th vehicle - to - vehicle communication user is For simplicity, it can be equivalently written as where Therefore, the total energy efficiency of vehicle-road users is where, and represents the data rate of vehicle-road communication user m; represents the total power consumption, where P c is the circuit power consumption. Similarly, is written as is written as where Due to the dynamic topology and channel delay in the vehicle network, it is challenging to obtain accurate channel state information. Based on the bounded channel uncertainty model, we have where, represents the uncertainty set, and respectively represent the estimated channel gains, and represent the corresponding estimation errors. and respectively represent the upper bounds of these estimation errors.

[0052] Furthermore, in step S2, the resource allocation model based on rate-splitting multiple access is constructed as:

[0053]

[0054] S3: Using methods such as the worst-case criterion, logarithmic approximation, variable relaxation, and variable substitution, the mixed-integer non-linear programming resource allocation model is transformed into a convex optimization model. Based on the worst-case criterion and the Dinkelbach method, the objective function can be transformed into where C4 is transformed into where C5 is transformed into where Based on the above transformation, the following deterministic optimization problem is obtained:

[0055]

[0056] Based on the logarithmic approximation, variable relaxation, and variable substitution methods, is transformed into where Similarly, is transformed into where is transformed into where Based on the above transformation, the following convex problem is obtained:

[0057]

[0058] It can be directly solved using the CVX toolbox.

[0059] S4: Fix the slack variable η(l), and use the CVX toolbox to calculate the convex problem:

[0060]

[0061] Obtain p n,c (l + 1),

[0062] S5: Through and log2(1 + r) ≥ αlog2(r) + β, Update

[0063] S6: Based on Update the total energy efficiency η of the vehicle-road communication based on rate-splitting multiple access in the vehicle networking communication system;

[0064] S7: Determine whether the total energy efficiency of the vehicle-road communication based on rate-splitting multiple access in the vehicle networking communication system converges. Specifically: when the total energy efficiency of the vehicle-road communication based on rate-splitting multiple access in the l-th iteration satisfies |η(l) - η(l - 1)| ≤ ε, it converges; otherwise, it does not converge and enters S8;

[0065] S8: Determine whether the current iteration number is greater than the maximum iteration number; if so, output η * , and then end; otherwise, update the current iteration number l, then enter the next iteration, and return to S4.

[0066] The application effect of the present invention will be described in detail below with reference to simulations.

[0067] Simulation conditions: The user path loss model for vehicle-to-roadside unit is where represents the distance between the vehicle and the roadside unit. The user path loss model for vehicle-to-vehicle communication is where is the distance between vehicles. Other simulation parameters are given in Table 1.

[0068] Table 1 Simulation parameter table

[0069]

[0070] Simulation results: In this simulation experiment, from Figure 3 It can be seen that as Pmax With the increase of max , the total energy efficiency of vehicle-road communication users first rises and then stabilizes, indicating that when the transmit power exceeds a certain threshold, further increasing the power will not bring additional energy efficiency improvement. The method of the present invention is superior to the resource allocation method based on non-orthogonal multiple access, and its advantage is attributed to the high-flexibility interference management ability brought by rate splitting. In addition, the resource allocation method based on orthogonal multiple access has the lowest energy efficiency, which is due to its relatively low spectral efficiency itself.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An energy efficiency optimization method for a power-constrained networked cooperative driving communication system, characterized in that: The method specifically includes the following steps: S1: Initialize the parameters of the vehicle - to - everything (V2X) communication system; The vehicle-to-everything (V2X) communication system includes a base station, N orthogonal sub-channels, K vehicle-to-vehicle (V2V) communication user pairs, and M vehicle-to-roadside (V2R) communication users; M n represents the number of V2R communication users on sub-channel n, and M n ≤ M, The base station uses rate-splitting multiple access (RSMA) technology to serve V2R communication users on each sub-channel; each V2V communication user pair consists of a V2V communication transmitter and a V2V communication receiver, and the transmitter sends safety information to the receiver. Each vehicle-to-vehicle communication user reuses the sub-channel of the V2R user for communication; S2: Construct a resource allocation model based on rate - splitting multiple access with the maximization of vehicle - to - road communication energy efficiency as the optimization objective according to constraints such as vehicle - side transmission power threshold, base - station transmission power threshold, vehicle - to - vehicle communication minimum rate, rate splitting, and channel uncertainty; S3: Use methods such as the worst - case criterion, variable relaxation, logarithmic approximation, and variable substitution to transform the mixed - integer non - linear programming resource allocation model into a convex optimization model for solution, so as to obtain the optimal vehicle - to - road communication energy efficiency.

2. The energy efficiency optimization method for a power-constrained networked cooperative driving communication system according to claim 1, characterized in that: In S1, the parameters of the vehicle-to-everything (V2X) communication system include: the transmission power of the private flow of the vehicle-road communication user The transmission power p of the public flow of the vehicle-road communication user n,c , the transmission power of the vehicle-to-vehicle communication user The circuit power consumption P c , the rate splitting ratio Sub-channel selection The channel of the vehicle-road communication user m The channel of the vehicle-to-vehicle communication user k The interference channel from the vehicle-to-vehicle communication user k to the vehicle-road communication user m The interference channel from the base station to the vehicle-to-vehicle communication user k The transmission power threshold of the vehicle terminal The transmission power threshold P of the base station max , the noise variance at the receiver of the vehicle-road communication user The noise variance at the receiver of the vehicle-to-vehicle communication user The total available bandwidth B of the system total , the minimum throughput of the vehicle-to-vehicle user The maximum number of iterations L max , the convergence accuracy ε and the number of iterations l.

3. The energy efficiency optimization method for a power-constrained networked cooperative driving communication system according to claim 2, characterized in that: The resource allocation model based on rate - splitting multiple access constructed in S2 is: Among them, represents the data rate of vehicle-road communication user m on sub-channel n, is the splitting ratio of the common rate; represents the total power consumption; represents the private rate of vehicle-road communication user m to decode the private signal on sub-channel n, and is the equivalent channel, is the equivalent noise, B = B total / N is the bandwidth of each sub-channel; is the upper bound of the rate at which the base station transmits the common signal; represents the common rate of vehicle-road communication user m to decode the common signal on sub-channel n, and represents the data rate of the k-th vehicle-to-vehicle communication user, and The channel uncertainty set is and respectively represent the estimated channel gains, and represent the corresponding estimation errors; and respectively represent the upper bounds of these estimation errors.

4. The energy efficiency optimization method for a power-constrained connected co-driving communication system according to claim 3, characterized in that: In S3, use the worst - case criterion method, variable substitution method, variable relaxation method, and logarithmic approximation method to transform the mixed - integer non - linear programming problem into the following convex problem: wherein, and are slack variables, 5. The energy efficiency optimization method for a power-constrained connected cooperative driving communication system according to claim 4, characterized in that: S3 specifically includes the following steps: S31: Use the worst - case criterion method to transform the mixed - integer non - linear programming problem model into a deterministic optimization problem model; S32: Transform the non-convex optimization problem model into a convex optimization problem by using variable substitution method, variable relaxation method, and logarithmic approximation method, and calculate the transmit power p n,c , rate splitting ratio sub-channel selection factor S33: Based on the obtained optimal variables, update the total energy efficiency η of vehicle - to - road users in the V2X communication system; S34: Determine whether the total energy efficiency of vehicle-road users in the vehicle-to-everything (V2X) communication system converges; if so, output the optimal total energy efficiency η of the vehicle-road users in the V2X communication system, and then end; otherwise, proceed to S35; * , and then end; otherwise, proceed to S35; S35: Determine whether the current iteration count is greater than the maximum iteration count; if so, output η * , then end; otherwise, update the current iteration count l, then enter the next iteration, and return to S31.

6. The energy efficiency optimization method for a power-constrained networked cooperative driving communication system according to claim 5, characterized in that: In S31, based on the worst-case criterion and the Dinkelbach method, the objective function is transformed into where The constraint C4 is transformed into where The constraint C5 is transformed into where Based on the above transformation, the following deterministic optimization problem is obtained:

7. The method for optimizing the energy efficiency of a power-constrained networked cooperative driving communication system according to claim 5, wherein: In S32, the non-convex optimization problem model is transformed into a convex optimization problem by using variable substitution method, variable relaxation method, and logarithmic approximation method; The lower bound of where Similarly, The lower bound of where The lower bound of where Based on the above transformation, the following convex problem is obtained: Solve directly using the CVX toolbox.

8. The energy efficiency optimization method for a power-constrained networked cooperative driving communication system according to claim 5, characterized in that: In S33, update the total energy efficiency of the vehicle-road users in the vehicle networking communication system to be:

9. The energy efficiency optimization method for a power-constrained networked cooperative driving communication system according to claim 5, characterized in that: In S34, determine whether the total energy efficiency of vehicle - to - road users in the V2X communication system converges. Specifically, when the total energy efficiency of vehicle - to - road users in the V2X communication system in the l - th iteration satisfies |η(l)-η(l - 1)|≤ε, it converges; otherwise, it does not converge.

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