A heterogeneous platoon network communication model fusing visible light communication and radio frequency communication

Abstract

The application discloses a heterogeneous vehicle group network communication model fusing visible light communication and radio frequency communication, belongs to the field of visible light communication technology and vehicle-mounted communication, introduces the concept of equivalent SINR on the basis of clustering vehicle nodes, takes the equivalent signal-to-noise ratio of a VLC channel between a cluster head CH and a cluster member CM as a distribution condition to perform channel distribution, and when the channel between the cluster head CH and the cluster member CM cannot pass or has low quality, takes neighbor vehicles between two vehicles as relay nodes to perform communication in a multi-hop mode, selects a channel with better signal-to-noise ratio as a communication mode of the current CH-CM and CM-CM, and specifically comprises the following steps: establishing a clustering model; analyzing VLC channel noise; analyzing RF channel noise; analyzing cluster head load balancing degree; establishing a channel distribution model; and establishing a channel distribution algorithm. The application can effectively guarantee the communication quality between vehicle nodes, improve vehicle network performance, and obtain greater transmission rate.

Description

Technical Field

[0001] This invention relates to the field of visible light vehicle communication, and in particular to a heterogeneous vehicle networking communication model that integrates visible light communication and radio frequency communication. Background Technology

[0002] Visible light communication uses visible light emitted by light-emitting diodes (LEDs) as the carrier for wireless communication. It offers advantages such as unlicensed spectrum, no electromagnetic interference, high bandwidth, and strong security, making it a promising complementary technology to short-range radio frequency (RF) wireless communication between vehicles. Consequently, heterogeneous VLC / RF vehicle-to-everything (V2X) communication networks have attracted widespread attention from researchers in recent years.

[0003] Addressing the issues of low communication quality and limited transmission rates between vehicle nodes in vehicular ad hoc networks is crucial for the further development of heterogeneous VLC / RF vehicular communication networks. Researchers have proposed various VLC / RF hybrid communication system models and analyzed their performance from different perspectives, but some limitations remain. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a heterogeneous vehicle networking communication model that integrates visible light communication and radio frequency communication. In view of the problem of low communication quality and limited transmission rate between vehicle nodes in the vehicle ad hoc network, a heterogeneous VLC / RF multi-hop clustered V2V communication model is constructed to ensure the communication quality between vehicle nodes, improve the performance of the vehicle network, and obtain a greater transmission rate.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A heterogeneous vehicle networking communication model integrating visible light communication and radio frequency communication introduces the concept of equivalent SINR based on vehicle node clustering. Channel allocation is performed using the equivalent signal-to-noise ratio (SNR) of the VLC channel between the cluster head (CH) and cluster member (CM). When the channel between the CH and CM is impassable or of low quality, neighboring vehicles between the two vehicles act as relay nodes, employing a multi-hop communication method. The channel with the best SNR is selected as the current CH-CM and CM-CM communication method. The specific implementation steps of the communication model are as follows:

[0007] Step 1, Establishing the clustering model;

[0008] Step 2, VLC channel noise analysis;

[0009] Step 3, RF channel noise analysis;

[0010] Step 4: Establish the channel allocation model;

[0011] Step 5: Establish the channel allocation algorithm.

[0012] A further improvement to the technical solution of this invention lies in the following: In step 1, the establishment of the clustering model specifically includes:

[0013] First, the relative distance and relative speed between vehicles are calculated. The relative distance in the model uses Manhattan distance; the relative distance D between any vehicle i and vehicle j is calculated. ij and relative velocity V ij The expression is:

[0014] D ij =|x i -x j |+|y i -y j | (1)

[0015] V ij =|v i -v j | (2)

[0016]

[0017]

[0018] Where, x i Let x be the x-coordinate of any vehicle i; j Let y be the x-coordinate of any vehicle j; i Let y be the ordinate of any vehicle i; j Let D be the ordinate of any vehicle j. ij v represents the relative distance between any vehicle i and vehicle j; i Let v be the speed of any vehicle i; j Let V be the speed of any vehicle j; ij Let be the relative speed between any vehicle i and vehicle j; n is the number of vehicle nodes in the cluster to which the vehicle node belongs.

[0019] The selection criterion for vehicle node i within the current cluster is:

[0020] CLUSTER(i)=α1D(i)+α2V(i) (5)

[0021] Among them, α1 and α2 are weighting factors, and different weight values ​​are set according to different scenarios; when the cluster head selection index CLUSTER(i) of node i is the smallest in its own cluster, then i is selected as the cluster head of this cluster.

[0022] A further improvement to the technical solution of this invention lies in the following: In step 2, the VLC channel noise analysis specifically includes the following steps:

[0023] 2.1 Define the received optical power P of the receiving vehicle. r (j) is:

[0024] P r (j)=P t *H(0) (6)

[0025] Among them, P t This refers to the transmission power of the vehicle's transmitter.

[0026] 2.2, The channel gain of the direct link from the transmitter to the receiver is:

[0027]

[0028] Where Dij is the relative distance between vehicle nodes; A is the effective area of ​​PD; and φ is the irradiance angle, i.e., the emission angle. Let be the angle of incidence, and is the optical filter gain; r is the path loss exponent; m and Let Lambertian emission order and optical concentrator gain be given by the following equation:

[0029]

[0030]

[0031] Where, φ 1 / 2 The half-power angle is η; the optical concentrator reflection index is η. This refers to the receiver's field of view.

[0032] 2.3, the signal-to-noise ratio of the VLC channel for the vehicle node is:

[0033]

[0034] Where μ is the photoelectric conversion constant, σ 2 This represents the total noise and interference energy.

[0035]

[0036] in, The thermal noise power generated by the receiver. The background optical noise power generated by the VLC channel of neighboring vehicles within the vehicle network is given by q, where q is the electron charge, B is the noise bandwidth, and I is the noise power. B I2 is the background photocurrent, I2 is the noise bandwidth factor, and N0 is the background photocurrent. v For noise power spectral density, B v Modulation bandwidth.

[0037] A further improvement to the technical solution of this invention lies in the following steps: In step 3, the RF channel noise analysis specifically includes the following steps:

[0038] 3.1, Define RF propagation path loss as:

[0039]

[0040] Where a is the road loss index; b is the road loss frequency dependence coefficient; c is the intercept coefficient; f c Here, we use the RF center carrier frequency in GHz. For line-of-sight (LOS) transmission, a = 18.7, b = 20, c = 46.8; for non-line-of-sight (NLOS) transmission, a = 36.8, b = 20, c = 46.8. The NLOS scenario is used here.

[0041] 3.2, the signal-to-noise ratio of the RF channel for the vehicle node is:

[0042]

[0043] in, For the channel gain between vehicles, P n denoted as background noise power, and n as the number of vehicle nodes within the cluster to which the vehicle belongs.

[0044] A further improvement to the technical solution of this invention lies in the following steps: In step 4, the establishment of the channel allocation model specifically includes the following steps:

[0045] 4.1 The maximum transmission rate, i.e., the channel capacity, can be obtained from the carrier bandwidth and SINR, specifically expressed as follows:

[0046]

[0047] Where B is the channel bandwidth; S is the signal power; and n0 is the noise power spectral density.

[0048] 4.2 Therefore, the maximum transmission rate between RF and VLC vehicles is:

[0049]

[0050]

[0051] 4.3, When RF and VLC reach the same maximum transmission rate, i.e., C RF (i,j)=C VLC When (i,j), the relationship between the signal-to-noise ratios of RF and VLC is obtained:

[0052]

[0053] Among them, RF SINR (i,j) is the equivalent value RF SINR (i,j) VLC B VLCFor VLC carrier bandwidth, B RF For the RF carrier bandwidth, Γ VLC and Γ RF Let VLC and RF be the channel coding loss factors, respectively. From the above equation, we can obtain the VLC loss factor for VLC when achieving the same maximum transmission rate. SINR (i,j) is converted to the equivalent value RF required to achieve the same transmission rate in RF. SINR (i,j) VLC ;

[0054] 4.4 The channel allocation is as follows:

[0055] When RF SINR (i,j) VLC ≥RF SINR (i,j), vehicles i and j communicate using VLC;

[0056] When RF SINR (i,j) VLC <RF SINR (i,j), and when communicating between clusters, nodes use RF for communication.

[0057] A further improvement to the technical solution of this invention lies in the following steps: In step 5, the channel allocation algorithm establishment specifically includes the following steps:

[0058] 5.1 The mobility-based clustering algorithm is as follows:

[0059]

[0060]

[0061] 5.2 The channel allocation algorithm for heterogeneous VLC / RF vehicle network is as follows:

[0062]

[0063] 5.3, analysis as follows:

[0064] 5.3.1 The vehicle nodes divide the entire environment into regions of equal size based on the obtained position and speed information. Each vehicle node automatically joins the cluster in the initialization region closest to the center x(i) of the initialization region according to its own position. The road has three lanes, a length of 100m, and a width of 3m for each lane. The coordinates of x(i) are ((250i+500) / N, 4.5), where N is the number of vehicle nodes and i is the order of the clusters.

[0065] 5.3.2 Each vehicle node calculates its own CLUSTER index as shown in Algorithm 1, broadcasts the information within the cluster, and selects the cluster head CH of each cluster;

[0066] 5.3.3 Based on the cluster head CH selected in step 5.3.2, calculate the VLC between the cluster head CH and the cluster member CM within the cluster according to Algorithm 2. SINR and RF SINR and VLC SINR Convert to equivalent RF SINR ; calculations show that if or RF SINR (i,j) VLC ≤RF SINR If the communication method between the cluster head CH and the cluster member CM is RF, then the communication method between the cluster head CH and the cluster member CM is VLC. As shown in Algorithm 2, if the channel between the cluster head CH and the cluster member CM is not passable or has low quality, the cluster member CM selects a neighboring vehicle between itself and the cluster head CH as a relay node and repeats Algorithm 2 to calculate and determine the communication method between itself and the cluster member CM.

[0067] The technological advancements achieved by this invention due to the adoption of the above technical solutions are as follows:

[0068] Based on vehicle node clustering, this invention introduces the concept of equivalent SINR, which improves the reliability of the system model, enhances the quality of heterogeneous VLC / RF communication between vehicles, and increases the transmission rate, providing a reliable theoretical foundation and experimental guidance for future technical research. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a schematic diagram of a VLC and RF heterogeneous vehicle network in an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of the actual generated vehicle position in an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram illustrating the relationship between cluster head load balancing and the number of vehicles in an embodiment of the present invention;

[0073] Figure 4 This is a flowchart of the algorithm in an embodiment of the present invention;

[0074] Figure 5 This is a schematic diagram illustrating the relationship between signal-to-noise ratio and the number of vehicles in an embodiment of the present invention. Detailed Implementation

[0075] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0076] This application provides a heterogeneous vehicle networking communication model that integrates visible light communication and radio frequency communication, solving the problem of "low communication quality and limited transmission rate between vehicle nodes in the existing technology". The general idea is to construct a heterogeneous VLC / RF multi-hop clustered V2V communication model, introduce the concept of equivalent SINR to improve the reliability of the system model, ensure the communication quality between vehicle nodes, improve vehicle network performance, and obtain a greater transmission rate.

[0077] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:

[0078] A heterogeneous vehicle networking communication model integrating visible light communication and radio frequency communication introduces the concept of equivalent signal-to-noise ratio (SINR) based on vehicle node clustering. Channel allocation is performed using the equivalent signal-to-noise ratio of the VLC channel between the cluster head (CH) and cluster member (CM). When the channel between the CH and CM is impassable or of low quality, neighboring vehicles between the two vehicles act as relay nodes, employing a multi-hop communication method. The channel with the best signal-to-noise ratio is selected as the current CH-CM and CM-CM communication method. The specific implementation steps of the communication model are as follows:

[0079] Step 1, Establishing the clustering model;

[0080] First, the relative distances and relative speeds between vehicles are calculated, with Manhattan distance used in the model. The vehicle layout on the road is as follows: Figure 1 As shown, the expressions for the relative distance and relative speed between any vehicle i and vehicle j are:

[0081] D ij =|x i -x j |+|y i -y j | (1)

[0082] V ij =|v i -v j | (2)

[0083]

[0084]

[0085] Where, x i Let x be the x-coordinate of any vehicle i; j Let y be the x-coordinate of any vehicle j; i Let y be the ordinate of any vehicle i; j Let D be the ordinate of any vehicle j. ij v represents the relative distance between any vehicle i and vehicle j; i Let v be the speed of any vehicle i; j Let V be the speed of any vehicle j; ij Let be the relative speed between any vehicle i and vehicle j; n is the number of vehicle nodes in the cluster to which the vehicle node belongs.

[0086] Then, the cluster head selection index of vehicle node i in the current cluster can be calculated from formulas (3) and (4):

[0087] CLUSTER(i)=α1D(i)+α2V(i) (5)

[0088] Here, α1 and α2 are weighting factors, with different weight values ​​set according to different scenarios. For example, in urban road scenarios, relative position (relative distance) has a higher weight, while in highway scenarios, relative speed has a higher weight. When the cluster head selection index CLUSTER(i) of node i is the smallest in its cluster, then i is selected as the cluster head.

[0089] Step 2, VLC channel noise analysis;

[0090] 2.1, The layout of vehicles on the road is as follows: Figure 1 and Figure 2 (in Figure 2 As shown in the diagram (number of vehicles = 40), the vehicles are on a three-lane straight road, and each vehicle is equipped with a transceiver. The received optical power of the receiving vehicle is:

[0091] P r (j)=P t *H(0) (6)

[0092] Among them, P t H(0) represents the transmit power of the vehicle transmitter; H(0) represents the channel gain of the direct link from the transmitter to the receiver.

[0093] 2.2, The channel gain of the direct link from the transmitter to the receiver in equation (6) is:

[0094]

[0095] Among them, D ij φ is the distance between vehicle nodes; A is the effective area of ​​the PD; φ is the irradiance angle (outgoing angle); φ is the incident angle, and is the optical filter gain; r is the path loss exponent; m and The Lambertian emission order and the optical concentrator gain are respectively given by the following formula:

[0096]

[0097]

[0098] Where, φ 1 / 2 The half-power angle is η; the optical concentrator reflection index is η. This refers to the receiver's field of view.

[0099] 2.3, the signal-to-noise ratio of the VLC channel for the vehicle node is:

[0100]

[0101] Where μ is the photoelectric conversion constant, σ 2 This represents the total noise and interference energy.

[0102]

[0103] in, The thermal noise power generated by the receiver. The background optical noise power generated by the VLC channel of neighboring vehicles within the vehicle network is given by q, where q is the electron charge, B is the noise bandwidth, and I is the noise power. B I2 is the background photocurrent, I2 is the noise bandwidth factor, and N0 is the background photocurrent. v For noise power spectral density, B v Modulation bandwidth.

[0104] Step 3, RF channel noise analysis;

[0105] The layout of vehicles on the road, such as Figure 1 and Figure 2 As shown, but not limited to the road communication shown in the diagram.

[0106] 3.1, Define RF propagation path loss as:

[0107]

[0108] Where a is the road loss index; b is the road loss frequency dependence coefficient; c is the intercept coefficient; f cThe center carrier frequency is RF, in GHz. For line-of-sight (LOS) transmission, a = 18.7, b = 20, c = 46.8; for non-line-of-sight (NLOS) transmission, a = 36.8, b = 20, c = 46.8. This invention adopts the NLOS scenario.

[0109] 3.2, the signal-to-noise ratio of the RF channel for the vehicle node is:

[0110]

[0111] in, For the channel gain between vehicles, P n denoted as background noise power, and n as the number of vehicle nodes within the cluster to which the vehicle belongs.

[0112] like Figure 3 As shown, the cluster head load balancing expression is:

[0113]

[0114] Where, n c It is the number of cluster heads, x i The number of members in cluster i, μ = (Nn) c ) / n c (N is the total number of nodes in the system) is the number of neighbors of the cluster head. Obviously, the higher the LBF value, the better the load distribution. For a perfectly balanced system, it tends to infinity.

[0115] Step 4: Establish the channel allocation model;

[0116] 4.1 The maximum transmission rate, i.e., the channel capacity, can be obtained from the carrier bandwidth and SINR, specifically expressed as follows:

[0117]

[0118] Where B is the channel bandwidth; S is the signal power; and n0 is the noise power spectral density.

[0119] 4.2 Therefore, the maximum transmission rate between RF and VLC vehicles is:

[0120]

[0121]

[0122] 4.3, When RF and VLC reach the same maximum transmission rate, i.e., C RF (i,j)=C VLC When (i,j), the relationship between the signal-to-noise ratios of RF and VLC is obtained:

[0123]

[0124] Among them, RF SINR (i,j) is the equivalent value RF SINR (i,j) VLC B VLC For VLC carrier bandwidth, B RF For the RF carrier bandwidth, Γ VLC and Γ RF Let VLC and RF be the channel coding loss factors, respectively. From the above equation, we can obtain the VLC loss factor for VLC when achieving the same maximum transmission rate. SINR (i,j) is converted to the equivalent value RF required to achieve the same transmission rate in RF. SIN R(i,j) VLC ;

[0125] 4.4 The channel allocation is as follows:

[0126] When RF SINR (i,j) VLC ≥RF SINR (i,j), vehicles i and j communicate using VLC;

[0127] When RF SINR (i,j) VLC <RF SINR (i,j), and when communicating between clusters, nodes use RF for communication.

[0128] Step 5, establish the channel allocation algorithm; such as Figure 4 As shown, the specific steps include:

[0129] 5.1 The mobility-based clustering algorithm is as follows:

[0130]

[0131]

[0132] 5.2 The channel allocation algorithm for heterogeneous VLC / RF vehicle network is as follows:

[0133]

[0134] 5.3, analysis as follows:

[0135] 5.3.1 Based on the obtained position and velocity information, vehicle nodes divide the entire environment into regions of equal size. Each vehicle node automatically joins the initialization region closest to the center x(i) of the initialization region according to its own position. The road has three lanes, a length of 100m, and a width of 3m per lane. The coordinates of x(i) are ((250i+500) / N, 4.5), where N is the number of vehicle nodes, i is the order of the cluster, and i = [1, N / 5].

[0136] 5.3.2 Each vehicle node calculates its own CLUSTER index as shown in Algorithm 1, broadcasts the information within the cluster, and selects the cluster head CH of each cluster.

[0137] 5.3.3 Based on the cluster head CH selected in step 5.3.2, calculate the VLC between the cluster head CH and the cluster member CM within the cluster according to Algorithm 2. SINR and RF SINR and VLC SINR Convert to equivalent RF SINR Calculations show that if or RF SINR (i,j) VLC ≤RF SINR If the communication method between the cluster head CH and the cluster member CM is RF, then the communication method between them is VLC; otherwise, it is VLC. As shown in Algorithm 2, if the channel between the cluster head CH and the cluster member CM is impassable or of low quality, the cluster member CM selects a neighboring vehicle to the cluster head CH as a relay node and repeats Algorithm 2 to calculate and determine the communication method with the cluster member CM.

[0138] This algorithm primarily employs a loop structure, with both time and space complexity being O(N). 2 N is the number of vehicle nodes. The algorithm has low complexity and can be applied to vehicle network environments. It can achieve a high transmission rate while improving the quality of vehicle network. Furthermore, clustering can improve the stability of the entire network to a certain extent.

[0139] System simulation work:

[0140] S1. The workshop two-input two-output visible light communication system model was configured as follows: The simulation environment was Python. It was assumed that all vehicles were static in the coordinate system. The simulation environment settings were: three lanes, each 100m long; the position and speed of each vehicle node were randomly generated; the vehicle node speed range was 60-80km / h; the vehicle length was 4m; the maximum number of vehicles deployed was 60; the influence of atmospheric flow and weather on VLC was not considered; and the VLC modulation method was 2-PPM. The simulation parameters are shown in Table 1.

[0141] Table 1. Simulation Parameters

[0142]

[0143]

[0144] S2, calculate the cluster head load balance according to formula (14), such as Figure 3 As shown, with the increase in the number of vehicle nodes, the cluster head load balancing of both clustering algorithms remains within the range of 1.0 ± 0.5, with little fluctuation, indicating good stability of the cluster heads. Channel allocation is performed between vehicles based on the signal-to-noise ratio (SNR) of the corresponding channels calculated using formulas (10), (13), and (18). The average SNR of the vehicle network under different numbers of vehicles and different power levels is shown in the figure. Figure 5 ( Figure 5 As shown in the figure (transmitter power = 1000mW), according to... Figure 5 It can be seen that the transmission power has a greater impact on the signal-to-noise ratio, while the number of vehicles has a smaller impact on the signal-to-noise ratio.

[0145] In the specific embodiments, the number of vehicles and the transmit power were analyzed for their impact on the average signal-to-noise ratio and average bit error rate of the vehicle network. However, this invention is not limited to the above performance analysis used to analyze heterogeneous VLC / RF vehicle networks in workshops.

[0146] 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 foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A heterogeneous vehicle networking communication method integrating visible light communication and radio frequency communication, characterized in that: Based on vehicle node clustering, the concept of equivalent SINR is introduced. Channel allocation is performed using the equivalent signal-to-interference-plus-noise ratio (SINR) of the VLC channel between the cluster head (CH) and cluster member (CM). When the channel between the CH and CM is impassable or of low quality, neighboring vehicles between the two vehicles act as relay nodes, employing a multi-hop communication method. The channel with the best SINR is selected as the current communication mode for CH-CM and CM-CM communication. The specific implementation steps of the communication model are as follows: Step 1, Establishing the clustering model; Step 2, VLC channel noise analysis; VLC channel noise analysis includes not only the analysis of channel noise, but also the received optical power of the receiving vehicle and the channel gain of the direct link from the transmitter to the receiver; specifically... Includes the following steps: 2.1 Define the received optical power of the receiving vehicle. for: in, This refers to the transmission power of the vehicle's transmitter. 2.2, The channel gain of the direct link from the transmitter to the receiver is: in, Let be the relative distance between any vehicle i and vehicle j; The effective area of ​​PD; This is the irradiance angle, i.e., the emission angle; Let be the angle of incidence, and , Let be the x-coordinate of any vehicle i. Let j be the x-coordinate of any vehicle j. Let be the ordinate of any vehicle i. Let j be the ordinate of any vehicle j; This refers to the gain of the optical filter. The path loss index; and Let Lambertian emission order and optical concentrator gain be respectively, given by the following equation: in, It is the half-power angle; The reflectance index of the optical concentrator; This refers to the receiver's field of view. 2.3, the signal-to-interference-plus-noise ratio (SIR) of the VLC channel for the vehicle node is: in, The photoelectric conversion constant, This represents the total noise and interference energy. in, =N v B v The thermal noise power generated by the receiver. This refers to the background optical noise power generated by the VLC channels of neighboring vehicles within the vehicle network. The amount of electron charge. For noise bandwidth, For background photocurrent, N is the noise bandwidth factor. v For noise power spectral density, B v Modulation bandwidth; Step 3, RF channel noise analysis; RF channel noise analysis includes not only the analysis of channel noise, but also RF propagation path loss; Step 4: Establish the channel allocation model; The channel allocation principle is determined based on the equivalent signal-to-interference-plus-noise ratio derived when RF and VLC reach the same maximum transmission rate.

2. The heterogeneous vehicle networking communication method integrating visible light communication and radio frequency communication according to claim 1, characterized in that: Step 1, the establishment of the clustering model specifically includes: First, the relative distance and relative speed between vehicles are calculated. The relative distance in the model uses Manhattan distance; the relative distance between any vehicle i and vehicle j is calculated. and relative velocity The expression is: in, Let i be the speed of any vehicle i; Let j be the speed of any vehicle. Let be the relative speed between any vehicle i and vehicle j; n is the number of vehicle nodes in the cluster to which the vehicle node belongs. The selection criterion for vehicle node i within the current cluster is: in, , As a weighting factor, different weight values ​​are set according to different scenarios; when the cluster head of node i selects an index... If i is the smallest in its cluster, then i is selected as the cluster head.

3. The heterogeneous vehicle networking communication method integrating visible light communication and radio frequency communication according to claim 1, characterized in that: Step 3, the RF channel noise analysis specifically includes the following steps: 3.1, Define RF propagation path loss as: in, Road loss index; This is the frequency dependence coefficient of road loss; This is the intercept coefficient; The RF center carrier frequency is in GHz; for line-of-sight (LOS) scenarios. , , For non-line-of-sight (NLOS) transmission scenarios , , This uses an NLOS scenario. 3.2, the signal-to-interference-plus-noise ratio (SIR) of the RF channel for the vehicle node is: in, For the channel gain between vehicles, denoted as background noise power, and n as the number of vehicle nodes within the cluster to which the vehicle resides.

4. The heterogeneous vehicle networking communication method integrating visible light communication and radio frequency communication according to claim 3, characterized in that: Step 4, the establishment of the channel allocation model specifically includes the following steps: 4.1 The maximum transmission rate, i.e., the channel capacity, can be obtained from the carrier bandwidth and SINR, specifically expressed as follows: Where B is the channel bandwidth and S is the signal power; The noise power spectral density; 4.2 Therefore, the maximum transmission rate between RF and VLC vehicles is: 4.3, when RF and VLC reach the same maximum transmission rate, i.e. Then, the relationship between the signal-to-interference-plus-noise ratio (SIN / N) of RF and VLC is obtained: in, For VLC carrier bandwidth, For RF carrier bandwidth, and Let VLC and RF be the channel coding loss factors, respectively. From the above equation, we can obtain the VLC channel coding loss factor when achieving the same maximum transmission rate. It is converted to the equivalent value required to achieve the same transmission rate in RF. ; 4.4, Channel allocation is as follows: when Vehicles i and j communicate using VLC; when Furthermore, when communicating between clusters, nodes use RF for communication.

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