CN104283596B - A kind of 3D beam form-endowing methods and equipment - Google Patents

Abstract

The present invention provides a 3D beamforming method and equipment. The method includes: obtaining the location information of each user that needs to be served in the current time slot, and determining the total throughput of each user relative to the beam angle vector αβ according to the obtained location information The function Use an iterative algorithm to determine the The largest beam angle vector αβ _M ; adjust the beam angles of the beams sent by each access point according to the acquired beam angle vector αβ _M . Compared with the method of directing the beam to the user in the prior art, the present invention can more effectively suppress the interference of the beam to other users and maximize the total throughput of the system.

Description

A 3D beamforming method and device

technical field

The present invention relates to the technical field of communications, and in particular to a 3D beamforming method and device.

Background technique

Heterogeneous network is a new network structure different from the traditional homogeneous network model. The heterogeneous network adds low-power transmitting nodes, including micro base stations (Microcell), pico base stations (Picocell), femto base stations (Femtocell), relays (Relay) and remote radio nodes (RRH). Compared with macro-cell base stations, the above-mentioned base stations are small in size, low in transmission power, and small in coverage, but they are easier to deploy than macro-cell base stations. Heterogeneous network deploys multiple types of base station coverage overlapping, which can solve the problem of coverage "blind area" and "busy area", especially suitable for solving the coverage problem of indoor and hotspot areas, which can significantly increase the system capacity and improve the system's spectral efficiency .

Beamforming technology was first used in smart antennas. The base station dynamically adjusts the horizontal direction of the downlink beam so that the beam points to the served user, thereby increasing the received power and reducing interference to other users. Horizontal beamforming cannot solve the interference problem between users in the same horizontal direction, therefore, techniques to control beam downtilt have emerged in recent years. By dynamically adjusting the beam inclination angle, it can not only effectively suppress the interference between users in the same radial direction, but also effectively solve the problems of low receiving power of users at the edge of the cell and blind spots in the center of the cell. With the development of technology, in recent years, a technology that simultaneously adjusts the horizontal angle and downtilt angle of the beam has emerged, that is, 3D beamforming. 3D beamforming requires more information to be exchanged between base stations, and the complexity is high. The existing technology is Horizontally and vertically partition the plot, and each partition corresponds to a fixed angle, so the flexibility is not high.

The overlapping coverage areas of various types of base stations in a heterogeneous network will generate a large number of cell edge areas, and users in the cell edge areas are easily interfered by adjacent base stations. The 3D beamforming technology can improve the received power of the user and reduce the interference to other users by directing the thinner beam to the served user, effectively improving the signal-to-interference ratio. Using 3D beamforming technology in heterogeneous networks has great potential for throughput improvement. However, if the beam is directed at the user at the edge of the cell and the users in the adjacent cell are very close, it will inevitably bring stronger interference, which will not only make no contribution to the improvement of system throughput, but will even increase the throughput. decline.

Contents of the invention

The invention provides a 3D beam forming method and equipment, which can better reduce the interference between adjacent cells in a heterogeneous network and improve the throughput of the system.

The present invention provides a 3D beamforming method, which is characterized in that it is applied in a heterogeneous network, where at least one macro cell in the heterogeneous network includes a macro base station and at least one low-power base station, and the macro base station and At least one low-power base station is used as an access point to form an access point group GN, and each access point uses a beam in a time slot to serve a user. The method includes:

Obtain the location information of each user that needs to be served in the current time slot;

According to the obtained position information, the function of the total throughput of each user relative to the beam angle vector αβ is determined The beam angle vector αβ is (α ₁ , α ₂ ... α _S , β ₁ , β ₂ ... β _S ), where α _s is used to represent the straight line where the crest of the beam sent by the sth access point is located The angle between the projection on the horizontal plane and the preset horizontal axis X, β _s is used to represent the angle between the straight line where the peak of the beam sent by the sth access point is located and the horizontal plane, S is the number of users, R _s (αβ) is the data transmission rate corresponding to the sth user;

Use an iterative algorithm to determine the The largest beam angle vector αβ _M ;

The beam angles of the beams sent by each access point are adjusted according to the acquired beam angle vector αβ _M.

Preferably, the function of determining the total throughput of each user relative to the beam angle vector αβ according to the acquired position information Specifically include:

Determine the function of the large-scale fading from each access point to each user relative to the beam angle vector αβ according to the obtained position information;

Pick As a function of the total throughput of each user with respect to the beam angle vector αβ, where,

belong to collection is an integer greater than 0;

Among them, ρ _s (α,β)=[ρ _s,1 (α ₁ ,β ₁ ),…,ρ _s,S (α _S ,β _S )], ρ _s,b (α _b ,β _b ) is The large-scale fading from the bth access point to the sth user is a function of the beam angle (α _b , β _b ) of the bth access point, _rs = (rs _,1 , rs _,2 , r _s,3 ... r _s,S ), where, when s=b, r _s,b =N; when s≠b, r _s,b =1, N is the number of antennas of each access point number; is the vector obtained after removing the sth element for ρ _s (α,β), The vector obtained after removing the sth element for r _s .

Preferably, the determining the function of the large-scale fading from each access point to each user relative to the beam angle vector αβ according to the acquired location information specifically includes: calculating the large-scale fading from each access point to each user respectively Fading as a function of the beam angle vector αβ, where, PL _s,b is the path loss from access point b to user s, Ψ _s,b is the shadow fading from access point b to user s, is the antenna pattern; is the angle between the line connecting user s and access point b and the X axis, is the angle between the straight line connecting user s and access point b and the horizontal plane; SLL _az and SLL _el are the side lobe levels of the horizontal and vertical patterns, respectively, and SLL _tot is the total side lobe level; E[A] It means to take the expected value of A, and A _max is the default value.

Preferably, the iterative algorithm is used to determine that The largest beam angle vector αβ _M , including:

S1. Select k beam angle vectors, the k beam angle vectors αβ include beam angle vectors αβ corresponding to each access point directly pointing to the served user equipment; where k is a preset value, and the k The beam angle vector αβ contains the corresponding beam angle vector when the antenna of each access point is directly pointing to the user it needs to serve;

S2, judging whether the k total throughputs corresponding to the k beam angle vectors αβ converge within the preset range, if not, turn to step S3, and if so, turn to step S4;

S3, select other beam angle vectors αβ other than the k beam angle vectors αβ to replace the beam angle vector αβ that minimizes the total throughput among the k beam angle vectors αβ, and return to step S2;

S4. Select the beam angle vector αβ that maximizes the total throughput among the k beam angle vectors αβ as the Maximum beam angle vector αβ _M .

Preferably, said use of the complex shape algorithm to obtain the total throughput After the maximum beam angle vector αβ _M , before adjusting the beam angles of the beams sent by each access point according to the acquired beam angle vector αβ _M , the method further includes:

Change the value of any angle in the beam angle vector αβ _M within the range of plus or minus T° according to the preset step size, and change the value of any angle in the beam angle vector αβ _M accordingly each time the value of any angle is changed The value of the angle makes the total throughput of the macro base station and the low-power base station take the maximum value, and the T is a preset value;

Judging whether the ratio of the maximum value of the total throughput to the total throughput corresponding to the beam angle vector αβ _M is greater than a preset value after changing any one of the angle values each time, and if it is judged to be yes, change the After any angle value, the beam angle vector that maximizes the total throughput is added to the candidate set;

Select the beam angle vector αβ _m that maximizes the interference suppression SLNR between macro base stations from the set to be selected;

The adjusting the beam angle of the beam sent by each access point according to the beam angle vector αβ _M specifically includes:

Adjusting beam angles of the macro base station and the low-power base station according to the beam angle vector αβ _m .

The present invention provides a 3D beamforming device, which is used as a macro base station in a heterogeneous network, and the device includes:

The location information acquisition module acquires the location information of each user that needs to be served in the current time slot;

Call the module to determine the function of the total throughput of each user relative to the beam angle vector αβ according to the obtained position information The beam angle vector αβ is (α ₁ , α ₂ ... α _S , β ₁ , β ₂ ... β _S ), where α _s is used to represent the straight line where the crest of the beam sent by the sth access point is located The angle between the projection on the horizontal plane and the preset horizontal axis X, β _s is used to represent the angle between the straight line where the peak of the beam sent by the sth access point is located and the horizontal plane, S is the number of users, R _s (αβ) is the data transmission rate corresponding to the sth user;

calculation module, using an iterative algorithm to determine the The largest beam angle vector αβ _M ;

An adjustment module, configured to adjust beam angles of beams sent by each access point according to the acquired beam angle vector αβ _M.

Preferably, the calling module is specifically configured to determine the function of the large-scale fading from each access point to each user with respect to the beam angle vector αβ according to the acquired position information, which takes As a function of the total throughput of each user with respect to the beam angle vector αβ, where,

belong to collection n is an integer greater than 0;

Among them, ρ _s (α,β)=[ρ _s,1 (α ₁ ,β ₁ ),…,ρ _s,S (α _S ,β _S )], ρ _s,b (α _b ,β _b ) is The large-scale fading from the bth access point to the sth user is a function of the beam angle (α _b , β _b ) of the bth access point, _rs = (rs _,1 , rs _,2 , r _s,3 ... r _s,S ), where, when s=b, r _s,b =N; when s≠b, r _s,b =1, N is the number of antennas of each access point number; is the vector obtained after removing the sth element for ρ _s (α,β), The vector obtained after removing the sth element for r _s

Preferably, the calling module is specifically used to calculate the function of the large-scale fading from each access point to each user relative to the beam angle vector αβ, wherein PL _s,b is the path loss from access point b to user s, Ψ _s,b is the shadow fading from access point b to user s, is the antenna pattern; is the angle between the line connecting user s and access point b and the X axis, is the angle between the straight line connecting user s and access point b and the horizontal plane; SLL _az and SLL _el are the side lobe levels of the horizontal and vertical patterns, respectively, and SLL _tot is the total side lobe level; E[A] It means to take the expected value of A, and A _max is the default value.

Preferably, the calculation module is specifically configured to perform the following steps:

S1. Select k beam angle vectors, where k is a preset value, and the k beam angle vectors αβ include the corresponding beam angle vectors when the antennas of each access point point directly to the users they need to serve;

S2, judging whether the k total throughputs corresponding to the k beam angle vectors αβ converge within the preset range, if not, turn to step S3, and if so, turn to step S4;

S3, select other beam angle vectors αβ other than the k beam angle vectors αβ to replace the beam angle vector αβ that minimizes the total throughput among the k beam angle vectors αβ, and return to step S2;

S4. Select the beam angle vector αβ that maximizes the total throughput among the k beam angle vectors αβ as the Maximum beam angle vector αβ _M .

Preferably, the device also includes:

A fine-tuning module for performing the following steps:

Change any angle value corresponding to the macro base station in the beam angle vector αβ _M within the range of plus or minus T° according to the preset step size, and change the beam angle vector αβ correspondingly every time the angle value is changed The values of other angles in _M make the total throughput of the macro base station and the low-power base station take the maximum value, and the T is a preset value;

Judging whether the ratio of the maximum value of the total throughput to the total throughput corresponding to the beam angle vector αβ _M is greater than a preset value after changing any one of the angle values each time, and if it is judged to be yes, change the After any angle value, the beam angle vector that maximizes the total throughput is added to the candidate set;

Select the beam angle vector αβ _m that maximizes the interference suppression SLNR between macro base stations from the set to be selected;

The adjusting module is specifically configured to adjust beam angles of beams sent by each access point according to the beam angle vector αβ _m .

In the present invention, the location information of each user that needs to be served in the current time slot is obtained, and the function of the total throughput of each user relative to the beam angle vector αβ is determined according to the obtained location information The beam angle vector αβ is (α ₁ , α ₂ ... α _S , β ₁ , β ₂ ... β _S ), where α _s is used to represent the straight line where the crest of the beam sent by the sth access point is located The included angle with the preset horizontal axis X, β _s is used to indicate the included angle between the straight line where the peak of the beam sent by the sth access point is located and the horizontal plane, S is the number of users, R _s (αβ) is the data transmission rate corresponding to the sth user; use an iterative algorithm to determine the The largest beam angle vector αβ _M ; adjust the beam angles of the beams sent by each access point according to the acquired beam angle vector αβ _M . Compared with the method of directing the beam to the user in the prior art, the present invention can more effectively suppress the interference of the beam to other users and maximize the total throughput of the system. At the same time, in the present invention, the calculation of the beam angles of each access point in the GN is uniformly performed on the macro base station of the GN, and each macro base station calculates the beam angle of the GN where it is located, while effectively suppressing the interference in the GN, It also prevents the work of calculating the beam angles in each GN from being performed on one device, reducing the workload of a single device.

Description of drawings

FIG. 1 is a schematic flowchart of a 3D beamforming method provided by an embodiment of the present invention;

FIG. 2 is a schematic flow chart of calculating a beam angle vector in a 3D beamforming method provided by an embodiment of the present invention;

FIG. 3 is a schematic flowchart of a part of a 3D beamforming method provided by an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of a 3D beamforming device provided by an embodiment of the present invention.

detailed description

The specific implementation manners of the present invention will be further described below in conjunction with the drawings and examples. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

An embodiment of the present invention provides a 3D beamforming method, which is applied to a heterogeneous network system. The heterogeneous network includes a macro base station and at least one low-power base station. As shown in FIG. 1 , the method includes:

Step 101, acquiring location information of each user that needs to be served in the current time slot.

In practical applications, there are multiple ways to obtain the location information of each user that needs to be served in the current time slot. For example, one of the methods is: in each GN, the LPN is connected to the MBS through a feeder, and the user location information is sent by its service node to the MBS. MBS report.

Step 102, according to the obtained location information, determine the function of the total throughput of each user relative to the beam angle vector αβ The beam angle vector αβ is (α ₁ , α ₂ ... α _S , β ₁ , β ₂ ... β _S ), where α _s is used to represent the straight line where the crest of the beam sent by the sth access point is located The included angle with the preset horizontal axis X, β _s is used to indicate the included angle between the straight line where the peak of the beam sent by the sth access point is located and the horizontal plane, S is the number of users, R _s (αβ) is the data transmission rate corresponding to the sth user;

Step 103, using an iterative algorithm to determine Maximum beam angle vector αβ _M .

Step 104, adjust the beam angles of the beams sent by each access point according to the acquired beam angle vector αβ _M.

In the embodiment of the present invention, a function of the total throughput relative to the beam angle vector αβ is predetermined and use the complex shape algorithm to choose such that the total throughput The largest beam angle vector αβ adjusts the beam angles of the beams transmitted by each access point. Compared with the way of directing the beam to the user in the prior art, the present invention can more effectively suppress the interference of the beam to other users and maximize the total throughput of the system.

Preferably, the above step 102 specifically includes:

Determine the function of the large-scale fading from each access point to each user relative to the beam angle vector αβ according to the obtained position information;

Pick As a function of the total throughput of each user with respect to the beam angle vector αβ; where,

belong to collection n is an integer greater than 0;

Among them, ρ _s (α,β)=[ρ _s,1 (α ₁ ,β ₁ ),…,ρ _s,S (α _S ,β _S )], ρ _s,b (α _b ,β _b ) is The large-scale fading from the bth access point to the sth user is a function of the beam angle (α _b , β _b ) of the bth access point, _rs = (rs _,1 , rs _,2 , r _s,3 ... r _s,S ), where, when s=b, r _s,b =N; when s≠b, r _s,b =1, N is the number of antennas of each access point number; is the vector obtained after removing the sth element for ρ _s (α,β), The vector obtained after removing the sth element for r _s . In this way, the complexity of calculating the beam angle vector αβ can be reduced.

Specifically, suppose is the channel matrix between base station b and user s, representing small-scale fading. Suppose the channel is an uncorrelated Rayleigh fading channel, so is independent and identically distributed and belongs to CN(0,1). x _b ∈□ ^N×1 is the transmitted signal of base station b, and the power limit is x _b can be written as x _s = f _s d _s . f _s is a normalized beamforming vector, that is, for the sth user, f _s =h _s,s /|h _s,s |. d _s represents the received data symbol of user s. From the above we can see, It obeys a chi-square distribution with a scaling factor of 1/2 and a degree of freedom of 2N, namely interference It obeys a chi-square distribution with a scaling factor of 1/2 and a degree of freedom of 2, namely in, Represents a chi-square distribution with n degrees of freedom. n _s □CN(0,1) is the normalized additive white Gaussian noise (AWGN) received by user s.

From this, the signal-to-interference-noise ratio of the user can be obtained as

Where α=[α ₁ ,…,α _S ], β=[β ₁ ,…,β _S ] represent the horizontal angle and downtilt angle of each base station beam, that is, the straight line where the peak of each transmitted beam is located on the horizontal plane The angle between the projection and the preset horizontal axis X and the angle between the straight line and the horizontal plane. According to Shannon's theorem, the average user data rate is

R _s (α,β)＝Ε[log ₂ (1+SINR _s (α,β))] (2)

Substitute (2) into (1) to get

Lemma: Suppose X _m is a random variable with chi-square distribution, the degree of freedom is 2r _m > 0, and X is the sum of X _m , that is Let μ=[μ ₁ ,…,μ _M ], r=[r ₁ ,…,r _M ], then for f(μ,r)=Ε[log ₂ (1+X)], we can get

in

i=[i ₁ ,i ₂ ,…,i _M ] belongs to the set Ω _t,l

Let r _s =1 _s +(N _t -1)e _s , ρ _s (α,β)=[ρ _s,1 (α ₁ ,β ₁ ),…,ρ _s,S (α _S ,β _S ) ], by the lemma

but

Preferably, the determining the function of the large-scale fading from each access point to each user relative to the beam angle vector αβ according to the acquired location information specifically includes: calculating the large-scale fading from each access point to each user respectively Fading as a function of beam angle vector αβ PL _s,b is the path loss from access point b to user s, Ψ _s,b is the shadow fading from access point b to user s, is the antenna pattern; b is the angle between the line connecting user s and access point b and the X axis, is the angle between the straight line connecting user s and access point b and the horizontal plane; SLL _az and SLL _el are the side lobe levels of the horizontal and vertical patterns, respectively, and SLL _tot is the total side lobe level; E[A] It means to take the expected value of A, and A _max is the default value.

Preferably, the above-mentioned step 103 may specifically include, as shown in FIG. 2 , various steps:

Step 201: Set the mapping factor λ and the dimension k of the complex shape, let _αβ0 be a starting point, determine another k-1 starting points by randomly selecting points in the feasible region, and calculate the objective function at each point (total throughput function) value. Here the dimension k may be a preset value.

Preferably, k=2S, the mapping factor λ is a preset value, and αβ ₀ represents the corresponding beam angle vector when each access point directs the peak of the beam to the user equipment. In practical applications, although the total throughput corresponding to the corresponding beam angle vector when the peak of the beam is directly directed to the user equipment is not necessarily the maximum value, it is also a relatively large value. In this manner, it can be ensured that the total throughput in the network is not less than the corresponding throughput when the crest of the beam directly points to the user equipment.

Step 202: Adjust the mapping factor λ to determine the worst point αβ _W , that is, the worst point here refers to the point with the smallest objective function, and calculate the center point αβ _C according to formula (7);

Step 203: Calculate αβ _R according to formula (8), and use αβ _R to replace the worst point to form a new complex shape.

αβ _R ＝αβ _C +λ(αβ _C -αβ _W ) (8)

Step 204, judge whether _αβR is the worst point, if not, go to step 206, if yes, go to step 205.

Step 205, reduce λ by half, and then go to step 203.

Step 206, judge that all k points are within several spatial units, if not, go to step 202, if yes, go to step 207.

Step 207, taking the vector that maximizes the objective function among the k vectors as the beam angle vector that maximizes the throughput.

It should be pointed out that the above-mentioned steps 201-207 are only a preferred implementation of step 103 in the embodiment of the present invention. In practical applications, those skilled in the art can think of many other iterative algorithms. Should fall into the protection scope of this application.

Preferably, after step 103 and before step 104, the 3D beamforming method provided by the embodiment of the present invention may further include the following steps 301-303 as shown in FIG. 3 :

Step 301, change any angle value corresponding to the macro base station in the beam angle vector αβ _M within the range of plus or minus T° according to the preset step size, and change each time any angle value corresponding to Values of other angles in the beam angle vector αβ _M make the total throughput of the macro base station and the low-power base station take the maximum value, and the T is a preset value.

Step 302, judging whether the ratio of the maximum value of the total throughput to the total throughput corresponding to the beam angle vector αβ _M is greater than the preset value after changing any one of the angle values each time, and when the judgment is yes, the time After changing any one of the angle values, the beam angle vector with the maximum total throughput is added to the candidate set.

Step 303, selecting a beam angle vector αβ _m that maximizes the SLNR of inter-macro base station interference suppression SLNR from the candidate set.

Specifically, in practical applications, each user is classified as a cell center user or a cell edge user. If an MBS user is a cell center user, it is far away from adjacent GN users, and the beams of the MBS will not face each other. Adjacent GN users cause severe interference, so inter-GN interference suppression is not performed; if an MBS user is an edge user, which may cause serious inter-GN interference, inter-GN interference suppression is performed based on the principle of maximum SLNR. Let the number of the studied base station be 1, there are Q-1 edge users in the adjacent GN, numbered from 2 to Q, the leaked signal can be written as

Then SLNR is

The problem to be solved for inter-GN interference suppression is to adjust the 3D beamforming angle to maximize the SLNR, namely

In order to ensure the convergence of the algorithm, let the variation range of each element of the vector αβ be plus or minus 5°, take the angle obtained in a) as the starting point, and first fix α ₁ (assuming that the two angle values corresponding to the macro base station are α1 , β1), take 0.5° as the step size, reduce β ₁ , for a certain α ₁ , β ₁ , adjust the 3D beamforming angle of the LPN in the GN within the range of plus or minus 5°, and get αβ to maximize the GN throughput If the throughput drops by no less than 5%, put this αβ into the candidate set Δ, once the boundary limit is reached, stop the search in this direction, change the direction, that is, increase β ₁ , and search for the candidate set in the same way element. Then change α ₁ with the same step size, fix α ₁ , and do the same search on β ₁ until the candidate set Δ is determined.

In order to apply the conclusion (4) obtained in a), write (10) as follows,

Since the function y=log ₂ (1+x) is a monotonically increasing function of x, when the RSLNR takes the maximum value, the SLNR also takes the maximum value. RSLNR can be further written as

Applying (4), and defining r ₁ =1 ₁ +(N-1)e ₁ , ρ ₁ (α,β)=[P ₁ ρ _1,1 (α,β),…,P ₁ ρ _Q,1 (α,β)] can be obtained

So finding (11) is equivalent to solving

Apply the exhaustive method to find the angle αβ ₁ that maximizes the RSLNR in the candidate set Δ, which is the optimal angle of each access point of the GN. Each GN finds its own optimal angle in parallel in the same way, so the total throughput of the 7 GNs in the center can be calculated. What needs to be explained here is that since the 7 GNs in the center are subject to both inter-GN and intra-GN interference, the 7 GNs in the research center are universal. In addition, when calculating the total throughput, it is necessary to consider the interference of the LPN to users in adjacent GNs.

On the basis of steps 301-303, step 104 specifically includes: adjusting the beam angles of the beams sent by each access point according to the beam angle vector αβ _m .

Based on the above steps 301-303, the embodiment of the present invention also fully considers the influence of the macro base station of the neighbor cell on each user in the cell when performing beamforming. In this way, the interference between the macro base stations can be suppressed.

In the technical solution provided by the embodiment of the present invention, by calculating the sum of the throughput of each served user and adjusting the beam angle of each access point, compared with the method of directly irradiating the beam to the user equipment in the prior art, it is possible to obtain Greater throughput. At the same time, in the embodiment of the present invention, it also provides a calculation method for estimating the sum of the throughput of each served user, which simplifies the calculation complexity. In addition, the size of the beam angle is adjusted according to the interference between macro cells, which further reduces the Users receive less interference, improving throughput.

Based on the same idea, an embodiment of the present invention also provides a 3D beamforming device, as shown in FIG. 4 , including:

The location information acquisition module 401, which acquires the location information of each user who needs to be served in the current time slot;

Call module 402 to determine the function of the total throughput of each user relative to the beam angle vector αβ according to the acquired position information The beam angle vector αβ is (α ₁ , α ₂ ... α _S , β ₁ , β ₂ ... β _S ), where α _s is used to represent the straight line where the crest of the beam sent by the sth access point is located The angle between the projection on the horizontal plane and the preset horizontal axis X, β _s is used to represent the angle between the straight line where the peak of the beam sent by the sth access point is located and the horizontal plane, S is the number of users, R _s (αβ) is the data transmission rate corresponding to the sth user;

Calculation module 403, using an iterative algorithm to determine The largest beam angle vector αβ _M ;

An adjustment module 404, configured to adjust the beam angles of the beams sent by each access point according to the acquired beam angle vector αβ _M.

Preferably, the calling module 402 is specifically used to determine the function of the large-scale fading from each access point to each user with respect to the beam angle vector αβ according to the obtained position information, which takes As a function of the total throughput of each user with respect to the beam angle vector αβ, where,

belong to collection n is an integer greater than 0;

Among them, ρ _s (α,β)=[ρ _s,1 (α ₁ ,β ₁ ),…,ρ _s,S (α _S ,β _S )], ρ _s,b (α _b ,β _b ) is The large-scale fading from the bth access point to the sth user is a function of the beam angle (α _b , β _b ) of the bth access point, _rs = (rs _,1 , rs _,2 , r _s,3 ... r _s,S ), where, when s=b, r _s,b =N; when s≠b, r _s,b =1, N is the number of antennas of each access point number; is the vector obtained after removing the sth element for ρ _s (α,β), The vector obtained after removing the sth element for r _s .

Preferably, the calling module 402 is specifically used to: respectively calculate the function of the large-scale fading from each access point to each user with respect to the beam angle vector αβ PL _s,b is the path loss from access point b to user s, Ψ _s,b is the shadow fading from access point b to user s, is the antenna pattern; is the angle between the line connecting user s and access point b and the X axis, is the angle between the straight line connecting user s and access point b and the horizontal plane; SLL _az and SLL _el are the side lobe levels of the horizontal and vertical patterns, respectively, and SLL _tot is the total side lobe level; E[A] It means to take the expected value of A, and A _max is the default value.

Preferably, the computing module 403 is specifically configured to perform the following steps:

S1. Select k beam angle vectors, where k is a preset value, and the k beam angle vectors αβ include the corresponding beam angle vectors when the antennas of each access point point directly to the users they need to serve;

S2, judging whether the k total throughputs corresponding to the k beam angle vectors αβ converge within the preset range, if not, turn to step S3, and if so, turn to step S4;

S3, select other beam angle vectors αβ other than the k beam angle vectors αβ to replace the beam angle vector αβ that minimizes the total throughput among the k beam angle vectors αβ, and return to step S2;

S4. Select the beam angle vector αβ that maximizes the total throughput among the k beam angle vectors αβ as the Maximum beam angle vector αβ _M .

Preferably, the device also includes:

Fine-tuning module 405, configured to perform the following steps:

Change any angle value corresponding to the macro base station in the beam angle vector αβ _M within the range of plus or minus T° according to the preset step size, and change the beam angle vector αβ correspondingly every time the arbitrary angle value is changed The values of other angles in _M make the total throughput of the macro base station and the low-power base station take the maximum value, and the T is a preset value;

Judging whether the ratio of the maximum value of the total throughput to the total throughput corresponding to the beam angle vector αβ _M is greater than a preset value after changing any one of the angle values each time, and if it is judged to be yes, change the After any angle value, the beam angle vector that maximizes the total throughput is added to the candidate set;

Select the beam angle vector αβ _m that maximizes the interference suppression SLNR between macro base stations from the set to be selected;

The adjustment module 404 is specifically configured to adjust the beam angles of the beams sent by each access point according to the beam angle vector αβ _m .

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, some improvements and modifications can also be made. These improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (6)

1. A 3D beamforming method, characterized in that it is applied in a heterogeneous network, where at least one macro cell in the heterogeneous network includes a macro base station and at least one low-power base station, and the one macro base station and at least one The low-power base stations are all used as access points to form an access point group GN, and each access point uses a beam in a time slot to serve a user. The method includes: Obtain the location information of each user that needs to be served in the current time slot; According to the obtained position information, the function of the total throughput of each user relative to the beam angle vector αβ is determined The beam angle vector αβ is (α ₁ , α ₂ ... α _S , β ₁ , β ₂ ... β _S ), where α _s is used to represent the straight line where the crest of the beam sent by the sth access point is located The angle between the projection on the horizontal plane and the preset horizontal axis X, β _s is used to represent the angle between the straight line where the peak of the beam sent by the sth access point is located and the horizontal plane, S is the number of users, R _s (αβ) is the data transmission rate corresponding to the sth user; Use an iterative algorithm to determine the The largest beam angle vector αβ _M ; adjusting beam angles of beams sent by each access point according to the acquired beam angle vector αβ _M ; The iterative algorithm used to determine the The largest beam angle vector αβ _M , including: S1. Select k beam angle vectors, where k is a preset value, and the k beam angle vectors αβ include the corresponding beam angle vectors when the antennas of each access point point directly to the users they need to serve; S2, judging whether the k total throughputs corresponding to the k beam angle vectors αβ converge within the preset range, if not, turn to step S3, and if so, turn to step S4; S3, select other beam angle vectors αβ other than the k beam angle vectors αβ to replace the beam angle vector αβ that minimizes the total throughput among the k beam angle vectors αβ, and return to step S2; S4. Select the beam angle vector αβ that maximizes the total throughput among the k beam angle vectors αβ as the The largest beam angle vector αβ _M ; The use of an iterative algorithm to obtain the total throughput After the maximum beam angle vector αβ _M , before adjusting the beam angles of the beams sent by each access point according to the acquired beam angle vector αβ _M , the method further includes: Change any angle value corresponding to the macro base station in the beam angle vector αβ _M within the range of plus or minus T° according to the preset step size, and change the beam angle vector αβ correspondingly every time the arbitrary angle value is changed The values of other angles in _M make the total throughput of the macro base station and the low-power base station take the maximum value, and the T is a preset value; Judging whether the ratio of the maximum value of the total throughput to the total throughput corresponding to the beam angle vector αβ _M is greater than a preset value after changing any one of the angle values each time, and if it is judged to be yes, change the After any angle value, the beam angle vector that maximizes the total throughput is added to the candidate set; Select the beam angle vector αβ _m that maximizes the signal leakage-to-noise ratio SLNR between macro base stations from the candidate set; The adjusting the beam angle of the beam sent by each access point according to the beam angle vector αβ _M specifically includes: The beam angles of the beams sent by each access point are adjusted according to the beam angle vector αβ _m . 2. The method according to claim 1, wherein the function of determining the total throughput of each user with respect to the beam angle vector αβ according to the acquired position information Specifically include: Determine the function of the large-scale fading from each access point to each user relative to the beam angle vector αβ according to the obtained position information; Pick As a function of the total throughput of each user with respect to the beam angle vector αβ; where, <mrow><mi>f</mi><mrow><mo>(</mo><mi>&amp;mu;</mi><mo>,</mo><mi>r</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>log</mi><mn>2</mn></msub><mrow><mo>(</mo><mi>e</mi><mo>)</mo></mrow><mo>&amp;lsqb;</mo><munderover><mi>&amp;Pi;</mi><mrow><mi>m</mi><mo>=</mo><mn>1</mn></mrow><mi>M</mi></munderover><mfrac><mn>1</mn><mrow><msup><msub><mi>&amp;mu;</mi><mi>m</mi></msub><msub><mi>r</mi><mi>m</mi></msub></msup></mrow></mfrac><mo>&amp;rsqb;</mo><munderover><mi>&amp;Sigma;</mi><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><mi>M</mi></munderover><munderover><mi>&amp;Sigma;</mi><mrow><mi>l</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>r</mi><mi>t</mi></msub></munderover><msup><mrow><mo>(</mo><mo>-</mo><mn>1</mn><mo>)</mo></mrow><mrow><msub><mi>r</mi><mi>t</mi></msub><mo>-</mo><mi>l</mi></mrow></msup><msubsup><mi>&amp;mu;</mi><mi>t</mi><mrow><msub><mi>r</mi><mi>t</mi></msub><mo>-</mo><mi>l</mi><mo>+</mo><mn>1</mn></mrow></msubsup><msub><mi>&amp;Psi;</mi><mrow><mi>t</mi><mo>,</mo><mi>l</mi></mrow></msub><mrow><mo>(</mo><mi>&amp;mu;</mi><mo>,</mo><mi>r</mi><mo>)</mo></mrow><msup><mi>e</mi><mrow><mo>(</mo><mn>1</mn><mo>/</mo><msub><mi>&amp;mu;</mi><mi>t</mi></msub><mo>)</mo></mrow></msup><munderover><mi>&amp;Sigma;</mi><mrow><mi>k</mi><mo>=</mo><mn>0</mn></mrow><mrow><msub><mi>r</mi><mi>t</mi></msub><mo>-</mo><mi>l</mi></mrow></munderover><msub><mi>E</mi><mrow><mi>k</mi><mo>+</mo><mn>1</mn></mrow></msub><mrow><mo>(</mo><mfrac><mn>1</mn><msub><mi>&amp;mu;</mi><mi>t</mi></msub></mfrac><mo>)</mo></mrow><mo>,</mo></mrow> i=[i ₁ ,i ₂ ,…,i _M ] belongs to the set Ω _t,l , n is an integer greater than 0, and M is the length of vector i; Among them, ρ _s (α,β)=[ρ _s,1 (α ₁ ,β ₁ ),…,ρ _s,S (α _S ,β _S )], ρ _s,b (α _b ,β _b ) is The large-scale fading from the bth access point to the sth user is a function of the beam angle (α _b , β _b ) of the bth access point, _rs = (rs _,1 , rs _,2 , r _s,3 ... r _s,S ), where, when s=b, r _s,b =N; when s≠b, r _s,b =1, and N is the number of antennas of each access point number; is the vector obtained after removing the sth element for ρ _s (α,β), The vector obtained after removing the sth element for r _s . 3. The method according to claim 2, wherein the determining the function of the large-scale fading from each access point to each user with respect to the beam angle vector αβ according to the acquired position information specifically comprises: respectively calculating The large-scale fading from each access point to each user is a function of the beam angle vector αβ, where PL _s,b is the path loss from access point b to user s, Ψ _s,b is the shadow fading from access point b to user s, <mrow><msubsup><mi>A</mi><mrow><mi>s</mi><mo>,</mo><mi>d</mi><mi>B</mi><mi>i</mi></mrow><mi>b</mi></msubsup><mrow><mo>(</mo><msub><mi>&amp;alpha;</mi><mi>b</mi></msub><mo>,</mo><msub><mi>&amp;beta;</mi><mi>b</mi></msub><mo>)</mo></mrow><mo>=</mo><msub><mi>A</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>-</mo><mi>m</mi><mi>i</mi><mi>n</mi><mrow><mo>(</mo><mi>m</mi><mi>i</mi><mi>n</mi><mo>&amp;lsqb;</mo><mn>12</mn><msup><mrow><mo>(</mo><mfrac><mrow><msubsup><mi>&amp;phi;</mi><mi>s</mi><mi>b</mi></msubsup><mo>-</mo><msub><mi>&amp;alpha;</mi><mi>b</mi></msub></mrow><msub><mi>&amp;phi;</mi>mi><mrow><mn>3</mn><mi>d</mi><mi>B</mi></mrow></msub></mfrac><mo>)</mo></mrow><mn>2</mn></msup><mo>,</mo><msub><mi>SLL</mi><mrow><mi>a</mi><mi>z</mi></mrow></msub><mo>&amp;rsqb;</mo><mo>+</mo><mi>m</mi><mi>i</mi><mi>n</mi><mo>&amp;lsqb;</mo><mn>12</mn><msup><mrow><mo>(</mo><mfrac><mrow><msubsup><mi>&amp;theta;</mi><mi>s</mi><mi>b</mi></msubsup><mo>-</mo><msub><mi>&amp;beta;</mi><mi>b</mi></msub></mrow><msub><mi>&amp;theta;</mi><mrow><mn>3</mn><mi>d</mi><mi>B</mi></mrow></msub></mfrac><mo>)</mo></mrow><mn>2</mn></msup><mo>,</mo><msub><mi>SLL</mi><mrow><mi>e</mi><mi>l</mi></mrow></msub><mo>&amp;rsqb;</mo><mo>,</mo><msub><mi>SLL</mi><mrow><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub><mo>)</mo></mrow></mrow> is the antenna pattern; is the angle between the line connecting user s and access point b and the X axis, is the angle between the straight line connecting user s and access point b and the horizontal plane; SLL _az and SLL _el are the side lobe levels of the horizontal and vertical pattern respectively, and SLL _tot is the total side lobe level; E[A] means Take the expected value of A, A _max is the preset value, φ _3dB is the half-power lobe width in the vertical direction, and θ _3dB is the half-power lobe width in the horizontal direction. 4. A 3D beamforming device, characterized in that it is used as a macro base station in a heterogeneous network, and the device includes: The location information acquisition module acquires the location information of each user that needs to be served in the current time slot; Call the module to determine the function of the total throughput of each user relative to the beam angle vector αβ according to the obtained position information The beam angle vector αβ is (α ₁ , α ₂ ... α _S , β ₁ , β ₂ ... β _S ), where α _s is used to represent the straight line where the crest of the beam sent by the sth access point is located The angle between the projection on the horizontal plane and the preset horizontal axis X, β _s is used to represent the angle between the straight line where the peak of the beam sent by the sth access point is located and the horizontal plane, S is the number of users, R _s (αβ) is the data transmission rate corresponding to the sth user; calculation module, using an iterative algorithm to determine the The largest beam angle vector αβ _M ; An adjustment module, configured to adjust the beam angles of the beams sent by each access point according to the acquired beam angle vector αβ _M ; The calculation module is specifically used to perform the following steps: S1. Select k beam angle vectors, where k is a preset value, and the k beam angle vectors αβ include the corresponding beam angle vectors when the antennas of each access point point directly to the users they need to serve; S2, judging whether the k total throughputs corresponding to the k beam angle vectors αβ converge within the preset range, if not, turn to step S3, and if so, turn to step S4; S3, select other beam angle vectors αβ other than the k beam angle vectors αβ to replace the beam angle vector αβ that minimizes the total throughput among the k beam angle vectors αβ, and return to step S2; S4. Select the beam angle vector αβ that maximizes the total throughput among the k beam angle vectors αβ as the The largest beam angle vector αβ _M ; A fine-tuning module for performing the following steps: Change any angle value corresponding to the macro base station in the beam angle vector αβ _M within the range of plus or minus T° according to the preset step size, and change the beam angle vector αβ correspondingly every time the angle value is changed The values of other angles in _M make the total throughput of the macro base station and the low-power base station take the maximum value, and the T is a preset value; Judging whether the ratio of the maximum value of the total throughput to the total throughput corresponding to the beam angle vector αβ _M is greater than a preset value after changing any one of the angle values each time, and if it is judged to be yes, change the After any angle value, the beam angle vector that maximizes the total throughput is added to the candidate set; Select the beam angle vector αβ _m that maximizes the interference suppression SLNR between macro base stations from the set to be selected; The adjusting module is specifically configured to adjust beam angles of beams sent by each access point according to the beam angle vector αβ _m . 5. The apparatus of claim 4, wherein The calling module is specifically used to determine the function of the large-scale fading from each access point to each user with respect to the beam angle vector αβ according to the obtained position information, which takes As a function of the total throughput of each user with respect to the beam angle vector αβ, where, <mrow><mi>f</mi><mrow><mo>(</mo><mi>&amp;mu;</mi><mo>,</mo><mi>r</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>log</mi><mn>2</mn></msub><mrow><mo>(</mo><mi>e</mi><mo>)</mo></mrow><mo>&amp;lsqb;</mo><munderover><mi>&amp;Pi;</mi><mrow><mi>m</mi><mo>=</mo><mn>1</mn></mrow><mi>M</mi></munderover><mfrac><mn>1</mn><mrow><msup><msub><mi>&amp;mu;</mi><mi>m</mi></msub><msub><mi>r</mi><mi>m</mi></msub></msup></mrow></mfrac><mo>&amp;rsqb;</mo><munderover><mi>&amp;Sigma;</mi><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow><mi>M</mi></munderover><munderover><mi>&amp;Sigma;</mi><mrow><mi>l</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>r</mi><mi>t</mi></msub></munderover><msup><mrow><mo>(</mo><mo>-</mo><mn>1</mn><mo>)</mo></mrow><mrow><msub><mi>r</mi><mi>t</mi></msub><mo>-</mo><mi>l</mi></mrow></msup><msubsup><mi>&amp;mu;</mi><mi>t</mi><mrow><msub><mi>r</mi><mi>t</mi></msub><mo>-</mo><mi>l</mi><mo>+</mo><mn>1</mn></mrow></msubsup><msub><mi>&amp;Psi;</mi><mrow><mi>t</mi><mo>,</mo><mi>l</mi></mrow></msub><mrow><mo>(</mo><mi>&amp;mu;</mi><mo>,</mo><mi>r</mi><mo>)</mo></mrow><msup><mi>e</mi><mrow><mo>(</mo><mn>1</mn><mo>/</mo><msub><mi>&amp;mu;</mi><mi>t</mi></msub><mo>)</mo></mrow></msup><munderover><mi>&amp;Sigma;</mi><mrow><mi>k</mi><mo>=</mo><mn>0</mn></mrow><mrow><msub><mi>r</mi><mi>t</mi></msub><mo>-</mo><mi>l</mi></mrow></munderover><msub><mi>E</mi><mrow><mi>k</mi><mo>+</mo><mn>1</mn></mrow></msub><mrow><mo>(</mo><mfrac><mn>1</mn><msub><mi>&amp;mu;</mi><mi>t</mi></msub></mfrac><mo>)</mo></mrow><mo>,</mo></mrow> i=[i ₁ ,i ₂ ,…,i _M ] belongs to the set Ω _t,l , n is an integer greater than 0, and M is the length of vector i; Among them, ρ _s (α,β)=[ρ _s,1 (α ₁ ,β ₁ ),…,ρ _s,S (α _S ,β _S )], ρ _s,b (α _b ,β _b ) is The large-scale fading from the bth access point to the sth user is a function of the beam angle (α _b , β _b ) of the bth access point, _rs = (rs _,1 , rs _,2 , r _s,3 ... r _s,S ), where, when s=b, r _s,b =N; when s≠b, r _s,b =1, and N is the number of antennas of each access point number; is the vector obtained after removing the sth element for ρ _s (α,β), The vector obtained after removing the sth element for r _s . 6. The device according to claim 5, wherein the calling module calculates the function of the large-scale fading from each access point to each user with respect to the beam angle vector αβ, wherein PL _s,b is the path loss from access point b to user s, Ψ _s,b is the shadow fading from access point b to user s, <mrow><msubsup><mi>A</mi><mrow><mi>s</mi><mo>,</mo><mi>d</mi><mi>B</mi><mi>i</mi></mrow><mi>b</mi></msubsup><mrow><mo>(</mo><msub><mi>&amp;alpha;</mi><mi>b</mi></msub><mo>,</mo><msub><mi>&amp;beta;</mi><mi>b</mi></msub><mo>)</mo></mrow><mo>=</mo><msub><mi>A</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>-</mo><mi>m</mi><mi>i</mi><mi>n</mi><mrow><mo>(</mo><mi>m</mi><mi>i</mi><mi>n</mi><mo>&amp;lsqb;</mo><mn>12</mn><msup><mrow><mo>(</mo><mfrac><mrow><msubsup><mi>&amp;phi;</mi><mi>s</mi><mi>b</mi></msubsup><mo>-</mo><msub><mi>&amp;alpha;</mi><mi>b</mi></msub></mrow><msub><mi>&amp;phi;</mi>mi><mrow><mn>3</mn><mi>d</mi><mi>B</mi></mrow></msub></mfrac><mo>)</mo></mrow><mn>2</mn></msup><mo>,</mo><msub><mi>SLL</mi><mrow><mi>a</mi><mi>z</mi></mrow></msub><mo>&amp;rsqb;</mo><mo>+</mo><mi>m</mi><mi>i</mi><mi>n</mi><mo>&amp;lsqb;</mo><mn>12</mn><msup><mrow><mo>(</mo><mfrac><mrow><msubsup><mi>&amp;theta;</mi><mi>s</mi><mi>b</mi></msubsup><mo>-</mo><msub><mi>&amp;beta;</mi><mi>b</mi></msub></mrow><msub><mi>&amp;theta;</mi><mrow><mn>3</mn><mi>d</mi><mi>B</mi></mrow></msub></mfrac><mo>)</mo></mrow><mn>2</mn></msup><mo>,</mo><msub><mi>SLL</mi><mrow><mi>e</mi><mi>l</mi></mrow></msub><mo>&amp;rsqb;</mo><mo>,</mo><msub><mi>SLL</mi><mrow><mi>t</mi><mi>o</mi><mi>t</mi></mrow></msub><mo>)</mo></mrow></mrow> is the antenna pattern; is the angle between the line connecting user s and access point b and the X axis, is the angle between the straight line connecting user s and access point b and the horizontal plane; SLL _az and SLL _el are the side lobe levels of the horizontal and vertical pattern respectively, and SLL _tot is the total side lobe level; E[A] means Take the expected value of A, A _max is the preset value, φ _3dB is the half-power lobe width in the vertical direction, and θ _3dB is the half-power lobe width in the horizontal direction.