A non-cooperative game congestion control method in D2D mode of Internet of Vehicles

Abstract

The present invention discloses a non-cooperative game congestion control method in a vehicle-to-vehicle (V2V) communication mode, comprising the following steps: establishing a highway mobile scenario, wherein the V2V communication link uses a dedicated frequency band for communication; establishing a wireless channel transmission model and a V2V communication interference model; the vehicle collects information on other vehicle nodes within the sensing range and establishes a neighbor node information table; measures the channel busy ratio (CBR); based on the neighbor node information table and the CBR, establishes a vehicle utility function and constructs an optimal power control model; based on the optimal solution of the utility function, dynamically adjusts the vehicle transmission power in a distributed manner to alleviate channel congestion. The present invention establishes a power control model based on a non-cooperative game, comprehensively considering the transmission status information of the surrounding vehicle nodes and the channel load conditions, and realizes collaborative decision-making and distributed congestion control. By dynamically adjusting the optimal transmission power, the packet reception rate is improved without reducing the system throughput, which can effectively alleviate channel congestion.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications and vehicle networking, and in particular to a non-cooperative game congestion control method in a vehicle networking D2D mode. Background Art

[0002] As a road safety solution, V2X (vehicle-to-everything) technology has become a mainstream solution to current traffic safety issues due to its safe and stable performance. C-V2X can be categorized into two communication modes, V2X-Cellular and V2X-Direct, based on their interfaces. V2X-Cellular forwards traffic via the cellular network's Uu interface, using cellular frequency bands. V2X-Direct, on the other hand, uses the PC5 interface and dedicated frequency bands for the connected vehicle (V2X) network, enabling direct communication between vehicles, between roads, and between people. This approach offers low latency and supports high mobile speeds, but requires robust resource allocation and congestion control algorithms.

[0003] In scenarios such as on highways or rural roads, vehicles are often in frequent cell switching or out of base station coverage, making centralized resource scheduling difficult. Therefore, direct communication based on the PC5 interface is a necessary communication method for V2V communication.

[0004] Due to the limited bandwidth of V2V-dedicated frequency bands and the complex and ever-changing driving environment, wireless resources are prone to link congestion when channel load increases, thus affecting driving safety. To improve data transmission quality and effectively utilize limited wireless resources, congestion control of wireless links is necessary.

[0005] The 3GPP standard introduces the Channel Busyness Ratio (CBR) into V2V congestion control as a parameter for evaluating channel congestion levels. Currently, most V2V congestion control solutions for LTE-V2X Mode 4 / NR-V2X Mode 2 are fully distributed. Each vehicle node adjusts communication parameters based on its own assessment of channel conditions and in accordance with a unified standard. This approach fails to comprehensively consider information about surrounding vehicles and fairness in congestion control, making it impossible to achieve overall system optimization. Therefore, it is crucial to share information between vehicles, integrate node information and channel status in each scenario, and study distributed congestion control strategies from the perspective of system optimization. For example, the "Congestion Control Method and Vehicle-Mounted Terminal" disclosed by Xu Jun et al. in Chinese Invention Publication Patent CN113473529A obtains the usage status of each sub-channel on the direct communication frequency band of the Internet of Vehicles, and determines whether there is channel congestion based on the usage status. If so, the method reduces its own transmission power and sends a group power control message to other vehicle-mounted terminals in the Internet of Vehicles, so that the other vehicle-mounted terminals reduce their respective transmission powers after receiving the group power control message. Although congestion control can be achieved, it does not take into account the information of other nodes in the communication environment. The node adjusts the communication parameters completely according to the CBR value measured by itself, and does not consider the fairness of power control of each node in the communication environment. Each node increases or decreases its own transmission power according to a unified standard. Summary of the Invention

[0006] The purpose of the present invention is to remedy the defects in the prior art and provide a non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles, aiming to alleviate channel congestion and improve the communication performance of the system.

[0007] To achieve the purpose of the present invention, the present invention provides a non-cooperative game congestion control method in a D2D mode of an Internet of Vehicles, the main steps of which include:

[0008] S1. Establish a highway mobility scenario under LTE-V2X Mode 4 / NR-V2X Mode 2. The V2V communication link uses a dedicated frequency band for communication, and each terminal independently selects resources.

[0009] S2. Establish a wireless channel transmission model and a V2V communication interference model, and calculate the received signal power and signal-to-interference-and-noise ratio at different communication distances;

[0010] S3. The vehicle collects information about other vehicle nodes within its sensing range, including link loss and interference between vehicles, establishes a neighbor node information table, and continuously updates the neighbor node information table.

[0011] S4. Evaluate the channel load condition by measuring the channel busy ratio CBR;

[0012] S5. Based on the neighbor node information table and CBR, establish the vehicle utility function and construct the optimal power control model;

[0013] S6. Dynamically adjust the vehicle transmission power according to the optimal solution of the utility function.

[0014] Furthermore, in step S1, a two-way multi-lane highway mobility model is established, the vehicle terminal uses the PC5 interface for data interaction, uses the 5.9GHz dedicated frequency band for communication, the vehicle performs autonomous resource scheduling, and accesses resources through perception, selection and reservation.

[0015] Furthermore, the specific process of step S2 is as follows:

[0016] S21. Establishing a wireless channel transmission model

[0017] Considering path loss and shadow fading, a wireless channel transmission model is established based on the WINNER+B1 model to calculate the signal received power at the receiving end.

[0018] Each vehicle periodically sends CAM information, which is received by all surrounding vehicles within the broadcast range. i The transmission power is expressed as P t，i，dBm , receiving vehicle V j The received signal power P at r，j，dBm The calculation formula is as follows:

[0019] P r，j，dBm =P t，i，dBm -(PL i，j，dB +SH i，j，dB )

[0020] Among them, PL i，j，dB Indicates the transmitter V i To the receiving end V j The path loss between them and the attenuation caused by obstacles are expressed as SH i，j，dB To express.

[0021] S22. Establishing a V2V communication interference model

[0022] Considering whether the data packets sent by the interference source can be effectively received, interference is divided into interference within the communication range and interference outside the communication range. Interference within the communication range is interference caused by vehicles using the same resources to broadcast CAM information, while interference outside the communication range is interference caused by the accumulation of data packets that cannot be effectively received due to low transmission power or long distance.

[0023] Receiving vehicle V j The signal-to-noise ratio γ i，j The calculation formula is as follows:

[0024]

[0025] Where N0 is the noise power at the receiving end, P r，x，dBm V j The power of the interference signal emitted by the xth transmitter within the communication range, N1 is the number of interfering nodes within the communication range, P r，y，dBm V j The power of the interference signal emitted by the yth transmitter outside the communication range, and N2 is the number of interfering nodes outside the communication range.

[0026] Furthermore, in step S3, the transmitting vehicle obtains the link loss and interference amount between it and all neighbor nodes within the perception range based on the wireless channel transmission model and the V2V communication interference model in step S2, establishes a local neighbor node information table based on this information, and updates the neighbor node information table before each transmission.

[0027] Furthermore, the channel busy ratio CBR in step S4 is defined as follows:

[0028]

[0029] subch total Indicates the number of all subchannels in the transmission period, ∑subch RSSI Indicates the number of subchannels whose RSSI measured by the user UE in the past 100 subframes exceeds the preconfigured threshold.

[0030] Furthermore, in step S4, the vehicle terminal measures the CBR once in each transmission cycle to evaluate the current channel status.

[0031] Furthermore, in step S5, based on the information of all neighboring nodes within the communication range, the vehicle V i The utility function of :

[0032]

[0033] Among them, B*log2(1+γ i，j ) is the Gaussian channel capacity formula, B is the channel bandwidth, n is the transmitting vehicle V i The number of all neighbor nodes. c(P t，i，dBm ) is the vehicle V i The cost function is:

[0034]

[0035] λ is the cost factor, which is a constant and is related to the channel parameters. The definition of λ is as follows:

[0036] λ=CBR*(PL i，j，dB +SH i，j，dB)

[0037] Vehicle V i The optimal power control problem, that is, the optimal power control model, is described as:

[0038]

[0039] Among them, s i For vehicle V i The strategy selected, that is, the power of the transmitter, s -i The strategy selected for other vehicles launching at the same time.

[0040] Furthermore, in step S6, the vehicles respectively reach a Nash equilibrium based on the information of neighboring nodes and the channel busyness ratio (CBR) value, and obtain the optimal transmission power; each vehicle performs power control in a distributed manner to maximize its own optimization benefits.

[0041] Furthermore, the PSO algorithm is used to solve the power control game model (optimal power control model), and the vehicle dynamically adjusts the transmission power in a distributed manner according to the optimal solution.

[0042] The present invention has the following advantages and effects compared to the prior art:

[0043] (1) The present invention discloses a non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles, which takes into account information such as link loss and interference of surrounding vehicle nodes and has the advantages of collaborative decision-making and distributed control.

[0044] (2) The present invention discloses a non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles, which considers introducing CBR and link loss into the cost function and solves the fairness problem in non-cooperative game power control.

[0045] (3) The present invention discloses a non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles. Without affecting the overall throughput of the system, it reduces the transmission power and improves the system data packet reception rate, effectively alleviates channel congestion, and ensures the reliability and stability of V2V communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a schematic diagram of a highway mobility scenario under LTE-V2X Mode 4 / NR-V2X Mode 2 disclosed in an embodiment of the present invention;

[0047] Figure 2 This is a flow chart of a non-cooperative game congestion control method in a D2D mode of an Internet of Vehicles disclosed in an embodiment of the present invention;

[0048] Figure 3Schematic diagram of the relationship between the distance between the transmitting and receiving vehicles and the data packet reception rate in an embodiment of the present invention;

[0049] Figure 4 Schematic diagram of the relationship between the distance between transmitting and receiving vehicles and throughput in an embodiment of the present invention;

[0050] Figure 5 Schematic diagram of the relationship between simulation time and measured CBR in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] like Figure 1 As shown in the figure, vehicle terminals in LTE-V2X Mode 4 / NR-V2X Mode 2 use the PC5 interface for data exchange. The PC5 interface is unaffected by network coverage and always supports V2X communication. To reduce resource selection conflicts and improve transmission reliability, vehicle terminals use the sensing-based semi-persistent scheduling algorithm (S-SPS) for autonomous resource selection. The S-SPS scheme leverages the periodic nature of V2X services and reserves resources during the sensing period to support data transmission.

[0053] Figure 2 The flowchart of the entire invention method is shown. The embodiment of the present invention provides a non-cooperative game congestion control method in a vehicle network D2D mode, which specifically includes the following steps:

[0054] S1. Establish a highway mobility scenario under LTE-V2X Mode 4 / NR-V2X Mode 2. The V2V communication link uses a dedicated frequency band for communication, and each terminal independently selects resources.

[0055] In some embodiments of the present invention, a two-way, multi-lane highway mobility model is established. Vehicle terminals use a PC5 interface for data exchange and a dedicated 5.9 GHz frequency band for communication. Vehicles perform autonomous resource scheduling and access resources through sensing, selection, and reservation. The specific process is as follows:

[0056] (1) The application scenario is a 2km long, two-way, three-lane highway, where vehicle positions are evenly distributed.

[0057] (2) The bandwidth of the dedicated frequency band for V2V communication is 10 MHz, and sub-channels are divided according to resource blocks (RBs).

[0058] (3) All vehicles in LTE-V2X Mode 4 / NR-V2X Mode 2 implement the perception-based semi-persistent scheduling protocol (SPS) to reserve resources for their transmissions. In the SPS protocol, when a vehicle node UE sends a data packet for the first time, a resource counter (RC) is allocated to it. Once the vehicle node UE selects a subchannel, the subchannel is reserved for the UE for RC transmission times. In addition, after each data packet is sent by the vehicle node UE, the RC value decreases by 1. When the value is zero, the vehicle node decides whether to retain the original resource or reselect the resource with a probability of 1-P. The probability value P can be set to any value between 0 and 0.8.

[0059] S2. Establish a wireless channel transmission model and a V2V communication interference model, and calculate the received signal power and signal-to-interference-and-noise ratio at different communication distances.

[0060] The specific process of step S2 is as follows:

[0061] (1) Establishing a wireless channel transmission model

[0062] Considering path loss and shadow fading, a channel transmission model is established based on the WINNER+B1 model to calculate the signal received power at the receiver.

[0063] Each vehicle periodically sends CAM information, which is received by all surrounding vehicles within the broadcast range. i The transmission power is expressed as P t，i，dBm , receiving vehicle V j The received signal power P at r，j，dBm The calculation formula is as follows:

[0064] P r，j，dBm =P t，i，dBm -(PL i，j，dBm +SH i，j，dBm )

[0065] Among them, PL i，j，dB Indicates the transmitter V i To the receiving end V j The path loss between them and the attenuation caused by obstacles are expressed as SH i，j，dB To express.

[0066] (2) Establishing a V2V communication interference model

[0067] Considering whether the data packets sent by the interference source can be effectively received, interference is divided into interference within the communication range and interference outside the communication range. Interference within the communication range is interference caused by vehicles using the same resources to broadcast CAM information, while interference outside the communication range is interference caused by the accumulation of data packets that cannot be effectively received due to low transmission power or long distance.

[0068] Receiving vehicle V j The signal-to-interference-noise ratio γ i，j The calculation formula is as follows:

[0069]

[0070] Where N0 is the noise power at the receiving end, P r，x，dBm V j The power of the interference signal emitted by the xth transmitter within the communication range, N1 is the number of interfering nodes within the communication range; P r，y，dBm V j The power of the interference signal emitted by the yth transmitter outside the communication range, N2 is the number of interference nodes outside the communication range, and i is the broadcast vehicle V i The subscript of .

[0071] S3. Based on the wireless channel transmission model and V2V communication interference model in step S2, the transmitting vehicle obtains the link loss and interference between it and all neighboring nodes within its sensing range, establishes a local neighbor node information table based on the link loss and interference, and updates the neighbor node information table before each transmission. The specific process is as follows:

[0072] Vehicle V i According to the surrounding neighbor nodes V j The path loss and shadow fading values ​​are calculated based on the distance between the vehicles and the local node information table. i The vehicle calculates the amount of interference between the node pairs formed with all surrounding neighbor nodes and saves this information in the local node information table.

[0073] S4. Measure the channel busy ratio (CBR) to assess the channel load. CBR is defined as follows:

[0074]

[0075] subch total Indicates the number of all subchannels within the transmission period (such as 100ms), ∑subch RSSI Indicates the number of subchannels whose RSSI measured by the user UE in the past 100 subframes exceeds the preconfigured threshold. In some embodiments of the present invention, the preconfigured RSSI threshold RSSI th =-107dBm.

[0076] In some embodiments of the present invention, since the CAM message has a transmission period of 100 ms, the vehicle terminal measures the CBR every 100 ms to evaluate the current channel status.

[0077] S5. Establish vehicle V based on the information of all neighboring nodes within the communication range. i The utility function is used to construct the optimal power control model. The specific process is as follows:

[0078] Using the complete information non-cooperative game model G{V; S1, S2, ..., S N ;u1,u2,...,u N} to describe the power control problem. Where V is all vehicles on the road, S = {S1, S2, ..., S N} is the power selection strategy set of the transmitting vehicle, N is the total number of vehicles on the road, S N The transmission power strategy selected for the Nth vehicle, u N is the utility function of the Nth vehicle, utility function u i (s i , s -i ) is used to describe the vehicle V i The total throughput obtained when the transmitter broadcasts a message is calculated as follows:

[0079]

[0080] Among them, B*log2(1+γ i，j ) is the Gaussian channel capacity formula, B is the channel bandwidth, n is the transmitting vehicle V i The number of all neighbor nodes, c(P t，idBm ) is the vehicle V i The cost function, s i For vehicle V i The strategy of choice, s -i The strategy selected for other vehicles launching at the same time.

[0081] In the non-cooperative game model, each vehicle user wants to maximize his own utility value, without considering that this behavior may reduce the utility value of other users. Therefore, the cost function c(P t，i，dBm ), imposes a certain degree of penalty on users, which increases with the increase of transmit power. This makes competition between users more rational, and users no longer blindly increase transmit power, which can improve the overall performance of the network to a certain extent.

[0082] c(P t，idBm ) is defined as follows:

[0083]

[0084] λ is the cost factor, which is a constant and is related to the channel parameters. The definition of λ is as follows:

[0085] λ=CBR*(PL i，j，dB+SH i，j，dB )

[0086] P min is the minimum transmit power required by the system.

[0087] According to the above formula, when the user's transmission power increases, the penalty will also increase, and the penalty imposed on the user will change with the CBR value and the distance between the transmitting vehicle and the receiving vehicle. j Distance from launching vehicle V i The further away you are, the greater the penalty for the vehicle's power selection.

[0088] Vehicle V i The optimal power control problem, that is, the optimal power control model, is described as:

[0089]

[0090] Among them, s i For vehicle V i The strategy selected, that is, the power of the transmitter, s -i The strategy selected for other launching vehicles at the same time, P max is the maximum transmit power required by the system.

[0091] S6. Vehicles reach a Nash equilibrium based on neighbor node information and the channel busyness ratio (CBR) value, and obtain the optimal transmission power. Each vehicle performs power control in a distributed manner to maximize its own optimization benefits.

[0092] In some embodiments of the present invention, the PSO algorithm is used to solve the optimal power control model, and the vehicle dynamically adjusts the transmission power in a distributed manner according to the optimal solution. The PSO algorithm continuously iterates to find the individual optimal position and individual optimal fitness of the particles, and finds the group optimal position and group optimal fitness by comparing the individual optimal values ​​during the iteration. The fitness function is the vehicle V i The utility function u i (s i , s -i ), the optimal solution of the group is vehicle V i The optimal transmit power that should be used.

[0093] In some embodiments of the present invention, in the performance simulation experiment, the main simulation parameters are shown in Table 1:

[0094] Table 1. Simulation parameter settings

[0095] Main parameters Configuration Values Carrier frequency 5.9GHz Number of subchannels in each subframe 3 The number of RBs in each subchannel 20 Modulation and Coding Strategy (MCS) 6 <![CDATA[Minimum transmission power P of the vehicle min > 10dBm <![CDATA[Vehicle maximum transmission power P max > 23dBm <![CDATA[Noise power N0]]> -95dBm Number of lanes 6 Lane width 4m Section length 2000m Vehicle speed 70, 100, 140 km / h Perception range 320m

[0096] In this embodiment, Figure 3 、 Figure 4 、 Figure 5 FIG. 4 is a schematic diagram comparing the present invention with a method of fixing the maximum transmit power.

[0097] Figure 3 Schematic diagram of the relationship between the distance between the transmitting and receiving vehicles and the data packet reception rate in an embodiment of the present invention. Figure 3 It can be seen that as the distance between the transmitting and receiving vehicles increases, the data packet reception rate gradually decreases. Compared with the vehicle using a fixed maximum power, the method of the present invention improves the data packet reception rate and enhances the reliability of V2V communication under congested conditions.

[0098] Figure 4 Schematic diagram of the relationship between the distance between the transmitting and receiving vehicles and the throughput in an embodiment of the present invention. Figure 4 It can be seen that as the distance between the transmitting and receiving vehicles increases, the throughput gradually decreases. Compared with the vehicle using a fixed maximum power, the method of the present invention can ensure that the throughput does not decrease after reducing the transmission power, thereby ensuring the stability of V2V communication under congestion conditions.

[0099] Figure 5 Schematic diagram of the relationship between simulation time and measured CBR in an embodiment of the present invention. Figure 5 It can be seen that compared with the vehicle using a fixed maximum power, the method of the present invention significantly reduces the channel busy ratio CBR value, effectively reduces the channel load, and alleviates the channel congestion.

[0100] The method provided in the aforementioned embodiment of the present invention establishes a non-cooperative game model, integrates neighbor node information and channel busyness ratio (CBR), and makes the selected communication parameters more optimal overall. By introducing a cost function into the non-cooperative game model and considering the impact of its own power adjustment on other nodes, the fairness problem in power control is effectively solved.

[0101] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A non-cooperative game congestion control method in a D2D mode of an Internet of Vehicles, characterized in that: The following steps are involved: S1. Establish a highway mobility scenario under LTE-V2X Mode 4 / NR-V2X Mode 2. The V2V communication link uses a dedicated frequency band for communication, and each terminal independently selects resources. S2. Establish a wireless channel transmission model and a V2V communication interference model, and calculate the received signal power and signal-to-interference-and-noise ratio at different communication distances. S3. The vehicle collects information about other vehicle nodes within its sensing range, including link loss and interference between vehicles, establishes a neighbor node information table, and continuously updates the neighbor node information table. S4. Measure the channel busy ratio (CBR) to evaluate the channel load condition. S5. Establish a vehicle utility function and an optimal power control model based on the neighbor node information table and the channel busy ratio (CBR). S6. Dynamically adjust the vehicle transmission power according to the optimal solution of the utility function.

2. The non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles according to claim 1, characterized in that: In step S1, a two-way multi-lane highway mobility model is established. The vehicle terminal uses the PC5 interface for data exchange and the 5.9GHz dedicated frequency band for communication. The vehicle performs autonomous resource scheduling and accesses resources through perception, selection and reservation.

3. The non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles according to claim 1, characterized in that: The step of establishing the wireless channel transmission model in step S2 includes: Considering path loss and shadow fading, a channel transmission model is established based on the WINNER+B1 model to calculate the signal received power at the receiver. Each vehicle periodically sends CAM information, which is received by all surrounding vehicles within the broadcast range. The broadcasting vehicle V i The transmission power is expressed as P t，i，dBm , receiving vehicle V j The received signal power P at r，j，dBm The calculation formula is as follows: P r，j，dBm =P t，i，dBm -(PL i，j，dB +SH i，j，dB ) Among them, PL i，j，dB Indicates the transmitter V i To the receiving end V j The path loss between them and the attenuation caused by obstacles are expressed as SH i，j，dB To express.

4. The non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles according to claim 1, characterized in that: The step of establishing the V2V communication interference model in step S2 includes: Considering whether the data packets sent by the interference source can be effectively received, the interference is divided into interference within the communication range and interference outside the communication range. The interference within the communication range is caused by vehicles using the same resources to broadcast CAM information. The interference outside the communication range is caused by the superposition of data packets that cannot be effectively received due to low transmission power or long distance. Receiving vehicle V j The signal-to-interference-noise ratio γ i，j The calculation formula is as follows: Where N0 is the noise power at the receiving end, P r，x，dBm V j The power of the interference signal emitted by the xth transmitter within the communication range, N1 is the number of interfering nodes within the communication range, P r，y，dBm V j The power of the interference signal emitted by the yth transmitter outside the communication range, and N2 is the number of interfering nodes outside the communication range.

5. The non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles according to claim 1, characterized in that: In step S3, based on the wireless channel transmission model and V2V communication interference model in step S2, the transmitting vehicle obtains the link loss and interference between it and all neighbor nodes within the sensing range, establishes a local neighbor node information table based on this information, and updates the neighbor node information table before each transmission.

6. The non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles according to claim 1, characterized in that: The channel busy ratio CBR in step S4 is defined as follows: subch total Indicates the number of all subchannels in the transmission period, ∑subch RSSI Indicates the number of subchannels whose RSSI measured by the user UE in the past 100 subframes exceeds the preconfigured threshold.

7. The non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles according to claim 1, characterized in that: In step S4, the vehicle terminal measures the CBR once in each transmission cycle to evaluate the current channel status.

8. The non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles according to claim 1, characterized in that: In step S5, based on the information of all neighboring nodes within the communication range, the vehicle V i The utility function of : Among them, B*log2(1+γ i，j ) is the Gaussian channel capacity formula, B is the channel bandwidth, n is the transmitting vehicle V i The number of all neighbor nodes of i，j To receive vehicle V j The signal-to-interference-noise ratio at c(P t，i，dBm ) is the vehicle V i The cost function is: λ is the cost factor, which is a constant and is related to the channel parameters. The definition of λ is as follows: λ=CBR*(PL i，j，dB +SH i，j，dB ) Vehicle V i The optimal power control problem, that is, the optimal power control model, is described as: Among them, s i For vehicle V i The strategy selected, that is, the power of the transmitter, s -i The strategy selected for other vehicles launching at the same time.

9. The non-cooperative game congestion control method in the D2D mode of the Internet of Vehicles according to claim 1, characterized in that: In step S6, the vehicles reach a Nash equilibrium based on the information of neighboring nodes and the channel busyness ratio (CBR) value, and obtain the optimal transmission power. Each vehicle performs power control in a distributed manner to maximize its own optimization benefits.

10. A non-cooperative game congestion control method in a vehicle-to-vehicle (V2D) network mode according to any one of claims 1 to 9, characterized in that: The PSO algorithm is used to solve the optimal power control model, and the vehicle dynamically adjusts the transmission power in a distributed manner according to the optimal solution.

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