CN111182454B - Intelligent access method for maximizing comprehensive benefits in heterogeneous wireless network - Google Patents
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Abstract
The invention discloses an intelligent access method for maximizing comprehensive benefits in a heterogeneous wireless network, which aims at solving the problems that the current access algorithm has frequent access and is lack of access strategy adjustment according to the moving speed of a user in the heterogeneous wireless network and provides an intelligent access algorithm for maximizing the comprehensive benefits. Firstly, calculating the maximum transmission rate of a user according to the received signal intensity and available bandwidth of the user, and taking the maximum transmission rate as the current benefit; secondly, the transmission rate of the user at next several moments is obtained by a prediction method, and the transmission rate is used as long-term benefit by combining a weight decreasing method; and finally, combining the current benefit and the long-term benefit according to the moving speed of the user. The experimental result shows that the invention can effectively reduce the access times, improve the system throughput and adapt to the access of users with different moving speeds.
Description
Technical Field
The invention belongs to an access method in a heterogeneous wireless network, and belongs to the field of mobile communication. In particular to an intelligent access algorithm for maximizing comprehensive benefits in a heterogeneous wireless network.
Background
With the rapid development of wireless network technology, the next generation network will be a heterogeneous wireless network composed of multiple wireless access technologies. The user can select more diversified networks for access, and because different types of networks have great differences in coverage, user experience and the like, the network selection method which is stable and efficient to research and supports the seamless access of the user becomes a current hotspot problem.
The document [ Yu H, Ma Y and Yu J, Network Selection Algorithm for multi-service multi-mode Terminals in Heterogeneous Wireless Networks [ J ], IEEE Access,2019,7: 46240-. A dynamically-preferred Network Selection algorithm is proposed in a document [ Goyal P, Lobiyal D K, Katti C P.Dynamic User Preference Based Network Selection for Vertical Handoff in Heterogeneous Networks [ J ]. Wireless Personal Communications,2017], and the algorithm can adjust the weight of parameters in real time according to the current Network condition, so that the original characteristics of the parameters are ensured, and the Network dynamics are considered at the same time. The above method only makes a decision according to the current benefit of the user, ignores the subsequent access situation of the user in the network, causes the user to frequently access the network, and cannot provide better Quality of Service (QoS) for the user. Document [ Yan X, Mani N, Sekeriodiglu Y A. traversing Distance Prediction Method to minimum Uncecesshandover from Cellular Networks to WLANs [ J ]. IEEE Communications Letters,2008,12(1):14-16] proposes a decision algorithm for predicting the moving Distance of a terminal in a network, minimizing the probability of access failure from the Cellular network to the WLAN network. The method only takes the failure probability of the user accessing the network as the basis of network selection, and cannot fully reflect the requirements of the user. The literature [ bright day, Zhao Ji hong, Qubirch ] vertical handover decision algorithm [ J ] communication report, 2014,35(9):67-78] adopts fuzzy logic to judge the time for accessing the network based on the distance between the user and the center of the network, the included angle between the speed direction of the user and the access point, and the moving speed of the user, and then selects the optimal access network by using an Analytic Hierarchy Process (AHP) and a simple weighting method. The method predicts the access time of the user, but does not effectively combine the current state of the user with the predicted result.
With the increase of the number of networks in the future network environment, the network diversity is more and more obvious. Currently, most papers ignore the long-term benefits of users, resulting in more unnecessary accesses; or only the user access time is considered to be predicted, and the current benefit and the long-term benefit are not well considered according to the moving speed of the user.
Disclosure of Invention
The invention aims to solve the problems in the prior art, comprehensively considers the current benefit and the long-term benefit of a user, provides an intelligent access method with maximized comprehensive benefits, and aims to give consideration to terminals with different speeds, ensure the service quality, reduce the access times, and simultaneously improve the throughput of the user so as to provide better service experience (QoS) for the user. An intelligent access method for maximizing comprehensive benefits in a heterogeneous wireless network is provided.
The technical scheme of the invention is as follows:
the invention discloses an intelligent access method for maximizing comprehensive benefits in a heterogeneous wireless network, which aims at the problem of frequent access caused by a large number of overlapping coverage of networks in a future network scene in the heterogeneous wireless network, and designs the following network access steps by combining the current benefits and long-term benefits of users:
101. according to the network which can be detected by the user, the current received signal strength, the interference signal strength and the available bandwidth of the network are measured, the maximum transmission rate is calculated and taken as the current benefit Q of the user1；
102. According to user's receivingN under the condition of user prediction of received signal strength, network coverage radius and average moving speedkThe predicted next N is calculated by using the position information of each moment and using a weight decreasing methodkTransmission rate per time as user long-term benefit Q2；
103. Adjusting the current benefit Q according to the current moving speed of the user1And long term benefit Q2And weighting, namely combining the current benefit and the long-term benefit of the user to obtain comprehensive benefit, finally comparing the comprehensive benefit value of each network, and selecting the network with the maximum comprehensive benefit as a target access network.
The method of the present invention is further analyzed and illustrated in detail below.
For convenience of explanation, a time slot (timeslot) concept is introduced, a continuous time is divided into discrete time intervals of equal length, and the network state at each time t is assumed to be constant.
The Received Signal Strength (RSS) of the network is a basic condition for a user to access the network, and the Received Signal Strength reflects the channel quality of the network. At time t, the received signal strength RSS received by user j from network iij(t) can be expressed as:
RSSij(t)＝ρi-κilg(dij(t))+ξ (1)
where ρ isiFor the transmission power of network i, [ kappa ]iIs the path loss factor of network i, dij(t) represents the distance of user j from a base station or access point in network i at time t, ξ is the compliance parameter (0,
) The random variable of the gaussian to be used,
different values are selected according to different networks.
The Signal-to-Noise Ratio (SNR) is a Ratio of Signal power to Noise power, and is an important parameter reflecting network quality. SNR of network i to user j at time tij(t) can be approximately expressed as:
wherein,
is the interfering signal strength in the network scenario.
The maximum transmission rate reflects the ability of the network to transmit information. According to the shannon formula, the maximum transmission rate R available to user j from network i at time tij(t) is:
Rij(t)＝bij(t)log2(1+SNRij(t)) (3)
wherein, bij(t) is the available bandwidth available to user j from network i at time t, bij(t) has different values according to different network conditions.
The transmission rate that the user wishes to obtain is the largest, and the invention defines the transmission rate that the user can obtain at the current moment as the current benefit. The user wishes to obtain the maximum current benefit, namely:
Q1＝maximizeRij(t) (4)
setting a point B and a point C as points of a user entering a network range and leaving the network range, wherein the length of the point BC is L, the point M is the midpoint of the point B and the point C, the point O is the position of a current network base station or an access point, and the point S is the position of the current user. By equation (3), the user can RSS based on the current received signal strengthij(t) estimating the distance lo of a user from a network base station or access points：
Further, the moving distance L of the user in the network can be estimated according to the distance between the user and the network base station:
lMS 2+lOM 2＝lOS 2 (7)
wherein R is the coverage radius of the network, the network radius data is sent in a broadcast mode in the decision process, and lOMDistance between network base station or access point and point in BC,/MSIndicating the distance of the current user position from the midpoint of BC. There are two cases of the position of point S: i.e. to the left of point M, at this point
Or to the right of point M, at this time
The same result was obtained by substituting the formula (7). v is the average speed of the user from point B to point S, TBSThe number of the sampling time from the network entering range to the current time is as follows:
wherein v issThe speed of the user at the s-th sampling instant.
From (6) and (7), the moving distance L of the user in the network is:
after the moving distance L of the user in the network is obtained, the position of the user at the next several times can be predicted in relation to the current user moving speed v. Setting the predicted position of the user at the kth moment as AkFrom the geometric properties:
From equations (6) and (10), the distance d from the user j to the base station or access point in the network i at the kth timeij(k) Comprises the following steps:
after the distance of the user to the network is obtained, the predicted transmission rate R available from the network i for the user j at the next k-th time can be obtainedij(k)：
If it is
Explaining that the user is still in the network coverage range at the kth moment, calculating by substituting the formula (1), (2) and (3) in sequence; if it is
Indicating that at time k the user has left the current area, the transmission rate is 0.
The user wishes to be on the networkIn which a period of service is obtained, rather than briefly accessing a network, and therefore the present invention will predict the next NkThe transmission rate at a time is defined as the long term benefit. The user wishes to obtain the maximum long-term benefits, namely:
the requirements are as follows:
wherein wkAs a weight for each time instant, NkIs the predicted number of times.
Transmission rate R relative to distant time instantsij(k) The results at a closer time are more important to the user. Therefore, the invention provides a method for decreasing the weight to represent the long-term benefit to reflect the user requirement, as follows:
it can be demonstrated that: when w ismaxWhen the number is equal to 1, the alloy is put into a container,
wmin，wmaxrespectively representing the minimum and maximum weights in the weight decreasing method.
Users want to maximize the current and long-term benefits, which is a Multi-objective Optimization Problem (MOP), consider converting a Multi-objective Optimization Problem (MOP) into a Single-objective Optimization Problem (SOP) using simple-weighted method (SAW), namely:
Q3＝w1Q1+(1-w1)Q2 (15)
wherein w1∈[0,1]，w1And 1-w1Respectively, the current benefit Q1And long term benefit Q2The weight of (c). When w is1When the total benefit is 1, the problem of maximum comprehensive benefitConverting into a current benefit maximization problem; when w is1When the value is 0, the comprehensive benefit maximization problem is converted into a long-term benefit maximization problem; when w is1When is epsilon (0,1), the current benefit Q1Is of importance with w1Is reduced, long-term benefit Q2Of importance with w1Is increased.
When the moving speed of the user is lower, the user has a higher possibility of staying in the network coverage area, so that the current benefit is more concerned; when the moving speed of the user is higher, the position state of the user changes faster, so that the long-term benefit is more concerned. In order to combine the current benefit with the long-term benefit, the present invention is intended to provide a method for adjusting the weights according to the user's moving speed, and the function needs to satisfy several conditions:
w(vmid)＝0.5,v＝vmid (16c)
wherein v isminAnd vmaxIs a weighted minimum and maximum velocity threshold,
when the user moving speed is too small, i.e. v is less than or equal to vminThen the user only pays attention to the current benefit and does not consider the long-term benefit, so w (v) is 1; when the user moves at an excessive speed, i.e. v ≧ vmaxThe user only pays attention to the long-term benefit and does not pay attention to the current benefit, and w (v) is 0; in other cases the size of w (v) increases with increasing v.
Based on the above conditions, the present invention proposes a sigmoid function to represent the weight and associates the weight with the moving speed:
wherein gamma is more than or equal to 2, gamma represents the steepness degree of the function, and the function is steeper when gamma is larger. Gamma can be adaptively adjusted according to the sensitivity of the user to the speed. Further, it can be confirmed that the formula (17) is monotonically decreased and quadratic.
Combining equation (15) and equation (17), the present invention obtains a network access algorithm that maximizes the overall benefit:
Q3＝maximize[w(v)Q1+(1-w(v))Q2] (18)
the formula integrates the current benefit and the long-term benefit of the user, and the weight is adjusted according to the current moving speed of the user, so that the method is suitable for the conditions of different moving speeds.
The invention has the following advantages and beneficial effects:
1. aiming at the problem that long-term benefit of a user is not considered in the existing access algorithm and network access times are more, the invention predicts the transmission rates at several moments according to the characteristics of a user moving route, and prioritizes the transmission rates in a weight decreasing mode, thereby effectively reducing the network access times and ping-pong effect.
2. According to the current moving speed of the user, an S-shaped function is used for combining the current benefit and the long-term benefit of the user, and the function can adjust the slope of the function according to the sensitivity of the user to the speed. Therefore, the system throughput is effectively improved, and the service quality of the user is improved.
3. There may be users moving faster or slower in a network scenario. The user position state with the fast moving speed changes fast, and if the network with the small coverage area of the access network has the situation that the network is accessed and the access algorithm is executed, the network resources are consumed, and the user experience is also reduced, so that the user is concerned about long-term benefits more. Users with slower moving speeds are more likely to stay within the network coverage area and are therefore more concerned about the current benefit. The invention adjusts the weight of the current benefit and the long-term benefit according to the moving speed, can better reflect the requirements of users and gives consideration to the high-speed and low-speed user access.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a prediction model according to the present invention;
FIG. 3 is a network environment topology diagram;
FIG. 4 is a comparison of access times between algorithms;
FIG. 5 is a comparison of network aggregate throughput between algorithms;
fig. 6 shows the access network probability variation according to the present invention.
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 of the invention is as follows:
the method comprehensively considers the current benefit and the long-term benefit of the user in the heterogeneous wireless network, gives consideration to the users with different moving speeds, can effectively reduce the access times and improve the system throughput.
The network access algorithm provided by the invention comprises the following steps:
step one, according to the network which can be detected by the current user, measuring the current received signal strength RSSij(t) interference signal strength
Available bandwidth bij(t) obtaining a maximum transmission rate Rij(t) and taking the maximum transmission rate as the current benefit Q of the user1。
Step two, when the user enters the network coverage area, recording the current moving speed v at every momentsAnd the number of times T that are moving together within the networkBSAccording to the recorded speed v of movementsThe average moving speed v of the user is calculated.
Step three, as shown in fig. 2, the positions of the user entering and leaving the network coverage are set as a point B and a point C, the length of BC is L, the midpoint of BC is M, the current position of the user is S, and the distance from the user to the network base station is set asOr access point distance of lOS. RSS based on current received signal strengthij(t) calculating the distance l of the user from the network base station or access pointOS：
Step four, from fig. 2, it can be obtained:
lMS 2+lOM 2＝lOS 2 (7)
by combining equations (6) and (7), the moving distance L of the user in the network can be obtained as:
step five, setting the predicted time number as NkSee FIG. 2, AkIs the predicted user position at the kth time instant. Then there are:
step six, the joint type (6) and the formula (10) can obtain the distance d of the user j to the network i at the kth momentij(k) Comprises the following steps:
step seven, after the distance between the user and the network is obtained, the predicted transmission rate R which can be obtained by the user j from the network i at the next k moment can be obtainedij(k)：
Step eight, obtaining the long-term benefit of the user:
wherein wkAs a weight for each time instant, NkIs the predicted number of times.
Step nine, based on the above conditions, the present invention proposes an S-shaped function to represent the weight, and the moving speed is associated with the weight:
wherein gamma is more than or equal to 2, gamma represents the steepness degree of the function, and the function is steeper when gamma is larger. Gamma can be adaptively adjusted according to the sensitivity of the user to the speed.
Step ten, combining the formula (15) and the formula (17), the invention obtains the network access algorithm which maximizes the comprehensive benefit:
Q3＝maximize[w(v)Q1+(1-w(v))Q2] (18)
to verify the invention, a simulation scenario as shown in FIG. 2 is set up: and (5) taking 5G, 4G and WLAN networks as heterogeneous network models, and building a simulation scene on an MATLAB platform for simulation analysis. In a 1000m × 1000m simulation scene, 1 4G network, 4 5G network and 3 WLAN networks are distributed, and the coverage radiuses of the 4G network and the 5G, WLAN network are 1200m, 250m and 150m respectively. The 4G network realizes full coverage in a simulation scene. The network and user locations are represented in planar rectangular coordinates (x, y). The network coordinates are as follows: 4G networks (500 ), 5G1 networks (250 ), 5G2 networks (300, 700), 5G3 networks (600,650), 5G4 networks (750,400), WLAN1 networks (500,300), WLAN2 networks (400,500), WLAN3 networks (800,700). Maximum number of user services of network: 20 4G networks, 15 5G networks and 10 WLAN networks. User access network bandwidth: 0.4MHz of a 4G network, 1MHz of a 5G network and 0.6MHz of a WLAN network.
The moving speed of a user is 0-30m/s, the moving direction is changed at random at regular intervals, the size of each time slot is 1 second, and the number of simulation time slots recorded each time is 100.
Interference signal strength
Predicting the number of time slots N k10, other parameters set: w is amax＝1，wmin＝0.1，vmin＝0m/s，vmax30m/s and 4. To further highlight the superiority of the present invention, the method (deployed) of the present invention is combined with the documents [ Yu H, Ma Y and Yu J, Network Selection Algorithm for multiple service multimodule Terminals in Heterogeneous Wireless Networks [ J],IEEE Access,2019,7:46240-46260]Network Selection algorithm (MMT) and documents [ Goyal P, Lobiyal D K, Katti C P.Dynamic User Preference Based Network Selection for Vertical Handoff in Heterogeneous Wireless Networks [ J].Wireless Personal Communications,2017]A dynamic preference network selection algorithm (UDP) for comparative analysis.
Fig. 4 shows the variation curve of the access times of the three algorithms with the increase of the number of users. It can be seen that the number of accesses for all three algorithms increases as the number of users in the area increases. When the number of users in the region is small, the access times of the three algorithms are increased quickly; when users are relatively many, the access times of the UDP algorithm rise relatively fast, and the MMT algorithm rises slowly due to comprehensive consideration of factors such as user preference, network attributes and service characteristics; the algorithm of the invention predicts the user movement behavior due to the definition of the long-term benefit of the user, and the access times are always lower than those of other two algorithms.
Fig. 5 shows the total network throughput as the number of users increases. It can be seen that the network overall throughput of all three algorithms increases as the number of users in the area increases. When the number of users is small, the network throughput rises quickly due to the fact that the capacity is sufficient; when the number of users is large, the number of users that the network can accommodate is insufficient, and the throughput of the network is slowly increased. The algorithm of the invention has network throughput superior to other algorithms, because the invention combines the current benefit and the long-term benefit of the user, maximizes the comprehensive benefit of the user and improves the service quality of the user.
Fig. 6 shows the variation of the probability of the user accessing the network with the moving speed of the user in the algorithm of the present invention. As can be seen from the figure, as the moving speed of the user increases, the user is more inclined to access the 4G network due to the larger coverage area of the 4G network; and the coverage of the 5G network and the WLAN network is smaller, and the probability of selecting the 5G network and the WLAN network by the user is smaller and smaller due to smaller long-term benefit when the moving speed of the user is higher. Therefore, the invention can effectively adjust the access behavior of the user according to the moving speed of the user and select a more reasonable network for the user to access.
From the angles of the user access times, the network throughput and the access network probability, the algorithm can adjust the behavior of the user for accessing the network according to the movement speed difference of the user while reducing the user access times, and effectively improves the user throughput. The invention predicts the service quality of the user by using a weight decreasing method in the long-term benefit model and adjusts the current benefit and the long-term benefit weight according to the user moving speed, thereby reducing the times of accessing the user to the network and improving the network throughput.
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 (2)
1. The intelligent access method for maximizing the comprehensive benefit in the heterogeneous wireless network is characterized by comprising the following steps of:
101. according to the network which can be detected by the user, the current received signal strength, the interference signal strength and the available bandwidth of the network are measured, the maximum transmission rate is calculated and taken as the current benefit Q of the user1；
102. Predicting N under the user according to the received signal strength of the user, the network coverage radius and the average moving speedkThe predicted next N is calculated by using the position information of each moment and using a weight decreasing methodkTransmission rate per time as user long-term benefit Q2Long term benefit Q2Is defined as:
wherein, the wkAs a weight for each time instant, NkFor the number of predicted time instants, Rij(k) Obtaining a transmission rate from the network i for the predicted k-th moment user j;
the weight decreasing method enables long-term benefits to reflect user requirements better, and specifically comprises the following steps:
when w ismaxWhen the number is equal to 1, the alloy is put into a container,
wmin，wmaxrespectively representing the minimum weight and the maximum weight in the weight decreasing method;
103. adjusting the current benefit Q according to the current moving speed of the user1And long term benefit Q2Weighting, combining the current benefit and the long-term benefit of the user to obtain the comprehensive benefit, finally comparing the comprehensive benefit value of each network, selecting the network with the maximum comprehensive benefit as the target access network, combining the current benefit and the long-term benefit of the user according to the moving speed of the user by using an S-shaped function, and converting into the user-friendly access networkMaximum overall benefit, Q3＝maximize[w(v)Q1+(1-w(v))Q2]Wherein w (v) is a composite benefit weight that varies according to the user's moving speed;
the sigmoid function associates a moving speed with a weight:
wherein gamma is more than or equal to 2, gamma represents the steepness of the function, the larger gamma is, the steeper the function is, v isminAnd vmaxIs a weighted minimum and maximum velocity threshold,
v is the current user moving speed.
2. The intelligent access method for maximizing the comprehensive benefits in the heterogeneous wireless network according to claim 1, wherein: the average moving speed acquiring method comprises the steps of recording the current moving speed v at every moment when a user enters a network coverage areasAnd the number of times T that are moving together within the networkBSAccording to the recorded speed v of movementsCalculating an average moving speed of a user
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