CN110391830A - A kind of robust multiple groups multicast Beamforming Method - Google Patents
A kind of robust multiple groups multicast Beamforming Method Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
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Abstract
The present invention relates to a kind of robust multiple groups multicast Beamforming Method, this method obtains two basic scenes in multiple groups multicast field first: the robust Wave beam forming problem relaxation for guaranteeing that the smallest SNR meets specified criteria and maximizes in user under the smallest SNR in user is that convex problem solves；Then convex row approach method is utilized, obtained solution for the non-convex problem in the next iteration that relaxes and is iteratively solved, the optimal solution of former problem is finally approached by constantly iterative step.Compared with prior art, the present invention has robust performance preferable, and complexity is relatively low, suitable for Practical Project the advantages that needs.
Description
Technical field
The present invention relates to multi-antenna technology fields, more particularly, to a kind of robust multiple groups multicast Beamforming Method.
Background technique
It is actual that multi-antenna technology is widely used in Long Term Evolution Advanced (LTE-A), 5G etc.
In communication system.Using the system of multi-antenna technology can be greatly improved under the premise of not increasing bandwidth traffic rate with communicate matter
Amount, thus largely studied by scholar.In some practical application scenes, the data received between different user are identical
, such as group chat, network direct broadcasting, video conference, the propagation under this scene are known as multicast transmission.In the field that multicast is propagated
Jing Zhong can waste a large amount of system resource, lead to system entire throughput if still using the unicast technique of traditional point-to-point type
Decline, communication quality can also reduce.Thus research multicast scene under high-efficiency transfer scheme become current research hotspot it
One.Multicast can be realized respectively in physical layer, link layer and network layer in a communications system.In the wireless transmission scheme of physical layer
The propagation of signal naturally has the characteristic of broadcast, and the multicasting technology based on physical layer takes full advantage of this characteristic, thus significantly
Save the running time-frequency resource of base station.The multicasting technology of physical layer is the important means for improving communication quality, lifting system performance.
It is existing classics robust multiple groups multicast Beamforming Method need to carry out a large amount of gaussian random step with it is subsequent
Solve scaling factor step and the high problem of bring computation complexity.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of robust multiple groups multicasts
Beamforming Method, this method robust performance is preferable, and complexity is relatively low, the needs suitable for Practical Project.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of robust multiple groups multicast Beamforming Method, this method obtain two basic fields in multiple groups multicast field first
Scape: guarantee that the smallest SNR meets specified criteria and maximizes the robust Wave beam forming problem in user under the smallest SNR in user
Relaxation is that convex problem is solved；
Then convex row approach method is utilized, by obtained solution for the non-convex problem and iteration in the next iteration that relaxes
It solves, the optimal solution of former problem is finally approached by constantly iterative step.
Preferably, this method specifically includes the following steps:
Step (1) considers additivity under the smallest SNR meets the robust Wave beam forming scene of specified criteria in guaranteeing user
Channel errors can find out the SINR of user, and be amplified to maximum to minimum and denominator by molecule scaling, obtain being influenced by error
SINR lower bound afterwards is relaxed the robust Wave beam forming problem under the scene to obtain new problem mould using SINR lower bound
Type；In maximizing user under the robust Wave beam forming scene of the smallest SNR, the modulus value using the plural number on complex plane is centainly big
In this property of the size of its real part, which is relaxed to obtain new problem model.
Step (2) is directed to the new problem model obtained under the robust Wave beam forming scene of two kinds of multiple groups multicasts by relaxation,
It recycles convex row approach method to be solved, obtains required Wave beam forming weight vector.
Preferably, the step (1) specifically:
Consideration is equipped with NtUser in the base station of root antenna and G grouping communicates, and each ustomer premises access equipment only has
Single antenna, i.e. communication system are MISO system；If considering additive channel error, the real channel of user i isWhereinFor the channel information of base station end estimation, ei∈CNFor corresponding error, and haveThen
Positioned at k-th of grouping GkI-th of user, then its SINR is represented by
Wherein wkExcipient weight coefficient, the h being grouped for k-thiFor the channel of user i,For the channel letter of base station end estimation
Breath, eiFor corresponding error, σiThe norm squared of error is corresponded to for i-th of user base station end channel information, l is first of grouping,
[]HIndicate conjugate transposition,IMFor unit battle array；
By molecule scaling to minimum, denominator scaling to maximum can must be by the lower bound that error is influenced rear SINR
Wherein wkThe excipient weight coefficient being grouped for k-th, wlThe excipient weight coefficient σ being grouped for firstεFor base station end channel
Information corresponds to the norm squared of error；
Centainly it is greater than this property of the size of its real part in conjunction with the modulus value of the plural number on SINR lower bound and complex plane, obtains
The smallest SNR meets specified criteria and maximizes the Shandong in user under two scenes of the smallest SNR in guarantee user after relaxation
Stick Wave beam forming problem is respectively as follows:
Wherein, PnFor the maximum power binding occurrence of n-th of antenna, r be Optimal Parameters variable,For given plural number power
Coefficient, γiSINR index, G for i-th of userkFor k-th of user grouping, NtFor antenna number；
Wherein t ∈ R is Optimal Parameters variable, R is real domain, Re [] is to take real part.
Preferably, the step (2) meets the robust Wave beam forming of specified criteria for the smallest SNR in guarantee user
Problem, the process using convex row approach method are as follows:
1) initial feasible solution generated at random is givenGiven condition of convergence Δ, the number of iterations i=0；
2) Solve problems obtain optimal solutionAnd optimal value εi+1；
3) it enablesThe number of iterations i=i+1；
4) it repeats 2) and 3) until εi+1-εi< Δ.
Preferably, the step (2) is for the robust for maximizing the robust Wave beam forming scene of the smallest SNR in user
Wave beam forming problem, the process using convex row approach method are as follows:
1) initial feasible solution being randomly generated is givenGiven iteration convergence condition Δ1With two convergence items step by step
Part Δ2, the number of iterations i=0, setting L=tmin, U=tmax；
2) fixedSolve problems model；
If 3) enabled the problem of step 2 without solutionIf there is solution in the problem of step 2, enableIt repeats
Step 2 is until U-L≤Δ2, obtain optimal solutionAnd optimal value εi+1, enableThe number of iterations i=i+1 is enabled, weight
Set L=tmin, U=tmax；
4) step 2 is repeated with 3 until εi+1-εi< Δ1。
Preferably, the optimal value of each iteration can constantly reduce, thus can gradually restrain.
Compared with prior art, the present invention using convex row approximate algorithm without carry out a large amount of gaussian random step with
And scaling factor step is solved, thus it has the advantages that calculating is very fast, with classical robust multiple groups multicast beamforming algorithm phase
Actual requirement of engineering is more suitable for than it.
Detailed description of the invention
Fig. 1 is influence curve figure of the error to transmission power needed for base station；
Fig. 2 is to be grouped interior user's number to the influence curve figure of transmission power needed for base station；
Fig. 3 is influence curve figure of the SINR to transmission power needed for base station；
Fig. 4 is contrast curve chart of the simulation time with user's variation in being grouped；
Fig. 5 is the contrast curve chart that simulation time changes with number of antennas；
Fig. 6 is influence curve figure of the channel errors size to user's traffic rate；
Fig. 7 is to be grouped interior number of users to the influence curve figure of user's traffic rate；
Fig. 8 is influence curve figure of the antenna power to user's traffic rate；
Fig. 9 is contrast curve chart of the simulation time with number of users variation in being grouped；
Figure 10 is the contrast curve chart that simulation time changes with number of antennas.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiment is a part of the embodiments of the present invention, rather than whole embodiments.Based on this hair
Embodiment in bright, those of ordinary skill in the art's every other reality obtained without making creative work
Example is applied, all should belong to the scope of protection of the invention.
It is following to illustrate to meet the specific implementation under specified criteria problem for the smallest SNR in guarantee user:
Consider that base station end is equipped with 8 antennas, there are two groupings in communication scenes, in each grouping there are four user.Letter
Road meet mean value be 0 variance be 1 independent Gaussian distribution, the received white Gaussian noise of base station end be 0dB, set SINR thresholding as
1.Fig. 1 show channel errors sizeChange influence to base station power, in Fig. 1 it can be seen from order to contain error
In the case where maintain preset SINR to limit, the power of base station end has certain raising, and error is bigger, and power increases
It is bigger.But it compared to RB-QoS-SDP algorithm, either 20 gaussian randomizations or 100 gaussian randoms, mentions herein
Power corresponding to algorithm is lower out, it means that only needs lower power, mentioned algorithm can remain preset herein
Threshold value.
Consider that base station end is equipped with 8 antennas, there are two groupings in communication scenes.It is 0 variance is 1 that channel, which meets mean value,
Independent Gaussian distribution, the received white Gaussian noise of base station end are 0dBw, set SINR thresholding as 1, the size of error norm square
ForFig. 2 show the influence for being grouped interior user's number variation to base station power.As seen from Figure 2, user in being grouped
When number increases, base station need to increase transmission power to meet the communication requirement of more users, but function needed for mentioned algorithm herein
Rate is lower than 100 gaussian randoms or the RB-QoS-SDP algorithm of 20 gaussian randoms.And it is noted that in simulation process
If user's number is excessive (6 or more) in being grouped in, and too serious due to interfering, probability of the problem without solution can substantially increase.
Consider that base station end is equipped with 8 antennas, there are two groupings in communication scenes, in each grouping there are four user.Letter
It is the independent Gaussian distribution that 0 variance is 1 that road, which meets mean value, and the received white Gaussian noise of base station end is 0dB, error norm square
Size isFig. 3 show the different SINR of setting, the variation of power needed for base station end.It as can be seen from Figure, is full
The higher SINR requirement of foot, base station need to increase transmission power, but power needed for mentioned algorithm is lower than 20 gaussian randoms herein
The RB-QoS-SDP algorithm of change and similar with the effect of 100 gaussian randoms.
Since this chapter algorithm proposed only needs iterative step, without carrying out a large amount of gaussian random step, thus RB-
QoS-SCA algorithm has the lower advantage of complexity.Fig. 4 and Fig. 5 show RB-QoS-SDP and the mentioned algorithm of this paper in difference
Grouping in comparison under user's number with simulation time under different antennae number.There are two groupings in the communication scenes,
The power of white Gaussian noise is set as 0dB, it is the independent Gaussian distribution that 0 variance is 1 that channel, which meets mean value, and the channel of base station end misses
Poor size isSINR threshold settings are 1.In the simulating scenes of Fig. 4, base station end is equipped with 8 antennas, by can in figure
To find out, mentioned algorithm is similar to the RB-QoS-SDP algorithm simulating time that 20 times are randomized herein and is far below 100 Gausses
The RB-QoS-SDP algorithm of randomization.In the simulating scenes of Fig. 5 in each grouping contain 4 users, You Tuzhong it can also be seen that
The simulation time of mentioned algorithm is far below the RB-QoS-SDP algorithm of 100 gaussian randoms herein, with 20 gaussian randoms
Simulation time it is similar.Thus mentioned algorithm not only has the advantages that outstanding performance while also having complexity low herein, more
Suitable for actual communication system.
It is following to illustrate to maximize the specific implementation in user under the robust Wave beam forming problem of the smallest SNR:
The condition that algorithm stops iteration is εi+1-εi< 0.001, two conditions stopped step by step are U-L < 0.001.
Consider that base station end is equipped with 4 antennas, there are two groupings in communication scenes, in each grouping there are two user.Letter
It is the independent Gaussian distribution that 0 variance is 1 that road, which meets mean value, and the received white Gaussian noise of base station end is 0dBw, every antenna institute energy
The maximum power of offer is 0dBw.Fig. 6 show influence of the channel errors size variation to minimum user's traffic rate.By in figure
As can be seen that with the increase of error, due to power be it is given, the traffic rate of minimum user has certain reduction, and accidentally
Difference is bigger, and the reduction of traffic rate is more obvious, but the mentioned algorithm of this paper for considering channel errors is compared to 20 times and 100 height
This randomization RB-MMF-SDP algorithm, the traffic rate of user are higher.
Consider that base station end is equipped with 4 antennas, there are two groupings in communication scenes, there are several users in each grouping.
If it is the independent Gaussian distribution that 0 variance is 1 that channel, which meets mean value, the received white Gaussian noise of base station end is 0dBw, every antenna
The maximum power that can be provided is 0dBw, and base station end channel existsError.Fig. 7 show user's number in grouping
Change the influence to minimum user's traffic rate, as can be seen from Figure, when being grouped interior user's number increase, base station is identical
Minimum user's traffic rate is able to satisfy under transmission power constantly to be reduced, but the minimum user communication that mentioned algorithm can provide herein
Rate is higher than the RB-MMF-SDP algorithm of 20 times and 100 times gaussian random steps, this illustrates the performance phase of proposed algorithm herein
It is more excellent than in RB-MMF-SDP algorithm.
Consider that base station end is equipped with 4 antennas, there are two groupings in communication scenes, in each grouping there are two user.If
It is the independent Gaussian distribution that 0 variance is 1 that channel, which meets mean value, and the received white Gaussian noise of base station end is 0dBw, base station end channel
In the presence ofError.Fig. 8 show influence of the individual antenna power limit variation to minimum user's traffic rate, by scheming
In as can be seen that the traffic rate of minimum user can constantly increase when the power that can be provided of antenna increases, but propose calculation herein
Minimum user's traffic rate that method can be provided is higher than the RB-MMF-SDP algorithm of 20 times and 100 times gaussian random steps, i.e.,
Under different antenna power limitations, the effect of mentioned algorithm is more excellent herein.
Mentioned algorithm is not due to carrying out a large amount of gaussian random step herein, thus its algorithm complexity is lower.Fig. 9
The simulation time of the mentioned algorithm method of this paper and RB-MMF-SDP algorithm is shown with number of users or antenna number in being grouped with Figure 10
The comparison of mesh variation.There are 2 users in simulating scenes in each grouping, it is the independent Gaussian point that 0 variance is 1 that channel, which meets mean value,
Cloth, the received white Gaussian noise of base station end are 0dBw, and the maximum power that every antenna can be provided is 0dBw, and base station end channel is deposited
InError.Gaussian random number is respectively 20 times and 100 times in RB-MMF-SDP, and mentioned algorithm stops herein
The condition of iteration.As seen from Figure 9, with the increase of number of users in being grouped, the calculating time of two kinds of algorithms can constantly increase
Add, but mentioned algorithm calculates RB-MMF-SDP algorithm of the time lower than 100 gaussian randoms herein, with 20 gaussian randoms
Time it is similar.By Figure 10 it is also seen that number of antennas increase will lead to the increase of optimized variable scale, thus algorithms of different
Calculating the time can increased, but mentioned algorithm calculates RB-MMF-SDP calculation of the time lower than 100 gaussian randoms herein
Method, it is similar with the simulation time of 20 gaussian random algorithms.Lower calculating is being spent by emulating visible the mentioned algorithm of this paper
In the case where time, effect is better than RB-MMF-SDP algorithm.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (6)
1. a kind of robust multiple groups multicast Beamforming Method, which is characterized in that this method obtains the two of multiple groups multicast field first
A basic scene: guarantee that the smallest SNR meets specified criteria and maximizes the robust wave beam in user under the smallest SNR in user
The relaxation of formation problem is that convex problem is solved；
Then convex row approach method is utilized, by obtained solution is for the non-convex problem in the next iteration that relaxes and iteration is asked
Solution, the optimal solution of former problem is finally approached by constantly iterative step.
2. a kind of robust multiple groups multicast Beamforming Method according to claim 1, which is characterized in that this method is specifically wrapped
Include following steps:
Step (1) considers additive channel under the smallest SNR meets the robust Wave beam forming scene of specified criteria in guaranteeing user
Error can find out the SINR of user, and be amplified to maximum to minimum and denominator by molecule scaling, obtain after being influenced by error
SINR lower bound is relaxed the robust Wave beam forming problem under the scene to obtain new problem model using SINR lower bound；In
It maximizes in user under the robust Wave beam forming scene of the smallest SNR, the modulus value using the plural number on complex plane is centainly greater than it
This property of the size of real part is relaxed to obtain new problem model to the robust Wave beam forming problem.
Step (2) is for the new problem model obtained under the robust Wave beam forming scene of two kinds of multiple groups multicasts by relaxation, then benefit
It is solved with convex row approach method, obtains required Wave beam forming weight vector.
3. a kind of robust multiple groups multicast Beamforming Method according to claim 2, which is characterized in that the step
(1) specifically:
Consideration is equipped with NtUser in the base station of root antenna and G grouping communicates, and each ustomer premises access equipment only has single day
Line, i.e. communication system are MISO system；If considering additive channel error, the real channel of user i isWhereinFor the channel information of base station end estimation, ei∈CNFor corresponding error, and haveThen it is located at k-th of grouping
GkI-th of user, then its SINR is represented by
Wherein wkExcipient weight coefficient, the h being grouped for k-thiFor the channel of user i,For the channel information of base station end estimation, ei
For corresponding error, σiThe norm squared of error is corresponded to for i-th of user base station end channel information, l is first of grouping, []HTable
Show conjugate transposition,IMFor unit battle array；
By molecule scaling to minimum, denominator scaling to maximum can must be by the lower bound that error is influenced rear SINR
Wherein wkThe excipient weight coefficient being grouped for k-th, wlThe excipient weight coefficient being grouped for first, σεFor base station end channel information
The norm squared of corresponding error；
Centainly it is greater than this property of the size of its real part in conjunction with the modulus value of the plural number on SINR lower bound and complex plane, is relaxed
The smallest SNR meets specified criteria and maximizes the robust wave in user under two scenes of the smallest SNR in guarantee user afterwards
Beam forms problem and is respectively as follows:
Wherein, PnFor the maximum power binding occurrence of n-th of antenna, r be Optimal Parameters variable,For given plural weight coefficient,
γiSINR index, G for i-th of userkFor k-th of user grouping, NtFor antenna number；
t≥0
Wherein t ∈ R is Optimal Parameters variable, R is real domain, Re [] is to take real part.
4. a kind of robust multiple groups multicast Beamforming Method according to claim 2, which is characterized in that the step
(2) the robust Wave beam forming problem for meeting specified criteria for the smallest SNR in guarantee user, utilizes convex row approach method
Process is as follows:
1) initial feasible solution generated at random is givenGiven condition of convergence Δ, the number of iterations i=0；
2) Solve problems obtain optimal solutionAnd optimal value εi+1；
3) it enablesThe number of iterations i=i+1；
4) it repeats 2) and 3) until εi+1-εi< Δ.
5. a kind of robust multiple groups multicast Beamforming Method according to claim 2, which is characterized in that the step
(2) it for the robust Wave beam forming problem for maximizing the robust Wave beam forming scene of the smallest SNR in user, is forced using convex row
The process of nearly method is as follows:
1) initial feasible solution being randomly generated is givenGiven iteration convergence condition Δ1With two conditions of convergence step by step
Δ2, the number of iterations i=0, setting L=tmin, U=tmax；
2) fixedSolve problems model；
If 3) enabled the problem of step 2 without solutionIf there is solution in the problem of step 2, enableRepeat step
2 until U-L≤Δ2, obtain optimal solutionAnd optimal value εi+1, enableThe number of iterations i=i+1 is enabled, L is reset
=tmin, U=tmax；
4) step 2 is repeated with 3 until εi+1-εi< Δ1。
6. a kind of robust multiple groups multicast Beamforming Method according to claim 4 or 5, which is characterized in that each iteration
Optimal value can constantly reduce, thus can gradually restrain.
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