The load-balancing method connected based on the random heuristic user determined
Technical field
The present invention relates to the load connected in a kind of super-intensive heterogeneous network based on the random heuristic user determined
Equalization methods, belongs to the networking technology area in radio communication.
Background technology
In the heterogeneous network of 5G (the 5th third-generation mobile communication technology) system, disposing highdensity small station becomes normal
The strategy seen, thus form super-intensive heterogeneous network.
In super-intensive heterogeneous network, the base station of dissimilar (such as can make a distinction according to launching power)
Constituting different layers, same type of base station is in same layer.The base station such as launching power little is referred to as small station, institute
Having composition small station, small station layer, launch high-power base station and be referred to as macro station, all macro stations constitute macro station layer.Due to not
Different with the transmitting power between the base station of layer, the reference signal from difference station causing user to receive receives merit
Rate (RSRP, Reference Signal Received Power) has the biggest gap, if user uses tradition
Maximum RSRP connection mechanism, greatly user can be caused to be linked into transmitting power relatively big, signal is preferable
Base station on, thus cause the congested of these base stations.And some other transmitting power is less, but there is abundant money
The base station in source is in idle condition.So so that offered load skewness weighs so that whole Internet resources are not
Can adequately and reasonably be used, cause resource utilization ratio low, overall performance of network declines.
In wireless network, the load of the number of users reflection base station that can connect with base station, so seeking in network each
The load balancing of base station can be regarded as and seek suitable user's method of attachment.In traditional cellular network, load is all
Weighing apparatus mechanism is exactly by the resource reasonable distribution in heavy duty and light load area, and the user in heavy duty network is turned
Move on to around load the lightest network, reach the state of Load Balanced distribution.So make whole system
Resource utilization is effectively promoted.But in super-intensive heterogeneous network, due to the transmitting merit between big small station
Rate differs greatly, and is unloaded to the user in small station by by the strong jamming from macro station from macro station, causes the letter of user
Number relatively low with interference plus noise ratio (SINR, Signal to Interference plus Noise Ratio).Cause using
The throughput degradation that family obtains from small station, it is impossible to make full use of small station resource.So in super-intensive heterogeneous network
Load balancing through frequently with suitable interference management techniques, reduce from macro station be unloaded to that the user in small station is subject to dry
Disturb, promote user to be unloaded to small station from the macro station of high capacity, further up to the load between macro station and small station
Equilibrium, thus promote network performance further.So introduce in super-intensive network almost blank subframe (ABS,
Almost Blank Subframe), namely macro station reserves according to certain ABS ratio in time domain
Almost blank subframe, during these subframes, small station user will be declined by the interference of macro station, improve small station money
The utilization rate in source.
Summary of the invention
Goal of the invention: for problems of the prior art with not enough, the present invention provides a kind of super-intensive isomery
The load-balancing method connected based on user in network, it is proposed that a kind of discrete based on random determine heuristic
User's method of attachment.Use the method in the present invention, it is possible to increase in super-intensive heterogeneous network load equilibrium and
The fairness of user rate, and the utilization rate of Internet resources.
The present invention, for the purpose of the logarithm speed summation maximizing all users, connects optimization to user, thus real
Existing load balancing.Owing to this problem complexity of direct solution is too high, it is to be appreciated that the channel gain information of the overall situation and
The connection strategy of all users, this can cause the signal resource exorbitant expenditure of whole system actual to implement.
Therefore, use a kind of discrete heuristic to allow each user dynamically and the base station of oneself optimum is selected independently.
All of decision all completes in user side, and user has one for its candidate base station that can connect and connects probability
Vector, according to current network environment, is randomly chosen serving BS, from choose according to connecting probability vector
Base station obtains the connection feedback of oneself, updates the connection probability vector of oneself, until connecting certain in probability vector
One member, close to 1, finally determines serving BS to be connected.
Technical scheme: a kind of load-balancing method connected based on the random heuristic user determined, to maximize
In super-intensive heterogeneous network, the logarithm speed summation of all users is the load balancing mould that target sets up that user connects
Type.This model promotes the fairness of network in the following areas, first, can promote with the speed promoting user for target
Enable user to be connected to relative free to be assigned on the small station of more multiple resource, thus improve and load between big small station
Harmony, secondly, logarithmic function is the function that a gain is successively decreased, and can improve the fairness of user rate.
The user determined by this model is connected can also promote overall network throughput performance.
Assuming in super-intensive heterogeneous network, all of major station all uses ABS ratio 0≤α≤1 of synchronization,
Each major station occupies the time resource of 1-α, during small station both can be operated in ABS, it is also possible to be operated in non-
During ABS, so each small station to be regarded as two logical base station, an ABS small station, occupy the time of α
Resource;One non-ABS small station, occupies the time resource of 1-α.User can be alternatively coupled to major station, ABS
Small station or non-ABS small station.
All of major station set M represents, size is Nm, all of ABS small station is by gathering PABSRepresent,
Size is Np, all of non-ABS small station is by gathering PnABSRepresenting, size is Np, wherein NpMore than Nm,
According to the difference of network, NpCan be NmSeveral times, Ji Shibei, even hundreds of times.All of user by
Set U represents, size is Nu。
The SINR value that user i receives from major station or non-ABS small station j:
The SINR value that user i receives from ABS small station j:
Wherein, PjIt is defined as being numbered the transmitting power of the base station of j, hijRepresent is from base station j to user i
Channel gain, comprise path loss, shadow fading and antenna gain, σ2Represent noise power.
Channel capacity c that user i obtains from base station jijIt is calculated by following formula:
cij=Wlog (1+SINRij) [3]
Wherein W represents the bandwidth of base station.All of base station uses identical bandwidth.
The optimization problem that the present invention proposes is shown below.
Constraints:
Wherein, xijIt is a binary variable, when user i is connected on the j of base station when, xij=1, be otherwise
0, kjBeing defined as on jth base station the number of users connected, X represents all xijSet.All of major station,
ABS small station, non-ABS small station composition collection of base stations, represent with B.Constraints [4b] is each in order to limit
User is connected solely on a base station, and constraints [4d] is in order to guarantee that all of user is connected.RijTable
Showing the speed that user i obtains from base station j, it is calculated by following formula:
Owing to this problem complexity of direct solution is too high, therefore, a discrete heuristic is used to allow each
User dynamically and is selected independently the base station of oneself optimum.All of decision all completes in user side, user for
Its candidate base station that can connect has one and connects probability vector, according to current network environment, according to connection
Probability vector is randomly chosen serving BS, obtains the connection feedback of oneself, update oneself from the base station chosen
Connection probability vector, until connecting some member in probability vector, close to 1, finally to determine clothes to be connected
Business base station.Detailed process is as follows:
1) base station selected strategy
At user side, each user is randomly chosen a base station according to randomized policy.Be fixedly selected one
Base station is different, and user i is according to a probability vectorSelect base station with having probability, wherein
qijRepresent that user i selects the probability of base station j.|ai| represent the number of the candidate base station of user i.If base station
Can provide better performance, then the connection probability of base station will be higher, and vice versa.In this way, often
Individual user can each be connected automatically on the BS of optimum.User i selects the probability of base station j to be given by:
WhereinAnd γ is a positive parameter, commonly referred to as temperature, the value of γ is close to 0
Time, can guarantee that the last solution of gained converges on optimal solution, but the value of γ is the biggest simultaneously, rate of convergence is the fastest,
For ensureing convergence rate and convergence effect, value generally uses 0.1~0.01 such scope.rijRepresent is to use
The connection feedback that family i obtains from base station j, defines according to utility function, and namely user obtains from base station instantly
Logarithm handling capacity.Assume that user i selects base station j as serving BS, be connected to the number of users on the j of base station
For kj.Then the feedback function of user i is given by:
2) connection feedback is calculated
User calculates the corresponding value of feedback in base station choosing connection according to [7] formula.All users in peak optimizating network
Throughput performance, coordination each other is necessary.So feedback function has a base station Value Factors
log(kj),It reflects the loading condition of base station。If kjRise,Illustrate to be connected to base stationjNumber of users
Increase, then the feedback that user i obtains from base station j declines.Bear if it is desired to increase base station in feedback function further
Carry consciousness, corresponding Value Factors p can be further added byij, it is proportional to kj。pijBe equivalent to the total of base station j connection
User is kjThe Value Factors of Shi Tixian loading condition.Feedback function becomesWhereinRepresent into
One step increases the feedback function after Value Factors.It is congested and certain that this Value Factors can reflect between user
Degree alleviates it.
3) base station is estimated to update
In state t, each user only obtains the feedback of the base station instantly selected, and provides next round and connects probability more
The value of feedback used in new:
What ε represented is a weight factor, and meets 0≤ε≤1, and reflection is that user is for connecting the feedback of base station
R in value renewalijWithShared weight ratio, and ε is the biggest,Shared weight is the biggest.Anti-according to newly obtain
Feedback value updates the connection probability vector of user.
4) final serving BS is determined
Circulation performs first three step, until a certain member variable is close to 1 in the connection probability vector of user
Time, then the base station that this variable is corresponding is the serving BS that user is final.Convergence rate to be increased, then can be even
When connecing the scope that probability reaches more than 0.9, determine that this base station is serving BS, accuracy will be increased further,
Then can choose the value of 0.95~more than 0.99 as determining border.
Beneficial effect: the present invention is with the logarithm speed summation of all users in maximization super-intensive heterogeneous network as mesh
Mark sets up the Load Balancing Model that user connects.With discrete carrying out based on the random heuristic determined
What user connected solves, and load effectively can be unloaded to the small station of relative free by the method from congested macro station
On.All of decision all completes in user side, and user terminal has only to know that the information of local just can be according to certainly
Oneself connection strategy is connected on instantly optimum base station, without know the SINR of other users and he
Connection strategy.Model have employed ABS technology, allow the user being connected on small station during ABS avoid
From the strong jamming of macro station, promote that user is unloaded on small station, ensures the performance of small station user simultaneously further.
The user that the model be given according to the present invention calculates connects, and promotes network performance while reaching load balancing.
Load-balancing method based on the present invention has the advantage that
1. user can effectively be unloaded to the small station of relative free from congested major station, between each base station
Load have higher fairness.
2. can effectively promote the speed of edge customer, the fairness of user performance improves
The most all of decision all completes in user side, and user terminal has only to know that the information of local just can root
It is connected on instantly optimum base station according to the connection strategy of oneself.
Detailed description of the invention
Below in conjunction with specific embodiment, it is further elucidated with the present invention, it should be understood that these embodiments are merely to illustrate this
Invention rather than restriction the scope of the present invention, after having read the present invention, those skilled in the art are to this
The amendment of the bright various equivalent form of values all falls within the application claims limited range.
The load-balancing method connected based on the random heuristic user determined, to maximize super-intensive heterogeneous network
In the logarithm speed summation of all users be the Load Balancing Model that target sets up that user connects.This model is following
Aspect promotes the fairness of network, first, user can be promoted with the speed promoting user to be connected to relatively for target
Free time can be assigned on the small station of more multiple resource, thus improves the harmony of load between big small station, secondly,
Logarithmic function is the function that a gain is successively decreased, and can improve the fairness of user rate.Determined by this model
User connects can also promote overall network throughput performance.
Assuming in super-intensive heterogeneous network, all of major station all uses ABS ratio 0≤α≤1 of synchronization,
Each major station occupies the time resource of 1-α, during small station both can be operated in ABS, it is also possible to be operated in non-
During ABS, so each small station to be regarded as two logical base station, an ABS small station, occupy the time of α
Resource;One non-ABS small station, occupies the time resource of 1-α.User can be alternatively coupled to major station, ABS
Small station or non-ABS small station.
All of major station set M represents, size is Nm, all of ABS small station is by gathering PABSRepresent,
Size is Np, all of non-ABS small station is by gathering PnABSRepresenting, size is Np, wherein NpMore than Nm,
According to the difference of network, NpCan be NmSeveral times, Ji Shibei, even hundreds of times.All of user by
Set U represents, size is Nu。
The SINR value that user i receives from major station or non-ABS small station j:
The SINR value that user i receives from ABS small station j:
Wherein, PjIt is defined as being numbered the transmitting power of the base station of j, hijRepresent is from base station j to user i
Channel gain, comprise path loss, shadow fading and antenna gain, σ2Represent noise power.
Channel capacity c that user i obtains from base station jijIt is calculated by following formula:
cij=Wlog (1+SINRij) [3]
Wherein W represents the bandwidth of base station.All of base station uses identical bandwidth.
The optimization problem that the present invention proposes is shown below.
Constraints:
Wherein, xijIt is a binary variable, when user i is connected on the j of base station when, xij=1, be otherwise
0, kjBeing defined as on jth base station the number of users connected, X represents all xijSet.All of major station,
ABS small station, non-ABS small station composition collection of base stations, represent with B.Constraints [4b] is each in order to limit
User is connected solely on a base station, and constraints [4d] is in order to guarantee that all of user is connected.RijTable
Showing the speed that user i obtains from base station j, it is calculated by following formula:
Owing to this problem complexity of direct solution is too high, therefore, a discrete heuristic is used to allow each
User dynamically and is selected independently the base station of oneself optimum.All of decision all completes in user side, user for
Its candidate base station that can connect has one and connects probability vector, according to current network environment, according to connection
Probability vector is randomly chosen serving BS, obtains the connection feedback of oneself, update oneself from the base station chosen
Connection probability vector, until connecting some member in probability vector, close to 1, finally to determine clothes to be connected
Business base station.Detailed process is as follows:
1) base station selected strategy
At user side, each user is randomly chosen a base station according to randomized policy.Be fixedly selected one
Base station is different, and user i is according to a probability vectorSelect base station with having probability, wherein
qijRepresent that user i selects the probability of base station j.|ai| represent the number of the candidate base station of user i.If base station
Can provide better performance, then the connection probability of base station will be higher, and vice versa.In this way, often
Individual user can each be connected automatically on the BS of optimum.User i selects the probability of base station j to be given by:
WhereinAnd γ is a positive parameter, commonly referred to as temperature, the value of γ is close to 0
Time, can guarantee that the last solution of gained converges on optimal solution, but the value of γ is the biggest simultaneously, rate of convergence is the fastest,
For ensureing convergence rate and convergence effect, value generally uses 0.1~0.01 such scope.rijRepresent is to use
The connection feedback that family i obtains from base station j, defines according to utility function, and namely user obtains from base station instantly
Logarithm handling capacity.Assume that user i selects base station j as serving BS, be connected to the number of users on the j of base station
For kj.Then the feedback function of user i is given by:
2) connection feedback is calculated
User calculates the corresponding value of feedback in base station choosing connection according to [7] formula.All users in peak optimizating network
Throughput performance, coordination each other is necessary.So feedback function has a base station Value Factors
log(kj),It reflects the loading condition of base station。If kjRise,Illustrate to be connected to the number of users of base station j
Increase, then the feedback that user i obtains from base station j declines.Bear if it is desired to increase base station in feedback function further
Carry consciousness, corresponding Value Factors p can be further added byij, it is proportional to kj。pijBe equivalent to the total of base station j connection
User is kjThe Value Factors of Shi Tixian loading condition.Feedback function becomesWhereinRepresent into
One step increases the feedback function after Value Factors.It is congested and certain that this Value Factors can reflect between user
Degree alleviates it.
3) base station is estimated to update
In state t, each user only obtains the feedback of the base station instantly selected, and provides next round and connects probability more
The value of feedback used in new:
What ε represented is a weight factor, and meets 0≤ε≤1, and reflection is that user is for connecting the feedback of base station
R in value renewalijWithShared weight ratio, and ε is the biggest,Shared weight is the biggest.Anti-according to newly obtain
Feedback value updates the connection probability vector of user.
4) final serving BS is determined
Circulation performs first three step, until a certain member variable is close to 1 in the connection probability vector of user
Time, then the base station that this variable is corresponding is the serving BS that user is final.Convergence rate to be increased, then can be even
When connecing the scope that probability reaches more than 0.9, determine that this base station is serving BS, accuracy will be increased further,
Then can choose the value of 0.95~more than 0.99 as determining border.
Embodiment
1. initialize each user connection probability for its candidate base station, if the candidate base station number of user i is
ai, then can connect probability vector and be initialized as
2. each user connects, according to it, the selection serving BS that probability vector is random in candidate base station.
3. the value of feedback that each user is given according to the serving BS selected by formula [7] calculating, calculates further according to formula [8]
Next round connects value of feedback to be used during probability vector updates.
4. each user updates connection probability vector according to formula [6] value of feedback being newly calculated in previous step.
5. repeated execution of steps 2,3,4, until certain element is close to 1 in the connection probability vector of user, than
If value is more than 0.95, then the base station of its correspondence is defined as the serving BS that this user is final.
The connection probability vector of the most all users has close to 1 element time, the most all of user connects really
Fixed.