CN110677861A - Network selection method facing 5G heterogeneous network - Google Patents

Network selection method facing 5G heterogeneous network Download PDF

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CN110677861A
CN110677861A CN201910914792.6A CN201910914792A CN110677861A CN 110677861 A CN110677861 A CN 110677861A CN 201910914792 A CN201910914792 A CN 201910914792A CN 110677861 A CN110677861 A CN 110677861A
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CN110677861B (en
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谢健骊
李翠然
高文娟
邸敬
吕安琪
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Lanzhou Jiaotong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/14—Reselecting a network or an air interface
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/16—Performing reselection for specific purposes
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Abstract

The invention discloses a network selection method facing a 5G heterogeneous network, which comprises the following steps: setting a network switching judgment condition according to the service type and the network time-varying characteristic; according to the service requirement, an MDP model and a return function of the MDP model are constructed based on the QoS attribute of a mobile user; determining the weight of the QoS attribute by adopting an analytic hierarchy process; when the network switching judgment condition is met, solving the MDP model through a genetic-simulated annealing algorithm; and selecting the network with the maximum long-term expected return value according to the solving result. The technical problem of network selection under the heterogeneous condition of the 5G ultra-dense network is solved.

Description

Network selection method facing 5G heterogeneous network
Technical Field
The invention relates to the field of telecommunications, in particular to a network selection method facing a 5G heterogeneous network.
Background
With the advent of the 5G era, the rapid increase of mobile terminals has brought exponential growth of mobile data, and an effective 5G wireless network architecture and an effective resource allocation algorithm are required to schedule resources of a system to process various requests of users. Many expert scholars have proposed various 5G radio access network architectures for how to efficiently process and allocate resources of the system. Among them, the heterogeneous network architecture is a research hotspot of experts and scholars.
In a 5G ultra-dense wireless heterogeneous network, multiple network systems coexist (e.g., 5G, 4G, 3G, WiFi, etc.), and a mobile user holding a multi-mode interface terminal does not need to be constrained to transmit data only through the same system network as a conventional single-mode terminal. The specific adoption of which network transmits data is the problem to be solved by the heterogeneous network selection method.
The currently proposed heterogeneous network selection method emphasizes the adoption of an intelligent optimization algorithm, access judgment is carried out according to the current network attribute of each network in the heterogeneous environment, and the influence of the dynamic change of the network attribute on a mobile user, a network return function and a switching judgment condition after a target network is selected for access is not considered. Meanwhile, the goal of the existing heterogeneous network selection method is to maximize the expected total return of each connection, but the service types of the mobile users are not distinguished. However, the increasing type of service in 5G, the response time of the mobile user to the network access and the accuracy of the access decision make higher demands. The single intelligent optimization algorithm cannot meet the network selection problem under the heterogeneous condition of the 5G ultra-dense network.
Disclosure of Invention
The invention aims to provide a network selection method facing a 5G heterogeneous network to solve the technical problem of network selection under the heterogeneous condition of the 5G ultra-dense network.
In order to achieve the purpose, the embodiment of the invention adopts the technical scheme that:
a network selection method facing a 5G heterogeneous network comprises the following steps:
setting a network switching judgment condition according to the service type and the network time-varying characteristic;
according to the service requirement, an MDP model and a return function of the MDP model are constructed based on the QoS attribute of a mobile user;
determining the weight of the QoS attribute by adopting an analytic hierarchy process;
when the network switching judgment condition is met, solving the MDP model through a genetic-simulated annealing algorithm;
and selecting the network with the maximum long-term expected return value according to the solving result.
Further, the MDP model includes:
decision time, network state space, action set, state transition probability, and a reward function.
Further, the network state record of the network state space includes:
the network label of the current connection, the available bandwidth of the candidate network, the latency of the candidate network, and the jitter of the candidate network.
Further, the reward function is:
r(s,a)＝f(s,a)-q(s,a)，
wherein the content of the first and second substances,
Figure BDA0002215758980000021
when the r (s, a) value is positive, indicating revenue; when the r (s, a) value is negative, indicating a fee;
f (s, a) is a return function of the network at the adjacent decision time, q (s, a) is a switching cost function, Cg,aFor the loss of performance when a user switches from network g to network g', when no network switch occurs Cg,aThe value is zero, r (s, a) is a return function of the network selected for switching at the decision time, and the larger the value of r (s, a) is, the better the network performance of the network selected for switching at the decision time is.
Further, in the above-mentioned case,
f(s,a)＝wBfB(s,a)+wDfD(s,a)+wJfJ(s,a)+wPfP(s,a)+wEfE(s,a)，
wherein f isB(s,a)，fD(s,a)，fJ(s,a)，fP(s, a) and fE(s, a) are respectively a bandwidth attribute, a time delay attribute, a jitter attribute, a packet loss rate attribute and a return function of a price attribute required by an access network; w is aB、wD、wJ、wPAnd wEAre all weight factors.
Further, setting a network switching decision condition according to the service type and the network time-varying characteristic, including:
judging whether the received signal strength of the candidate target network is greater than a pre-switching threshold value or not, and if so, judging the current service type;
judging whether a condition of switching triggering is met or not according to the judged service type;
when the switching trigger condition is satisfied, the mobile user executes switching and selects the best network.
Further, the service types include voice service, video service and data service;
the conditions for the handover trigger are as follows: dnew＜Dvo-th，Dnew＜Dvi-thAnd Bnew＞BthAnd Dnew＜Dda-thAnd B2-B1＞0；
Wherein D isnew，BnewTime delay and bandwidth, respectively, of candidate target networks, Dvo-th，Dvi-thAnd Dda-thDelay thresholds for voice, video and data traffic, respectively, B1，B2Respectively, the available bandwidth of the current connection network and the candidate target network.
Further, the pre-switching threshold is variable, and the setting of the pre-switching threshold includes:
the bandwidth threshold at the moment t is equal to the average bandwidth value of the trigger network within the decision time d and is expressed as
Figure BDA0002215758980000031
Wherein, BTTriggering network bandwidth at decision time T, and n is a candidate target network in decision time periodThe available bandwidth of the network is higher than BthThe number of times.
Further, the relationship between the network bandwidth change and the bandwidth threshold at different time instants is represented as:
where beta is the bandwidth threshold adjustment factor, t is time, B1，B2Respectively, the available bandwidth of the current connection network and the candidate target network.
The technical scheme of the invention has the following beneficial effects:
according to the technical scheme, different types of service requirements are considered, an MDP model and a return function thereof are constructed based on QoS (quality of service) attributes of mobile users, and an Analytic Hierarchy Process (AHP) is adopted to calculate network attribute weight; network switching judgment conditions are designed according to different user service types and network time-varying characteristics, and the MDP model is solved through a genetic-simulated annealing (GA-SA) algorithm, so that a user can be seamlessly switched to a network with the maximum long-term expected return value. The technical problem of network selection under the heterogeneous condition of the 5G ultra-dense network is solved.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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Fig. 1 is a flowchart of a network selection method for a 5G heterogeneous network according to an embodiment of the present invention;
fig. 2 is a heterogeneous wireless network model according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating Markov decision time and period according to an embodiment of the present invention;
fig. 4 is a flowchart of an optimal handover decision algorithm according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Markov decision process: markov decision process, MDP;
analytic hierarchy process: analytical hierarchy process, AHP;
genetic-simulated annealing algorithm: genetic algorithm and standardized analysis, GA-SA;
genetic algorithm: genetic algorithm, GA;
and (3) simulating an annealing algorithm: simulated connecting, SA.
In order to reduce the running time of the algorithm, dynamically adapt to different network conditions, and intelligently make a reasonable switching decision, the embodiment considers that a mixed intelligent optimization algorithm is adopted to solve the model, so that the mobile user can obtain better service.
As shown in fig. 1, a network selection method for a 5G heterogeneous network includes:
s101: setting a network switching judgment condition according to the service type and the network time-varying characteristic;
s102: according to the service requirement, an MDP model and a return function of the MDP model are constructed based on the QoS attribute of a mobile user;
s103: determining the weight of the QoS attribute by adopting an analytic hierarchy process;
s104: when the network switching judgment condition is met, solving the MDP model through a genetic-simulated annealing algorithm;
s105: and selecting the network with the maximum long-term expected return value according to the solving result.
In a specific application scenario, the heterogeneous wireless network model is shown in fig. 2.
A network selection method facing a 5G heterogeneous network comprises the following steps:
s1, when the state of the heterogeneous wireless network frequently changes, in order to avoid the ping-pong effect, corresponding network selection triggering conditions need to be set for different types of services, as follows:
1) pre-switching decisions. Received signal strength RSS as candidate target networknewGreater than or equal toSwitching threshold RSSthJudging the current service type; otherwise, no handover occurs.
2) And switching to trigger judgment. The conditions that the voice service, the video service and the data service meet the switching trigger are respectively as follows: dnew＜Dvo-th，Dnew＜Dvi-thAnd Bnew＞BthAnd Dnew＜Dda-thAnd B2-B1Is greater than 0. Wherein D isnew，BnewTime delay and bandwidth, respectively, of candidate target networks, Dvo-th，Dvi-thAnd Dda-thDelay thresholds for voice, video and data traffic, respectively, B1，B2Respectively, available bandwidth of the current connection network and the candidate target network.
3) When the switching trigger condition is satisfied, the mobile user executes switching and selects the best network. Otherwise, no handover is performed.
The state of the heterogeneous wireless network has time-varying property, and the fixed switching threshold value cannot better reflect the time-varying property of the network. Defining: the bandwidth threshold at the moment t is equal to the average bandwidth value of the trigger network within the decision time d and is expressed as
Figure BDA0002215758980000061
Wherein, BTThe bandwidth of the trigger network at the decision time T is determined, n is the available bandwidth of the candidate target network in the decision time period is higher than BthThe number of times. The relationship between the change in network bandwidth and the bandwidth threshold at different times can be expressed as
Figure BDA0002215758980000062
Wherein β is a bandwidth threshold adjustment factor. Therefore, the network switching times can be reduced through the dynamic setting of the bandwidth threshold.
S2, when the network status satisfies the handover triggering condition, the mobile user will perform network selection. Modeling a network selection problem in a 5G heterogeneous wireless network into a Markov decision model with discrete time and continuous state, wherein network state information is sampled in a decision period of a mobile user, and an immediate return function at each sampling moment forms a return function sequence so as to maximize an expected return value as an optimal network selection decision of the mobile user.
The markov decision model is mainly composed of five parts: decision time T, T ═ 0,1, 2.., N, network state space S, action set a, state transition probability P [ S' | S, a]And a return function r(s)t,at). As shown in fig. 3. Let TP represent a decision period, and the network state observed by the decision maker at decision time t is recorded as st，stThe network label of the current connection and the available bandwidth, time delay, jitter and the like of the candidate network. The action is the concrete expression of the strategy at the decision time, and the mobile user can select the current state information stSelection action atAnd switching is carried out. P [ s' | s, a]Indicating the state s at the current timetNext, select a certain action atProbability of transition to the next moment state s'. Mobile user according to given state stE S and selected action atE.g. A, obtaining a return value r(s) during the decisiont,at)。
Suppose there are M candidate networks in the overlapping coverage area of the heterogeneous wireless network, and the QoS attribute parameters of the mobile user include: the current available bandwidth (B), delay (D), jitter (J), packet loss rate (P) and price (E) of the network, the state space S of the network can be represented as:
S＝{1,2,...,M}×B1×D1×J1×P1×E1×B2×D2×J2×P2×E2×...×BM×DM×JM×PM×EM(3)，
wherein, BM，DM，JM，PMAnd EMRespectively representing the current available bandwidth, time delay, jitter, packet loss rate and price of the connection network. In a heterogeneous network environment, a mobile user can connect to multiple networks simultaneously, so the action set is an M-dimensional vector, which is expressed as:
a＝(a1,a2,...,aM) (4)，
wherein, ai∈{0,1},i＝1,2,...,M,ai1 denotes that the mobile subscriber is connected to the network i, ai0 means that the mobile user disconnects from network i. Let current network state s ═ g, b1,...,bm,d1,...,dm,j1,...,jm,p1,...,pm,e1,...,em]If the selected action is a, the network state s ' at the next time is [ g ', b '1,...,b′m,d′1,...,d′m,j′1,...,j′m,p′1,...,p′m,e′1,...,e′m]The transition probability of (c) is:
Figure BDA0002215758980000071
where g and g' represent the networks to which the mobile user is connected at the current and next decision time, respectively, and bi,b′i∈B，di,d′i∈D，ji,j′i∈J，pi,p′i∈P，ei,e′i∈E。
When the mobile user moves from state s to state s', an immediate return is obtained, and a larger value indicates better network performance for the selected handover at the decision time. Define the immediate return function as:
r(s,a)＝f(s,a)-q(s,a) (6)，
Figure BDA0002215758980000072
the value of r (s, a) in the formula (6) is positive, indicating the receipt; when the value is negative, a fee is indicated.
Wherein f (s, a) is a return function of the network at the adjacent decision time, q (s, a) is a switching cost function, Cg,aThis value is zero when no handover occurs, for the loss of performance when the user switches from network g to g'.
S3, respectively designing the return functions of different networks according to the voice, video and data service type characteristics, so that the user can reasonably select the network and reduce unnecessary switching times. The bandwidth is a benefit type attribute, the attribute has the requirements of minimum value and maximum value, and the return function is sigmoid function f (x) ═ 1+ e-a(x-b))-1(for voice and video traffic) and the exponential function h (x) 1-e-cx(corresponding to data traffic). The delay is a cost-type attribute, and its return function is g (x) ═ 1-f (x), where the parameters a and b are used to adjust the steepness and threshold of the function, respectively. The return function of jitter is expressed as a logarithmic function, p (x) 1- (d + eln (x + h)). The packet loss rate is a cost-type attribute with only a minimum requirement, and its return function is defined as a linear decreasing function, u (x) is 1- (x/g), where a negative value is adjusted to zero. The return function of the price is expressed by a linear piecewise function
Figure BDA0002215758980000081
Where i represents the minimum service price that the user is willing to pay, and when i is 0, it represents that the user wants to obtain a free service. j represents the highest price that the user can pay, and if x > j, the price of the network exceeds the payment capability of the user, and the user does not want to access the network.
Considering the QoS attributes of mobile users, the expression of the reward function f (s, a) can be written as:
f(s,a)＝wBfB(s,a)+wDfD(s,a)+wJfJ(s,a)+wPfP(s,a)+wEfE(s,a) (9)
wherein f isB(s,a)，fD(s,a)，fJ(s,a)，fP(s, a) and fE(s, a) are respectively a bandwidth attribute, a time delay attribute, a jitter attribute, a packet loss rate attribute and a return function of a price attribute required by an access network;
s4, weight factor wxThe status or the role of the QoS attribute in constructing the return function is reflected, and the result of the comprehensive decision is directly influenced. In the alternative networkUnder the condition of load balancing, users with different service types have different requirements on the QoS selected by switching. Therefore, an Analytic Hierarchy Process (AHP) is used to determine the weight value of the QoS attribute;
s5, the network selection goal based on the MDP model is to maximize the expected return value for each connection, and the optimization problem is expressed as:
Figure BDA0002215758980000091
where v(s) is the maximum expected return. The expected reward at time T +1 for each state s is
Figure BDA0002215758980000092
In summary, each decision time produces an immediate return function, and as time goes on, an immediate return function sequence is obtained. Genetic Algorithm (GA) is a search algorithm for solving the optimization problem, has strong global search capability, but has slow convergence speed, weak local search capability and easy influence of parameter setting on the operation result. The local search capability of the mutated connecting (SA) is strong, and the deficiency of the GA algorithm can be made up. Therefore, the GA-SA algorithm is used to solve the maximum problem of the expected return function.
The steps of the GA-SA algorithm are as follows: initializing parameters, including setting initial temperature T0 to 10, maximum iteration number MaxIt to 300, population size popsize to 50, number of networks NN to 3, QoS parameters (bandwidth, delay, jitter, packet loss rate, price), etc., and setting v for each state s0(s) ═ 0, and ε > 0, and T ═ 0.
The programming is carried out according to the method, and the specific implementation is as follows:
step1 encodes and generates an initial population, which is composed of randomly generated data sequences, representing candidate solutions to the optimization problem.
Step2, substituting the initial population into the objective function, and calculating the fitness value of each chromosome in the current population according to the fitness function, wherein the larger the objective function value is, the smaller the fitness value is. The objective function is v ═ max | v(s) |, and when the value is positive, the fitness function is Fit (v(s) ═ 1/(1+ v (s)); when the value is negative, the fitness function may be written as Fit (v (s)) ═ 1+ | v (s)) |.
Step3, executing selection operation, adopting elite reservation strategy to avoid population degeneration, so that excellent individuals can fully participate in the evolution process, and the convergence speed is accelerated. And after each step of operation is finished, checking whether the fitness of the optimal individual is improved, and if the fitness is degraded, replacing the worst individual after the operation with the optimal individual before the operation.
Step4 performs crossover and mutation operations. The invention adopts a Two-point crossing mode, wherein the Two-point crossing (Two-point crossing) refers to that Two crossing points are randomly arranged in Two individual code strings which are matched with each other, and then part of chromosomes of Two individuals between the Two set crossing points are exchanged. Mutation refers to the replacement of the gene values at certain loci in an individual's chromosomal code string with other alleles at that locus to form a new individual.
And Step5, verifying the search result, generating a new individual, and storing the individual with the highest fitness.
Step6 terminates the evolution. If the iteration termination condition | v is satisfiedT+1-vTIf | < epsilon (1-lambda)/(2 lambda) or the maximum iteration number, executing Step7, otherwise, adding 1 to the value of T and returning to Step2 to continue the iteration.
Step7, for any state S ∈ S in the network state space, executing the GA-SA algorithm, and accessing the network with the maximum expected reward function value by the mobile user.
And determining a switching strategy at each decision moment through a GA-SA algorithm, and calculating an expected return value of a switching network according to the formulas (9) and (10), wherein the network with the largest expected return value is the optimal access network, so that the optimal switching decision algorithm of the mobile station is completed. The main steps of the algorithm are shown in fig. 4.
The network selection problem in the 5G heterogeneous wireless network is modeled into a Markov decision model, parameters required by the model are determined by adopting an analytic hierarchy process and a utility function theory, and the network model is solved by a genetic-simulated annealing algorithm, so that the optimal network selection problem under the 5G ultra-dense network heterogeneous condition is practically solved, and a reasonable switching judgment condition is set according to different service characteristics, so that the technical problem of a ping-pong effect in network selection is avoided.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A network selection method facing a 5G heterogeneous network is characterized by comprising the following steps:
setting a network switching judgment condition according to the service type and the network time-varying characteristic;
according to the service requirement, an MDP model and a return function of the MDP model are constructed based on the QoS attribute of a mobile user;
determining the weight of the QoS attribute by adopting an analytic hierarchy process;
when the network switching judgment condition is met, solving the MDP model through a genetic-simulated annealing algorithm;
and selecting the network with the maximum long-term expected return value according to the solving result.
2. The method for selecting the network oriented to the 5G heterogeneous network according to claim 1, wherein the MDP model comprises:
decision time, network state space, action set, state transition probability, and a reward function.
3. The method of claim 2, wherein the network state information of the network state space comprises:
the network label of the current connection, the available bandwidth of the candidate network, the latency of the candidate network, and the jitter of the candidate network.
4. The method of claim 2, wherein the reward function is:
r(s,a)＝f(s,a)-q(s,a)，
wherein the content of the first and second substances,
Figure FDA0002215758970000011
when the r (s, a) value is positive, indicating revenue; when the r (s, a) value is negative, indicating a fee;
f (s, a) is a return function of the network at the adjacent decision time, q (s, a) is a switching cost function, Cg,aFor the loss of performance when a user switches from network g to network g', when no network switch occurs Cg,aThe value is zero, r (s, a) is a return function of the network selected for switching at the decision time, and the larger the value of r (s, a) is, the better the network performance of the network selected for switching at the decision time is.
5. The method for selecting a network for a 5G heterogeneous network according to claim 4,
f(s,a)＝wBfB(s,a)+wDfD(s,a)+wJfJ(s,a)+wPfP(s,a)+wEfE(s,a)，
wherein f isB(s,a)，fD(s,a)，fJ(s,a)，fP(s, a) and fE(s, a) are respectively a bandwidth attribute, a time delay attribute, a jitter attribute, a packet loss rate attribute and a return function of a price attribute required by an access network; w is aB、wD、wJ、wPAnd wEAre all weight factors.
6. The method for selecting the network oriented to the 5G heterogeneous network according to claim 1, wherein the setting of the network handover decision condition according to the service type and the network time-varying characteristic includes:
judging whether the received signal strength of the candidate target network is greater than a pre-switching threshold value or not, and if so, judging the current service type;
judging whether a condition of switching triggering is met or not according to the judged service type;
when the switching trigger condition is satisfied, the mobile user executes switching and selects the best network.
7. The method of claim 6, wherein the service types include voice service, video service, and data service;
the conditions for the handover trigger are as follows: dnew＜Dvo-th，Dnew＜Dvi-thAnd Bnew＞BthAnd Dnew＜Dda-thAnd B2-B1＞0；
Wherein D isnew，BnewTime delay and bandwidth, respectively, of candidate target networks, Dvo-th，Dvi-thAnd Dda-thDelay thresholds for voice, video and data traffic, respectively, B1，B2Respectively, the available bandwidth of the current connection network and the candidate target network.
8. The method for selecting a network for a 5G heterogeneous network according to claim 6, wherein the pre-switching threshold is variable, and the setting of the pre-switching threshold comprises:
the bandwidth threshold at the moment t is equal to the average bandwidth value of the trigger network within the decision time d and is expressed as
Figure FDA0002215758970000031
Wherein, BTThe bandwidth of the trigger network at the decision time T is determined, n is the available bandwidth of the candidate target network in the decision time period is higher than BthThe number of times.
9. The method of claim 8, wherein the relationship between the network bandwidth change and the bandwidth threshold at different time points is expressed as:
Figure FDA0002215758970000032
where beta is the bandwidth threshold adjustment factor, t is time, B1，B2Respectively, the available bandwidth of the current connection network and the candidate target network.
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