# Data Transmission Control Method of Electrical Equipment of Automobile Based on the 5G Communication Technology

Authors: Yajun Han
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Conflicts emerge when the data transmission occurs between electrical equipment in an automobile. Hence, transmission delays appear as issues. For this reason, a method, called data transmission control, for electrical equipment of automobiles based on the 5G communication technology is proposed in this paper. Firstly, the wavelet feature decomposition was utilized to partition the frequency spectrum, and thus the statistical characteristics of electrical equipment of automobiles concerning the transmitted data were obtained. Then, the high-order approximate distribution method was adopted to construct a channel for a 5G network data transmission. Afterward, the control logic structure of the data transmission was built, and the problem, called the conflict of the data transmission, was alleviated through concurrent data collection and processing methods. On this basis, coding coefficients of a constructed global coding matrix were selected to encode and transmit source information. Also, the number of redundant data packets at each layer was adjusted. Finally, the data transmission control of the electrical equipment of the automobile was realized through the linear combination of the network nodes. The simulation results showed that the throughput of the proposed method was always higher than 7.7 MB/s, the bit error rate was around 0 when the signal-to-noise ratio was lower than 3, and the transmission delay was always below 0.5 s, which could provide a reference for the efficient and safe operation of automobiles.


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## Body

## 1. Introduction

With the rapid development of information technology, the functions of an automobile have become increasingly diverse, and automobile design has become more humane. The continuous improvement of automobile performance has caused a sharp increase in the number of electrical equipment installed in automobiles, which has, however, led to connection and communication between devices being more difficult [1–3]. Therefore, engineers in charge of automobile electronics identify the problems concerning the low communication quality and high data transmission delay as a priority to be resolved when equipment exchanges data transmission.Wireless transmission and a classification algorithm of multisource information of devices based on compressed sensing were proposed to deal with the problems mentioned previously in [4]. By constructing a model called multihop information transmission, the information transmission problem was converted into a compressed sensing problem of multichannel measurement signals. Thus, the time-domain feature, i.e., the total variation of the vibration signal, was introduced, and the evaluation algorithm of the compensation distance was used to verify the superiority of the time-domain feature index for the reconstructed signal. The characteristic part and data part of audio and video files to form a sequence of encoded data packets were divided based on the theory of network data packet transmission in [5]. Hence, the data packets were reordered in sequence and transmitted to ensure the data transmission security of audio and video files. However, the above-mentioned methods have their limitations and cannot properly meet the requirements for low-latency transmission of electrical equipment data of automobiles in the current 5G era.The 5G communication is the main development direction of future mobile communication. The development and application of the 5G communication technology have brought new opportunities to the connection and communication of electrical equipment of automobiles. When compared with the 4G technology, the communication capacity of the 5G is increased by more than 1000 times, and the spectrum efficiency, energy consumption, transmission delay, and user experience have all been significantly improved.This research built a new service system based on the communication service system of the original network, rationalizing the information contained in the network, performing network tasks more quickly and safely, and thus reducing transmission delay. Therefore, integrating the concept of sustainable development to better satisfy the production and operation needs of automobile enterprises can be realized.In this paper, a data transmission control method for electrical equipment of automobiles was proposed based on the 5G communication technology whose communication network channels were analyzed, and a control model of the data transmission was built. Besides, network coding technology was employed to perform error protection on automobile commands to achieve the goal of the accurate and efficient data transmission control of electrical equipment of automobiles on this basis.


## 2. Materials and Methods

### 2.1. Analysis concerning the 5G Communication Network Channels of Electrical Equipment of Automobiles

To achieve the data transmission control for electrical equipment of automobiles using a 5G communication network, the network channel is first analyzed completely. There are a large number of electrical equipment in automobiles, and different devices in the communication network constitute different nodes. If you haveM sink nodes to collect communication data, the amplitude of the background noise signal in the network data transmission is denoted by Ac. Then, the coherent distribution source has P interference signals [6, 7]. The data transmission signal of the equipment in the 5G communication network is denoted by x, and the characteristic sequence of the initial input network communication data is described by(1)wn=w0,…,wN−1,where wn is a discrete network communication data sequence of finite length, 0≤n≤N−1. Then, the data transmission channel model of the electrical equipment’s 5G communication network is defined by(2)Xk=∑n=0N−1wnexp−j2πZnk,where k takes a value of 0≤k≤Z−1, denoting the communication data length of the network, and the bandwidth of the network data transmission is denoted by Z=DFTx. The signal is processed by a discrete orthogonal wavelet transform. In the discrete distribution sequence a, a wavelet transform is utilized to complete the signal equalization of the 5G network transmission, and the results of the spectral feature extraction are separated by the wavelet feature decomposition to obtain the feature quantity of statistical information concerning the data transmission, which is denoted by(3)B=B0,…,BN−1.In the network transmission channel, spectrum separation is utilized to obtain discrete data sampling pointsj under different bandwidths, and the energy function for the data transmission of the network communication is given by(4)Ej=∑kCjk2.For the transmission channels with the integer levels ofN0 and N1, the control algorithm based on baud interval equalization is used to obtain the spectral characteristic components of the transmission symbols denoted by(5)Cjk=xt,φj,kt,where Cj and Ej represent the low-pass and high-pass filter transfer functions of the 5G network communication data, respectively. Then, the characteristics of the output energy spectrum of network communication are defined by(6)E=xt2=∑j∑kCjk2=∑jEj.Sincest, the transmission signal vector, and nt, the noise vector, of the network transmission data are independent of each other, only the fusion singular value decomposition is needed to complete the channel separation. It is assumed that the data orders are denoted by Nj, N0j, and N1j with different network data transmission lengths, and Pj represents the power spectral density of the network data in the energy accumulation range. If the real part is denoted by nrk and the imaginary part is denoted by nik, the input noise signal, nk, is called independent Gaussian noise [8]. The passband of the network communication data transmission is denoted by cj, and the wavelet function decomposition is used to complete the channel equalization, and the analytical output of the channel equalization formula is defined by(7)C0←DFTx,(8)Cj,Wj←AFBCj−1,N0j,N1j,(9)wj←DFT−1Wj,(10)cJ←DFT−1CJ,where 1≤j≤J and cj and wj are the threshold function and resolution function of data transmission, respectively. The characteristic of the power spectral density is expressed by(11)Tj=σ2lnN1−J2×Ej∑j=1J+1Ej,j=1,2,…,J,σ2lnN×Ej∑j=1J+1Ej,j=J+1.The algorithm, called baud interval equalization, is used to determine the overall communication network status of the device and to clarify the equalization index of the data transmission channel, thereby providing a solid theoretical basis for subsequent transmission control [9].


### 2.2. Construction of the Control Model of the Data Transmission

The key to the data transmission control model of electrical equipment of automobiles is to use protocol conversion and big data concurrency control method [10] to complete the real-time collection, analysis, and efficient transmission of instructions for the internal equipment data of automobiles. The framework of the transmission logic is depicted in Figure 1.Figure 1
The logical framework of the data transmission.The logical framework of the data transmission is composed of Controller Area Network (CAN) protocol [11] converter, communication server, monitoring client, and other parts. The communication server is the central part of the model that is responsible for maintaining the connection between the downstream socket of the CAN protocol converter and the upstream socket of the client and is also responsible for data frame transmission control and analysis. The database saves the non-current data stored in the communication server concerning the historical database. The client extracts the real-time data of the device by calling the interface with the communication server and uses the historical database to search for non-real-time data [12].There are two types of data in the model, i.e., control instructions and monitoring data. The control commands of automobile start and stop issued by the user will be assembled into a CAN data frame through the communication server and then sent to the electrical equipment using the protocol converter. The monitoring data contain the basic information of the equipment of the automobile. These data adopt the active periodic transmission method [13]. After being packaged into protocol frames by the protocol converter, they are transmitted to the communication server. Then, the communication server calls different processing threads and queues according to different data types, and the data frame is decomposed into application layer data that can be understood by the monitoring client.In large electrical equipment, the communication server needs to process, forward, and assemble tens of thousands of CAN data frames per second [14]. Due to cost and other factors, it is impossible to improve the hardware configuration to enhance the data processing capability of the device. Thus, a complete data processing dealing with the concept of large-scale data concurrency control necessitates a theoretical base. Referring to the framework of Figure 1 as a prerequisite, the data processing flow of the model is described as follows. For the characteristics of the CAN protocol data unit, a data submodel for an application layer of a CAN protocol is constructed. Thus, this model can complete the rapid transmission of CAN data frames with different priorities and different data volumes. According to the requirement to monitor timeliness, a socket-based data transmission submodel is employed to present relevant data on the client in real-time. Afterward, a concurrent data collection and processing method is established to effectively alleviate the problem of data transmission conflicts and enhance the efficiency of data transmission control based on the data submodel [15].


### 2.3. Transmission Control Based on Network Coding Technology

Network coding is a new data transmission control mode in the communication field. Network coding nodes can not only complete data forwarding and copying but also encode information before transmission, which can maximize network throughput and robustness [16]. Dedicated short-range communications developed and tested by automotive technology providers are under threat, and next-generation 5G technology could be the final answer [17].In the general network coding method, the coding node selects random values as the coding core, forms a rectangular matrix through random numbers, and finally forms the global coding matrix of the network coding. Because the coding matrix has certain constraints, the receiving end can perform the decoding functionality when a full-rank global coding matrix is provided, which only leads to a higher probability of packet loss rate and delay interference in data transmission.This paper designs a data transmission control method based on a special structure coding matrix to improve the reliability of data transmission. The 5G communication network is regarded as a directed acyclic graphG=F,H, where F is a set of network nodes and H is a set of directed edges. The directed edges represent the directed links between nodes. The 5G communication network coding is employed to limit the capacity channel of the coding unit. If the link capacity is higher than 1, then it is divided into multiple capacity channels.The core concept of coding is that the information characters forwarded by the output channel of each coding node are the linear combination of all input information characters for this node in a finite field [18, 19]. The set of input channels of the node f is denoted by Inf=e:taile=f, the set of output channels is denoted by Outf=e:heade=f, and taile and heade are the tail and head of the arc e, respectively.After encoding the output channelee∈Outf of the node f, the information character operation and analysis formula forwarded by it is given by(12)ye=∑e′∈Infme′,eye′.The source and the receiving nodes are associated with the global coding matrix (GCM), which is defined by(13)Y=L×S,where Y is the decoded data packet, L is the global coding matrix, and S represents the initial information. When the database receives linear independent data, the global coding matrix needs to be updated. When it has a full rank, the receiving node uses the Gaussian elimination to decode, which is denoted by(14)S=L−1×Y.When network coding methods are generally utilized to complete data transmission control, coding coefficients must be selected in the global coding matrix to perform the coding and transmission of source information. The global coding matrix formed by the coding coefficients is represented by(15)L=l11l12…l1Nl21l22…l2N⋮⋮⋱⋮lN1lN2…lNN,where l11∼lNN represents the global coding coefficient and N is the total number of output data packets. It is assumed that pi is called the loss rate of the linear independent data packet; then, the number of linear independent data packets in the output data packet is given by(16)Ni=N1−piN.The corresponding throughput is denoted by(17)Throughputi=NiN=1−piN.The time to receiveN linear independent data packet is denoted by(18)Ti=Throughputi−1=1−pi−N.The global coding matrix designed in this paper has a non-strict triangular structure, which is composed of submatricesM1, M2, M3, and Mi that are divided into two parts of ki and ri. While ki is a strict triangular matrix [20], the source node generates a total of ki+ri=ni data packets.Adjusting the value ofki or ri can allocate the code rate of the initial data. O denotes the number of data layers coded through the network. The source node generates a total of N=∑i=1Oki network coding data.The data redundancy rate is denoted by(19)α=∑i=1Ori∑i=1Oki.Adjusting the number of redundant data packets in each layer can protect automobile commands from errors [21]. If the optimal number of redundant data packets is represented by the vector r¯, the code rate allocation problem is equivalent to the following constraint optimization problem defined by(20)Jr¯∗=min∑i=1ODdi,ti,qiPi,where Di is the rate distortion of the ith layer code stream and Pi is the packet loss rate of the ith layer code stream. The number of redundant data packets corresponding to each layer is converged by(21)∑i=1Ori=R,(22)riki≥ri+1ki+1.The calculation steps of the data transmission control method proposed herein are shown in Figure2.Figure 2
The calculation process of the proposed method.According to the subordinate layer number of the data packet in the header information, it is obvious whether all the data packets received by the intermediate nodei belong to the same layer of the layered code stream. If it belongs to the same layer, a linear combination is adopted according to equation (23) to realize random linear network coding and is sent to the next node concurrently, which is defined by(23)γk=∑liinPi⋅gik,k∈liout,where liin is called the input channel corresponding to the intermediate node, liout is called the output channel corresponding to the intermediate node, k is called the random output channel, Pi represents all data packets received by the node, and gik is called the network coding coefficient.Finally, when the linear network coding of the random 5G communication is completed by sending the data to receiving end through the transmission channel, the data transmission dealing with the control of the electrical equipment of an automobile is achieved with much more efficiency and reliability.


## 2.1. Analysis concerning the 5G Communication Network Channels of Electrical Equipment of Automobiles

To achieve the data transmission control for electrical equipment of automobiles using a 5G communication network, the network channel is first analyzed completely. There are a large number of electrical equipment in automobiles, and different devices in the communication network constitute different nodes. If you haveM sink nodes to collect communication data, the amplitude of the background noise signal in the network data transmission is denoted by Ac. Then, the coherent distribution source has P interference signals [6, 7]. The data transmission signal of the equipment in the 5G communication network is denoted by x, and the characteristic sequence of the initial input network communication data is described by(1)wn=w0,…,wN−1,where wn is a discrete network communication data sequence of finite length, 0≤n≤N−1. Then, the data transmission channel model of the electrical equipment’s 5G communication network is defined by(2)Xk=∑n=0N−1wnexp−j2πZnk,where k takes a value of 0≤k≤Z−1, denoting the communication data length of the network, and the bandwidth of the network data transmission is denoted by Z=DFTx. The signal is processed by a discrete orthogonal wavelet transform. In the discrete distribution sequence a, a wavelet transform is utilized to complete the signal equalization of the 5G network transmission, and the results of the spectral feature extraction are separated by the wavelet feature decomposition to obtain the feature quantity of statistical information concerning the data transmission, which is denoted by(3)B=B0,…,BN−1.In the network transmission channel, spectrum separation is utilized to obtain discrete data sampling pointsj under different bandwidths, and the energy function for the data transmission of the network communication is given by(4)Ej=∑kCjk2.For the transmission channels with the integer levels ofN0 and N1, the control algorithm based on baud interval equalization is used to obtain the spectral characteristic components of the transmission symbols denoted by(5)Cjk=xt,φj,kt,where Cj and Ej represent the low-pass and high-pass filter transfer functions of the 5G network communication data, respectively. Then, the characteristics of the output energy spectrum of network communication are defined by(6)E=xt2=∑j∑kCjk2=∑jEj.Sincest, the transmission signal vector, and nt, the noise vector, of the network transmission data are independent of each other, only the fusion singular value decomposition is needed to complete the channel separation. It is assumed that the data orders are denoted by Nj, N0j, and N1j with different network data transmission lengths, and Pj represents the power spectral density of the network data in the energy accumulation range. If the real part is denoted by nrk and the imaginary part is denoted by nik, the input noise signal, nk, is called independent Gaussian noise [8]. The passband of the network communication data transmission is denoted by cj, and the wavelet function decomposition is used to complete the channel equalization, and the analytical output of the channel equalization formula is defined by(7)C0←DFTx,(8)Cj,Wj←AFBCj−1,N0j,N1j,(9)wj←DFT−1Wj,(10)cJ←DFT−1CJ,where 1≤j≤J and cj and wj are the threshold function and resolution function of data transmission, respectively. The characteristic of the power spectral density is expressed by(11)Tj=σ2lnN1−J2×Ej∑j=1J+1Ej,j=1,2,…,J,σ2lnN×Ej∑j=1J+1Ej,j=J+1.The algorithm, called baud interval equalization, is used to determine the overall communication network status of the device and to clarify the equalization index of the data transmission channel, thereby providing a solid theoretical basis for subsequent transmission control [9].


## 2.2. Construction of the Control Model of the Data Transmission

The key to the data transmission control model of electrical equipment of automobiles is to use protocol conversion and big data concurrency control method [10] to complete the real-time collection, analysis, and efficient transmission of instructions for the internal equipment data of automobiles. The framework of the transmission logic is depicted in Figure 1.Figure 1
The logical framework of the data transmission.The logical framework of the data transmission is composed of Controller Area Network (CAN) protocol [11] converter, communication server, monitoring client, and other parts. The communication server is the central part of the model that is responsible for maintaining the connection between the downstream socket of the CAN protocol converter and the upstream socket of the client and is also responsible for data frame transmission control and analysis. The database saves the non-current data stored in the communication server concerning the historical database. The client extracts the real-time data of the device by calling the interface with the communication server and uses the historical database to search for non-real-time data [12].There are two types of data in the model, i.e., control instructions and monitoring data. The control commands of automobile start and stop issued by the user will be assembled into a CAN data frame through the communication server and then sent to the electrical equipment using the protocol converter. The monitoring data contain the basic information of the equipment of the automobile. These data adopt the active periodic transmission method [13]. After being packaged into protocol frames by the protocol converter, they are transmitted to the communication server. Then, the communication server calls different processing threads and queues according to different data types, and the data frame is decomposed into application layer data that can be understood by the monitoring client.In large electrical equipment, the communication server needs to process, forward, and assemble tens of thousands of CAN data frames per second [14]. Due to cost and other factors, it is impossible to improve the hardware configuration to enhance the data processing capability of the device. Thus, a complete data processing dealing with the concept of large-scale data concurrency control necessitates a theoretical base. Referring to the framework of Figure 1 as a prerequisite, the data processing flow of the model is described as follows. For the characteristics of the CAN protocol data unit, a data submodel for an application layer of a CAN protocol is constructed. Thus, this model can complete the rapid transmission of CAN data frames with different priorities and different data volumes. According to the requirement to monitor timeliness, a socket-based data transmission submodel is employed to present relevant data on the client in real-time. Afterward, a concurrent data collection and processing method is established to effectively alleviate the problem of data transmission conflicts and enhance the efficiency of data transmission control based on the data submodel [15].


## 2.3. Transmission Control Based on Network Coding Technology

Network coding is a new data transmission control mode in the communication field. Network coding nodes can not only complete data forwarding and copying but also encode information before transmission, which can maximize network throughput and robustness [16]. Dedicated short-range communications developed and tested by automotive technology providers are under threat, and next-generation 5G technology could be the final answer [17].In the general network coding method, the coding node selects random values as the coding core, forms a rectangular matrix through random numbers, and finally forms the global coding matrix of the network coding. Because the coding matrix has certain constraints, the receiving end can perform the decoding functionality when a full-rank global coding matrix is provided, which only leads to a higher probability of packet loss rate and delay interference in data transmission.This paper designs a data transmission control method based on a special structure coding matrix to improve the reliability of data transmission. The 5G communication network is regarded as a directed acyclic graphG=F,H, where F is a set of network nodes and H is a set of directed edges. The directed edges represent the directed links between nodes. The 5G communication network coding is employed to limit the capacity channel of the coding unit. If the link capacity is higher than 1, then it is divided into multiple capacity channels.The core concept of coding is that the information characters forwarded by the output channel of each coding node are the linear combination of all input information characters for this node in a finite field [18, 19]. The set of input channels of the node f is denoted by Inf=e:taile=f, the set of output channels is denoted by Outf=e:heade=f, and taile and heade are the tail and head of the arc e, respectively.After encoding the output channelee∈Outf of the node f, the information character operation and analysis formula forwarded by it is given by(12)ye=∑e′∈Infme′,eye′.The source and the receiving nodes are associated with the global coding matrix (GCM), which is defined by(13)Y=L×S,where Y is the decoded data packet, L is the global coding matrix, and S represents the initial information. When the database receives linear independent data, the global coding matrix needs to be updated. When it has a full rank, the receiving node uses the Gaussian elimination to decode, which is denoted by(14)S=L−1×Y.When network coding methods are generally utilized to complete data transmission control, coding coefficients must be selected in the global coding matrix to perform the coding and transmission of source information. The global coding matrix formed by the coding coefficients is represented by(15)L=l11l12…l1Nl21l22…l2N⋮⋮⋱⋮lN1lN2…lNN,where l11∼lNN represents the global coding coefficient and N is the total number of output data packets. It is assumed that pi is called the loss rate of the linear independent data packet; then, the number of linear independent data packets in the output data packet is given by(16)Ni=N1−piN.The corresponding throughput is denoted by(17)Throughputi=NiN=1−piN.The time to receiveN linear independent data packet is denoted by(18)Ti=Throughputi−1=1−pi−N.The global coding matrix designed in this paper has a non-strict triangular structure, which is composed of submatricesM1, M2, M3, and Mi that are divided into two parts of ki and ri. While ki is a strict triangular matrix [20], the source node generates a total of ki+ri=ni data packets.Adjusting the value ofki or ri can allocate the code rate of the initial data. O denotes the number of data layers coded through the network. The source node generates a total of N=∑i=1Oki network coding data.The data redundancy rate is denoted by(19)α=∑i=1Ori∑i=1Oki.Adjusting the number of redundant data packets in each layer can protect automobile commands from errors [21]. If the optimal number of redundant data packets is represented by the vector r¯, the code rate allocation problem is equivalent to the following constraint optimization problem defined by(20)Jr¯∗=min∑i=1ODdi,ti,qiPi,where Di is the rate distortion of the ith layer code stream and Pi is the packet loss rate of the ith layer code stream. The number of redundant data packets corresponding to each layer is converged by(21)∑i=1Ori=R,(22)riki≥ri+1ki+1.The calculation steps of the data transmission control method proposed herein are shown in Figure2.Figure 2
The calculation process of the proposed method.According to the subordinate layer number of the data packet in the header information, it is obvious whether all the data packets received by the intermediate nodei belong to the same layer of the layered code stream. If it belongs to the same layer, a linear combination is adopted according to equation (23) to realize random linear network coding and is sent to the next node concurrently, which is defined by(23)γk=∑liinPi⋅gik,k∈liout,where liin is called the input channel corresponding to the intermediate node, liout is called the output channel corresponding to the intermediate node, k is called the random output channel, Pi represents all data packets received by the node, and gik is called the network coding coefficient.Finally, when the linear network coding of the random 5G communication is completed by sending the data to receiving end through the transmission channel, the data transmission dealing with the control of the electrical equipment of an automobile is achieved with much more efficiency and reliability.


## 3. Results and Analysis

To prove the practicability of the proposed data transmission control method for electrical equipment of automobiles based on the 5G communication technology, simulation experiments were carried out to investigate the effective throughput under different data transmission conditions employing the proposed method, the compressed sensing method, and the packet sequence method. The link capacity of each channel was set to 6 Mb, the round-trip delay of the link was 45 ms, and the average packet loss rate was set to 12%. The correlation between the effective throughput and the number of channels is shown in Figure3.Figure 3
The correlation between the effective throughput and the number of channels.The range of packet loss rate was set to be 0–35%, and the other parameters are the same as above. Figure4 compares the effective throughput of the three methods.Figure 4
The comparison of the effective throughput of the three methods.Figure4 depicts the throughput of the three methods. The throughput of the proposed method was always higher than 7.7 MB/s, which was significantly better than the compressed sensing method and the packet sequence method when there was an increase in the packet loss rate. Because the proposed method could accurately analyze the real-time conditions of the internal channels of the 5G communication network, it ensured the effective progress of the data transmission and maintained a relatively stable state.Table1 presents the comparison of the bit error rates of the three methods concerning the data transmission.Table 1
The comparison of the bit error rates.
SNR (dB)The proposed methodCompressed sensing methodPacket sequence method150.0740.1740.185120.0510.1650.17690.0320.1350.14260.0240.1010.10830.0000.0870.09400.0000.0590.062Table1 shows that the proposed method tended to have the smallest bit error rate when the SNR ratio had a trend to decrease. Besides, when the signal-to-noise ratio was lower than 3, the bit error rate converge to 0, which was significantly better than that of the compressed sensing method and the data packet sequence method. Specifically, the compressed sensing method had poor data collection capabilities, leading to low data authenticity. In the data packet sequence method, the quality of the transmission control protocol could not be easily controlled, and the method process was relatively ideal. The proposed method employed the network coding technology to complete the linear combination of data and reduce redundant data transmission. Thus, relatively better performance has been achieved.The experiment also presented and compared the results of the data transmission delay extracted from the three methods by plotting them in Figure5.Figure 5
The comparison of the data transmission delay of the three methods.Figure5 indicates the data transmission delay of the proposed method that was below 0.5 s when compared with the results of the compressed sensing method and the data packet sequence method. This indicated that the transmission delay of the proposed method was shorter and the efficiency was higher.The algorithm adjusts the number of redundant packets at each layer and improves the packet transmission success rate through the linear combination of network nodes.Figure6 shows the change of packet transmission success rate with packet ratio when the total number of packets is constant. It can be seen that as the proportion of delay-sensitive packets increases, the packet transmission success rate of the algorithm in this paper increases gradually compared with the other two methods.Figure 6
The success rate of packet transmission varying with the proportion of packets.Figure7 demonstrates the change of packet transmission success rate with the number of nodes. It can be seen that the transmission success rate of the three algorithms increases gradually with the increase of the number of nodes. This is because as the number of nodes increases, the connectivity of the network increases and the success rate of hop-by-hop transmission between nodes increases.Figure 7
The success rate of packet transmission varying with the number of nodes.Figure8 shows the change of packet transmission success rate with node speed. It can be seen that the packet transmission success rate of the three algorithms is almost not affected by the node speed, and the performance of the proposed method and compressed sensing method is better than that of the packet sequence method.Figure 8
The comparison of the methods with the proposed one used in this manuscript.Figure9 demonstrates the change of packet transmission success rate with packet transmission distance. It can be seen that when the packet transmission distance is less than 500 m, the packet transmission success rate of the three algorithms is similar. Then, as the transmission distance gradually increases, the data packets of the three algorithms are transmitted. The success rate gradually declined. The proposed method achieves the best performance.Figure 9
Packet transmission distance/m.


## 4. Conclusion

To improve the reliability of the data transmission and ensure the safe driving of automobiles, a data transmission control method for electrical equipment of automobiles was proposed based on the 5G communication technology. The results of the simulation experiment showed that the proposed method had a better channel equalization, lower transmission delay, and stronger adaptive ability when compared with the compressed sensing method and the data packet sequence method. When the signal-to-noise ratio was obtained less than 3, the bit error rate of the proposed method converges to 0, which means that a better data transmission quality and higher reliability could be realized.With the rapid development of automobile technologies, many kinds of electrical equipment are installed in automobiles. Therefore, there is a need to have more kinds of electronic control unit equipment for automobiles, and the lines would become extremely complicated. Hence, there would be more frequent conflicts in the data transmission of electrical equipment of automobiles. The proposed method has many advantages such as high channel equalization, low transmission delay, and lower bit error rate. The application of the proposed method is expected to improve the real-time performance and reliability of data transmission for electrical equipment of automobiles.From the development perspective of the history of the automobile, automobile electrification has become a revolutionary step in the automobile industry, and the electrical equipment of the automobile has become the most important supporting foundation for the control systems of the automobile. With the increasing maturity of new energy automobiles, unmanned driving, and onboard information system technologies, the automobile industry has been advancing in the directions of intelligence, networking, and deep electronics. Therefore, the future direction of this research aims at focusing continuously on exploring solutions of other aspects of the problem to further provide important achievements for safe driving.



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*Source: 2139629-2022-03-24.xml*