CN111988834B - Heterogeneous Internet of vehicles network selection method, system and device - Google Patents

Heterogeneous Internet of vehicles network selection method, system and device Download PDF

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CN111988834B
CN111988834B CN202010879517.8A CN202010879517A CN111988834B CN 111988834 B CN111988834 B CN 111988834B CN 202010879517 A CN202010879517 A CN 202010879517A CN 111988834 B CN111988834 B CN 111988834B
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power
utility function
user
network
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CN111988834A (en
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申玉洁
刘鑫一
王威
侯俊
庞继龙
谢景丽
徐志麟
狄陈琪
刘岩
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Changan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/18—Selecting a network or a communication service
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Abstract

The invention discloses a method, a system and a device for selecting a heterogeneous Internet of vehicles network, wherein the method specifically comprises the following steps: obtaining a utility function weight coefficient, a logarithm form utility function weight and a network transmitting power initial value through a user satisfaction experience function, and setting an allowable error; calculating the maximum transmission rate of all networks by using the concept of the effective capacity; calculating a power value corresponding to the maximum transmission rate, comparing an absolute value of a difference between power values obtained by two adjacent iterations with an allowable error to obtain a power theoretical value corresponding to the maximum transmission rate, obtaining a satisfaction degree utility function value when the same user faces different networks, and selecting a network with the maximum satisfaction degree utility function value as an access network; the heterogeneous Internet of vehicles network selection method introduced by the invention introduces the concept of effective capacity, not only considers the satisfaction degree of users, but also considers the transmission efficiency, obtains the maximum value of the utility value of the satisfaction degree in a limited power range, and is used for selecting the network.

Description

Heterogeneous Internet of vehicles network selection method, system and device
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a method, a system and a device for selecting a heterogeneous Internet of vehicles network.
Background
In recent years, the demand of Internet of vehicles (IoV) users for accessing broadband wireless networks with high quality at any time and any place is becoming stronger, and wireless communication technologies which take various scenes into consideration are rapidly developed. Emerging interconnected Vehicle and autonomous Vehicle technologies may improve overall network operating efficiency, mobility, and traffic safety through Vehicle-to-Vehicle (to Vehicle) and Vehicle-to-Infrastructure (to Infrastructure) Communications based on real-time Dedicated Short Range Communications (DSRC). Mobile cellular networks have evolved from GSM (Global System for Mobile communications) to UMTS (Universal Mobile Telecommunications System) and then to LTE (Long Term Evolution) and 802.11 standard Wireless Local Area Networks (WLAN) to today's 5G; with the continuous development of diversification and Multi-network convergence of user demands, multi-source Heterogeneous Wireless Networks (MHWNs) have come into existence.
In multi-source heterogeneous vehicle networking, a user terminal (vehicle) has multiple networks available, such as WLAN, cellular networks, and other wireless networks. How users choose these networks as access networks is very important. Due to the differences of the transmission quality of wireless network links, the overlapping of wireless network signals, the richness of mobile terminal equipment services and other factors, a reliable and efficient network access selection algorithm is needed to meet the requirement of vehicle network user access network selection.
Until now, experts in the field have made a lot of research on access selection algorithms and have proposed many algorithms. Generally, the method is classified into an access selection algorithm based on load balancing, received signal strength and Quality of Service (QoS) according to a criterion; the algorithm is divided into access selection algorithms based on utility functions, neural networks, game theory, multi-attribute decision making and the like according to the selected mathematical method.
To provide network selection, "network selection of vehicle application triggered telematics device system" by ford global technology corporation (application date: 20/02/2019, application number: 201910127575.2, publication number: 110191492A) and "access network selection method and system" by chinese telecommunications limited (application date: 12/11/2013, application number: CN201310671484.8, publication number: CN 104717723B) and "method for facilitating network access selection for wireless communication devices" by swedish ericsson limited (application date: 13/10/2017, application number: 201780095735.9, publication number: 111194563 a). But the defects in the methods are as follows: only the network switching response is considered, and the user satisfaction and the transmission efficiency are not considered, so that the user is likely to face the conditions of low QoS guarantee and waste of communication resources when the network selection is carried out.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a heterogeneous Internet of vehicles network selection method, which introduces the concept of effective capacity, considers the satisfaction degree of users and the transmission efficiency, obtains the maximum value of a satisfaction degree utility function in a limited power range and is used for selecting a network.
In order to achieve the purpose, the technical scheme of the invention is as follows: a heterogeneous Internet of vehicles network selection method comprises the following steps:
obtaining a utility function weight coefficient, a logarithm form utility function weight and a network transmitting power initial value through a user satisfaction experience function, and setting an allowable error;
aiming at different networks, acquiring the maximum transmission rate of all the networks under the principle of effective capacity;
calculating a power value corresponding to the maximum transmission rate by using a Newton iteration method, comparing the absolute value of the difference between the power values obtained by two adjacent iterations with an allowable error, and ending the Newton iteration operation until the absolute value of the difference between the power values obtained by two adjacent iterations is smaller than the set allowable error to obtain a theoretical power value corresponding to the maximum transmission rate;
obtaining a satisfaction utility function value of the same user facing different networks according to the user satisfaction experience function and the power theoretical value, and selecting the network with the maximum satisfaction utility function value as an access network;
wherein the user satisfaction experience function is
Figure BDA0002653689140000031
In the formula, beta represents the weight of the utility function in a logarithmic form, and a user selects the corresponding weight to change the shape of the utility function according to different requirements of services; p denotes a transmission power value of the user.
The method specifically comprises the following steps:
step 1, initialization: determining a utility function weight coefficient alpha and a weight beta of a log-form utility function according to a user satisfaction experience function, and determining an initial power value p of a user access network 0 Setting an allowable error c;
step 2, according to the concept of effective capacity, calculating the maximum transmission rate of all networks
Figure BDA0002653689140000032
A value of (d);
step 3, calculating the power value p corresponding to the maximum transmission rate by a Newton iteration method i+1 I denotes the ith iteration;
step 4, the power value p obtained by the i +1 th iteration obtained in the step 3 i+1 And the power value p obtained in the ith iteration i The absolute value of the difference is compared with the allowable error c set in step 1, if | p i+1 -p i If | is less than c, returning the theoretical value p * ＝p i +1 If | p i+1 -p i If i > c, then i = i +1 until a condition of | p is found i+1 -p i When | < c, returning the theoretical value p * ＝p i+1 (ii) a Obtaining the theoretical optimal power value p * Then, experience the function according to the satisfaction degree of the user and return theoretical value p * And obtaining a satisfaction degree utility function value, and selecting the network with the maximum satisfaction degree utility function value as an access network.
In step 2, the effective capacity is specifically expressed as:
Figure BDA0002653689140000033
where γ represents the signal-to-noise ratio (S) of the userNR), theta denotes a QoS index, E [ x ]]The method comprises the following steps of obtaining an expected operation for x, wherein T represents the time slot length of one transmission process, and B represents the system bandwidth.
In the step 3, p is calculated by using a Newton iteration method i+1 The values are specifically as follows:
for optimal p * The value, first of all, of the function,
Figure BDA0002653689140000041
the variable p is then derived:
Figure BDA0002653689140000042
the function expression is given as:
Figure BDA0002653689140000043
then solving the formula (3) to obtain the evaluated value;
wherein:
Figure BDA0002653689140000044
Figure BDA0002653689140000045
in the formula of alpha λ (i)、α ν (i) Is a constant greater than 0.
In step 4, an optimal value p is calculated through a convex optimization theory * The method comprises the following steps:
considering the quality of service of all accessible networks in combination, it is desirable to be able to obtain as large a satisfaction utility function as possible, the network selection being expressed as an optimization problem:
Figure BDA0002653689140000051
Figure BDA0002653689140000052
c.2:p≤p max ,p≥0 (6)
wherein, the user satisfaction experience function is expressed as u (p), and the user transmission efficiency is expressed as
Figure BDA0002653689140000053
Figure BDA0002653689140000054
Figure BDA0002653689140000055
According to the convex optimization theory, the obtained formula (6) is concave optimization, and the local optimal solution is the global optimal solution of the formula (6).
Solving equation (6) to obtain the global optimal solution is as follows:
firstly, obtaining a Lagrange expression of an objective function:
Figure BDA0002653689140000056
the following formula holds:
Figure BDA0002653689140000057
the Lagrangian dual formula is then given:
Figure BDA0002653689140000061
the variable p is then derived as:
Figure BDA0002653689140000062
then, solving the formula (13) by using a Newton iteration method to obtain:
Figure BDA0002653689140000063
wherein:
Figure BDA0002653689140000064
Figure BDA0002653689140000065
in the formula of alpha λ (i)、α ν (i) A constant greater than 0.
A heterogeneous Internet of vehicles network selection system comprises an initialization module, a maximum transmission rate acquisition module, a power calculation module and a comparison selection module; the initialization module obtains a utility function weight coefficient, a log-form utility function weight and a network transmitting power initial value through a user satisfaction experience function, and sets an allowable error;
the maximum transmission rate acquisition module acquires the maximum transmission rates of all networks based on the principle of effective capacity;
the power calculation module calculates a power value corresponding to the maximum transmission rate by using a Newton iteration method, and then compares the absolute value of the difference between the power values obtained by two adjacent iterations with an allowable error until the absolute value of the difference between the power values obtained by two adjacent iterations is smaller than the set allowable error, and ends the Newton iteration operation to obtain a theoretical power value corresponding to the maximum transmission rate;
and the comparison selection module obtains a satisfaction utility function value of the same user facing different networks according to the user satisfaction experience function and the power theoretical value, and selects the network with the maximum satisfaction utility function value as an access network.
A computer device comprises one or more processors and a memory, wherein the memory is used for storing computer executable programs, the processor reads part or all of the computer executable programs from the memory and executes the computer executable programs, and when the processor executes part or all of the computer executable programs, the heterogeneous Internet of vehicles network selection method can be realized.
A computer-readable storage medium, in which a computer program is stored, and which, when executed by a processor, is capable of implementing the heterogeneous internet of vehicles network selection method of the present invention.
Compared with the prior art, the invention has the following advantages:
the utility function method is adopted to construct the network selection model, the network selection algorithm based on the utility function can comprehensively consider various factors, and a utility function is designed, then calculation is carried out, function values are sequenced, and finally the network with the highest utility function value is accessed; the network access selection strategy based on the utility function has higher decision speed and lower algorithm complexity, and comprehensively considers the performance indexes of various networks and the service characteristics of users; introducing a concept of effective capacity, comprehensively considering user satisfaction and transmission efficiency, taking power as a variable and a satisfaction utility function value as a selection standard, and selecting a network which is most suitable for a user according to different satisfaction utility function values among different access networks; in the calculation process, a convex optimization theory is adopted, a network selection model is modeled into an optimization model, the optimization theory and a Newton iteration method are utilized to calculate a corresponding optimal value, and the Newton iteration method has higher convergence speed and is closer to a local maximum value than other algorithms based on gradient descent or non-gradient methods; simulation results show that different satisfaction utility function values are obtained in a limited power range when different network environments are considered, and finally a network with the maximum satisfaction utility function value is selected as an access network.
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FIG. 1 is a block diagram of an implementation flow of the present invention.
Fig. 2 is a diagram of experience function versus access resource in the present invention.
Fig. 3 is a graph of the satisfaction utility function value versus different transmit powers and different values of a and theta in the present invention.
Figure 4 is a graph of satisfaction utility function values for different networks in the present invention.
Figure 5 is a graph of the satisfaction utility function values of the proposed strategy versus other strategies.
Detailed Description
The invention is described in detail below with reference to the drawings.
Firstly, determining a utility function weight coefficient alpha and a weight beta of a log-form utility function according to a user satisfaction experience function, and determining an initial power value p of a user access network 0 And setting an allowable error c; then for different networks, the effective capacity formula is used
Figure BDA0002653689140000081
Where γ represents the signal-to-noise ratio (SNR) of the user, θ represents the QoS index, E [ x ]]The method comprises the steps of calculating an expectation operation on x, T representing the continuous time slot length of one transmission process, B representing the system bandwidth, and calculating the maximum information transmission rate
Figure BDA0002653689140000082
Obtaining maximum information transmission rate
Figure BDA0002653689140000083
Then, the power value p is calculated by Newton's iteration method i+1 The superscript i denotes the ith iteration; if p i+1 -p i If | is less than c, returning the theoretical value p * ＝p i+1 If | p i+1 -p i If | is greater than c, i = i +1, and the iterative algorithm is continued until | p is found to be satisfied i+1 -p i The value of | < c returns to the theoretical value p * ＝p i+1 (ii) a Obtaining the theoretical optimal power value p * Then, according to the user satisfaction degreeA function of the experiment and a theoretical value of p * And obtaining a satisfaction degree utility function value, and selecting the network with the maximum satisfaction degree utility function value as an access network.
Referring to the attached figure 1, the specific implementation steps of the invention are as follows:
step 1, initialization: determining a utility function weight coefficient alpha and a weight beta of a log-form utility function according to a user satisfaction experience function, and determining an initial power value p of a user access network 0 And setting an allowable error c;
step 2, according to the concept of effective capacity, calculating the maximum transmission rates of different networks
Figure BDA0002653689140000091
A value of (d);
step 3, calculating p by a Newton iteration method i+1 The superscript i denotes the ith iteration;
the method specifically comprises the following steps:
step (31) for optimal p * The value, first using newton's method, converges faster than other algorithms based on either gradient descent or non-gradient methods, closer to the local maximum, first having the function:
Figure BDA0002653689140000092
wherein: alpha represents a weight coefficient of the utility function; θ represents a QoS index; n is a radical of 0 Representing a noise power spectral density; b represents the system bandwidth; p represents the transmission power of the user; e A [x]Representing an exponential integration function.
The variable p is then derived:
Figure BDA0002653689140000093
the function expression is given as:
Figure BDA0002653689140000094
the evaluated value is then obtained by solving equation (3).
Wherein:
Figure BDA0002653689140000095
Figure BDA0002653689140000101
in the formula of alpha λ (i)、α ν (i) Is a constant greater than 0.
Step 4, p obtained in step 3 is used i+1 -p i Is compared with the allowable error c, if p i+1 -p i If | is less than c, returning the theoretical value p * ＝p i+1 If | p i+1 -p i If | is greater than c, i = i +1, returning to the step 3, and continuing the iterative algorithm until | p is found to be satisfied i+1 -p i The value of | < c returns the theoretical value p * ＝p i+1 (ii) a Obtaining the theoretical optimal power value p * Then, experience the function according to the user satisfaction and return to the theoretical value p * Obtaining a satisfaction degree utility function value, and selecting a network with the maximum satisfaction degree utility function value as an access network;
the method comprises the following specific steps: considering the quality of service of all accessible networks comprehensively, it is expected that as large a satisfaction utility function as possible can be obtained, and network selection can be expressed as an optimization problem:
Figure BDA0002653689140000102
wherein: the user satisfaction experience function is expressed as u (p) and the user transmission efficiency is expressed as
Figure BDA0002653689140000103
Figure BDA0002653689140000104
Figure BDA0002653689140000105
According to the convex optimization theory, the obtained formula (6) is concave optimization, the local optimal solution is the global optimal solution of the formula (6), a Largrange expression of a function is given, and the optimization problem in the formula (6) is solved.
Solving the optimization problem in equation (6) is specifically as follows:
firstly, obtaining a Lagrange expression of an objective function:
Figure BDA0002653689140000111
herein, the following formula holds:
Figure BDA0002653689140000112
the Lagrangian dual formula is then given:
Figure BDA0002653689140000113
the variable p is then derived as:
Figure BDA0002653689140000114
and then solving the formula (13) by using a Newton iteration method.
Figure BDA0002653689140000115
Wherein:
Figure BDA0002653689140000116
Figure BDA0002653689140000121
in the formula of alpha λ (i)、α ν (i) A constant greater than 0. Based on the theoretical optimal power value p obtained in the step 4 * And selecting the network with the maximum satisfaction utility function value as the access network.
The effect of the present invention can be further illustrated by the following simulation examples.
1. Simulation conditions are as follows:
in the simulation of the invention, the simulation environment is Matlab2016, on the premise of meeting a certain delay requirement, network selection is mainly carried out among three networks, namely 5G, WLAN and DSRC, and the environment is considered as an urban area.
2. Simulation content and results:
fig. 2 is a graph of a relationship between access resources and a user experience function, and it can be known from the graph that, when three curves are observed, as the power value increases, the increasing speed is fast at the beginning, and the slowly increasing speed is slow, and finally, the curves tend to be stable; the three curves are compared with each other to find that the weights beta of the log-form utility functions are different, the shapes of the functions are also different, and users can select different beta values according to different service requirements.
Fig. 3 is a graph of the satisfaction utility function value versus different transmit powers and different values of α and θ. As can be seen, the satisfaction utility value increases with increasing power, approaching its maximum value within a limited range; different values of alpha and theta are set, and the larger the value of theta is, the smaller the satisfaction utility function value becomes, which indicates that the limit pursuit of time delay can be achieved at the cost of sacrificing other performances; the value of alpha can be changed according to different requirements of users, when the requirement of the users on transmission efficiency is higher, a smaller value of alpha is selected, the corresponding 1-alpha is enlarged, and the obtained satisfaction utility function value is smaller.
Fig. 4 is a graph of the satisfaction utility function value versus power for different networks, when a single variable is guaranteed, i.e. assigned the same values of α and β, where α =0.5 and β =2. As can be seen from the figure, in a limited power range, the satisfaction utility value increases with the increase of power, so as to obtain different satisfaction utility function values, and the WLAN network has a greater utility function value than the other two networks, and at this time, the WLAN network is preferentially selected as the access network.
Fig. 5 is a diagram comparing the proposed policy with other network selection policies. At this time, simulation experiments are carried out in the same network environment, and the satisfaction degree utility function values of the strategy provided by the invention are respectively 1.99 times and 3.56 times of those of the network selection strategy based on the transmitting power and the network selection strategy based on the transmission efficiency. The strategy proves to be an efficient and reliable network selection method.
The heterogeneous Internet of vehicles network selection method disclosed by the invention obtains the optimal power value through the steps (2) - (3) according to the effective capacity formula and the Newton iteration method; comparing | p by steps (4) - (5) i+1 -p i Judging whether to continue iterative operation or end operation for network selection according to the magnitude relation between the | and the allowable error c; the Newton iteration method has higher convergence speed and is closer to a local maximum value than other algorithms based on gradient descent or non-gradient methods; the heterogeneous Internet of vehicles network selection method introduced by the invention introduces the concept of effective capacity, not only considers the satisfaction degree of users, but also considers the transmission efficiency, and obtains the maximum value of the utility value of the satisfaction degree in a limited power range, thereby being used for selecting the network.
Optionally, the present invention further provides a computer device, including but not limited to one or more processors and a memory, where the memory is used to store a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, and when the processor executes part or all of the computer executable program, the processor can implement part or all of the steps of the heterogeneous internet of vehicles network selection method according to the present invention, and the memory can also be used to store vehicle-mounted sensor information, road information, and map information.
A computer-readable storage medium, in which a computer program is stored, where the computer program, when executed by a processor, can implement the method for selecting a heterogeneous internet of vehicles network according to the present invention.
The computer equipment can adopt an on-board computer, a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation.
The processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or an off-the-shelf programmable gate array (FPGA).
The memory of the invention can be an internal storage unit of a vehicle-mounted computer, a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation, such as a memory and a hard disk; external memory units such as removable hard disks, flash memory cards may also be used.
Computer-readable storage media may include computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The computer-readable storage medium may include: read-only memory
A Read Only Memory (ROM), a Random Access Memory (RAM), a Solid State Drive (SSD), or an optical disc. The Random Access Memory may include a resistive Random Access Memory (ReRAM) and a Dynamic Random Access Memory (DRAM).

Claims (7)

1. A heterogeneous Internet of vehicles network selection method is characterized by comprising the following steps:
obtaining a utility function weight coefficient, a logarithm form utility function weight and a network transmitting power initial value through a user satisfaction experience function, and setting an allowable error;
aiming at different networks, acquiring the maximum transmission rate of all the networks based on the principle of effective capacity;
calculating a power value corresponding to the maximum transmission rate by using a Newton iteration method, comparing the absolute value of the difference between the power values obtained by two adjacent iterations with an allowable error, and ending the Newton iteration operation until the absolute value of the difference between the power values obtained by two adjacent iterations is smaller than the set allowable error to obtain a theoretical power value corresponding to the maximum transmission rate;
obtaining a satisfaction utility function value of the same user facing different networks according to the user satisfaction experience function and the power theoretical value, and selecting the network with the maximum satisfaction utility function value as an access network;
beta represents the weight of the utility function in the logarithmic form, and the user selects the corresponding weight to change the shape of the utility function according to different requirements of the service; p represents a transmission power value of a user; specifically, considering the service quality of all accessible networks comprehensively, it is desirable to obtain as large a satisfaction utility function as possible, and the network selection is expressed as an optimization problem:
Figure FDA0003816043230000011
wherein, the user satisfaction experience function is expressed as u (p), and the user transmission efficiency is expressed as
Figure FDA0003816043230000012
Figure FDA0003816043230000013
Figure FDA0003816043230000014
According to the convex optimization theory, the concave optimization of the formula (1) is obtained, and the local optimal solution is the global optimal solution of the formula (1);
the effective capacity is specifically expressed as:
Figure FDA0003816043230000015
where γ represents the signal-to-noise ratio of the user, θ represents the QoS index, E [ x ]]The method comprises the following steps of calculating an expected operation on x, wherein T represents the time slot length of one transmission process, and B represents the system bandwidth.
2. The heterogeneous Internet of vehicles network selection method according to claim 1, comprising the following steps:
step 1, initialization: determining a utility function weight coefficient alpha and a weight beta of a log-form utility function according to a user satisfaction experience function, and determining an initial power value p of a user access network 0 Setting an allowable error c;
step 2, according to the concept of effective capacity, calculating the maximum transmission rate of all networks
Figure FDA0003816043230000021
A value of (d);
step 3, calculating the power value p corresponding to the maximum transmission rate by a Newton iteration method i+1 I denotes the ith iteration;
step 4, the power value p obtained by the i +1 th iteration obtained in the step 3 i+1 And the power value p obtained in the ith iteration i The absolute value of the difference is compared with the allowable error c set in step 1, if | p i+1 -p i If | is less than c, returning the theoretical value p * ＝p i+1 If | p i+1 -p i If | is > c, i = i +1 until | p is found to be satisfied i+1 -p i When | < c, returning the theoretical value p * ＝p i+1 (ii) a Obtaining the theoretical optimal power value p * Then, experience the function according to the satisfaction of the user and return theoretical value p * And obtaining a satisfaction degree utility function value, and selecting the network with the maximum satisfaction degree utility function value as an access network.
3. The method for selecting the heterogeneous Internet of vehicles network according to claim 2, wherein in the step 3, p is calculated by Newton's iteration method i+1 The values are specifically as follows:
for optimal p * The value, first of all the function,
Figure FDA0003816043230000022
wherein, alpha represents the weight coefficient of the utility function; b is the system bandwidth; p is the transmission power of the user; n is a radical of 0 Is the noise power spectral density; e A [x]Is an exponential integration function;
the variable p is then derived:
Figure FDA0003816043230000023
the function expression is given as:
Figure FDA0003816043230000031
then solving the formula (3) to obtain the evaluated value;
wherein:
Figure FDA0003816043230000032
Figure FDA0003816043230000033
in the formula of alpha λ (i)、α ν (i) Is a constant greater than 0.
4. The heterogeneous internet of vehicles network selection method according to claim 2, wherein solving equation (6) to obtain a global optimal solution is specifically as follows:
firstly, obtaining a Lagrange expression of an objective function:
Figure FDA0003816043230000034
the following formula holds:
Figure FDA0003816043230000035
the Lagrangian dual formula is then given:
Figure FDA0003816043230000041
the variable p is then derived as:
Figure FDA0003816043230000042
then, solving the formula (13) by using a Newton iteration method to obtain:
Figure FDA0003816043230000043
wherein:
Figure FDA0003816043230000044
Figure FDA0003816043230000045
in the formula of alpha λ (i)、α ν (i) A constant value larger than 0, wherein alpha represents a weight coefficient of the utility function;b is the system bandwidth; p is the transmission power of the user; n is a radical of 0 Is the noise power spectral density; e A [x]Is an exponential integration function.
5. A heterogeneous Internet of vehicles network selection system is characterized by comprising an initialization module, a maximum transmission rate acquisition module, a power calculation module and a comparison selection module; the initialization module obtains a utility function weight coefficient, a log-form utility function weight and a network transmitting power initial value through a user satisfaction experience function, and sets an allowable error;
the maximum transmission rate acquisition module acquires the maximum transmission rates of all networks based on the principle of effective capacity; the effective capacity is specifically expressed as:
Figure FDA0003816043230000051
where γ represents the signal-to-noise ratio (SNR) of the user, θ represents the QoS index, E [ x ]]The method comprises the following steps of (1) calculating an expectation operation on x, wherein T represents the continuous time slot length of one transmission process, and B represents the system bandwidth;
the power calculation module calculates a power value corresponding to the maximum transmission rate by using a Newton iteration method, and then compares the absolute value of the difference between the power values obtained by two adjacent iterations with an allowable error until the absolute value of the difference between the power values obtained by two adjacent iterations is smaller than the set allowable error, and ends the Newton iteration operation to obtain a theoretical power value corresponding to the maximum transmission rate;
the comparison selection module obtains a satisfaction utility function value of the same user facing different networks according to the user satisfaction experience function and the power theoretical value, selects a network with the maximum satisfaction utility function value as an access network, comprehensively considers the service quality of all accessible networks, and expects to obtain the satisfaction utility function as large as possible, wherein the network selection is expressed as an optimization problem:
Figure FDA0003816043230000052
s.t.c.1:
Figure FDA0003816043230000053
c.2:p≤p max ,p≥0 (1)
wherein, the user satisfaction experience function is expressed as u (p), and the user transmission efficiency is expressed as
Figure FDA0003816043230000054
Figure FDA0003816043230000055
Figure FDA0003816043230000056
Wherein, beta represents the weight of the utility function in logarithmic form, and p represents the transmission power of the user;
according to the convex optimization theory, the obtained formula (1) is concave optimization, and the local optimal solution is the global optimal solution of the formula (1).
6. A computer device, comprising one or more processors and a memory, wherein the memory is used for storing a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, and the processor can realize the heterogeneous internet of vehicles network selection method according to any one of claims 1 to 4 when executing part or all of the computer executable program.
7. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method for selecting the heterogeneous internet of vehicles according to any one of claims 1 to 4 is implemented.
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