CN109194373B - Large-scale MIMO beam domain combined unicast and multicast transmission method - Google Patents

Large-scale MIMO beam domain combined unicast and multicast transmission method Download PDF

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CN109194373B
CN109194373B CN201810933114.XA CN201810933114A CN109194373B CN 109194373 B CN109194373 B CN 109194373B CN 201810933114 A CN201810933114 A CN 201810933114A CN 109194373 B CN109194373 B CN 109194373B
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王闻今
熊佳媛
尤力
陈旭
李科新
高西奇
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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
    • H04B7/0413—MIMO systems
    • 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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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0058—Allocation criteria
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
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Abstract

The invention provides a large-scale MIMO beam domain combined unicast and multicast transmission method, wherein a large-scale antenna array is configured at a base station side of wireless communication, and a large-scale beam set covering the whole cell is generated through beam forming. And the base station communicates with the users in the cell by adopting a mode of combining beam domain unicast and multicast on the same time-frequency resource. And the base station counts the channel state information according to the beam domain of each user in the cell, and performs power distribution on the multicast signals of the beam domain and the unicast signals sent to each user. The beam domain power allocation is based on an MM iterative algorithm and a deterministic equivalence method, a beam domain power allocation matrix is obtained by iteratively solving a convex optimization problem, and dynamic updating is carried out along with the change of statistical channel state information. The invention solves the power distribution optimization problem of the beam domain combined unicast and multicast transmission of which the base station side only knows the statistical channel information, improves the unicast and multicast transmission rate of the system and effectively reduces the complexity of realization.

Description

Large-scale MIMO beam domain combined unicast and multicast transmission method
Technical Field
The invention belongs to the field of communication, and particularly relates to a beam domain wireless transmission method for performing combined unicast and multicast by using a large-scale antenna array under the same time-frequency resource.
Background
In a massive MIMO system, a base station arranges a massive antenna array to simultaneously serve multiple users. By adopting the large-scale MIMO technology, the interference among users can be effectively reduced, and the frequency spectrum utilization rate and the power efficiency of the wireless communication system are greatly improved. The beam domain transmission refers to that a base station side converts a transmission signal into a beam domain through unified unitary transformation, signal transmission is carried out on a beam domain channel, and the spatial angle resolution of a large-scale antenna array and the locality characteristic of a user channel in the beam domain are fully utilized.
Under the scene of combined unicast and multicast, a base station simultaneously sends multicast signals for all users in a cell and unicast signals for single users on the same time-frequency resource. In this scenario, it is often necessary to construct and solve a problem about transmission signal power allocation so that the weighted average of the unicast rate and the multicast rate of the entire system is maximized, and the optimization objective function of such a problem is often non-convex and it is often difficult to obtain a global optimal solution. Meanwhile, in the optimization process, the calculation of the unicast and multicast rates needs to be carried out, and the realization complexity is high. Therefore, the invention provides a low-complexity large-scale MIMO beam domain combined unicast and multicast transmission method utilizing statistical channel state information.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a transmission method for performing combined unicast and multicast by using statistical channel state information under the scene that a cell base station simultaneously transmits a unicast signal and a multicast signal.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a large-scale MIMO beam domain combined unicast and multicast transmission method comprises the following steps:
(1) under the scene that a base station and a user group carry out combined unicast and multicast communication, the base station configured with the large-scale antenna array generates a beam set capable of covering the whole cell by a method of simulating multi-beam forming or digital multi-beam forming or simulating and digital mixed beam forming;
(2) the base station utilizes the beam domain statistical channel state information of users in the cell to construct and solve the optimization problem of beam domain combined unicast and multicast power distribution to distribute power to the transmitted signals; the optimization target of the beam domain united unicast and multicast power distribution optimization problem is to maximize the weighted average of the unicast rate and the multicast rate of the system, and the optimization variable is a covariance matrix of a multicast signal sent by a base station and a unicast signal to each user; the constraint condition is that the covariance matrix of the total signal sent by the base station meets the power constraint;
(3) in the moving process of each user, along with the change of the statistical channel state information between the base station and each user, the base station side dynamically implements beam domain power allocation and combines with the dynamic update of the unicast and multicast process.
In the step (1), the base station generates a large-scale wave beam set capable of covering the whole cell to realize the wave beam domain division of the space resources, the base station performs combined unicast and multicast communication on the same time-frequency resource by the users in the cell, and the process of the combined unicast and multicast communication is implemented on the wave beam domain.
And (3) the base station performs power distribution on the transmission signal by using the beam domain statistical channel state information of the users in the cell in the step (2). And the base station estimates beam domain statistical channel state information required for implementing beam domain power allocation according to a detection signal sent by a cell user in an uplink channel detection stage. The specific allocation method is based on the MM (Minorize-validation) iterative algorithm and a deterministic equivalence method.
The power allocation method based on the MM iterative algorithm and the deterministic equivalence method comprises the following steps:
(a) and performing first-order Taylor expansion approximation on interference rate items in the weighted average expression of the unicast rate and the multicast rate in the current iteration process, converting a non-convex problem into a convex optimization problem about beam domain power distribution, and then solving by using an interior point method or other optimization methods.
And substituting the solution of the optimization problem in the current iteration process into the optimization target to generate the optimization problem of the next iteration, and solving again. The steps are repeated until the difference value of the weighted average of the unicast rate and the multicast rate of the system in the two adjacent iterative processes is smaller than a given threshold value, and the solution of the last iterative process is the solution of the optimization problem.
(b) According to the large-dimension random matrix theory, the determinacy equivalent expressions of the unicast total rate item and the multicast total rate item containing the interference rate in the weighted average expression of the unicast rate and the multicast rate of the system are respectively calculated, and the expectation calculation with high complexity is avoided.
In the step 3), as each user moves dynamically, the beam domain statistical channel state information between the base station and each user changes, and the base station re-implements the beam domain power allocation according to the changed statistical channel state information, thereby implementing dynamic update of the joint unicast and multicast process. The change of the beam domain statistical channel state information is related to a specific application scenario, a typical statistical time window is several times or tens of times of a short-time transmission time window, and the acquisition of the related statistical channel state information is also performed over a larger time width.
Has the advantages that: compared with the prior art, the invention has the following advantages:
1. the base station and the cell users implement the combined unicast and multicast communication under the same time-frequency resource of the wave beam domain, and can be matched with the space characteristics of wireless channels of the base station and the cell users, so that the improvement of power efficiency and spectral efficiency brought by using a large-scale antenna array is obtained.
2. The method designs the sending signal by using the beam domain statistical channel state information of the cell user, the required beam domain statistical channel state information of each user can be obtained by sparse detection signals, and the provided combined unicast and multicast transmission method is simultaneously suitable for a time division duplex system and a frequency division duplex system.
3. By using an MM iterative algorithm and a deterministic equivalent method, the implementation complexity of the combined unicast and multicast communication is remarkably reduced, and the method can obtain approximately optimal performance.
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Fig. 1 is a flowchart of a large-scale MIMO beam domain combined unicast and multicast wireless transmission method using statistical channel state information.
Fig. 2 is a diagram of a massive MIMO combined unicast and multicast system.
Fig. 3 is a flow chart of a power allocation method based on MM iterative algorithm and deterministic equivalence method.
Detailed Description
In order to make the technical field of the invention better understand, the following description is combined with the accompanying drawings in the embodiment of the invention.
As shown in fig. 1, a large-scale MIMO beam domain combined unicast and multicast transmission method using statistical channel state information disclosed in the embodiments of the present invention mainly includes the following steps:
1) the base station configures a large-scale antenna array, and generates a large-scale beam set capable of covering the whole cell by a beam forming method. In this step, the base station generates a large-scale beam set capable of covering the whole cell by using an analog multi-beam forming method or a digital multi-beam forming method, thereby realizing beam domain division of space resources. The base station carries out combined unicast and multicast communication with the cell users on the same time-frequency resource, and the process of the combined unicast and multicast communication is implemented on a wave beam domain.
2) And the base station utilizes the beam domain statistical channel state information of the users in the cell to construct and solve the optimization problem of beam domain combined unicast and multicast power distribution to carry out power distribution on the transmitted signals.
3) In the dynamic moving process of each user, along with the change of the beam domain statistical channel state information between the base station and the cell user, the base station side dynamically implements beam domain power allocation and combines with the dynamic updating of the unicast and multicast process.
The method according to the embodiment of the present invention is described in detail below by taking the large-scale MIMO-unicast-multicast scenario shown in fig. 2 as an example. Considering a single-cell scenario, the base station configures M (M is 10)2Or 103Order of magnitude) transmit antennas spaced one-half wavelength apart.
Figure BDA0001767135390000041
And configuring N receiving antennas for each user for the cell user set. The base station can transform the transmitted space domain signals to the beam domain by adopting an analog multi-beam forming method or a digital multi-beam forming method or an analog and digital mixed beam forming method.
In the channel detection stage, the cell users send uplink detection signals, and the base station estimates the beam domain statistical channel state information of the cell users according to the received detection signals, namely
Figure BDA0001767135390000042
Wherein HkFor the beam domain channel matrix of the kth user, operator ⊙ is the Hadamard product of the matrix, the conjugate of the matrix,
Figure BDA00017671353900000415
representing the desired operation.
Suppose that the beam domain joint unicast and multicast signals sent by the base station are
Figure BDA0001767135390000044
Wherein xmIn order to multicast the signals, the network element,
Figure BDA0001767135390000045
is a unicast signal from the base station to user k. The covariance matrix of the beam-domain multicast signal is
Figure BDA0001767135390000046
The covariance matrix of the unicast signal sent to user k is
Figure BDA0001767135390000047
The multicast rate can be expressed as:
Figure BDA0001767135390000048
Figure BDA0001767135390000049
Figure BDA00017671353900000410
is the interference rate term in the multicast procedure,
Figure BDA00017671353900000411
also, the unicast rate may be expressed as
Figure BDA00017671353900000412
Figure BDA00017671353900000413
Is the interference rate term of the base station to user k in the unicast process,
Figure BDA00017671353900000414
in the above expression, log represents a logarithmic operation, det represents a determinant of a matrix, and H is a conjugate transpose of the matrix.
Weighted average of system unicast and multicast rates
Figure BDA0001767135390000051
η ∈ [0,1] is the weight factor for multicast.
In view of the low correlation on the base station side of the beam domain channel, the base station transmits mutually independent data streams on the individual beams, i.e. the multicast signal covariance matrix ΛmAnd unicast signal covariance matrix
Figure BDA0001767135390000052
Are all diagonal matrices. It is noted that in the beam-domain joint unicast and multicast transmission process, in order to obtain higher system and rate, the covariance matrix of the transmitted signals is needed
Figure BDA0001767135390000053
Optimizing, namely performing power distribution on the transmitting beam at the base station side, namely solving the following optimization problem:
Figure BDA0001767135390000054
wherein, P is the total power constraint of the base station, tr (-) represents the trace of the calculation matrix, and ≧ represents the matrix non-negative definite.
The objective function of the problem is non-convex, the global optimal solution is difficult to obtain, and the realization complexity is high. Therefore, the embodiment of the invention adopts an MM iterative algorithm and a deterministic equivalence method to solve the beam domain multicast power distribution optimization problem.
The power allocation method based on the MM iterative algorithm and the deterministic equivalence method comprises the following steps:
(a) and performing first-order Taylor expansion approximation on an interference rate item in the multicast rate item and an interference rate item in the unicast rate item in the current iteration process, and converting the non-convex problem into a convex optimization problem about beam domain power distribution as follows:
Figure BDA0001767135390000061
wherein the content of the first and second substances,
Figure BDA0001767135390000062
are respectively as
Figure BDA0001767135390000063
And
Figure BDA0001767135390000064
the elements on the diagonal of which can be represented as
Figure BDA0001767135390000065
Figure BDA0001767135390000066
Then, the optimization problem in the formula (6) is solved by using an interior point method or other optimization methods.
And substituting the solution of the optimization problem in the current iteration process into the optimization target to generate the optimization problem of the next iteration, and solving again. The steps are repeated until the difference value of the weighted average of the unicast rate and the multicast rate of the system in the two adjacent iterative processes is smaller than a given threshold value, and the solution of the last iterative process is the solution of the optimization problem.
(b) In order to reduce the operation complexity, according to the large-dimension random matrix theory, weighted average terms of the unicast rate and the multicast rate of the system are respectively calculated
Figure BDA0001767135390000067
And
Figure BDA0001767135390000068
deterministic equivalent expression
Figure BDA0001767135390000069
And
Figure BDA00017671353900000610
Figure BDA00017671353900000611
Figure BDA00017671353900000612
wherein the content of the first and second substances,
Figure BDA00017671353900000613
Figure BDA00017671353900000614
Figure BDA00017671353900000615
Figure BDA0001767135390000071
Figure BDA0001767135390000072
four auxiliary variables are obtained by iterative calculation:
Figure BDA0001767135390000073
Figure BDA0001767135390000074
Figure BDA0001767135390000075
Figure BDA0001767135390000076
wherein the content of the first and second substances,
Figure BDA00017671353900000724
and
Figure BDA00017671353900000725
the representation generates a diagonal matrix with elements on the diagonal being
Figure BDA0001767135390000079
Figure BDA00017671353900000710
Thereby obtaining a deterministic equivalent expression of a weighted average of the unicast rate and the multicast rate of the system
Figure BDA00017671353900000711
Fig. 3 shows the implementation process of the MM iterative algorithm and the power allocation method of the deterministic equivalence method, and the detailed process of the algorithm is as follows:
step 1: initializing covariance matrix of transmitted signal
Figure BDA00017671353900000712
The iteration number indication i is set to 0. Covariance matrix of signal to be transmitted at initialization
Figure BDA00017671353900000713
When, a uniform power distribution can be assumed, i.e.The K +1 covariance matrices are all
Figure BDA00017671353900000714
Where I is an M identity matrix.
Step 2: initial value for computing system with equal certainty of weighted average of unicast rate and multicast rate
Figure BDA00017671353900000715
The method comprises iteratively calculating deterministic equivalent auxiliary variables according to covariance matrix of initialized transmitted signals
Figure BDA00017671353900000716
Calculating by using the auxiliary variable until the auxiliary variable converges
Figure BDA00017671353900000717
And
Figure BDA00017671353900000718
the certainty is equivalent, and calculation is performed by substituting into equation (21).
And step 3: by using
Figure BDA00017671353900000719
Calculating the current iteration gradient term
Figure BDA00017671353900000720
And
Figure BDA00017671353900000721
linearization of multicast interference rate terms using MM iterative algorithm
Figure BDA00017671353900000722
And unicast interference rate term
Figure BDA00017671353900000723
And obtaining the optimization problem in the iteration process as shown in the formula (6).
And 4, step 4: by using
Figure BDA0001767135390000081
Iterative computation of deterministic equivalent auxiliary variables
Figure BDA0001767135390000082
Figure BDA0001767135390000083
Until the auxiliary variable converges.
And 5: obtaining the weighted average term of the unicast rate and the multicast rate of the system according to the formulas (9) and (10)
Figure BDA0001767135390000084
And
Figure BDA0001767135390000085
deterministic equivalent expression
Figure BDA0001767135390000086
And
Figure BDA0001767135390000087
and replacing in optimization objective with deterministic equivalent expressions
Figure BDA0001767135390000088
And
Figure BDA0001767135390000089
step 6: solving the optimization problem by using an interior point method or other convex optimization methods to obtain the solution of the current iteration
Figure BDA00017671353900000810
And 7: using the solution to the current iterative optimization problem, the deterministic equivalence of the weighted average of the system unicast and multicast rates is calculated using equation (21)
Figure BDA00017671353900000811
And 8: comparison
Figure BDA00017671353900000812
And
Figure BDA00017671353900000813
if the difference between the two is less than the preset threshold epsilon, the iteration is ended, at this moment
Figure BDA00017671353900000814
I.e. the solution to the optimization problem. Otherwise, the step 2 is returned to by i + 1.
In the moving process of each user, along with the change of the beam domain statistical channel state information between the base station and the user, the base station side repeats the steps according to the updated statistical channel state information to carry out beam domain combined unicast and multicast power distribution. Thereby realizing the dynamic update of the joint unicast and multicast transmission process. The change of the beam domain statistical channel state information is related to a specific application scenario, a typical statistical time window is several times or tens of times of a short-time transmission time window, and the acquisition of the related statistical channel state information is also performed on a larger time width.
It should be noted that the above mentioned embodiments are only specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and all such changes or substitutions should be covered by the scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.

Claims (5)

1. A large-scale MIMO beam domain combined unicast and multicast transmission method is characterized in that: the method comprises the following steps:
(1) under the scene that a base station and a user group carry out combined unicast and multicast communication, the base station configured with the large-scale antenna array generates a beam set capable of covering the whole cell by a method of simulating multi-beam forming or digital multi-beam forming or simulating and digital mixed beam forming;
(2) a base station utilizes beam domain statistics channel state information of users in a cell to construct and solve a beam domain combined unicast and multicast power distribution optimization problem based on an MM iterative algorithm and a deterministic equivalence method to perform power distribution on a transmission signal; the beam domain joint unicast and multicast power allocation optimization problem is expressed as:
Figure FDA0002400058310000011
Figure FDA0002400058310000012
Figure FDA0002400058310000013
wherein η ∈ [0,1]]Is a weight factor for the multicast and is,
Figure FDA0002400058310000014
is the user set in the cell, K is the total number of users in the cell,
Figure FDA0002400058310000015
for the multicast rate of the base station,
Figure FDA0002400058310000016
for unicast rate from base station to user k, ΛmA covariance matrix of the beam-domain multicast signals is transmitted for the base station,
Figure FDA0002400058310000017
a covariance matrix of a beam domain unicast signal from a base station to a user k, P is total power constraint of the base station, tr (-) represents a trace of a calculation matrix, and the matrix is not negative definite if not less than 0; among the above optimization objectives:
Figure FDA0002400058310000018
Figure FDA0002400058310000019
wherein the content of the first and second substances,
Figure FDA00024000583100000110
Hkis the wave beam domain channel matrix from the base station to the user k, I is an identity matrix, superscript H is the conjugate transpose of the solution matrix, det is the determinant of the solution matrix,
Figure FDA00024000583100000111
to meet the expectations;
(3) in the moving process of each user, along with the change of the statistical channel state information between the base station and each user, the base station side dynamically implements beam domain power allocation and combines with the dynamic update of the unicast and multicast process.
2. The massive MIMO beam-domain joint unicast multicast transmission method of claim 1, characterized in that: in the step (1), the base station generates a large-scale wave beam set capable of covering the whole cell to realize the wave beam domain division of the space resources, the base station performs combined unicast and multicast communication with users in the cell on the same time-frequency resource, and the process of the combined unicast and multicast communication is implemented on the wave beam domain.
3. The massive MIMO beam-domain joint unicast multicast transmission method of claim 1, characterized in that: and the beam domain statistical channel state information is estimated and obtained by the base station according to the received uplink detection signal sent by the user in the cell.
4. The massive MIMO beam-domain joint unicast multicast transmission method of claim 1, characterized in that: the MM iterative algorithm and the deterministic equivalence method used for solving the optimization problem in the step (2) comprise the following two aspects:
(a) in the current iteration process, performing first-order Taylor series expansion approximation on interference rate terms in weighted average expressions of the unicast rate and the multicast rate of the system, converting a non-convex problem into a convex optimization problem about beam domain power distribution, and converting the optimization problem into a solution of the following problems:
Figure FDA0002400058310000021
Figure FDA0002400058310000022
Figure FDA0002400058310000023
wherein the content of the first and second substances,
Figure FDA0002400058310000024
Figure FDA0002400058310000025
Figure FDA0002400058310000026
Figure FDA0002400058310000027
and
Figure FDA0002400058310000028
an M × M diagonal matrix, the elements on the diagonal are:
Figure FDA0002400058310000029
Figure FDA00024000583100000210
wherein M is the number of base station antennas, N is the number of user antennas,
Figure FDA0002400058310000031
counting channel state information for a beam domain, marking iteration times by a superscript i, marking a row and column number of a matrix by a subscript t, taking ⊙ as the Hadamard product of the matrix, and taking a superscript as the conjugate of the matrix;
substituting the solution of the optimization problem in the current iteration process into the optimization target to generate the optimization problem of the next iteration, and solving again until the difference value of the weighted average of the unicast rate and the multicast rate of the system in the two adjacent iteration processes is less than a given threshold value, wherein the solution of the last iteration process is the solution of the optimization problem;
(b) respectively calculating according to the large-dimension random matrix theory
Figure FDA0002400058310000032
And
Figure FDA0002400058310000033
deterministic equivalent expression
Figure FDA0002400058310000034
And
Figure FDA0002400058310000035
the expectation calculation with high complexity is avoided;
Figure FDA0002400058310000036
Figure FDA0002400058310000037
wherein the content of the first and second substances,
Figure FDA0002400058310000038
Figure FDA0002400058310000039
Figure FDA00024000583100000310
Figure FDA00024000583100000311
Figure FDA00024000583100000312
four auxiliary variables are obtained by iterative calculation:
Figure FDA00024000583100000313
Figure FDA00024000583100000314
Figure FDA00024000583100000315
Figure FDA00024000583100000316
Figure FDA00024000583100000317
and
Figure FDA00024000583100000318
the representation generates a diagonal matrix with elements on the diagonal being
Figure FDA00024000583100000319
5. The massive MIMO beam-domain joint unicast multicast transmission method of claim 1, characterized in that: in the dynamic moving process of each user, along with the change of statistical channel state information between a base station and each user, the base station side dynamically implements beam domain power distribution and combines the dynamic update of the unicast and multicast process; the change of the beam domain statistical channel state information is related to a specific application scene, and the statistical time window is several times or ten times of the short-time transmission time window.
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