CN107613532B - Fuzzy vertical switching method based on lingering time prediction in vehicle heterogeneous network - Google Patents

Fuzzy vertical switching method based on lingering time prediction in vehicle heterogeneous network Download PDF

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CN107613532B
CN107613532B CN201710769363.5A CN201710769363A CN107613532B CN 107613532 B CN107613532 B CN 107613532B CN 201710769363 A CN201710769363 A CN 201710769363A CN 107613532 B CN107613532 B CN 107613532B
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马彬
张文静
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Chongqing University of Post and Telecommunications
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Abstract

The invention discloses a two-type fuzzy vertical switching method based on lingering time prediction in a vehicle heterogeneous network. In the vehicle heterogeneous network, the influence of the stay time on the switching performance is considered for the problem that the switching performance is reduced due to the fact that the stay time of the vehicle terminal is too short and the ambiguity and the randomness exist in the judgment parameters at the same time. When switching judgment is carried out, firstly, the lingering time of the terminal in each candidate network is predicted, then, corresponding two-type membership function is designed according to the lingering time, available bandwidth and the ambiguity and randomness factors existing in user access cost, a two-type fuzzy inference system is constructed, and finally, the optimal switching network is selected according to system output. Experimental results show that the method can effectively reduce average switching times, switching failure rate and ping-pong switching times and improve system throughput.

Description

Fuzzy vertical switching method based on lingering time prediction in vehicle heterogeneous network
Technical Field
The invention belongs to a vertical switching method in a vehicle heterogeneous wireless network, and belongs to the field of mobile communication. And more particularly to a method for vertical handover using sojourn time prediction and two-type fuzzy inference.
Background
With the development of intelligent transportation systems and the integration of heterogeneous wireless network technologies, the dependence of vehicle terminals in high-speed movement on networks is increasing day by day, and vehicle users expect to obtain optimal network services at any time and any place, including stronger stability, higher bandwidth, lower cost and the like. In the vehicle heterogeneous wireless network, the vertical handover technology is a key technology for meeting the network requirements of users. However, due to the high speed mobility of the vehicle terminal and the complex diversity of the network environment, how to ensure the high efficiency and stability of the data transmission of the vehicle terminal with an effective vertical handover mechanism is one of the important issues facing the vehicle communication system. In the vertical handover decision, user preference, importance degree of parameters, and the like have certain ambiguity, and in addition, the obtained attribute values have ambiguity due to measurement errors and the dynamic property of the network, and in order to describe and process the ambiguity information more accurately and improve the decision accuracy, many researchers in recent years apply fuzzy logic to the vertical handover method.
In the document Calhan A, center C, Case study on handoff strategies for wireless handoff networks J. Computer Standards & Interfaces 2013,35(1): 170-. In the literature [ Kaleem F, Mehbodniya A, IslamA, et al, dynamic target wireless network selection technical utilization [ J ]. China Communications,2013,10(1):1-16] parameters such as terminal speed, network load and throughput are considered, a multi-criterion vertical switching method is provided, and a hierarchical fuzzy reasoning system is adopted to judge whether to perform switching. The document [ Kustawan I, Chi K. Handoff decision using a kalman filter and fuzzy logic in heterologous networks [ J ]. IEEE communications letters,2015,19(12): 2258-. However, the method only uses the speed as a decision parameter, neglects the influence of the stay time of the terminal in the network on the handover decision, and is easy to cause ping-pong effect, thereby reducing the handover performance. The document [ bright day, Zhao Ji hong, Qubeth, etc.. A fuzzy logic-based vertical switching decision method for multi-terminal cooperation [ J ] communication science, 2014,35(9):67-78] proposes a fuzzy logic-based vertical switching decision method for multi-terminal cooperation, and an author takes three main parameters influencing the stay time of a terminal in a WLAN as the input of a fuzzy inference system and sets 2 switching threshold values to judge the switching time, wherein the three parameters are respectively: the distance between the terminal and the access point, the speed of the terminal and the included angle of the speed direction relative to the access point. However, in the sojourn time prediction model, the author only simply regards the motion rule of the terminal as uniform linear motion, neglects the influence of the change of the speed and the direction after switching on the sojourn time, and does not really reflect the movement rule of the terminal. In addition, in the above methods, a fuzzy system is used to deal with the ambiguity problem of the decision parameter, but in the actual decision process, there exist not only ambiguity but also randomness in the input parameter, such as randomness caused by measurement noise and time-varying nature of the network during measurement, and a fuzzy system cannot well describe the randomness of the parameter. Therefore, the decision method based on the one-type fuzzy system cannot effectively improve the service quality after the terminal accesses the network.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. The fuzzy vertical switching method based on the stay time prediction in the vehicle heterogeneous network is provided, the average switching times and the ping-pong effect are effectively reduced, the system throughput is effectively improved, and therefore the service quality of the terminal is improved. The technical scheme of the invention is as follows:
a fuzzy vertical switching method based on lingering time prediction in a vehicle heterogeneous network is characterized by comprising the following steps:
101. firstly, according to the characteristic that a vehicle only runs along a road route in a vehicle heterogeneous network, predicting the stay time of a terminal in each candidate network by using a route planned by a vehicle-mounted navigation system;
102. secondly, designing a two-type membership function of each decision parameter according to the characteristic that the decision parameters have both ambiguity and randomness, wherein the decision parameters comprise the stay time, the bandwidth and the communication cost predicted in the step 101, carrying out fuzzy reasoning according to the designed two-type membership function, the output of a fuzzy reasoning system is the score value of a candidate network, finally comparing the score values of each network, selecting the network with the largest score value as an optimal switching network, and finishing vertical switching.
Further, the vehicle heterogeneous wireless network includes: the BS is a base station of LTE, the WLAN1, the WLAN2 and the WLAN3 are three access points deployed on the roadside respectively, the B and the D are intersections respectively, the E and the F are intersections of coverage boundaries of the WLAN2 and the WLAN3 and roads respectively, the position A is assumed to be a starting point, the C is assumed to be an end point, and the optimal route planned by the navigation system is A-B-C.
Further, in the step 101, according to the planned route, the routes of the vehicle in the three candidate networks are calculated, so as to calculate the stay time of the terminal in the candidate networks, which specifically includes the steps of:
the range in WLAN1 may be expressed as:
SWLAN1＝dAB+dBC(1)
wherein d isABIs the length of the road between points A and B, dBCThe length of the road between the two points B and C;
the range in WLAN2 may be expressed as:
SWLAN2＝dAB+dBE(2)
wherein d isBEThe length of the road between the two points B and E;
the range in WLAN3 may be expressed as:
SWLAN3＝dAB+dBF(3)
wherein d isBFThe length of the road between the two points B and F;
suppose that in the sampling period TsThe total number of instantaneous speed samples taken in is N, the average speed V of the vehicle can be expressed as:
Figure BDA0001394741080000041
wherein, VnIs a period TsThe nth instantaneous speed sample value of the vehicle;
from the distance S and the speed
Figure BDA0001394741080000048
The residence time of the vehicle within each candidate network can be calculated, and the residence time of the terminal within WLAN1, WLAN2, and WLAN3 can be expressed as:
Figure BDA0001394741080000042
Figure BDA0001394741080000043
Figure BDA0001394741080000044
further, in step 102, a type-ii membership function is designed according to the uncertainty of each decision parameter, and each parameter has three fuzzy sets, which are: low/short, medium, high/long, each fuzzy set is expressed by adopting a two-type membership function in a Gaussian type interval, and the expression of the upper and lower membership function in the two-type membership function in the Gaussian type interval is shown as a formula (8) and a formula (9):
Figure BDA0001394741080000045
Figure BDA0001394741080000046
wherein the content of the first and second substances,
Figure BDA0001394741080000047
the variation range of the average value.
Further, when the decision parameter is the residence time and is used as the evaluation index, the mean variation range of the "short" is [0,2 ]; the mean variation range of "medium" is [14,16 ]; the mean variation range of "long" is [28,30 ];
when the judgment parameter is the bandwidth and is used as an evaluation index, the variation range of the mean value of 'low' is [0,2 ]; the mean variation range of "medium" is [6,8 ]; the mean of "high" varies by a range of [12,14 ].
When the judgment parameter is communication cost and is used as an evaluation index, the mean value change range of 'low' is [0,0.1 ]; the mean variation range of "medium" is [0.45,0.55 ]; the mean value of "high" varies by [0.9,1 ].
Outputting two types of membership functions to evaluate the switching performance of the candidate network according to the score value, wherein the variation range of the low mean value is [0,10 ]; the mean variation range of "medium" is [45,55 ]; the mean value of "high" varies by [90,100 ].
The invention has the following advantages and beneficial effects:
aiming at the problem of frequent switching caused by too short stay time of the vehicle terminal in the network after vertical switching, the stay time of the terminal in each candidate network is predicted by utilizing the route planned by the vehicle navigation system according to the characteristic that the vehicle only runs along the road route, so that the average switching times and ping-pong effect are effectively reduced.
The two-type membership function of each parameter is designed according to the uncertain characteristics of the decision parameters, and the decision parameters have uncertain characteristics such as fuzziness and randomness simultaneously due to high-speed mobility of a vehicle terminal, time-varying characteristics of a network state and errors and noises existing during parameter measurement. Therefore, the two-type fuzzy system is introduced to process the uncertainty problem in the judgment parameters, and the system throughput is effectively improved, so that the service quality of the terminal is improved.
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FIG. 1 is a schematic diagram of a vehicle heterogeneous wireless network model in accordance with a preferred embodiment of the present invention;
FIG. 2 is a function of two types of membership in linger time;
FIG. 3 is a two-type membership function for bandwidth;
FIG. 4 is a two-type membership function for cost;
FIG. 5 is a graph of output two type membership functions;
FIG. 6 is a schematic diagram of a two-type fuzzy inference system;
FIG. 7 is a comparison of handover failure rates for different methods;
FIG. 8 is a comparison of throughput for different methods;
FIG. 9 is a comparison of average switching times for different methods;
fig. 10 is a comparison of unnecessary switching times for different methods.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the method comprehensively considers the influence of the stay time of the vehicle terminal in the candidate network on the switching performance and the characteristics of uncertainty such as ambiguity, randomness and the like in the judgment parameters, can effectively reduce the average switching times, the switching failure rate and the ping-pong switching times, and improves the system throughput.
The vertical switching method provided by the invention comprises the following steps:
step one, designing a two-type membership function of each parameter according to the characteristics that the decision parameters have ambiguity and randomness simultaneously in the actual vertical switching process, wherein the decision parameters comprise residence time, bandwidth and communication cost. Designing the two-type membership function shown in FIGS. 1-4 according to each decision parameter and the output uncertainty characteristics
In the vehicle heterogeneous wireless network model, the BS is a base station of LTE, the WLAN1, the WLAN2 and the WLAN3 are three access points deployed on the roadside respectively, the B and the D are intersections respectively, and the E and the F are intersections of coverage boundaries of the WLAN2 and the WLAN3 and roads respectively. Assuming that the position A is a starting point and the position C is an end point, the optimal route planned by the navigation system is A-B-C.
Step three, calculating the routes of the vehicle in three candidate networks according to the planned route:
the range in WLAN1 may be expressed as:
SWLAN1＝dAB+dBC(1)
wherein d isABIs the length of the road between points A and B, dBCThe length of the road between the two points B and C.
The range in WLAN2 may be expressed as:
SWLAN2＝dAB+dBE(2)
wherein d isBEThe length of the road between points B and E.
The range in WLAN3 may be expressed as:
SWLAN3＝dAB+dBF(3)
wherein d isBFThe length of the road between points B and F.
Because the speed of the vehicle changes in the whole running process and the vehicle speed sensor can only measure the instantaneous speed of the vehicle, the average value of the instantaneous speed in a certain period time is taken as the running speed of the vehicleSpeed. Suppose that in the sampling period TsThe total number of instantaneous speed samples taken in is N, the average speed V of the vehicle can be expressed as:
Figure BDA0001394741080000071
wherein, VnIs a period TsAnd the nth instantaneous speed sample of the vehicle.
Step four, the distance S and the speed are calculated
Figure BDA0001394741080000072
The vehicle residence time within each candidate network may be calculated. The terminal residence time within WLAN1, WLAN2, and WLAN3 may be expressed as:
Figure BDA0001394741080000073
Figure BDA0001394741080000074
Figure BDA0001394741080000075
and step five, inputting the bandwidth and the communication cost of the candidate network and the lingering time calculated in the step one into a two-type fuzzy reasoning system shown in the figure 6, carrying out fuzzy reasoning according to a two-type membership function designed in the step one, finally comparing the score values of all networks, selecting the network with the maximum score value as the optimal switching network, and finishing vertical switching.
Firstly, the fuzzy and random characteristics existing in the decision parameters in the vertical switching process are analyzed, and then a two-type membership function of each parameter is designed according to specific uncertain characteristics. Specific analyses and designs are given below:
(1) residence time: in the switching decision process, due to the time-varying characteristics of the measurement error and the speed magnitude, the lingering time has randomness, so that the mean value of the membership function changes near the central point. When the average lingering time is [0,2], more switching failures occur, which seriously affects the user experience; when the mean linger time is [14,16], the performance of the user in the WLAN is improved, and the performance reduction caused by switching can be compensated; when the mean value of the linger time is [28,30], the user can perform data transmission in the WLAN for a long time, the service quality of the terminal is improved, and when the linger time is more than 30s, the service quality is not influenced by the linger time. When the residence time is more than 30s, the upper and lower membership degrees of the fuzzy set "long" are both 1.
(2) Bandwidth: in the process of switching judgment, due to the dynamic characteristic of network load, the available bandwidth has certain randomness, so that the membership degree has uncertainty. When the average value of the bandwidth is [0,2], it indicates that the load in the WLAN network is too heavy at this time, the handover may be blocked, and the terminal should avoid switching to the network; when the average value of the bandwidth is [6,8], the performance of the candidate network is moderate; when the average value of the bandwidths is [12,14], the candidate network has the best performance, and the terminal can switch to the network in time.
(3) The cost is as follows: due to the fact that different users have different understandings of the same cost value, the membership function of the fuzzy set has uncertainty, and therefore the membership is fuzzy. When the average value of the access charges is [0,0.1], the cost of data transmission is very low, and the expectation of a user can be completely met; when the average cost is 0.45,0.55, the cost is slightly higher, but within the range that the user can receive; when the average cost is 0.9,1, the cost is too high for the user to receive, and the user can avoid accessing the network.
(4) Scoring: the score is an output two-type membership function which is used for evaluating the switching performance of the candidate network and is a back-piece membership function in the fuzzy inference system. When the average value of the scores is [0,10], the switching value of the switching of the candidate network is too low to be suitable for switching; when the average value is [45,55], the performance of the candidate network is moderate; when the average value is [90,100], not only is the bandwidth of the candidate network high and the cost low, but also the lingering time of the vehicle terminal in the network is long. Therefore, it is suitable as a target network.
Based on the above analysis, the present invention designs two types of membership functions as shown in FIGS. 2-5.
In order to verify the invention, a simulation experiment is carried out on an MATLAB platform, and the following simulation scenes are set: a network composed of two access technologies of LTE and WLAN is used as a heterogeneous network model, and a simulation scene is set up on an MATLAB platform for simulation analysis. Assume that there are 1 LTE and 3 WLANs distributed in the scene with radii of 1000m and 150m, respectively. The LTE network realizes full coverage in a simulation scenario, and the coverage and road conditions of the WLAN network are shown in fig. 1. The charges of WLAN1, WLAN2, and WLAN3 networks are: 0.2, 0.4 and 0.7, with a maximum capacity of 14 Mbps.
In the simulation process, it is assumed that the arrival rate of the new vehicle terminal in the scene follows a poisson distribution with λ ═ 1. To reduce the complexity of the experiment, the data rates of the users in LTE and WLAN are assumed to be 64kbps and 384kbps, respectively, and the traffic model is downlink communication. To further highlight the superiority of the present invention, the method Proposed by the present invention (P-VHA) was compared with the residence time-based screening method (Sojurn time-dependent Vertical Handover Algorithm, STS-VHA) in the literature [ Long Xu, Yi Li.A network selection scheme on TOPSIS in heterologous networks environment [ J ]. Journal of Harbin institute of Technology,2014,21(1):43-48] and the literature [ Kustawan I, ChiK. Handff determination a kalman filter and Fuzzy Logic in heterologous analysis [ J ]. IEEE network Letters, Logic, 10-12 ] based on the Algorithm analysis system (Type-IFL 2258). In addition, the simulation time is 60s for each different speed value.
When the stay time of the terminal in the network is less than the switching delay and the switching is not completed, the terminal leaves the target network, and the switching is considered to be failed. In this chapter, the switching delay is set to 1s, and the failure rates of the three methods in switching at different speeds are analyzed and compared, as shown in fig. 7. It can be seen that the handover failure rates of both IFL-VHA and STS-VHA increase significantly with increasing speed, whereas the handover failure rate of the method herein increases slowly. Furthermore, the handover failure rate of the present method is always the lowest with the same speed magnitude. This is because IFL-VHA only considers the magnitude of the terminal speed, ignoring the effect of linger time; when the STS-VHA establishes the stay time prediction model, only the movement of the terminal is regarded as uniform linear motion, and the fact that the motion direction of the terminal changes along with the road route in the actual environment is ignored. In the method, the moving characteristics of the vehicle terminal are considered when the stay time is analyzed and predicted, and the distance of the terminal in the candidate network is calculated according to the road route, so that the successful switching probability is effectively improved.
Fig. 8 is a graph of throughput versus terminal speed for the three methods. It can be seen that the throughput of all three methods increases significantly with increasing speed when the speed v <4m/s, whereas the throughput of all three methods no longer changes significantly when the speed v >8 m/s. In addition, the throughput of the method is always the highest, IFL-VHA is the second, and STS-VHA is the lowest, because STS-VHA builds the linger time prediction model too simply to easily exclude networks with better performance; the IFL-VHA ignores the motion characteristics of the vehicle terminal and the randomness characteristics of the judgment parameters, which easily causes the frequent switching and is not beneficial to the effective utilization of network resources; the method comprehensively considers multiple uncertain factors of the actual motion rule and the judgment parameter of the vehicle terminal, thereby effectively improving the system throughput.
Fig. 9 is a graph of average handover times versus terminal speed for the three methods. It can be seen that the average number of handovers both for the method herein and for STS-VHA increases with increasing speed, whereas the average number of handovers does not increase significantly any more for IFL-VHA at speeds v >6 m/s. Furthermore, the average number of handovers is slightly higher in the method herein than in the IFL-VHA method when the speed is greater than v >12 m/s. The method considers the influence of the terminal stay time and the multiple uncertainties of the decision parameters on the switching performance, avoids frequent switching by predicting the stay time of the terminal in the candidate network, and further adopts a two-type fuzzy inference system to process the multiple uncertainties existing in the decision parameters, thereby effectively reducing the average switching times.
Fig. 10 is a graph showing the variation of the number of unnecessary handovers with the terminal speed in three methods, where unnecessary handovers refers to handovers performed by a terminal two or more times continuously within a time Δ T, and Δ T is 5s in this chapter. It can be seen that the number of unnecessary handovers increases with increasing speed for all three methods. In addition, when the terminal speed v is less than 4m/s, the unnecessary switching times of the IFL-VHA method are higher than STS-VHA, and when the speed v is more than 18m/s, the unnecessary switching times of the method are higher than that of the IFL-VHA, because the IFL-VHA takes the speed as a decision factor, the switching is less likely to occur when the speed is higher, and therefore, the unnecessary switching times are increased more slowly, and the method fully considers the actual stay time of the terminal in the network, thereby reducing the useless switching.
From the two angles of average switching times and unnecessary switching times, the method further improves the QoS of the vehicle terminal and analyzes reasons while ensuring the switching performance, considers the terminal stay time and the randomness characteristic existing in the judgment parameter, designs a corresponding two-type membership function according to the ambiguity and randomness factors existing in the judgment parameter at the same time, and effectively processes the uncertain problem of the parameter, thereby reducing the average switching times and the unnecessary switching times.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (3)

1. A fuzzy vertical switching method based on linger time prediction in a vehicle heterogeneous network is characterized by comprising the following steps:
101. firstly, according to the characteristic that a vehicle only runs along a road route in a vehicle heterogeneous network, predicting the stay time of a terminal in each candidate network by using a route planned by a vehicle-mounted navigation system;
102. secondly, designing a two-type membership function of each judgment parameter according to the characteristic that the judgment parameters have both ambiguity and randomness, wherein the judgment parameters comprise the stay time, the bandwidth and the communication cost predicted in the step 101, carrying out fuzzy reasoning according to the designed two-type membership function, the output of a fuzzy reasoning system is the score value of a candidate network, finally comparing the score values of each network, selecting the network with the largest score value as an optimal switching network, and finishing vertical switching;
step 102, a two-type membership function is designed according to the uncertain characteristics of each decision parameter, each parameter has three fuzzy sets, which are: low/short, medium, high/long, each fuzzy set is expressed by adopting a two-type membership function in a Gaussian type interval, and the expression of the upper and lower membership function in the two-type membership function in the Gaussian type interval is shown as a formula (8) and a formula (9):
Figure FDA0002515273120000011
Figure FDA0002515273120000012
wherein x represents an element, σ represents a mean square error,
Figure FDA0002515273120000013
is the variation range of the average value;
when the decision parameter is the residence time and is used as an evaluation index, the mean variation range of the 'short' is [0,2 ]; the mean variation range of "medium" is [14,16 ]; the mean variation range of "long" is [28,30 ];
when the judgment parameter is the bandwidth and is used as an evaluation index, the variation range of the mean value of 'low' is [0,2 ]; the mean variation range of "medium" is [6,8 ]; the mean of "high" ranges from [12,14 ];
when the judgment parameter is communication cost and is used as an evaluation index, the mean value change range of 'low' is [0,0.1 ]; the mean variation range of "medium" is [0.45,0.55 ]; the mean variation range of "high" is [0.9,1 ];
outputting two types of membership functions to evaluate the switching performance of the candidate network according to the score value, wherein the variation range of the low mean value is [0,10 ]; the mean variation range of "medium" is [45,55 ]; the mean value of "high" varies by [90,100 ].
2. The fuzzy vertical handover method based on linger time prediction in the vehicle heterogeneous network according to claim 1, wherein the vehicle heterogeneous wireless network comprises: the BS is a base station of LTE, the WLAN1, the WLAN2 and the WLAN3 are three access points deployed on the roadside respectively, the B and the D are intersections respectively, the E and the F are intersections of coverage boundaries of the WLAN2 and the WLAN3 and roads respectively, the position A is assumed to be a starting point, the C is assumed to be an end point, and the optimal route planned by the navigation system is A-B-C.
3. The fuzzy vertical switching method based on linger time prediction in the vehicle heterogeneous network according to claim 2, wherein in the step 101, the routes of the vehicle in the three candidate networks are calculated according to the planned route, so as to calculate the linger time of the terminal in the candidate networks, specifically comprising the steps of:
the range in WLAN1 may be expressed as:
SWLAN1＝dAB+dBC(1)
wherein d isABIs the length of the road between points A and B, dBCThe length of the road between the two points B and C;
the range in WLAN2 may be expressed as:
SWLAN2＝dAB+dBE(2)
wherein d isBEThe length of the road between the two points B and E;
the range in WLAN3 may be expressed as:
SWLAN3＝dAB+dBF(3)
wherein d isBFIs a road between two points B and FA length;
suppose that in the sampling period TsThe total number of instantaneous speed samples taken in is N, the average speed V of the vehicle can be expressed as:
Figure FDA0002515273120000031
wherein, VnIs a period TsThe nth instantaneous speed sample value of the vehicle;
from the distance S and the speed
Figure FDA0002515273120000032
The residence time of the vehicle within each candidate network can be calculated, and the residence time of the terminal within WLAN1, WLAN2, and WLAN3 can be expressed as:
Figure FDA0002515273120000033
Figure FDA0002515273120000034
Figure FDA0002515273120000035
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