CN111031512B - Unmanned fleet communication real-time performance analysis method under traffic interference - Google Patents

Unmanned fleet communication real-time performance analysis method under traffic interference Download PDF

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CN111031512B
CN111031512B CN201911128045.6A CN201911128045A CN111031512B CN 111031512 B CN111031512 B CN 111031512B CN 201911128045 A CN201911128045 A CN 201911128045A CN 111031512 B CN111031512 B CN 111031512B
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吴琼
葛红梅
夏思洋
刘汉旭
董晓丹
李正权
武贵路
李宝龙
刘洋
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Jiangnan University
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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]
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
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Abstract

The invention provides a real-time performance analysis method for unmanned fleet communication under traffic interference, which can analyze the real-time performance of 802.11p fleet communication under typical traffic interference, and the scheme has moderate calculation complexity, simple and reasonable system model and is suitable for practical application. According to the technical scheme, the dynamic state of each vehicle in the unmanned fleet under traffic interference is calculated in real time; determining a network communication model according to the dynamic state of the vehicles, and expressing the communication state between the vehicles at any moment; then, a transmission queue model of the AC0 and AC1 queues is defined, and is used for representing the dynamic behavior of the transmission queue of each vehicle; and finally, solving two real-time performance parameters of 802.11 p-based fleet communication under traffic interference by using the transmission queue model.

Description

Unmanned fleet communication real-time performance analysis method under traffic interference
Technical Field
The invention relates to the technical field of vehicle wireless communication, in particular to a real-time performance analysis method for unmanned fleet communication under traffic interference.
Background
The Internet of vehicles (Vehicular Ad Hoc Networks: VANETs) is a research focus in recent years. The Platoon (platon) is a group of vehicles that travel in the same direction on a road. In a fleet, the head vehicle is driven by the driver in the direction and speed of travel, and the following vehicles are kept in line with the head vehicle. The vehicles run in a form of a fleet, so that the safety of roads can be improved, the fuel consumption is reduced, and meanwhile, the traffic jam is effectively reduced.
802.11p is a wireless communication technology specially applied to a vehicle-mounted network, and an edca (enhanced Distributed Channel access) mechanism is adopted by a protocol media access control layer to provide a Channel access priority; specifically, the 802.11p EDCA mechanism utilizes a plurality of transmission queues (AC0, AC1, AC2, AC3) of different priorities to transmit different types of information, the security information has a high time requirement, so it is transmitted by a high priority queue; urgent safety information is transmitted by the highest priority queue AC0, and periodic safety information is transmitted by the second highest priority queue AC 1; entertainment and other information with less high time requirements are transmitted by the low priority queues AC2 and AC 3. When two queues in one vehicle transmit data simultaneously, the high-priority queue continues to transmit data, while the low-priority queue is equivalent to a collision and needs to back off again. 802.11p in the vehicle network defines two real-time communication performance parameters: time delay of data packets, transfer rate of data packets; the time delay of the data packet refers to the time from the time when the data packet arrives at the transmission queue of the sending vehicle to the time when the data packet is received by the receiving vehicle or discarded; the transmission rate of the data packet refers to the ratio of the data volume successfully received by the vehicle in the communication range of the sending vehicle to the data volume sent by the sending end; the two parameters are used for judging the timeliness and reliability of information transmission between vehicles in the internet of vehicles, and the safety of the operation of the whole internet of vehicles can be judged based on the performance of the information transmission between the vehicles. However, the communication performance research aiming at 802.11p in the vehicle-mounted network is mainly focused on a static performance research direction, few people research the real-time dynamic performance of 802.11p, and particularly under the traffic interference factor, no people research the real-time performance change condition of 802.11p fleet communication is related at present.
Disclosure of Invention
In order to solve the problem that the change condition of the real-time performance of fleet communication in the Internet of vehicles is lack of a confirmation method under traffic interference factors, the invention provides a real-time performance analysis method of unmanned fleet communication under traffic interference, which can analyze the real-time performance of 802.11p fleet communication under typical traffic interference.
The technical scheme of the invention is as follows: the real-time performance analysis method for unmanned fleet communication under traffic interference comprises the following steps:
s1: set any vehicle V in the motorcade ij Follows the vehicle following model IDM model;
it is characterized by also comprising the following steps:
s2: defining a network communication model H (t) reflecting the communication state between vehicles at any time, wherein H (t) is represented as follows:
Figure BDA0002277474130000011
wherein the content of the first and second substances,
Figure BDA0002277474130000012
for reaction of vehicles V i,j And a vehicle V k,l The connectivity at the time of the instant t,
Figure BDA0002277474130000013
indicating vehicle V at time t k,l In a vehicle V i,j Otherwise, is not in the vehicle V i,j Within the transmission range of (c);
any element of the network connectivity model H (t)
Figure BDA0002277474130000014
The expression of (c) is:
Figure BDA0002277474130000021
wherein (x) i,j (t),y i,j (t)) means vehicles V in an unmanned fleet i,j Position at time t, x i,j (t) represents the position of the vehicle in the horizontal direction, y i,j (t) represents a position in the vertical direction of the vehicle,
Figure BDA0002277474130000022
indicating vehicle V i,j The transmission range of (a);
s3: defining a transmission queue model;
the transmit queue access procedure follows a general distribution; the service time is independent and distributed, and the mean value and the variance exist;
setting: AC 0 Queue, the arrival of the data packet obeys the poisson process, and the arrival rate at the time t is lambda 0 (t)；
AC 1 Queue, data packet arrive periodically, arrival rate at t time is lambda 1 (t)；
AC 0 The transmission queue has the following model equation expression:
Figure BDA0002277474130000023
AC 1 the transmission queue has the following model equation expression:
Figure BDA0002277474130000024
wherein L is q (t) represents AC q (q is 0,1) average number of packets in transmission queue at time t,
Figure BDA0002277474130000025
represents L q Rate of change of (t), μ q (t) represents the packet-coated service rate at time t, p q (t) denotes time t AC q Server utilization of the transmission queue; c. C q (t) 2 Represents AC q Squared coefficient of variation of queue service time；a n Is related to the coefficient of variation c q Coefficient of the polynomial of interest, λ 1 (t) denotes time t AC 1 The arrival rate of queued packets;
s4: defining an 802.11p MAC service process model;
the expression of the 802.11p MAC service model is as follows:
Figure BDA0002277474130000026
wherein the content of the first and second substances,
Figure BDA0002277474130000027
represents AC q Probability mother function of queue service time, W 0 Represents AC 0 Contention window value, σ, of queue at backoff order 0 0 (t) represents AC 0 The transmission probability of the transmission queue at the time t, R represents the maximum retransmission times, H q (z) a probability mother function representing an average time of each slot;
tr (z) is a probability mother function representing a transmission time;
G 1,r (z) is represented by a back-off order of r When is AC 1 A probability mother function of queue back-off time;
s5: solving: two real-time performance parameters of 802.11 p-based fleet communications under traffic interference;
time delay PD of data packet q The expression of (c) is:
Figure BDA0002277474130000031
wherein PD is q (t-Deltat) represents the target vehicle V i,j The packet delay at time t-at,
Figure BDA0002277474130000032
represents L q (t) rate of change of (t); packet transmission rate PDR q The expression of (a) is:
Figure BDA0002277474130000033
wherein:
Figure BDA0002277474130000034
indicating arrival of data at target vehicle V i,j In time, the data amount that the vehicle should receive in its transmission range, its expression is:
Figure BDA0002277474130000035
Figure BDA0002277474130000036
f s q (t) indicates a target vehicle V i,j The data of the service, the data volume successfully received by the vehicle in the transmission range is expressed as:
Figure BDA0002277474130000037
Figure BDA0002277474130000038
Figure BDA0002277474130000039
indicates the target vehicle V at time t i,j To vehicle V k,l The collision probability of the transmitted data is expressed as:
Figure BDA00022774741300000310
Figure BDA00022774741300000311
indicating the target vehicle V i,j To vehicle V k,l When data is transmitted, the collision probability caused by the exposed terminal is expressed as:
Figure BDA00022774741300000312
Figure BDA00022774741300000313
Figure BDA0002277474130000041
indicating the target vehicle V i,j To vehicle V k,l When data is sent, the collision probability caused by a hidden terminal is expressed as:
Figure BDA0002277474130000042
s6: based on real-time performance parameters: time delay PD of the data packet q The transfer rate PDR of the data packet q Judging the performance of the system;
time delay PD of said data packet q Maximum value smaller than vehicle information update time interval Deltat, and transfer rate PDR of the data packet q And when the safety information is higher than the preset threshold value, the unmanned vehicles in the motorcade can timely receive the safety information transmitted from other vehicles.
It is further characterized in that:
in step S3, the squared coefficient of variation c of the queue at time t q (t) 2 The expression of (a) is as follows:
Figure BDA0002277474130000043
wherein, Ds q (t) represents AC q Variance of queue service time;Ts q (t) represents AC q The mean of the service times of the queues;
in step S4, the expression of the probability mother function tr (z) of the transmission time is:
Figure BDA0002277474130000044
T tr represents AC q The transmission time of the queue is expressed as:
Figure BDA0002277474130000045
wherein the PHY is H And MAC H Respectively representing the physical layer and MAC layer packet header lengths, EP]Indicating the size of the data packet, R b Indicating the basic transmission rate, R d Represents the data transmission rate, δ represents the propagation delay;
in step S4, when the back-off order is r, AC 1 Probability mother function G of queue back-off time 1,r The expression of (z) is:
Figure BDA0002277474130000046
wherein H q (z) a probability mother function, W, representing the mean time per slot 1,r Represents AC 1 Contention window value, W, of queue at jth backoff stage 1,M Represents AC 1 Maximum contention window value of queue, M denotes AC 1 The maximum backoff order of the queue is 1, R represents retransmission limit and is 2;
the probability mother function H of the average time of each time slot q The expression of (z) is:
Figure BDA0002277474130000051
wherein, T slot Each representsA time of one slot;
AIFS q represents AC q An arbitration frame interval of the transmission queue;
Figure BDA0002277474130000052
representing time t AC q A backoff freeze probability of the transmission queue;
the arbitrated frame space AIFS q The expression of (a) is:
AIFS q ＝AIFSN q ×T slot +SIFS
wherein, AIFSN q The number of arbitration frame intervals is represented, and SIFS represents the minimum frame interval;
the AC q Backoff freeze probability of queue at time t
Figure BDA0002277474130000053
The expression of (a) is:
Figure BDA0002277474130000054
wherein A represents AC 1 Queue demand versus AC 0 The number of time slots for queue multi-detection;
N tr (t) vehicle V in unmanned fleet at time t i,j Number of vehicles within transmission range;
σ q (q is 0,1) represents AC q (q ═ 0,1) transmission probabilities for the queues;
the unmanned vehicle V at the time t i,j Number of vehicles N in transmission range tr The expression of (t) is:
Figure BDA0002277474130000055
wherein n represents the number of fleets, m represents the number of vehicles in a fleet, and n represents the number of fleets;
the AC is q Transmission probability sigma of queue q (t)The expression of (a) is:
Figure BDA0002277474130000061
where ρ is q (t) denotes AC at time t q Queue server utilization;
W 1,0 represents AC 1 Contention window value at backoff order 0;
m represents AC 1 The maximum backoff order of the queue;
Figure BDA0002277474130000062
represents AC q The arrival probability of the data packets of the queue is q equal to 0, 1;
the AC is 1 Contention window value W at backoff order 0 1,0 The expression of (a) is:
W 1,0 ＝CW 1,min +1；
the AC is 1 The expression of the maximum backoff order M of the queue is:
Figure BDA0002277474130000063
CW 1,max represents AC 1 Maximum contention window, CW, of the queue 1,min Represents AC 1 The minimum contention window of (c);
the AC is 1 Packet arrival probability of a queue
Figure BDA0002277474130000064
The expression of (a) is:
Figure BDA0002277474130000065
the invention provides a method for analyzing the real-time performance of communication of an unmanned fleet under traffic interference, which comprises the steps of firstly calculating the dynamics of each vehicle in the unmanned fleet under the traffic interference in real timeA state; determining a network communication model H (t) according to the dynamic state of the vehicles, and expressing the communication state between the vehicles at any moment; then define AC 0 Queue, AC 1 A transmission queue model of the queue, for representing the dynamic behaviour of the transmission queue of each vehicle; then defining an 802.11p MAC service process model, solving a service time variation coefficient in a transmission queue model by using the service process model and a network communication model, and finally solving two real-time performance parameters of 802.11 p-based fleet communication under traffic interference by using the transmission queue model; in the technical scheme of the invention, the motion state of the vehicle is deduced in real time by utilizing the motion rule of the vehicle, and a real-time network communication model is established, so that the connectivity of the whole network is definite; based on a real-time network communication model, the technical scheme of the invention comprehensively considers the backoff freezing and the hidden terminal influence and deduces the formula of the time delay and the transfer rate of the data packet; according to the technical scheme, all influence factors are comprehensively considered, the method is more suitable for actual conditions, and the constructed model is more accurate; based on the technical scheme of the invention, the real-time performance change condition of 802.11p fleet communication under typical traffic interference can be solved, and basic data is provided for subsequent research; according to the technical scheme, the system model is simple and reasonable, and is suitable for popularization and application.
Drawings
FIG. 1 is a schematic diagram of a ground fleet of vehicles in a networked vehicle system;
FIG. 2 is a graph illustrating an exemplary speed change process of a target vehicle under a typical traffic disturbance;
FIG. 3 is λ 0 (t) and lambda 1 (t) an example graph of the mean of the service times of the MAC layer when the value is 20;
FIG. 4 is λ 0 (t) and lambda 1 (t) a time delay example graph of a data packet when the value is 20;
FIG. 5 is λ 0 (t) and λ 1 (t) an example graph of the transfer rate of the packet when the value is 20.
Detailed Description
As shown in figure 1 of the attached drawings of the specification, the schematic diagram of a ground fleet of vehicles in the vehicle networking system is shown, and the ground fleet of vehicles is stored on a roadIn a plurality of fleets, the vehicles in the rectangular frame are a fleet P i (Platoo), wherein the first train in each fleet is the head train (Leader Vehicle), the rest of the vehicles are the slave trains (Member vehicles), and the inter-train distance (inter-Spacing) between the trains in the same fleet is the inter-fleet distance s ij (ii) a The distance between fleets of vehicles (Inter-platon Spacing) is the Inter-fleet distance D ij (ii) a The vehicles in the platoon are denoted v ij Where i denotes the platoon and j denotes the jth vehicle in the platoon, i.e. the head vehicle of the first platoon, denoted v 11 The jth vehicle in the ith vehicle fleet is v ij (ii) a Each vehicle is in communication connection with the cloud server and other vehicles in the same fleet through a wireless network; the kinematic relevant data such as the instant speed, the acceleration, the running time, the geographic position (coordinate) and the like of each vehicle are collected and calculated by a sensor in the vehicle and then are shared in a communication network; assume that the Vehicle at the center of the circle in fig. 1 is a Vehicle (Disturbed Vehicle) in which an unexpected sudden change in form speed occurs
Figure BDA0002277474130000071
When the traffic interference factor occurs, the speed, acceleration and other kinematic states of other vehicles in the same fleet and vehicles in other fleets can be changed; the number of vehicles in the vehicle communication range and the number of hidden terminals can be greatly changed in real time, so that the time delay and the transfer rate of data packets can be increased or decreased; at this moment, the change conditions of two parameters, namely the time delay of a data packet and the transfer rate of the data packet, between vehicles need to be acquired so as to judge whether the vehicles can receive emergency safety information and data packets related to periodic safety information in time, and if each vehicle can receive the safety information in time, the arrangement of pedestrians and passengers can be ensured when unmanned vehicles in the internet of vehicles can generate traffic interference of a certain kind; if the safety information cannot be received in time, it can be judged that the safety cannot be ensured when vehicles in the Internet of vehicles have certain traffic interference.
The invention discloses a real-time performance analysis method of 802.11p fleet communication aiming at traffic interference, which comprises the following steps.
S1: defining a vehicle following model under traffic interference; referring to fig. 2 of the drawings in the specification, the speed change process of a vehicle disturbed by traffic is as follows: [ t ] of 0 ,t 1 ]From a steady speed v with a constant deceleration stb Is reduced to v low ，[t 1 ,t 2 ]In a low speed state v low For a period of time, [ t ] 2 ,t f ]At constant acceleration from v low To v stb (ii) a I.e. the target vehicle is moving from a steady speed v stb Time t d Making uniform deceleration movement to a certain lower speed v low And after t s The vehicle runs at low speed within time, and finally performs uniform acceleration to recover to the original stable speed, wherein the acceleration time is t a (ii) a Any vehicle V in the motorcade i,j Follows the vehicle following model IDM model.
S2: defining a network communication model H (t) reflecting the communication state between vehicles at any time, wherein H (t) is represented as follows:
Figure BDA0002277474130000081
wherein the content of the first and second substances,
Figure BDA0002277474130000082
for reaction of vehicles V i,j And a vehicle V k,l The connectivity at the time of the t-time,
Figure BDA0002277474130000083
indicating vehicle V at time t k,l In a vehicle V i,j Otherwise, is not in the vehicle V i,j Within the transmission range of (c);
any element of the network connectivity model H (t)
Figure BDA0002277474130000084
The expression of (c) is:
Figure BDA0002277474130000085
wherein (x) i,j (t),y i,j (t)) means vehicles V in an unmanned fleet i,j Position at time t, x i,j (t) represents the position of the vehicle in the horizontal direction, y i,j (t) represents a position in the vertical direction of the vehicle,
Figure BDA0002277474130000086
indicating vehicle V i,j The transmission range of (a);
for vehicle V i,j Position (x) thereof i,j (t),y i,j Y in (t)) i,j (t) is constant, x i,j (t) can be solved in real time by an iterative method, if the kinematic information of the vehicle at the initial moment is applied, the kinematic information of the time (t-2 Δ t) is obtained by the iterative method, and the kinematic information of the vehicle at the time t is calculated by iteration as follows:
a.t abscissa x of vehicle position at time i,j The expression of (t) is as follows:
Figure BDA0002277474130000087
wherein, a i,j (t-Deltat) represents an arbitrary vehicle V i,j Acceleration at time (t- Δ t), x i,j (t- Δ t) and v i,j The expression (t- Δ t) is as follows:
Figure BDA0002277474130000088
v i,j (t-△t)＝v i,j (t-2△t)+a i,j (t-2△t)+o(△t 2 )
b. for disturbed vehicles, a i,j The expression of (t- Δ t) is as follows:
Figure BDA0002277474130000091
wherein, t 1 ＝t 0 +t d And t is f ＝t 0 +t d +t s +t a ；
c. For following vehicles, it follows the IDM model, a i,j The expression of (t- Δ t) is as follows:
Figure BDA0002277474130000092
wherein a represents the maximum acceleration, v 0 Which is indicative of the maximum speed of the vehicle,
Figure BDA0002277474130000093
indicating vehicle V i,j And a vehicle V i,j-1 An ideal spacing therebetween;
S i,j (t-Deltat) represents the vehicle V i,j And a vehicle V i,j-1 The actual distance between them, expressed as:
S i,j (t-△t)＝x i,j (t-△t)-x i,j (t-△t)-L+o(△t 2 )；
wherein L represents a vehicle length;
Figure BDA0002277474130000094
indicating vehicle V i,j And a vehicle V i,j-1 The expression of the ideal spacing between the two is:
Figure BDA0002277474130000095
wherein s is 0 Indicating minimum inter-fleet spacing, T h Is an ideal headway, and b is a comfortable acceleration;
△v i,j (t-Deltat) represents the vehicle V i,j And a vehicle V i,j-1 The speed difference between them, expressed as:
△v i,j (t-△t)＝v i,j (t-△t)-v i,j-1 (t-△t)+o(△t 2 )。
s3: defining a transmission queue model; invention transmissionThe input queue access process follows a general distribution and the service time is independent and equally distributed, with mean and variance. For AC 0 Queue, the arrival of the data packet obeys the poisson process, and the arrival rate at the time t is lambda 0 (t)；AC 1 Queue, data packet arrive periodically, arrival rate at t time is lambda 1 (t); different transmission queues (AC) 0 And AC 1 ) Corresponding to the different models, it is represented as follows:
AC 0 the transmission queue has the following model equation expression:
Figure BDA0002277474130000096
the AC1 transmission queue has the following model equation expression:
Figure BDA0002277474130000101
wherein L is q (t) represents AC q (q is 0,1) average number of packets of the transmission queue at time t,
Figure BDA0002277474130000102
represents L q Rate of change of (t), μ q (t) represents the packet service rate at time t, ρ q (t) denotes time t AC q Server utilization of the transmission queue;
c q (t) 2 represents AC q The square variation coefficient of queue service time is expressed as:
Figure BDA0002277474130000103
wherein Ds is q (t) represents AC q Variance of queue service time; ts q (t) represents AC q The mean of the service times of the queues;
adopting Runge-Kutta algorithm to solve AC after delta t every other time interval q Queue model, get L q And
Figure BDA0002277474130000104
s4: defining an 802.11p MAC service process model; consider the 802.11p MAC service process as z The linear system of the domain, the expression of the service process model is as follows:
Figure BDA0002277474130000105
wherein the content of the first and second substances,
Figure BDA0002277474130000106
represents AC q Probability mother function of queue service time, W 0 Represents AC 0 Contention window value, σ, of queue at backoff order 0 0 (t) represents AC 0 The transmission probability of the transmission queue at the time t, wherein R represents the maximum retransmission times;
tr (z) represents a probability mother function of the transmission time, whose expression is:
Figure BDA0002277474130000107
T tr represents AC q The transmission time of the queue is expressed as:
Figure BDA0002277474130000108
wherein the PHY is H And MAC H Respectively representing the physical layer and MAC layer packet header lengths, EP]Indicating the size of the data packet, R b Indicating the basic transmission rate, R d Represents the data transmission rate, δ represents the propagation delay;
G 1,r (z) is represented by a back-off order of r When is AC 1 The probability mother function of queue back-off time is expressed as:
Figure BDA0002277474130000111
H q (z) a probability mother function representing the average time of each slot, expressed as:
Figure BDA0002277474130000112
wherein, T slot Representing the time of each slot;
AIFS q represents AC q The arbitration frame interval of the transmission queue is expressed as:
AIFS q ＝AIFSN q ×T slot +SIFS
wherein, AIFSN q The number of arbitration frame intervals is represented, and SIFS represents the minimum frame interval;
Figure BDA0002277474130000113
representing the time AC q The backoff freezing probability of the transmission queue is expressed as follows:
Figure BDA0002277474130000114
wherein A represents AC 1 Queue demand versus AC 0 The number of the time slots of the queue multi-detection;
N tr (t) vehicle V in unmanned vehicle fleet at time t i,j The number of vehicles in the transmission range is expressed as:
Figure BDA0002277474130000115
wherein n represents the number of fleets and m represents the number of vehicles in one fleet;
σ q (q is 0,1) represents the transmission probability of the queue, and the expression is as follows:
Figure BDA0002277474130000121
where ρ is q (t) denotes AC at time t q Queue server utilization;
W 1,0 represents AC 1 The contention window value at backoff order 0 is expressed as:
W 1,0 ＝CW 1,min +1；
m represents AC 1 The maximum backoff order of the queue is expressed as:
Figure BDA0002277474130000122
CW 1,max represents AC 1 Maximum contention window, CW, of the queue 1,min Represents AC 1 The minimum contention window of (c);
Figure BDA0002277474130000123
represents AC q The data packet arrival probability of the queue is expressed as:
Figure BDA0002277474130000124
step S4 is integrated to solve AC q Mean value of service time Ts of queue q (t) and the variance Ds q (t), the expression is:
Figure BDA0002277474130000125
Figure BDA0002277474130000126
s5: under the condition of solving traffic interference, two real-time performances of the 802.11 p-based fleet communication are solved, namely the time delay of a data packet and the transfer rate of the data packet:
(1) time delay PD of data packet q The expression of (a) is:
Figure BDA0002277474130000131
wherein PD is q (t-Deltat) represents the target vehicle V i,j Packet delay at time t- Δ t;
(2) packet transmission rate PDR q The expression of (c) is:
Figure BDA0002277474130000132
wherein f is s q (t) indicates a target vehicle V i,j The data of the service, the data volume successfully received by the vehicle in the transmission range is expressed as:
Figure BDA0002277474130000133
Figure BDA0002277474130000134
Figure BDA0002277474130000135
indicating the target vehicle V at time t i,j To vehicle V k,l The collision probability of the transmitted data is expressed as:
Figure BDA0002277474130000136
Figure BDA0002277474130000137
indicating the target vehicle V i,j To vehicle V k,l Number of transmissionsAccording to the time, the collision probability caused by the exposed terminal is expressed as:
Figure BDA0002277474130000138
Figure BDA0002277474130000139
Figure BDA00022774741300001310
indicating the target vehicle V i,j To vehicle V k,l When data is sent, the collision probability caused by a hidden terminal is expressed as:
Figure BDA00022774741300001311
Figure BDA00022774741300001312
indicating arrival of data at target vehicle V i,j In time, the data amount that the vehicle should receive in its transmission range, its expression is:
Figure BDA00022774741300001313
s6: based on real-time performance parameters: time delay PD of data packet q Packet transfer rate PDR q Judging the performance of the system;
time delay PD of data packet q The maximum value is smaller than the vehicle information updating time interval delta t, which shows that the unmanned vehicle can timely receive the latest safety information, namely, each measure of the vehicle is made based on the latest environmental condition, and the transfer rate PDR of the data packet q And at the lowest, not less than 50%, which indicates that the data packet can be successfully transmitted. I.e. the time delay of the data packet is less than the update interval and the transfer rate of the data packet is good, the vehicleThe vehicle can receive the safety information from the surrounding vehicles in time and successfully and can transmit the safety information to the surrounding vehicles in time and successfully, and the whole system is in a better state.
In a laboratory environment, simulation is carried out based on the technical scheme of the invention, and the accuracy and the feasibility of the technical scheme of the invention are confirmed. The simulation environment of the invention is MATLAB 2010a, the vehicle updating time interval is 0.01s, and the parameter of 802.11p is the standard parameter. And simulating the communication process of the whole fleet by using the 802.11p protocol by using the 802.11p basic parameters to obtain a simulation value.
Referring to FIG. 3 of the drawings, the arrival rates λ of packets for the AC0 queue and the AC1 queue 0 (t) and lambda 1 (t) the variation of the average service time of the MAC layer with time when the value of (t) is 20. The abscissa is time in seconds and the ordinate is the average service time in milliseconds. The solid line and the dotted line respectively represent theoretical values of the MAC mean time of service of the AC0 queue and the AC1 queue analyzed based on the technical solution of the present invention, and the triangle and the diamond respectively represent simulated values of the MAC mean time of service of the AC0 queue and the AC1 queue simulated by software. Meanwhile, as can be seen from the figure, the line representing the AC0 queue is below the line of the AC1 queue, and the priority of the AC0 queue is higher than that of the AC1 queue, so that the high-priority queue has a lower average access time than the low-priority queue, which indicates that the data calculated based on the invention meets the actual requirements on the queue transmission time and priority in actual work.
Referring to FIG. 4 of the drawings, the arrival rates λ of packets for the AC0 queue and the AC1 queue 0 (t) and lambda 1 When the value of (t) is 20, the real-time Delay of communication, namely the Delay of a data Packet (Packet Delay), changes along with time. The abscissa is time in seconds, and the ordinate is the time delay of the packet in milliseconds. The solid line and the dotted line respectively represent theoretical values (AC0-analysis/AC1-analysis) for analyzing the packet delay of the AC0 queue and the AC1 queue based on the technical scheme of the invention, and the triangle and the diamond respectively represent theoretical valuesThe simulation values (AC0-simulation/AC1-simulation) of the packet delays of the AC0 queue and the AC1 queue simulated by software are obviously very close to the analysis values, which shows that the technical scheme of the invention can obtain an accurate result of the packet delay.
Referring to FIG. 5 of the drawings, the arrival rates λ of packets for the AC0 queue and the AC1 queue 0 (t) and lambda 1 (t) when the value is 20, the transfer rate of the packet changes with time. The abscissa is time in seconds, and the ordinate is Packet Delivery Ratio (Packet Delivery Ratio). The solid line and the dotted line respectively represent theoretical values of the packet transfer rates of the AC0 queue and the AC1 queue analyzed based on the technical scheme of the present invention, and the simulated values of the packet transfer rates of the AC0 queue and the AC1 queue simulated by software in the triangle and the diamond are obviously very close to the analyzed values, which indicates that the correct result can be obtained by calculating the packet transfer rates based on the technical scheme of the present invention.
Meanwhile, from the perspective of judging system performance based on real-time performance parameters: as can be seen from fig. 3, the maximum value of the AC0 queue or AC1 queue delay is much smaller than the information update time interval (10 ms in the present invention) of the unmanned vehicle, indicating that the unmanned vehicle can receive information in time; as can be seen from fig. 4, the transfer rate of the queue data packet is relatively good, which is as high as 80% and not less than 55%, indicating that the security information can be successfully transferred; based on the two sets of data in fig. 3 and 4, it can be seen that the unmanned vehicle as the experimental target can timely receive safety information (emergency, brake failure, speed change, etc.) from other vehicles; the unmanned vehicle can make response measures (speed reduction or lane change or even stop advancing and the like) in advance according to the obtained safety information, so that traffic accidents are avoided, and the safety of pedestrians and passengers is guaranteed.

Claims (1)

1. The real-time performance analysis method for unmanned fleet communication under traffic interference comprises the following steps:
s1: set any vehicle V in the motorcade ij Follows the vehicle following model IDM model;
it is characterized by also comprising the following steps:
s2: defining a network communication model H (t) reflecting the communication state between vehicles at any time, wherein H (t) is represented as follows:
Figure FDA0003746012950000011
wherein the content of the first and second substances,
Figure FDA0003746012950000012
for reaction of vehicles V i,j And a vehicle V k,l The connectivity at the time t is the time t,
Figure FDA0003746012950000013
indicating vehicle V at time t k,l In a vehicle V i,j Otherwise, is not in the vehicle V i,j Within the transmission range of (c); wherein m represents the number of vehicles in a fleet and n represents the number of fleets;
any element in the network connectivity model H (t)
Figure FDA0003746012950000014
The expression of (a) is:
Figure FDA0003746012950000015
wherein (x) i,j (t),y i,j (t)) means vehicles V in an unmanned fleet i,j Position at time t, x i,j (t) represents the position of the vehicle in the horizontal direction, y i,j (t) represents a position in the vertical direction of the vehicle,
Figure FDA0003746012950000016
indicating vehicle V i,j The transmission range of (a);
s3: defining a transmission queue model;
the transmit queue access procedure follows a general distribution; the service time is independent and distributed, and the mean value and the variance exist;
setting: AC 0 A queue, wherein the arrival of the data packet follows the poisson process, and the arrival rate at the time t is lambda 0 (t)；
AC 1 Queue, data packet arrive periodically, arrival rate at t time is lambda 1 (t)；
AC 0 The transmission queue has the following model equation expression:
Figure FDA0003746012950000017
AC 1 the transmission queue has the following model equation expression:
Figure FDA0003746012950000019
wherein L is q (t) represents AC q The average number of packets at time t of the transmit queue,
Figure FDA0003746012950000018
represents L q Rate of change of (t), μ q (t) represents the packet-coated service rate at time t, p q (t) denotes time t AC q Server utilization of the transmission queue; c. C q (t) 2 Represents AC q The squared coefficient of variation of queue service time; a is n Is related to the coefficient of variation c q Coefficient of the polynomial of interest, λ 1 (t) denotes time t AC 1 The arrival rate of queued packets; wherein q is 0, 1;
s4: defining an 802.11p MAC service process model;
the expression of the 802.11p MAC service model is as follows:
Figure FDA0003746012950000021
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003746012950000022
represents AC q Probability mother function of queue service time, W 0 Represents AC 0 Contention window value, σ, of queue at backoff order 0 0 (t) represents AC 0 The transmission probability of the transmission queue at time t, R represents the maximum retransmission times, H q (z) a probability mother function representing an average time of each slot; wherein q is 0, 1;
tr (z) is a probability mother function representing a transmission time;
G 1,r (z) denotes AC when the back-off order is r 1 A probability mother function of queue back-off time;
s5: solving: two real-time performance parameters of 802.11 p-based fleet communications under traffic interference;
time delay PD of data packet q The expression of (a) is:
Figure FDA0003746012950000023
wherein PD is q (t-Deltat) represents the target vehicle V i,j The packet delay at time t-at,
Figure FDA0003746012950000028
represents L q (t) rate of change of (t);
packet transmission rate PDR q The expression of (c) is:
Figure FDA0003746012950000024
wherein:
Figure FDA0003746012950000025
indicating arrival of data at target vehicle V i,j In time, the data amount that the vehicle should receive in its transmission range, its expression is:
Figure FDA0003746012950000026
Figure FDA0003746012950000027
indicating the target vehicle V i,j The data of the service, the data volume successfully received by the vehicle in the transmission range is expressed as:
Figure FDA0003746012950000031
Figure FDA0003746012950000032
indicates the target vehicle V at time t i,j To vehicle V k,l The collision probability of the transmitted data is expressed as:
Figure FDA0003746012950000033
Figure FDA0003746012950000034
indicating the target vehicle V i,j To vehicle V k,l When data is transmitted, the collision probability caused by the exposed terminal is expressed as:
Figure FDA0003746012950000035
Figure FDA0003746012950000036
indicating the target vehicle V i,j To vehicle V k,l When data is sent, the collision probability caused by a hidden terminal is expressed as:
Figure FDA0003746012950000037
s6: based on real-time performance parameters: time delay PD of the data packet q The transfer rate PDR of the data packet q Judging the performance of the system;
time delay PD of said data packet q Maximum value smaller than vehicle information update time interval Deltat, and transfer rate PDR of said data packet q When the safety information is higher than the preset threshold value, the unmanned vehicles in the motorcade can timely receive the safety information transmitted from other vehicles;
in step S3, the squared coefficient of variation c of the queue at time t q (t) 2 The expression of (a) is as follows:
Figure FDA0003746012950000038
wherein, Ds q (t) represents AC q Variance of queue service time; ts q (t) represents AC q The mean of the service times of the queues;
in step S4, the expression of the probability mother function tr (z) of the transmission time is:
Figure FDA0003746012950000039
T tr represents AC q The transmission time of the queue is expressed as:
Figure FDA00037460129500000310
wherein the PHY is H And MAC H Respectively representing physical layer and MAC layer packet header lengths, E P]Indicating the size of the data packet, R b Indicating the basic transmission rate, R d Represents the data transmission rate, δ represents the propagation delay;
in step S4, when the back-off order is r, AC 1 Probability mother function G of queue back-off time 1,r The expression of (z) is:
Figure FDA0003746012950000041
wherein H q (z) a probability mother function, W, representing the mean time per slot 1,r Represents AC 1 Contention window value, W, of queue at jth backoff stage 1,M Represents AC 1 Maximum contention window value of queue, M denotes AC 1 The maximum backoff order of the queue is 1, R represents retransmission limit and is 2;
the probability mother function H of the average time of each time slot q The expression of (z) is:
Figure FDA0003746012950000042
wherein, T slot Representing the time of each slot;
AIFS q represents AC q An arbitration frame interval of the transmission queue;
Figure FDA0003746012950000043
representing time t AC q A backoff freeze probability of the transmit queue;
the arbitrated frame space AIFS q The expression of (a) is:
AIFS q ＝AIFSN q ×T slot +SIFS
wherein, AIFSN q Indicating arbitration frame intervalSIFS represents the minimum inter-frame space;
the AC is q Backoff freezing probability of queue at time t
Figure FDA0003746012950000044
The expression of (c) is:
Figure FDA0003746012950000045
wherein A represents AC 1 Queue demand versus AC 0 The number of time slots for queue multi-detection;
N tr (t) vehicle V in unmanned vehicle fleet at time t i,j Number of vehicles within transmission range;
σ q represents AC q The transmission probability of the queue;
the unmanned vehicle V at the time t i,j Number of vehicles N in transmission range tr The expression of (t) is:
Figure FDA0003746012950000051
wherein n represents the number of fleets and m represents the number of vehicles in a fleet; the AC is q Transmission probability sigma of queue q The expression of (t) is:
Figure FDA0003746012950000052
where ρ is q (t) denotes AC at time t q Queue server utilization;
W 1,0 represents AC 1 A contention window value at backoff order 0;
m represents AC 1 The maximum backoff order of the queue;
Figure FDA0003746012950000053
represents AC q The arrival probability of the data packets of the queue is q equal to 0, 1;
the AC 1 Contention window value W at backoff order 0 1,0 The expression of (a) is:
W 1,0 ＝CW 1,min +1；
the AC 1 The expression for the maximum backoff order M of the queue is:
Figure FDA0003746012950000054
CW 1,max represents AC 1 Maximum contention window, CW, of the queue 1,min Represents AC 1 The minimum contention window of (c);
the AC is 1 Packet arrival probability of a queue
Figure FDA0003746012950000055
The expression of (a) is:
Figure FDA0003746012950000061
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103795646A (en) * 2012-11-02 2014-05-14 北京大学 Distributed priority congestion control method for IEEE802.11P vehicle-mounted system
CN104967671A (en) * 2015-06-01 2015-10-07 南京邮电大学 Adaptive EDCA method based on vehicle network density
CN109640290A (en) * 2018-11-30 2019-04-16 北京邮电大学 Service differentiating method, device and equipment based on EDCA mechanism in car networking

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103795646A (en) * 2012-11-02 2014-05-14 北京大学 Distributed priority congestion control method for IEEE802.11P vehicle-mounted system
CN104967671A (en) * 2015-06-01 2015-10-07 南京邮电大学 Adaptive EDCA method based on vehicle network density
CN109640290A (en) * 2018-11-30 2019-04-16 北京邮电大学 Service differentiating method, device and equipment based on EDCA mechanism in car networking

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Time-Dependent Performance Analysis of the 802.11p-Based Platooning Communications Under Disturbance;Qiong Wu等;《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》;20201231;全文 *

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