CN112714417A - C-V2X traffic flow control method for cellular internet of vehicles - Google Patents

C-V2X traffic flow control method for cellular internet of vehicles Download PDF

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CN112714417A
CN112714417A CN202110127213.0A CN202110127213A CN112714417A CN 112714417 A CN112714417 A CN 112714417A CN 202110127213 A CN202110127213 A CN 202110127213A CN 112714417 A CN112714417 A CN 112714417A
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queue
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vehicles
data packet
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CN112714417B (en
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丁飞
李治良
沙宇晨
张美楠
张楠
张登银
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/10—Flow control between communication endpoints

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Abstract

The invention discloses a method for controlling a traffic flow of a cellular Internet of vehicles C-V2X, and aims to solve the technical problem that the traditional traffic flow control cannot adapt to a time-varying scene of the traffic flow of the Internet of vehicles C-V2X, so that the network delay is high. It includes: converting the service flow of the Internet of vehicles into a data packet queuing queue by using a service flow model which is constructed in advance, and iteratively obtaining the mapping relation between the queue threshold value and the average queuing delay of the Internet of vehicles through queue analysis; calculating the average queuing delay of the current time window according to the queuing delay of the data packet; based on the mapping relation between the queue threshold and the average queuing delay, carrying out closed-loop feedback control according to the average queuing delay of the current time window and the preset time delay requirement of the Internet of vehicles, and dynamically adjusting the queue threshold of the next time window; and realizing the traffic flow control of the Internet of vehicles according to the queue threshold value of the next time window. The invention can effectively reduce the network delay of various service flows under the C-V2X network scene.

Description

C-V2X traffic flow control method for cellular internet of vehicles
Technical Field
The invention relates to a method for controlling a C-V2X traffic flow in a cellular Internet of vehicles, belonging to the technical field of wireless communication.
Background
With the development of wireless communication technology and network technology, network convergence gradually becomes a development trend in the communication field. Under the background of this era, the car networking gradually developed into the vehicle-mounted heterogeneous network that car intranet, intercar network, vehicle-mounted ad hoc network integration, user and vehicle-mounted terminal are under the heterogeneous network environment that many different networks such as cellular network, V2X direct connection network cover jointly, in addition, vehicle-mounted terminal is bearing and is including: traffic safety, road condition broadcast, streaming media and other services. In the face of multi-service requirements under multiple networks, a service flow distribution technology becomes an effective method for solving high-speed wireless communication of a vehicle-mounted heterogeneous network.
Currently, in the 5G standardization scheme of 3GPP, a offloading scheme for a Local Network (Local Network) is proposed to achieve the effect of routing a specific traffic flow to the Local Network. The current main service offloading scheme includes a multiple-homing scheme in an Uplink Classifier (Uplink Classifier) scheme and an Internet Protocol Version 6 (Internet Protocol Version 6), however, offloading rules of both schemes need to be configured in advance in an smf (session Management function) before service flow initiation, and belong to a static service offloading policy, which cannot meet requirements of various types of burst service flows, and at the same time, it is difficult to meet distribution requirements of specific service flows in a C-V2X network, and network delay of service flows is high.
Disclosure of Invention
In order to solve the problem that the traditional service flow control cannot adapt to the time-varying scene of the C-V2X service flow of the Internet of vehicles, which causes higher network delay, the invention provides a method for controlling the service flow of the cellular Internet of vehicles C-V2X, which models the service flow of the Internet of vehicles based on the superposition of multiple Markov Modulation Bernoulli Processes (MMBP), researches the relation between a queue threshold value and average queuing delay in the service flow of the Internet of vehicles, realizes the service flow control by dynamically adjusting the queue threshold value, and can effectively reduce the network delay of various service flows under the C-V2X network scene.
In order to solve the technical problems, the invention adopts the following technical means:
the invention provides a method for controlling traffic flow of a cellular Internet of vehicles C-V2X, which comprises the following steps:
converting the service flow of the Internet of vehicles into a data packet queuing queue by using a service flow model which is constructed in advance, and iteratively obtaining the mapping relation between the queue threshold value and the average queuing delay of the Internet of vehicles through queue analysis;
acquiring the queuing delay of the data packets of the Internet of vehicles service flow according to a preset time window, and calculating the average queuing delay of the current time window according to the queuing delay of the data packets;
based on the mapping relation between the queue threshold and the average queuing time delay, carrying out closed-loop feedback control according to the average queuing time delay of the current time window and the preset time delay of the Internet of vehicles, and dynamically adjusting the queue threshold of the next time window;
and receiving and sending data packets according to the queue threshold value of the next time window, so as to realize the traffic flow control of the Internet of vehicles.
Further, the construction method of the service flow model comprises the following steps:
according to network parameters of the Internet of vehicles, information source models of the Internet of vehicles are constructed based on MMBP, wherein each information source model represents a communication terminal in the Internet of vehicles, and only one data packet is generated in one time slot by one information source model;
acquiring an N-MMBP model by utilizing superposition and aggregation of N information source models, wherein N is the number of communication terminals in the Internet of vehicles;
generating a data packet by using an N-MMBP model, performing Internet of vehicles service flow simulation, and calculating an information source state transition probability matrix and a data packet arrival matrix of the Internet of vehicles service flow;
and forming a service flow model by using the information source state transition probability matrix and the data packet arrival matrix.
Further, the network parameters of the internet of vehicles comprise the number of communication terminals in the internet of vehicles, state transition parameters of each communication terminal, arrival probability and data packet sending probability of the internet of vehicles.
Further, each source model includes two states: communication state S0And a silent state S1And S is0And S1The time length of the state transition is longer than one time slot; the method for calculating the information source state transition probability matrix comprises the following steps:
obtaining the next state transition probability of each information source model by an exhaustion method, wherein the specific expression is as follows:
Figure BDA0002923881850000031
wherein, SPiRepresenting the next state transition probability, p, of the ith source modeliState transition parameter, q, representing the communication state of the ith source modeliState transition parameter, ST, representing the silence state of the ith source modeliIndicating the state of the ith source model in the current time slot, STi' denotes the state of the ith source model in the next slot, i ═ 1,2, …, N;
order S0＝0，S1Carrying out binary coding on the state of the N-MMBP model in each time slot to obtain a corresponding binary number, converting the binary number into a decimal number, and obtaining the state number of the N-MMBP model;
and calculating the state transition probability of the N-MMBP model according to the next state transition probability of each information source model and the state number of the N-MMBP model, wherein the specific expression is as follows:
Figure BDA0002923881850000041
wherein S ism,nThe state transition probability of the N-MMBP model from the state m to the state N is represented, m is the state number of the N-MMBP model of the current time slot, N is the state number of the N-MMBP model of the next time slot, and m and N belong to [1,2 ]N]；
Constructing an information source state transition probability matrix of the N-MMBP model based on the state transition probability of the N-MMBP model:
Figure BDA0002923881850000042
wherein S represents the source state transition probability matrix of the N-MMBP model.
Further, if the internet of vehicles in a time slot generates δ data packets, δ is greater than or equal to 0 and less than or equal to N, the calculation method of the data packet arrival matrix corresponding to the δ data packets is as follows:
forming a data packet generation combination set by optional delta information source models in the N-MMBP model, and forming a data packet generation combination set matrix SRC by using all the data packet generation combination sets1(δ)：
Figure BDA0002923881850000043
Wherein H is the maximum combination number of the source model generating delta data packets in the N-MMBP model,
Figure BDA0002923881850000044
Xbmeans that the b-th packet generates a combined set, xb,cRepresenting the c-th source model in the b-th data packet generation combined set, wherein b is 1,2, …, H, and c is 1,2, … and delta;
according to SRC1(δ) calculating a packet generation probability corresponding to each packet generation combination set:
Figure BDA0002923881850000051
wherein apbIndicating the packet generation probability corresponding to the b-th packet generation combination set,
Figure BDA0002923881850000052
representing a source model xb,cIn the state of (a) to (b),
Figure BDA0002923881850000053
representing a source model xb,cIn a communication state S0The probability of arrival of the lower one,
Figure BDA0002923881850000054
representing a source model xb,cIn a silent state S1(ii) a lower arrival probability;
obtaining and SRC according to N-MMBP model1(delta) corresponding packet non-generating combined set matrix SRC2(δ)：
Figure BDA0002923881850000055
Wherein, YbRepresents XbCorresponding b-th data packet no-generation combined set, yb,dIndicating that the b-th data packet does not generate the d-th source model in the combined set, wherein d is 1,2, …, N-delta;
according to SRC2(delta) calculating the data packet non-generation probability corresponding to each data packet non-generation combination set:
Figure BDA0002923881850000056
wherein, NapbIndicating the data packet non-generation probability corresponding to the b-th data packet non-generation combination set,
Figure BDA0002923881850000057
representing the source model yb,dIn the state of (a) to (b),
Figure BDA0002923881850000058
representing the source model yb,dIn a communication state S0The probability of arrival of the lower one,
Figure BDA0002923881850000059
representing the source model yb,dIn a silent state S1(ii) a lower arrival probability;
according to apb、NapbAnd source state transition profileAnd (3) a rate matrix, wherein the data packet arrival probability of the N-MMBP model is calculated by the following specific formula:
Figure BDA0002923881850000061
wherein, am,nRepresenting the arrival probability of the corresponding data packet when the N-MMBP model is converted from the state m to the state N and delta data packets are generated;
based on am,nConstructing a data packet arrival matrix of the N-MMBP model:
Figure BDA0002923881850000062
wherein, A (delta) represents a packet arrival matrix when the N-MMBP model generates delta packets.
Further, the specific operation of iteratively obtaining the mapping relationship between the queue threshold and the average queuing delay of the internet of vehicles through queue analysis is as follows:
selecting queue thresholds one by one according to a preset queue threshold value range;
generating a data packet queuing queue by using a service flow model, and calculating a joint steady-state probability vector of the queue length under each queue threshold under the constraint of each selected queue threshold;
based on Little theorem, calculating the average queuing delay corresponding to each queue threshold value by using the joint steady-state probability vector of the queue length;
and obtaining the mapping relation between the queue threshold values and the average queuing delay of the Internet of vehicles according to all the queue threshold values and the average queuing delay corresponding to the queue threshold values.
Further, the method for calculating the joint steady-state probability vector of the queue length under the queue threshold K comprises the following steps:
transmitting data packets to the data packet queuing queue by using a service flow model, obtaining the queue length of the data packet queuing queue under each time slot under the constraint of a queue threshold value K,
Figure BDA0002923881850000071
utilizing the probability coupling of the data packet arrival matrix of the service flow model and the data packet transmission probability of the Internet of vehicles to calculate a queue length state transition matrix of a data packet queuing queue at the front time slot and the rear time slot;
performing steady-state queuing analysis on the queue length state transition matrix by using a Markov chain solver algorithm, and calculating a combined steady-state probability vector of the queue length by using the following specific formula:
π＝u(I-X+eu)-1 (10)
Figure BDA0002923881850000072
wherein pi represents a joint steady-state probability vector of the queue length under the queue threshold K, u is any row vector of X, I is an identity matrix of K multiplied by K, QT represents a queue length state transition matrix, QTf,gQueue length state transition elements representing the (f +1) th row and the (g +1) th column in QT, i.e. queue length state transition elements for a queue length transition from f to g, e is a column vector of length K,
Figure BDA0002923881850000073
f∈[0,K]，g∈[0,K]。
further, the method for dynamically adjusting the queue threshold of the next time interval comprises:
calculating the predicted value of the queuing delay of the data packet in the next time window according to the average queuing delay of the current time window, wherein the calculation formula is as follows:
D'z+1＝2×TDz-Dz+Gz-1 (12)
wherein, D'z+1Packet queuing delay prediction, TD, for z +1 time windowzRepresenting the average queuing delay, D, of the z-th time windowzPacket queuing delay, G, representing the z-th time windowz-1The time delay difference of the z-1 th time window is represented;
calculating the average queuing delay predicted value of the next time window according to the data packet queuing delay predicted value:
Figure BDA0002923881850000081
wherein, TD'z+1Representing the predicted value of the average queuing delay of the z +1 th time window;
and selecting the queue threshold value of the next time window by utilizing the mapping relation between the queue threshold value and the average queuing delay time according to the difference value between the average queuing delay predicted value of the next time window and the preset time delay of the Internet of vehicles.
The following advantages can be obtained by adopting the technical means:
the invention provides a method for controlling a C-V2X service flow of a cellular Internet of vehicles, which comprises the steps of establishing a service flow model according to the communication characteristics of data packets of the Internet of vehicles, converting the service flow of the Internet of vehicles into a discrete time queuing queue by using the service flow model, obtaining the mapping relation between a queue threshold value and an average queuing delay of the Internet of vehicles, and adjusting the queue threshold value to realize the adjustment of the average queuing delay. The method can dynamically adjust the queue threshold according to the average queuing delay and the preset vehicle networking delay (vehicle networking service flow communication requirement), and receives and discards the data packet according to the queue threshold, thereby reducing the probability of network congestion and improving the utilization rate and the average throughput of a vehicle networking system; queuing time delay is changed and restrained through a self-adaptive queue threshold mechanism, and the change of MAC layer time delay can be effectively compensated, so that bounded node time delay is realized, the delay of a wireless node meets the QoS requirement of corresponding Internet of vehicles service, and the network delay of various service flows under the C-V2X network scene is effectively reduced.
In addition, the invention also fully considers two service types of C-V2X: safe communication service, non-safe communication service, utilize multiple Markov to modulate Bernoulli's process (MMBP) and carry on the model construction, the model performance is closer to the business of car networking, have improved the accuracy of the model; the method of the invention is agnostic to the MAC layer and the application layer, and the queuing mechanism is transparent to the MAC layer, so the method of the invention does not need the information of the MAC layer and the cooperation mechanism of the MAC layer to limit the node delay, and optimizes the network cost.
Drawings
FIG. 1 is a flow chart illustrating the steps of a method for controlling traffic flow in a cellular Internet of vehicles C-V2X according to the present invention;
FIG. 2 is a network scenario diagram of the Internet of vehicles C-V2X according to an embodiment of the present invention;
FIG. 3 is a diagram of a source model in an embodiment of the invention;
FIG. 4 is a diagram illustrating a state transition of the N-MMBP model according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the accompanying drawings as follows:
the invention provides a method for controlling traffic flow of a cellular Internet of vehicles C-V2X, which specifically comprises the following steps as shown in figure 1:
step 1, converting the service flow of the Internet of vehicles into a data packet queuing queue by using a service flow model which is constructed in advance, and iteratively obtaining the mapping relation between a queue threshold value and average queuing delay of the Internet of vehicles through queue analysis;
step 2, acquiring data packet queuing delay of the Internet of vehicles service flow according to a preset time window, and calculating the average queuing delay of the current time window according to the data packet queuing delay;
step 3, based on the mapping relation between the queue threshold and the average queuing delay, performing closed-loop feedback control according to the average queuing delay of the current time window and the preset time delay of the Internet of vehicles, and dynamically adjusting the queue threshold of the next time window;
and 4, receiving and sending the data packet according to the queue threshold value of the next time window, so as to realize the traffic flow control of the Internet of vehicles.
The network scenario of the car networking C-V2X is shown in fig. 2, and the C-V2X network includes a plurality of communication terminals (people, cars, drive test facility units, etc.) and two service types (secure communication service, non-secure communication service).
Aiming at a network scene of the Internet of vehicles C-V2X, the invention constructs a service flow model, and the specific construction process is as follows:
step A, constructing information source models of the Internet of vehicles based on MMBP according to network parameters of the Internet of vehicles, wherein each information source model represents a communication terminal in the Internet of vehicles and is used for generating data packets and sending the generated data packets to communication channels of the Internet of vehicles; a source model generates only one packet in a slot. The network parameters of the internet of vehicles are configured parameters after the internet of vehicles system is formed, and mainly comprise the number of communication terminals in the internet of vehicles, state transition parameters of each communication terminal, arrival probability, data packet sending probability of the internet of vehicles and the like.
According to the characteristics of the communication terminal, each source model comprises two states: communication state S0And a silent state S1The two states may be switched over with time, as shown in fig. 3. Setting state transition parameters (p, q) and arrival probability (alpha) of a source model1,α2) (ii) a Wherein, the state transition parameter refers to the probability of a specific state transition occurring in two time slots before and after the source model, such as transition from a communication state to a communication state or transition from the communication state to a silent state; the arrival probability refers to the probability that the data packet arrives at the communication channel of the internet of vehicles (namely, the data packet queue in the invention) after the data packet is generated by the information source model.
The invention can adjust the S of the information source model0And S1A state to generate different traffic patterns with different traffic strengths or burstiness for traffic from different traffic classes.
And step B, obtaining an N-MMBP model by utilizing superposition and aggregation of N information source models, wherein N is the number of the communication terminals in the Internet of vehicles. As the types of the C-V2X service flows in the Internet of vehicles are different, such as traffic safety early warning type service flows in safety communication and streaming media service flows in non-safety communication, the arrival process of a single information source model cannot represent multiple types of aggregated C-V2X service flows, and therefore the method uses the flows of multiple information source models to be superposed to establish a more reasonable Internet of vehicles aggregated service flow model.
Step C, simulating the Internet of vehicles service flow by using the N-MMBP model, and calculating the information source state transition probability matrix and the data packet of the Internet of vehicles service flowThe matrix is reached. The invention can utilize the N-MMBP model to simulate the data packet receiving and sending overshoot in the Internet of vehicles to form different Internet of vehicles service flows, sets the arrival probability of two states in the information source model to be the same, can generate a smooth and constant service flow model to form a Bernoulli source, and can also generate a data packet receiving and sending overshoot by using the S-MMBP model to form a Bernoulli source0And S1Is set to 0 to model voice or video traffic.
In the vehicle networking service flow simulation process, along with scene change, the state of each information source model in the N-MMBP model can be changed, different data packets can be generated in different scenes, and in order to better simulate the vehicle networking service flow, all possible scenes are deduced on the basis of the N-MMBP model, and an information source state transition probability matrix and a data packet arrival matrix are obtained.
Derivation of traffic flow of vehicle networking by using time slot as unit, state S of information source model0And S1The time length of the state transition between the two is longer than one time slot, i.e. the state transition of the source model generally occurs in two time slots. The method for calculating the information source state transition probability matrix comprises the following steps:
c-1, as can be seen from fig. 3, the diagonal arrival matrix Λ obtained by the state transition matrix P of each source model is:
Figure BDA0002923881850000111
Figure BDA0002923881850000121
wherein p represents the probability that the state of the source model is not transferred and the state is the communication state, 1-p represents the probability that the state of the source model is transferred from the communication state to the silent state, 1-q represents the probability that the state of the source model is transferred from the silent state to the communication state, q represents the probability that the state of the source model is not transferred and the state is the silent state, and alpha represents the probability that the state of the source model is not transferred and the state is the silent state1Representing the generation and output of a source model in a communication stateProbability of arrival of packets, α2And the arrival probability of the data packet generated and output by the source model in the silent state is represented.
According to the state transition matrix P, obtaining the next state transition probability of each information source model by an exhaustion method, namely enumerating all the conditions of state transition of each information source model in front and back time slots, wherein the specific expression is as follows:
Figure BDA0002923881850000122
wherein, SPiRepresenting the next state transition probability, p, of the ith source modeliA state transition parameter representing a communication state of the ith source model, i.e. a probability that a state is unchanged in preceding and following slots and the state is a communication state, qiThe state transition parameter representing the silent state of the ith source model, i.e. the probability that the state is unchanged and the state is a silent state in the preceding and following slots, STiIndicating the state of the ith source model in the current time slot, STi' denotes the state of the ith source model in the next slot, i ═ 1,2, …, N.
C-2, because the N-MMBP model comprises a plurality of information source models, each information source model can discover state transition, and in order to represent the state transition conditions of all the information source models in the N-MMBP model, the invention introduces a binary coding process. Order S0＝0，S1And (2) converting the state of each information source model in each time slot into a number of 0 or 1, arranging the numbers corresponding to all the information source models in the N-MMBP model in sequence, realizing binary coding operation on the state of the N-MMBP model, obtaining a binary number corresponding to the N-MMBP model in each time slot, converting the binary number into a decimal number, and obtaining the state number of the N-MMBP model. Assuming that N of the N-MMBP model is 6, that is, there are 6 source models, and in the current timeslot, the states of the 6 source models are: s0,S0,S0,S1,S0,S1The corresponding binary number is 000101B, the decimal number of 5 can be obtained through the binary system conversion, and 5 is the N-MMBP modulusThe status number of the type current slot.
C-3, according to the next state transition probability of each information source model and the state number of the N-MMBP model, the state transition of the N-MMBP model in the previous and subsequent time slots can be deduced, as shown in fig. 4, assuming that N of the N-MMBP model is 6, and the state of the N-MMBP model in the current time slot is S0,S0,S0,S1,S0,S1The state number is m-5-000101B, and the state of the next slot N-MMBP model is: s0,S1,S0,S1,S1,S0The state number N010110B 22, the state transition probability value of each source model in the N-MMBP model can be obtained according to the formula (14), and the state transition probability of the N-MMBP model can be further calculated: s5,22＝p1×(1-p2)×p3×q4×(1-p5)×(1-q6)。
According to the above-mentioned extrapolation process, the expression of the state transition probability of the N-MMBP model is:
Figure BDA0002923881850000131
wherein S ism,nThe state transition probability of the N-MMBP model from the state m to the state N is represented, m is the state number of the N-MMBP model of the current time slot, N is the state number of the N-MMBP model of the next time slot, and m and N belong to [1,2 ]N]。
C-4, constructing an information source state transition probability matrix of the N-MMBP model based on the state transition probability of the N-MMBP model to obtain the state transition probability of the N-MMBP model under all conditions, wherein the specific expression is as follows:
Figure BDA0002923881850000141
wherein S represents the source state transition probability matrix of the N-MMBP model.
In each time slot, only one data packet is generated by one information source model, delta data packets are generated by the Internet of vehicles in one time slot, namely the number of the information source models generating the data packets in the N-MMBP model is delta, delta is more than or equal to 0 and less than or equal to N, the arrival probability is associated with the non-arrival probability according to the state and the arrival times delta of each information source model in the N-MMBP model and is coupled with an information source state transition probability matrix S, and a data packet arrival matrix corresponding to the delta data packets in the N-MMBP model can be deduced, wherein the specific process is as follows:
c-5, under the premise that the specific information source model generates the data packet, analyzing all possible combinations: the probability that each source model is selected as a data packet generation source is equal, and a set formed by the data packet generation sources (i.e. a data packet generation combination set) is necessarily a subset of all the source model sets, so that the maximum combination number of source models generating delta data packets in the N-MMBP model is H,
Figure BDA0002923881850000142
selecting delta information source models from the N-MMBP model to form a data packet generation combination set, traversing all information source models to obtain H data packet generation combination sets in total, and forming a data packet generation combination set matrix SRC by using all data packet generation combination sets1(δ)：
Figure BDA0002923881850000151
Wherein, XbMeans that the b-th packet generates a combined set, xb,cIndicating that the b-th packet generates the c-th source model in the combined set, b is 1,2, …, H, c is 1,2, …, δ.
C-6 according to SRC1(δ) calculating a packet generation probability corresponding to each packet generation combination set:
Figure BDA0002923881850000152
wherein apbIndicating the packet generation probability corresponding to the b-th packet generation combination set,
Figure BDA0002923881850000153
representing a source model xb,cIn the state of (a) to (b),
Figure BDA0002923881850000154
representing a source model xb,cIn a communication state S0The probability of arrival of the lower one,
Figure BDA0002923881850000155
representing a source model xb,cIn a silent state S1The probability of arrival of.
C-7, forming a data packet generation combination set in the step C-5, inevitably generating a data packet non-generation combination set, namely a combination set of a source model without generating a data packet, and obtaining and SRC according to the N-MMBP model1(delta) corresponding packet non-generating combined set matrix SRC2(δ)：
Figure BDA0002923881850000156
Wherein, YbRepresents XbCorresponding b-th data packet no-generation combined set, i.e. X is rejectedbAfter the source model in (1), the N-MMBP model does not have a combination set of the source models for generating the data packet, yb,dIndicating that the b-th packet does not generate the d-th source model in the combined set, and d is 1,2, …, N-delta.
C-8 according to SRC2(delta) calculating the data packet non-generation probability corresponding to each data packet non-generation combination set:
Figure BDA0002923881850000161
wherein, NapbIndicating the data packet non-generation probability corresponding to the b-th data packet non-generation combination set,
Figure BDA0002923881850000162
representing the source model yb,dIn the state of (a) to (b),
Figure BDA0002923881850000163
representing the source model yb,dIn a communication state S0The probability of arrival of the lower one,
Figure BDA0002923881850000164
representing the source model yb,dIn a silent state S1The probability of arrival of.
C-9, according to apb、NapbAnd an information source state transition probability matrix, and calculating the data packet arrival probability of the N-MMBP model, wherein the specific formula is as follows:
Figure BDA0002923881850000165
wherein, am,nRepresenting the packet arrival probability corresponding to when the N-MMBP model transitions from state m to state N and δ packets are generated.
C-10, based on am,nConstructing a data packet arrival matrix of the N-MMBP model under all scenes:
Figure BDA0002923881850000166
a (delta) represents a data packet arrival matrix when the N-MMBP model generates delta data packets, and the matrix comprises the arrival probability of the delta data packets generated under all state transition conditions.
And D, forming a service flow model by using the information source state transition probability matrix and the data packet arrival matrix, wherein the service flow model can simulate the data packet generation process in all scenes in the Internet of vehicles.
Due to the limited capacity and the QoS requirement of the communication channel of the Internet of vehicles, data packets generated by the communication terminal are not always received by the communication channel of the Internet of vehicles, and some data packets may be sent out by the communication channel of the Internet of vehicles for the purpose of ensuring the communication quality, and the data packets are discarded. According to the actual situation of the service flow of the Internet of vehicles, after the service flow model is obtained, the process that the data packet arrives and leaves in the Internet of vehicles is converted into the discrete time queuing model, and the data packet queuing queue is generated.
The data packet queue in the invention is equal to the communication channel of the Internet of vehicles, the data packet generated by the information source model is queued into the queue, the length of the queue is limited due to the limited channel capacity, K data packets can be accommodated at most, K is the queue threshold value,
Figure BDA0002923881850000171
in the discrete time queuing model, time is divided into slots with fixed length intervals, and assuming that there is one and only one packet allowed to leave the queue in each slot, the probability of a packet being sent out (leaving) in a slot is β, the queuing rule for the queue is FIFO (first in first out), so that the packet that entered the queue earliest will be discarded when the number of packets in the queue exceeds the queue threshold.
In step 1, the data packet queuing conditions of the internet-of-vehicles data packet queuing queues under different queue thresholds are obtained by changing the size of the queue threshold, so as to obtain average queuing time delays corresponding to the different queue thresholds, and realize the mapping from the queue threshold to the average queuing time delay, and the specific operation is as follows:
step 101, selecting queue thresholds one by one according to a preset queue threshold value range [1, L ], wherein L is the maximum queue length.
Step 102, generating a data packet queuing queue by using a service flow model, and calculating a joint steady-state probability vector of the queue length under each queue threshold under the constraint of each selected queue threshold.
And 103, based on Little theorem, calculating the average queuing time delay corresponding to each queue threshold by using the joint steady-state probability vector of the queue length, wherein the average queuing time delay refers to the average network delay of the vehicle networking service flow in a period of time. In the embodiment of the invention, a method for calculating average queuing delay by using a joint steady-state probability vector of queue length is the prior art.
And step 104, obtaining a mapping relation between the queue threshold values of the Internet of vehicles and the average queuing delay according to all the queue threshold values and the average queuing delays corresponding to the queue threshold values, and recording the mapping relation into a form to be stored in a database for subsequent calling.
In step 102, a queue threshold K (K ∈ [1, L ])]And is
Figure BDA0002923881850000181
) The calculation method of the combined steady-state probability vector of the lower queue length comprises the following steps:
(1) and sending data packets to the data packet queuing queue by using the service flow model, obtaining the queue length of the data packet queuing queue under each time slot under the constraint of a queue threshold value K, and leaving the data packets in the queue if the number of the data packets in the queue exceeds the queue threshold value K after the data packets sent by the service flow model reach the queue, so as to ensure that the queue length is not more than K.
(2) According to the queue length of the data packet queuing queue under the front and the back time slots, the queue length change of the data packet queuing queue, namely the difference value of the queue length under the front and the back time slots can be obtained. In the present invention, there are 3 main cases of queue length variation of a packet queuing queue: 1. the queue length is unchanged; 2. the queue length is reduced by 1 due to the data packet leaving the queue; 3. the queue length increases. According to the difference of the number of the data packets generated by the service flow model, the queue length of the current time slot, etc., the above 3 cases can be further divided, as shown in the following table:
TABLE 1
Figure BDA0002923881850000182
Figure BDA0002923881850000191
Figure BDA0002923881850000201
In table 1, QL indicates the queue length of the packet queuing queue at the current time slot.
On the basis of table 1, the state transition probabilities corresponding to 3 cases of queue length variation can be obtained by using the coupling of the packet arrival matrix of the service flow model and the packet transmission probability of the internet of vehicles.
State transition probability with constant queue length
Figure BDA0002923881850000202
The following were used:
Figure BDA0002923881850000203
a (0) represents a data packet arrival matrix of a service flow model which does not send data packets to a data packet queuing queue, beta is the sending probability of the data packet queuing queue sending out the data packets, A (1) represents a data packet arrival matrix of the service flow model which sends 1 data packet, and A (gamma) represents a data packet arrival matrix of the service flow model which sends gamma data packets.
The probability of state transition D for a queue length reduction of 1 due to packet departure is as follows:
D＝A(0)*β (26)
state transition probability of queue length increased by N (N is more than or equal to 1 and less than or equal to N)
Figure BDA0002923881850000204
The following were used:
Figure BDA0002923881850000211
wherein, a (n) represents that the traffic flow model sends a packet arrival matrix of n packets.
And calculating a queue length state transition matrix of the data packet queue under the front time slot and the rear time slot by using formulas (25), (26) and (27) in combination with the number N of communication terminals in the Internet of vehicles and a queue threshold value K. Since there is an empty queue in the present invention, the queueThe number of elements of the length state transition matrix is (K +1) × (K +1), and the value range of the subscript of each element is [0, K]If the queue length state transition matrix is QT, the queue length state transition element in the first row and the first column of QT can be expressed as QT0,0The queue length state transition element of the K +1 th row and the K +1 th column in QT can be expressed as QTK+1,K+1All possible queue length changes are contained in the queue length state transition matrix.
In the embodiment of the present invention, K is 7, and queue length state transition matrices corresponding to N of different sizes are given when K is 7:
Figure BDA0002923881850000221
(3) performing steady-state queuing analysis on the queue length state transition matrix by using a Markov chain solver algorithm, and calculating a combined steady-state probability vector of the queue length, wherein a specific calculation formula is as follows:
π＝u(I-X+eu)-1 (29)
Figure BDA0002923881850000222
wherein pi represents a joint steady-state probability vector of the queue length under the queue threshold K, u is an arbitrary row vector of X, I is an identity matrix of K multiplied by K, e is a column vector of length K,
Figure BDA0002923881850000223
QTf,gqueue length state transition elements representing the (f +1) th row and the (g +1) th column in a queue length state transition matrix, namely queue length state transition elements for transferring the queue length of a data packet queuing queue from f to g, wherein f belongs to [0, K ]]，g∈[0,K]。
In equation (29), the joint steady-state probability vector π needs to satisfy the following equation:
π·QTf,g＝0 (31)
π·e＝1 (32)
in step 2, the time flow is divided into windows according to a preset time window, then the data packet queuing delay of the vehicle networking service flow is obtained time window by time window, and the data packet queuing delay is the communication delay of the data packet in the vehicle networking service flow in the current time window. The average queuing delay of the current time window can be calculated according to the queuing delay of the data packet, and the specific formula is as follows:
Figure BDA0002923881850000231
wherein, TDzRepresents the average queuing delay, D, corresponding to the z-th time windowzIndicating the packet queuing delay for the z-th time window.
In the embodiment of the present invention, the specific operation of step 3 is as follows:
step 301, calculating the predicted value of the queuing delay of the data packet in the next time window according to the average queuing delay of the current time window, and obtaining the predicted value of the average queuing delay of the next time window through averaging operation.
The formula for calculating the predicted value of the queuing delay of the data packet is as follows:
D'z+1＝2×TDz-Dz+Gz-1 (34)
wherein, D'z+1Packet queuing delay prediction, G, for z +1 time windowz-1Representing the delay difference for the z-1 th time window.
The delay difference is calculated as follows:
Figure BDA0002923881850000241
wherein D isjPacket queuing delay, G, representing the jth time windowz-2Representing the delay difference of the z-2 time library creation.
Step 302, comparing the predicted value of the average queuing delay of the next time window with a preset vehicle networking delay (network delay requirement of a vehicle networking service flow), and selecting a queue threshold value by using a mapping relation between the queue threshold value and the average queuing delay according to the difference value between the predicted value of the average queuing delay and the vehicle networking delay, wherein the selection strategy is as follows: selecting a queue threshold value with smaller average queuing time delay or closer to the time delay of the Internet of vehicles; and changing the actual average queuing delay of the next time window of the Internet of vehicles by using the selected queue threshold value, thereby reducing the difference value between the actual average queuing delay and the Internet of vehicles delay.
The method can utilize real-time average queuing time delay and the time delay of the Internet of vehicles to carry out closed-loop feedback control, dynamically adjust the queue threshold value of the next time window, selectively receive the data packets according to the adjusted queue threshold value in step 4, and send out part of the data packets in the business flow of the Internet of vehicles according to the first-in first-out principle when the data packets generated by the business flow of the Internet of vehicles exceed the queue threshold value, thereby ensuring the overall performance of the business flow of the Internet of vehicles, limiting the communication delay of the business flow to a lower level and realizing the control of the business flow of the Internet of vehicles.
The method can control the service flow by actively adjusting the queue threshold value, limit the communication delay of the data flow formed by aggregating a plurality of C-V2X service flows to a low level, adapt to the time-varying scene of the C-V2X service flow in the Internet of vehicles, meet the distribution requirement of specific service flow in the C-V2X network, and effectively reduce the network delay of various service flows under the C-V2X network scene.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (8)

1. A method for controlling traffic flow of a cellular Internet of vehicles C-V2X is characterized by comprising the following steps:
converting the service flow of the Internet of vehicles into a data packet queuing queue by using a service flow model which is constructed in advance, and iteratively obtaining the mapping relation between the queue threshold value and the average queuing delay of the Internet of vehicles through queue analysis;
acquiring the queuing delay of the data packets of the Internet of vehicles service flow according to a preset time window, and calculating the average queuing delay of the current time window according to the queuing delay of the data packets;
based on the mapping relation between the queue threshold and the average queuing time delay, carrying out closed-loop feedback control according to the average queuing time delay of the current time window and the preset time delay of the Internet of vehicles, and dynamically adjusting the queue threshold of the next time window;
and receiving and sending data packets according to the queue threshold value of the next time window, so as to realize the traffic flow control of the Internet of vehicles.
2. The method for controlling the traffic flow of the cellular internet of vehicles C-V2X according to claim 1, wherein the traffic flow model is constructed by the following steps:
according to network parameters of the Internet of vehicles, information source models of the Internet of vehicles are constructed based on MMBP, wherein each information source model represents a communication terminal in the Internet of vehicles, and only one data packet is generated in one time slot by one information source model;
acquiring an N-MMBP model by utilizing superposition and aggregation of N information source models, wherein N is the number of communication terminals in the Internet of vehicles;
generating a data packet by using an N-MMBP model, performing Internet of vehicles service flow simulation, and calculating an information source state transition probability matrix and a data packet arrival matrix of the Internet of vehicles service flow;
and forming a service flow model by using the information source state transition probability matrix and the data packet arrival matrix.
3. The method as claimed in claim 2, wherein the network parameters of the car networking comprise the number of communication terminals in the car networking, the state transition parameter of each communication terminal, the arrival probability and the data packet transmission probability of the car networking.
4. The method of claim 2, wherein each source model comprises two states: communication state S0And a silent state S1And S is0And S1The time length of the state transition is longer than one time slot; the method for calculating the information source state transition probability matrix comprises the following steps:
obtaining the next state transition probability of each information source model by an exhaustion method, wherein the specific expression is as follows:
Figure FDA0002923881840000021
wherein, SPiRepresenting the next state transition probability, p, of the ith source modeliState transition parameter, q, representing the communication state of the ith source modeliState transition parameter, ST, representing the silence state of the ith source modeliIndicating the state of the ith source model in the current time slot, STi' denotes the state of the ith source model in the next slot, i ═ 1,2, …, N;
order S0＝0，S1Carrying out binary coding on the state of the N-MMBP model in each time slot to obtain a corresponding binary number, converting the binary number into a decimal number, and obtaining the state number of the N-MMBP model;
and calculating the state transition probability of the N-MMBP model according to the next state transition probability of each information source model and the state number of the N-MMBP model, wherein the specific expression is as follows:
Figure FDA0002923881840000022
wherein S ism,nThe state transition probability of the N-MMBP model from the state m to the state N is represented, m is the state number of the N-MMBP model of the current time slot, N is the state number of the N-MMBP model of the next time slot, and m and N belong to [1,2 ]N]；
Constructing an information source state transition probability matrix of the N-MMBP model based on the state transition probability of the N-MMBP model:
Figure FDA0002923881840000031
wherein S represents the source state transition probability matrix of the N-MMBP model.
5. The method for controlling traffic flow of the cellular Internet of vehicles C-V2X according to claim 4, wherein the Internet of vehicles generates delta data packets within a time slot, and delta is greater than or equal to 0 and less than or equal to N, then the method for calculating the arrival matrix of the data packets corresponding to the delta data packets is as follows:
forming a data packet generation combination set by optional delta information source models in the N-MMBP model, and forming a data packet generation combination set matrix SRC by using all the data packet generation combination sets1(δ)：
Figure FDA0002923881840000032
Wherein H is the maximum combination number of the source model generating delta data packets in the N-MMBP model,
Figure FDA0002923881840000033
Xbmeans that the b-th packet generates a combined set, xb,cRepresenting the c-th source model in the b-th data packet generation combined set, wherein b is 1,2, …, H, and c is 1,2, … and delta;
according to SRC1(δ) calculating a packet generation probability corresponding to each packet generation combination set:
Figure FDA0002923881840000041
wherein apbIndicating the packet generation probability corresponding to the b-th packet generation combination set,
Figure FDA0002923881840000042
representing a source model xb,cIn the state of (a) to (b),
Figure FDA0002923881840000043
representing a source model xb,cIn a communication state S0The probability of arrival of the lower one,
Figure FDA0002923881840000044
representing a source model xb,cIn a silent state S1(ii) a lower arrival probability;
obtaining and SRC according to N-MMBP model1(delta) corresponding packet non-generating combined set matrix SRC2(δ)：
Figure FDA0002923881840000045
Wherein, YbRepresents XbCorresponding b-th data packet no-generation combined set, yb,dIndicating that the b-th data packet does not generate the d-th source model in the combined set, wherein d is 1,2, …, N-delta;
according to SRC2(delta) calculating the data packet non-generation probability corresponding to each data packet non-generation combination set:
Figure FDA0002923881840000046
wherein, NapbIndicating the data packet non-generation probability corresponding to the b-th data packet non-generation combination set,
Figure FDA0002923881840000047
representing the source model yb,dIn the state of (a) to (b),
Figure FDA0002923881840000048
representing the source model yb,dIn a communication state S0The probability of arrival of the lower one,
Figure FDA0002923881840000049
representing the source model yb,dIn a silent state S1(ii) a lower arrival probability;
according to apb、NapbAnd an information source state transition probability matrix, and calculating the data packet arrival probability of the N-MMBP model, wherein the specific formula is as follows:
Figure FDA0002923881840000051
wherein, am,nRepresenting the arrival probability of the corresponding data packet when the N-MMBP model is converted from the state m to the state N and delta data packets are generated;
based on am,nConstructing a data packet arrival matrix of the N-MMBP model:
Figure FDA0002923881840000052
wherein, A (delta) represents a packet arrival matrix when the N-MMBP model generates delta packets.
6. The method for controlling traffic flow of the cellular internet of vehicles C-V2X according to claim 1, wherein the specific operation of iteratively obtaining the mapping relation between the queue threshold and the average queuing delay of the internet of vehicles through queue analysis is as follows:
selecting queue thresholds one by one according to a preset queue threshold value range;
generating a data packet queuing queue by using a service flow model, and calculating a joint steady-state probability vector of the queue length under each queue threshold under the constraint of each selected queue threshold;
based on Little theorem, calculating the average queuing delay corresponding to each queue threshold value by using the joint steady-state probability vector of the queue length;
and obtaining the mapping relation between the queue threshold values and the average queuing delay of the Internet of vehicles according to all the queue threshold values and the average queuing delay corresponding to the queue threshold values.
7. The method for controlling traffic flow of the cellular internet of vehicles C-V2X according to claim 6, wherein the method for calculating the joint steady-state probability vector of the queue length under the queue threshold K is as follows:
transmitting data packets to the data packet queuing queue by using a service flow model, obtaining the queue length of the data packet queuing queue under each time slot under the constraint of a queue threshold value K,
Figure FDA0002923881840000061
utilizing the probability coupling of the data packet arrival matrix of the service flow model and the data packet transmission probability of the Internet of vehicles to calculate a queue length state transition matrix of a data packet queuing queue at the front time slot and the rear time slot;
performing steady-state queuing analysis on the queue length state transition matrix by using a Markov chain solver algorithm, and calculating a combined steady-state probability vector of the queue length by using the following specific formula:
π＝u(I-X+eu)-1
Figure FDA0002923881840000062
wherein pi represents a joint steady-state probability vector of the queue length under the queue threshold K, u is any row vector of X, I is an identity matrix of K multiplied by K, QT represents a queue length state transition matrix, QTf,gQueue length state transition elements representing the (f +1) th row and the (g +1) th column in QT, i.e. queue length state transition elements for a queue length transition from f to g, e is a column vector of length K,
Figure FDA0002923881840000063
f∈[0,K]，g∈[0,K]。
8. the method for controlling traffic flow of cellular internet of vehicles C-V2X according to claim 1, wherein the method for dynamically adjusting the queue threshold of the next time interval comprises:
calculating the predicted value of the queuing delay of the data packet in the next time window according to the average queuing delay of the current time window, wherein the calculation formula is as follows:
D'z+1＝2×TDz-Dz+Gz-1
wherein, D'z+1Packet queuing delay prediction, TD, for z +1 time windowzRepresenting the average queuing delay, D, of the z-th time windowzPacket queuing delay, G, representing the z-th time windowz-1The time delay difference of the z-1 th time window is represented;
calculating the average queuing delay predicted value of the next time window according to the data packet queuing delay predicted value:
Figure FDA0002923881840000071
wherein, TD'z+1Representing the predicted value of the average queuing delay of the z +1 th time window;
and selecting the queue threshold value of the next time window by utilizing the mapping relation between the queue threshold value and the average queuing delay time according to the difference value between the average queuing delay predicted value of the next time window and the preset time delay of the Internet of vehicles.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114300082A (en) * 2022-03-14 2022-04-08 四川大学华西医院 Information processing method and device and computer readable storage medium
CN114363944A (en) * 2021-12-13 2022-04-15 信通院车联网创新中心(成都)有限公司 C-V2X-based equipment communication performance test system and test method thereof
CN115442846A (en) * 2021-06-02 2022-12-06 中国移动通信集团黑龙江有限公司 Data distribution method and device
CN116112446A (en) * 2022-11-30 2023-05-12 重庆紫光华山智安科技有限公司 Delay feedback method based on message queue, computer equipment and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130136000A1 (en) * 2011-11-29 2013-05-30 Hughes Networks Systems, Llc Method and system for controlling tcp traffic with random early detection and window size adjustments
CN106304165A (en) * 2016-08-12 2017-01-04 辛建芳 The method for analyzing performance of the D2D honeycomb heterogeneous network based on queuing theory
CN111988242A (en) * 2020-07-23 2020-11-24 大连大学 Hybrid queue scheduling method of heaven-earth integrated intelligent network

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130136000A1 (en) * 2011-11-29 2013-05-30 Hughes Networks Systems, Llc Method and system for controlling tcp traffic with random early detection and window size adjustments
CN106304165A (en) * 2016-08-12 2017-01-04 辛建芳 The method for analyzing performance of the D2D honeycomb heterogeneous network based on queuing theory
CN111988242A (en) * 2020-07-23 2020-11-24 大连大学 Hybrid queue scheduling method of heaven-earth integrated intelligent network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
杜辙;郭宗莲;岳丽全;: "队列长度动态加权公平调度算法在列车网络中的应用", 计算机应用研究, no. 1 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115442846A (en) * 2021-06-02 2022-12-06 中国移动通信集团黑龙江有限公司 Data distribution method and device
CN114363944A (en) * 2021-12-13 2022-04-15 信通院车联网创新中心(成都)有限公司 C-V2X-based equipment communication performance test system and test method thereof
CN114363944B (en) * 2021-12-13 2024-01-16 信通院车联网创新中心(成都)有限公司 Equipment communication performance test system based on C-V2X and test method thereof
CN114300082A (en) * 2022-03-14 2022-04-08 四川大学华西医院 Information processing method and device and computer readable storage medium
CN114300082B (en) * 2022-03-14 2022-06-10 四川大学华西医院 Information processing method and device and computer readable storage medium
CN116112446A (en) * 2022-11-30 2023-05-12 重庆紫光华山智安科技有限公司 Delay feedback method based on message queue, computer equipment and storage medium
CN116112446B (en) * 2022-11-30 2024-06-07 重庆紫光华山智安科技有限公司 Delay feedback method based on message queue, computer equipment and storage medium

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