Novel Internet of Vehicles congestion simulation method for 5G V2X Uu interface

Abstract

The invention discloses a novel Internet of Vehicles congestion simulation method oriented to a 5G V2X Uu interface, a semi-physical simulation test platform oriented to the 5G V2X Uu interface is included, the test platform architecture comprises a traffic simulator, data packet software, a receiving end OBU, a sending end OBU and a 5G comprehensive tester, and the problem that the semi-physical simulation is limited by the number of terminals and is difficult to simulate a congestion scene is solved. By limiting available resources of the network, the requirement of semi-physical simulation on the number of terminals is reduced, and meanwhile, the increase of the number of vehicles in the network is simulated by increasing the network traffic load. The adjustment of the traffic load is realized by setting a data packet sending period and the size of a data packet. And selecting a required channel model to calculate fading according to a traffic simulation scene and vehicle time-position information, and then adding the fading into a corresponding data packet. And for a V2N2V link, respectively adding uplink fading and downlink fading for a data packet according to different environments and positions of receiving and transmitting end vehicles.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a novel vehicle networking service congestion simulation method for a 5G V2X Uu interface. Background Art

[0002] As cellular systems evolve from 4G Long Term Evolution (LTE) to 5G, C-V2X evolves from LTE-V2X to NR-V2X. NR-V2X is a V2X technology based on 5G NR. 5G NR is designed to reduce latency, improve the reliability and spectrum efficiency of cellular networks, so its introduction can improve communication performance such as latency of the Uu interface. In traditional cellular networks, in order to reduce latency, key V2X services related to driving safety are implemented through the PC5 interface, that is, through direct or sidelink connections between V2V or V2I. Services based on the Uu interface are usually used for infotainment, comfort or traffic management services. However, the high speed and functions introduced by 5G NR can reduce latency, greatly increasing the possibility of supporting safety-related V2X services through the Uu interface, namely V2N, V2N2V and N2V. On the other hand, the Internet of Vehicles is a typical scenario of 5G ultra-high reliability and low latency, which is related to driving safety and needs to ensure the reliability and latency required by the business under network congestion. Therefore, it is necessary to test, verify and study the communication performance such as the delay of the Uu interface in the congested scenario of the Internet of Vehicles.

[0003] There are currently three main simulation test methods for congestion scenarios in the Internet of Vehicles: software simulation, field testing, and semi-physical simulation. Software simulation uses specialized traffic simulation software and network simulation software to create a model of the Internet of Vehicles and simulate behaviors in aspects such as communication protocols, vehicle movement, and signal transmission. It has the advantages of powerful functions, easy operation, repeatable simulation, and good flexibility, and is the most widely used method in congestion scenario simulation. However, the software tools used in this method often require high computing resources. As the number of vehicles in the simulation scenario increases, the consumption of computing resources also increases rapidly. Moreover, the software simulation process is too ideal and has a low degree of fit to the actual transmission environment. Especially for complex simulation systems such as the Internet of Vehicles that have time-varying, fast fading, and experience multiple propagation scenarios, there is a large deviation between the pure software simulation results and the actual scenarios. Unlike software simulation, field testing is carried out in a real environment, and it is obvious that the simulation of the propagation environment has higher accuracy. However, using field testing to simulate congestion scenarios requires a lot of manpower and material resources, more time and resources, and the test results are greatly affected by the environment and are difficult to reproduce effectively. Moreover, Internet of Vehicles communications are characterized by complex scenarios, fast-changing network topology, and diverse business types, making many test scenarios difficult to construct effectively.

[0004] In order to reduce the test cost and shorten the test cycle while ensuring the accuracy and repeatability of the test results, semi-physical simulation came into being. Semi-physical simulation is a hybrid simulation method that combines the actual vehicle terminal equipment and the virtual network environment, allowing the test to be carried out indoors while also ensuring the accuracy of the simulation.

[0005] The congestion scenario of the Internet of Vehicles facing the Uu interface is characterized by a large number of terminal devices and a complex propagation environment. The current research on the performance analysis of Internet of Vehicles communication using semi-physical simulation has shortcomings in the simulation test of congestion scenarios:

[0006] Traditional IoV semi-physical simulation uses a large number of terminal devices to simulate congestion scenarios when resources are idle. Since semi-physical simulation uses actual IoV terminal devices, the number of terminal devices it can support is limited, and it is impossible to simulate scenarios with more than a certain number of vehicles in real time. It does not have good scalability and is difficult to simulate congestion scenarios.

[0007] In the current semi-physical simulation of the propagation environment in congested scenarios, it is often assumed that the fading experienced by each vehicle terminal signal is the same. However, in the real propagation environment, since the position of each vehicle and the surrounding environment are different, the fading experienced by each vehicle is often different. In particular, for the V2N2V link, due to the different positions of the transmitting vehicle and the receiving vehicle, the data packet will experience different fading in the uplink and downlink respectively.

[0008] One of the existing technical solutions (publication number: CN202010603223.2, name: Method for Rapidly Evaluating the Congestion Status of the Internet of Vehicles) is the closest prior art to the present invention. It discloses a method for rapidly evaluating the congestion status of the Internet of Vehicles network. First, a physical resource block allocation model is established under C-scene V2 scene X, and then a theoretical connection between the number of physical resource blocks in the macro cell and the transmission distance is established, and then the probability that the number of required physical resource blocks is not less than the number of available physical resource blocks in different scenarios is derived, that is, the theoretical analytical formula of the network congestion probability. The congestion status under a certain network parameter configuration is quickly evaluated through the theoretical analytical formula of the network congestion probability. The system simulation results verify the theoretical analytical formula of the network congestion probability, and show that adding roadside units under C-scene V2 scene X can significantly reduce the probability of network congestion and improve the quality of network communication services. This invention mainly focuses on the evaluation of the congestion status of the Internet of Vehicles network, and proposes a theoretical evaluation model. It does not involve the simulation of the Internet of Vehicles congestion scenario and the test verification of key performance such as latency in this scenario.

[0009] "Modeling, Simulation and Analysis of Multi-Scenario Urban Traffic Mobility in Internet of Vehicles", this paper analyzes the application scenarios of urban traffic vehicle mobility in the vehicle-mounted self-organizing network environment. In the collaborative simulation environment of VanetMobiSim traffic simulator and NS-2 network simulator, models are constructed for two typical application scenarios in urban intelligent transportation: intersections and two-way fast four-lane roads. At the same time, AODV and DSDV routing protocols are simulated and analyzed in these two application scenarios. The performance of different protocols in the same scenario is compared through four evaluation indicators: end-to-end delay, jitter rate, packet loss rate and control packet overhead. In addition, for the AODV protocol, the impact of environmental factors such as vehicle speed, vehicle density, maximum number of connections and number of packets sent per unit time on the protocol communication effect is analyzed.

[0010] Differences: This article uses software simulation tools to simulate the congestion scenario of the Internet of Vehicles. Unlike the simulation of congestion scenarios in this invention, this article directly simulates performance indicators such as latency and packet loss rate under different congestion levels by increasing the traffic density in the network.

[0011] "Congestion Control Strategy based on Channel Load Prediction for Internet of Vehicles" is published in the 3rd issue of "Electronic Design Applications" in 2022. This paper designs and implements a congestion control strategy based on channel load prediction for Internet of Vehicles (C2SLP) to address the channel congestion problem. The strategy is divided into three modules. First, the detection function in the carrier sense multiple access protocol is used to obtain the idle and busy status of the channel for load evaluation. Then, the result is substituted into the autoregressive integrated moving average model (ARIMA) to predict the channel load value at the next moment. Finally, the load prediction value is compared with the preset standard value. According to the comparison result, the power control algorithm is used to adjust the transmission power to avoid channel congestion in advance.

[0012] Difference: This method focuses more on congestion control rather than congestion simulation in the study of vehicle network congestion scenarios. By observing the current load of the network, the load at the next moment can be predicted, so as to adjust the network parameters in advance and avoid network congestion.

[0013] In recent years, 5G NR has attracted widespread attention, and its introduction has increased the possibility of 5G Uu interface supporting low-latency and high-reliability applications in the Internet of Vehicles. The Internet of Vehicles needs to be able to guarantee the reliability and latency required by the business even when the network is congested. Therefore, a test method needs to be designed for testing and research on the transmission performance of the 5G Uu interface during business congestion. After comparing the advantages and disadvantages of field testing, simulation software testing, and semi-physical simulation testing, the present invention adopts semi-physical simulation testing. However, due to the number of on-board terminal devices (On Board Unit, OBU), semi-physical simulation testing is insufficient in large-scale terminal equipment testing.

[0014] In order to solve this problem, the present invention designs a new method for simulating congestion of Internet of Vehicles services. This method can be used to test and verify the transmission performance of the 5G Uu interface in congestion scenarios, providing a reference for 5G Internet of Vehicles network configuration and system optimization. Summary of the invention

[0015] In view of the shortcomings of the prior art, the purpose of the present invention is to design a semi-physical simulation test platform for the 5G V2X Uu interface, through which key communication performance indicators such as delay and packet loss rate of the three communication modes of V2N, N2V and V2N2V can be tested. Based on this platform, a new method for simulating congestion scenarios is provided using limited vehicle terminal equipment, overcoming the problem that traditional semi-physical simulation tests are difficult to simulate congestion scenarios.

[0016] In order to achieve the above object, the present invention adopts the following technical solutions:

[0017] A novel vehicle network congestion simulation method for 5G V2X Uu interface, including a semi-physical simulation test platform for 5G V2X Uu interface, wherein the test platform architecture includes a traffic simulator, data packet software, a receiving end OBU, a transmitting end OBU, a 5G comprehensive tester and clock synchronization, wherein the data packet software includes data packet sending configuration software, data packet forwarding software and data packet receiving statistics software,

[0018] The traffic simulator simulates multi-vehicle traffic scenarios and transmits vehicle time-position information to the corresponding data packet software;

[0019] The data packet sending configuration software adds fading to the generated data packet according to the time-position information of the originating vehicle, and the originating OBU sends the data packet, and the comprehensive tester receives the data packet. The added fading is the uplink fading.

[0020] The comprehensive tester adds fading to the received data packet according to the time-position information of the receiving vehicle, and forwards the data packet to the receiving OBU. The added fading is the downlink fading.

[0021] The receiving end OBU receives the data packet, and the data packet receiving software calculates the key performance indicators, including but not limited to delay and packet loss rate;

[0022] Clock synchronization ensures that the clocks of the OBU and the comprehensive tester are synchronized to ensure accurate delay test results;

[0023] The congestion simulation process includes:

[0024] Step S1, first configure the 5G network parameters;

[0025] Step S2, then limiting the uplink available resources to increase the traffic load;

[0026] Traffic load is configured by adjusting the packet sending period and packet size; the smaller the sending period and the larger the packet size, the more vehicles there are;

[0027] Step S3, the data packet sending configuration software selects a corresponding channel model to calculate uplink fading according to the simulated road traffic scene and the time-position information of the originating vehicle;

[0028] Step S4, then adding corresponding uplink fading to the data packet and sending the data packet by the transmitting OBU;

[0029] Step S5, the forwarding software selects a corresponding channel model to calculate downlink fading according to the simulated road traffic scene and the time-position information of the receiving vehicle;

[0030] Step S6, then adding downlink fading and forwarding the data packet;

[0031] Step S7: receiving data packets and performing performance testing.

[0032] Preferably,

[0033] The traffic simulator is software with traffic simulation function. Typical traffic scenes are constructed by traffic simulation software, and vehicle flow is set in the traffic scene to simulate multi-vehicle traffic scenes.

[0034] Preferably,

[0035] The data packet transmission configuration software and data packet forwarding software set the channel model according to the time-position information of the vehicle and the test requirements, and add fading to the data packet;

[0036] The data packet sending configuration software supports the generation of vehicle network Uu interface data packets. The information set by the data packet sending configuration software includes but is not limited to the data packet type, size and sending cycle.

[0037] Preferably,

[0038] The receiving end OBU and the transmitting end OBU both support sending and receiving data packets through the Uu interface. The transmitting end OBU sends data packets at a set power according to the configuration of the data packet type, size and sending cycle of the data packet sending configuration software, and the receiving end OBU receives data packets forwarded by the comprehensive tester.

[0039] Preferably,

[0040] The 5G comprehensive tester is used to simulate the 5G network. The network parameters are configured including but not limited to the 5G networking mode, duplex mode, test frequency range, subcarrier spacing and number of available resource blocks, or as a 5G base station simulator, to send and receive data packets at a set frequency and power.

[0041] Preferably,

[0042] The V2X is a V2N2V link;

[0043] In step S7, the data packet is received and a delay model and a packet loss rate model are constructed; wherein,

[0044] The delay model is specifically:

[0045] The overall latency of V2N2V is as follows:

[0046] D V2N2V =D V2N +D proc +D N2V +D que (Formula 1)

[0047] Among them, D V2N is the delay from the originating vehicle to the 5G base station, D N2V is the delay from the 5G base station to the receiving vehicle, D proc is the data packet network processing delay, which is the time between the moment the base station receives a data packet from a vehicle in the uplink and the moment it sends the data packet in the downlink; D que It is the queuing delay caused by congestion.

[0048] The delay from vehicle terminal data packet i to 5G base station is calculated as follows:

[0049]

[0050] Among them, U i is the size of the packet, is the link capacity from data packet i to the base station, B is the transmission bandwidth; is the signal-to-noise ratio of data packet i from the originating vehicle to the 5G base station, P t，v is the vehicle terminal transmission power, is the path loss, is the small-scale fading experienced by the uplink, n0 is the noise power spectral density;

[0051] The network processing delay of the vehicle terminal data packet i is calculated as follows:

[0052]

[0053] Among them, α i is the complexity of the service carried by data packet i, and E is the network processing capability.

[0054] The delay of data packet i from the 5G base station to the receiving vehicle is as follows:

[0055]

[0056] In V2N2V communication links, the delay caused by network congestion can be modeled according to the queuing model;

[0057] The queuing delay in congestion scenarios depends on the traffic load and network service rate.

[0058] The traffic load is configured by the packet sending period T and the packet size U. Assuming that the sending period of each vehicle is T, the packet size is U, and the number of sending vehicles is N, the traffic load can be modeled as the arrival rate of the packet, which is The service rate is μ m =(1-α)C m , α is the resource occupancy rate, C m is the capacity of link m,

[0059] m∈{V2N, N2V}, then the queuing delay is:

[0060]

[0061] Based on the above model, the network congestion degree measurement indicators are as follows:

[0062] γ cong =max(λ / μ V2N ,λ / μ N2V ) #(6)

[0063] γ cong The larger the value, the greater the network congestion. The highest congestion level is 1. V2N and μ N2V When any of the values ​​reaches the λ value, it indicates that the current vehicle terminal has occupied all available resources;

[0064] Preferably,

[0065] The packet loss rate model is specifically:

[0066] The conditions for determining whether data packet i is lost are as follows:

[0067]

[0068] Loss = 0, 1 represents successful and failed data packet reception respectively;

[0069] D req is the latency requirement of the Internet of Vehicles service, P r is the packet receiving power, P thre is the packet receiving power threshold;

[0070] When the delay of data packet i is higher than the required delay or the receiving power is lower than the threshold value, the data packet is judged as receiving failure, otherwise, it is received successfully.

[0071] The present invention also provides an electronic device, comprising: a memory, a processor, and a congestion simulation method program stored in the memory and executable on the processor, wherein when the congestion simulation method program is executed by the processor, the new vehicle network congestion simulation method for the 5G V2X Uu interface as described in any one of claims 1 to 8 is implemented.

[0072] The present invention also provides a computer-readable storage medium, on which a congestion simulation method program is stored. When the congestion simulation method program is executed by a processor, the new vehicle network congestion simulation method for the 5G V2X Uu interface as described in any one of claims 1 to 8 is implemented.

[0073] Beneficial effects:

[0074] In view of the problem that traditional semi-physical simulation is difficult to simulate congestion scenarios due to the limited number of supported terminal devices, the present invention provides a new method for simulating congestion scenarios in the Internet of Vehicles, which can simulate congestion scenarios using limited terminal devices. In view of the problem that semi-physical simulation is not very accurate in simulating the propagation environment of congestion scenarios, the present invention selects the required channel model to calculate the fading based on the traffic simulation scenario and the vehicle time-position information, and then adds the fading to the corresponding data packet. In particular, for the V2N2V link, uplink and downlink fading are added to the data packet separately according to the different environments and positions of the transmitting and receiving vehicles.

[0075] The present invention overcomes the problem that semi-physical simulation is limited by the number of terminals and is difficult to simulate congestion scenarios. By limiting the available network resources, the requirements for the number of terminals in semi-physical simulation are reduced, and by increasing the network traffic load, the increase in the number of vehicles in the network is simulated. The traffic load is adjusted by setting the packet sending period and the packet size. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the brain wave-based train emergency braking control method and system. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0077] Figure 1 This is a schematic diagram of the architecture of the 5G V2X Uu interface semi-physical simulation test platform of the present invention;

[0078] Figure 2 It is a schematic diagram of the congestion scenario simulation process of the present invention;

[0079] Figure 3 A schematic diagram of a V2N2V scenario provided for this embodiment;

[0080] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0081] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0082] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0083] To facilitate understanding of the embodiments of the present invention, two specific embodiments are taken as examples for further explanation below, and these two embodiments do not constitute limitations on the embodiments of the present invention.

[0084] The present invention designs a semi-physical simulation test platform for the 5G V2X Uu interface, through which key communication performance indicators such as delay and packet loss rate of the three communication modes of V2N, N2V and V2N2V can be tested. Based on this platform, a new method for simulating congestion scenarios is provided using limited vehicle terminal equipment, overcoming the problem that traditional semi-physical simulation tests are difficult to simulate congestion scenarios. Figure 1The figure shows the architecture of the semi-physical simulation test platform for the 5G V2X Uu interface. The hardware part mainly includes the OBU and comprehensive tester at the transceiver end, and the software part includes the traffic simulator and data packet sending configuration, forwarding and receiving statistics software. Since the V2N2V link includes the V2N and N2V links, Figure 1 The explanation is mainly carried out by taking V2N2V as an example, and the simulation of V2N and N2V will not be elaborated.

[0085] exist Figure 1 In the 5G V2X Uu interface semi-physical simulation platform architecture shown in the figure, the main functions of each module in the V2N2V mode are as follows:

[0086] 1) Traffic simulator: Traffic simulator refers to software with traffic simulation function. Typical traffic scenarios are constructed through traffic simulation software. Traffic flow can be set in the traffic scenario to simulate multi-vehicle traffic scenarios. The traffic simulator sends vehicle time-location information to the corresponding data packet software.

[0087] 2) Data packet software: Data packet software mainly includes data packet sending configuration, forwarding and receiving statistics software. Data packet sending configuration software and forwarding software set the channel model according to the time-location information of the vehicle and the test requirements, and add fading to the data packet. Data packet sending configuration software supports the generation of vehicle network Uu interface data packets, and can also set the data packet type, size, sending cycle and other information. Data packet receiving statistics software counts the received data packet information and calculates key performance indicators such as latency and packet loss rate.

[0088] 3) Transceiver OBU: OBU supports sending and receiving data packets through the Uu interface. The transmitting OBU sends data packets at the set power according to the configuration of the data packet type, size, sending cycle, etc. of the data packet sending configuration software. The receiving OBU receives the data packets forwarded by the comprehensive tester.

[0089] 4) 5G comprehensive tester: The comprehensive tester is used to simulate the 5G network. It can configure network parameters such as 5G networking mode, duplex mode, test frequency range, subcarrier spacing, and number of available resource blocks. It can also be used as a 5G base station simulator to send data packets at a set frequency and power, and can also receive data packets.

[0090] 5) Clock synchronization: Ensure that the clocks of the OBU and the comprehensive tester are synchronized to ensure accurate delay test results.

[0091] In this architecture, the traffic simulator simulates a multi-vehicle traffic scenario and passes the vehicle time-position information to the corresponding data packet software. The data packet sending configuration software adds fading to the generated data packet based on the time-position information of the transmitting vehicle, and the transmitting OBU sends the data packet, and the comprehensive tester receives the data packet. The added fading is the uplink fading. The comprehensive tester adds fading to the received data packet based on the time-position information of the receiving vehicle, and forwards the data packet to the receiving OBU. The added fading is the downlink fading. The receiving OBU receives the data packet, and the data packet receiving software calculates key performance indicators such as delay and packet loss rate.

[0092] Figure 1 The semi-physical simulation test shown includes a receiving OBU and a transmitting OBU, a total of two OBUs. In order to simulate congestion scenarios, the present invention provides a novel vehicle network congestion scenario simulation method, which can use limited terminal devices to simulate congestion scenarios. Specifically, on the one hand, by limiting the available network resources, the requirements for the number of terminals in the semi-physical simulation are reduced, and on the other hand, by increasing the network traffic load, the increase in the number of vehicles in the network is simulated.

[0093] like Figure 2 As shown, it is based on Figure 1 The congestion scenario simulation process of the semi-physical simulation test platform includes the following specific steps:

[0094] Step S1, first configure the 5G network parameters;

[0095] Use the comprehensive tester to configure network parameters including but not limited to 5G networking mode, duplex mode, test frequency range, subcarrier spacing, and number of available resource blocks;

[0096] Step S2, then limiting the uplink available resources to increase the traffic load;

[0097] Traffic load is configured by adjusting the packet sending period and packet size; the smaller the sending period and the larger the packet size, the more vehicles there are;

[0098] Step S3, the data packet sending configuration software selects a corresponding channel model to calculate uplink fading according to the simulated road traffic scene and the time-position information of the originating vehicle;

[0099] Step S4, then adding corresponding uplink fading to the data packet and sending the data packet by the transmitting OBU;

[0100] Step S5, the forwarding software selects a corresponding channel model to calculate downlink fading according to the simulated road traffic scene and the time-position information of the receiving vehicle;

[0101] Step S6, then adding downlink fading and forwarding the data packet;

[0102] Step S7: receiving data packets and performing performance testing.

[0103] against Figure 2 The congestion scenario simulation process described above constructs the following delay model and packet loss rate model through theoretical analysis.

[0104] by Figure 3 Take the V2N2V scenario as an example. The overall latency of V2N2V is as follows:

[0105] D V2N2V =D V2N +D proc +D N2V +D que #(1)

[0106] Among them, D V2N is the delay from the originating vehicle to the 5G base station, D N2V is the delay from the 5G base station to the receiving vehicle, D proc is the data packet network processing delay, which is the time between the moment the base station receives a data packet from a vehicle in the uplink and the moment it sends the data packet in the downlink. que It is the queuing delay caused by congestion.

[0107] The delay from vehicle terminal data packet i to 5G base station is calculated as follows:

[0108]

[0109] Among them, U i is the size of the packet, is the link capacity from data packet i to the base station, B is the transmission bandwidth. is the signal-to-noise ratio of data packet i from the originating vehicle to the 5G base station, P t，v is the vehicle terminal transmission power, is the path loss, is the small-scale fading experienced in the uplink, and n0 is the noise power spectral density.

[0110] The network processing delay of the vehicle terminal data packet i is calculated as follows:

[0111]

[0112] Among them, α i is the complexity of the service carried by data packet i, and E is the network processing capability.

[0113] The delay of data packet i from 5G base station to receiving vehicle The calculation is similar, as follows:

[0114]

[0115] In a V2N2V communication link, the delay caused by network congestion can be modeled according to the queuing model. The queuing delay in the congestion scenario depends on the traffic load and the network service rate. Figure 2 In the congestion scenario simulation method shown in Figure 1, the traffic load can be configured by the packet sending period T and the packet size U. Assuming that the sending period of each vehicle is T, the packet size is U, and the number of sending vehicles is N, the traffic load can be modeled as the arrival rate of the packet, which is The service rate is μ m =(1-α)C m ,

[0116] α is the resource utilization rate, C m is the capacity of link m, m∈{V2N,N2V}, then the queuing delay is:

[0117]

[0118] Based on the above model, the network congestion degree measurement indicators are as follows:

[0119] γ cong =max(λ / μ V2N ,λ / μ N2V )#(6)

[0120] γ cong The larger the value, the greater the network congestion. The highest congestion level is 1. V2N and μ N2V When any of the values ​​reaches the λ value, it indicates that the current vehicle terminal has occupied all available resources.

[0121] The conditions for determining whether data packet i is lost are as follows:

[0122]

[0123] Loss = 0, 1 represents the successful and failed reception of the data packet respectively. req is the latency requirement of the Internet of Vehicles service, P r is the packet receiving power, P thre is the power threshold for receiving data packets. When the delay of data packet i is higher than the required delay or the receiving power is lower than the threshold, the data packet is judged as receiving failure, otherwise, it is received successfully.

[0124] The semi-physical simulation test platform architecture for the Uu interface of the 5G vehicle network of the present invention can be used to test key communication performance indicators such as delay, packet loss rate, etc. of the three communication modes of V2N, N2V, and V2N2V.

[0125] The present invention overcomes the problem that semi-physical simulation is limited by the number of terminals and is difficult to simulate congestion scenarios. By limiting the available network resources, the requirements for the number of terminals in semi-physical simulation are reduced, and by increasing the network traffic load, the increase in the number of vehicles in the network is simulated. The traffic load is adjusted by setting the packet sending period and the packet size.

[0126] The present invention proposes a method for simulating the propagation environment of a congested scenario. According to the traffic simulation scenario and the time-position information of the vehicle, the required channel model is selected to calculate the fading, and then the fading is added to the corresponding data packet. In particular, for the V2N2V link, uplink and downlink fading are added to the data packet respectively according to the different environments and positions of the transmitting and receiving vehicles.

[0127] Based on the new congestion scenario simulation method mentioned above, the queuing model is used to analyze and obtain the theoretical model of congestion scenario delay and packet loss rate, and an evaluation index for measuring the network congestion status is proposed, providing a theoretical basis for the setting of simulation test parameters.

[0128] In addition, an embodiment of the present application also proposes a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, on which is stored a congestion simulation program of the present invention, and when the behavioral congestion simulation program is executed by a processor, the method of the present application as described above is implemented.

[0129] The various embodiments of the electronic device and computer-readable storage medium of the present application can all refer to the various embodiments of the congestion simulation method of the present application, which will not be repeated here.

[0130] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0131] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0132] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for an electronic device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0133] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A new vehicle network congestion simulation method for 5G V2X Uu interface, characterized in that: It includes a semi-physical simulation test platform for 5G V2XUu interface. The test platform architecture includes a traffic simulator, data packet software, a receiving end OBU, a transmitting end OBU, a 5G comprehensive tester and clock synchronization. The data packet software includes data packet sending configuration software, data packet forwarding software and data packet receiving statistics software. The traffic simulator simulates multi-vehicle traffic scenarios and transmits vehicle time-position information to the corresponding data packet software; The data packet sending configuration software adds fading to the generated data packet according to the time-position information of the originating vehicle, and the originating OBU sends the data packet, and the comprehensive tester receives the data packet. The added fading is the uplink fading. The comprehensive tester adds fading to the received data packet according to the time-position information of the receiving vehicle, and forwards the data packet to the receiving OBU. The added fading is the downlink fading. The receiving end OBU receives the data packet, and the data packet receiving software calculates the key performance indicators, including but not limited to delay and packet loss rate; Clock synchronization ensures that the clocks of the OBU and the comprehensive tester are synchronized to ensure accurate delay test results; The congestion simulation process includes: Step S1, first configure the 5G network parameters; Step S2, then limiting the uplink available resources to increase the traffic load; Traffic load is configured by adjusting the packet sending period and packet size; the smaller the sending period and the larger the packet size, the more vehicles there are; Step S3, the data packet sending configuration software selects a corresponding channel model to calculate uplink fading according to the simulated road traffic scene and the time-position information of the originating vehicle; Step S4, then adding corresponding uplink fading to the data packet and sending the data packet by the transmitting OBU; Step S5, the forwarding software selects a corresponding channel model to calculate downlink fading according to the simulated road traffic scene and the time-position information of the receiving vehicle; Step S6, then adding downlink fading and forwarding the data packet; Step S7: receiving data packets and performing performance testing.

2. According to claim 1, a new vehicle network congestion simulation method for 5G V2X Uu interface is characterized in that: The traffic simulator is software with traffic simulation function. Typical traffic scenes are constructed by traffic simulation software, and vehicle flow is set in the traffic scene to simulate multi-vehicle traffic scenes.

3. According to a new vehicle network congestion simulation method for 5G V2X Uu interface according to claim 1, it is characterized in that: The data packet transmission configuration software and data packet forwarding software set the channel model according to the time-position information of the vehicle and the test requirements, and add fading to the data packet; The data packet sending configuration software supports the generation of vehicle network Uu interface data packets. The information set by the data packet sending configuration software includes but is not limited to the data packet type, size and sending cycle.

4. According to a novel vehicle network congestion simulation method for 5G V2X Uu interface according to claim 1, it is characterized in that: The receiving end OBU and the transmitting end OBU both support sending and receiving data packets through the Uu interface. The transmitting end OBU sends data packets at a set power according to the configuration of the data packet type, size and sending cycle of the data packet sending configuration software, and the receiving end OBU receives data packets forwarded by the comprehensive tester.

5. According to a novel vehicle network congestion simulation method for 5G V2X Uu interface according to claim 1, it is characterized in that: The 5G comprehensive tester is used to simulate the 5G network. The network parameters are configured including but not limited to the 5G networking mode, duplex mode, test frequency range, subcarrier spacing and number of available resource blocks, or as a 5G base station simulator, to send and receive data packets at a set frequency and power.

6. According to a novel vehicle network congestion simulation method for 5G V2X Uu interface according to claim 1, it is characterized in that: The V2X is a V2N2V link; In step S7, the data packet is received and a delay model and a packet loss rate model are constructed; wherein, The delay model is specifically: The overall latency of V2N2V is as follows: D V2N2V =D V2N +D proc +D N2V +D que (official1) Among them, D V2N is the delay from the originating vehicle to the 5G base station, D N2V is the delay from the 5G base station to the receiving vehicle, D proc is the data packet network processing delay, which is the time between the time when the base station receives a data packet from the vehicle in the uplink and the time when the data packet is sent in the downlink; D que It is the queuing delay caused by congestion. The delay from vehicle terminal data packet i to 5G base station is calculated as follows: Among them, U i is the size of the packet, is the link capacity from data packet i to the base station, B is the transmission bandwidth; is the signal-to-noise ratio of data packet i from the originating vehicle to the 5G base station, P t，v is the vehicle terminal transmission power, is the path loss, is the small-scale fading experienced by the uplink, n0 is the noise power spectral density; The network processing delay of the vehicle terminal data packet i is calculated as follows: Among them, α i is the complexity of the service carried by data packet i, and E is the network processing capability. The delay of data packet i from the 5G base station to the receiving vehicle is as follows: In V2N2V communication links, the delay caused by network congestion can be modeled according to the queuing model; The queuing delay in congestion scenarios depends on the traffic load and network service rate. The traffic load is configured by the packet sending period T and the packet size U. Assuming that the sending period of each vehicle is T, the packet size is U, and the number of sending vehicles is N, the traffic load can be modeled as the arrival rate of the packet, which is The service rate is μ m =(1-α)C m , α is the resource occupancy rate, C m is the capacity of link m, m∈{V2N,N2V}, then the queuing delay is: Based on the above model, the network congestion degree measurement indicators are as follows: c cong =max(λ / μ) V2N ,l / m N2V )#(6) γ cong The larger the value, the greater the network congestion. The highest congestion level is 1. V2N and μ N2V When any of the values ​​reaches the λ value, it indicates that the current vehicle terminal has occupied all available resources.

7. According to claim 6, a new vehicle network congestion simulation method for 5G V2X Uu interface is characterized in that: The packet loss rate model is specifically: The conditions for determining whether data packet i is lost are as follows: Loss = 0, 1 represents successful and failed data packet reception respectively; D req is the latency requirement of the Internet of Vehicles service, P r is the packet receiving power, P thre is the packet receiving power threshold; When the delay of data packet i is higher than the required delay or the receiving power is lower than the threshold value, the data packet is judged as receiving failure, otherwise, it is received successfully.

8. An electronic device, characterized in that: include: A memory, a processor, and a congestion simulation method program stored in the memory and executable on the processor, wherein when the congestion simulation method program is executed by the processor, the new vehicle network congestion simulation method for the 5G V2X Uu interface as claimed in any one of claims 1 to 8 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a congestion simulation method program, and when the congestion simulation method program is executed by the processor, it implements the new vehicle network congestion simulation method for the 5G V2X Uu interface as described in any one of claims 1 to 8.

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