CN109104225B - Large-scale MIMO beam domain multicast transmission method with optimal energy efficiency - Google Patents

Large-scale MIMO beam domain multicast transmission method with optimal energy efficiency Download PDF

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CN109104225B
CN109104225B CN201810889400.0A CN201810889400A CN109104225B CN 109104225 B CN109104225 B CN 109104225B CN 201810889400 A CN201810889400 A CN 201810889400A CN 109104225 B CN109104225 B CN 109104225B
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base station
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尤力
陈旭
王闻今
孙晨
卢安安
高西奇
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Southeast University
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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
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H04B7/043—Power distribution using best eigenmode, e.g. beam forming or beam steering
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention provides a large-scale MIMO beam domain multicast transmission method with optimal energy efficiency, which mainly comprises the following steps: a cell base station is configured with a large-scale antenna array, generates a large-scale beam set to cover the whole cell through beam forming, and performs multicast communication with users on the generated beam; and the base station estimates the statistical channel state information of each user according to the received user uplink detection signal, and performs beam domain power distribution with optimal energy efficiency according to the statistical channel state information. The proposed energy efficiency optimal beam domain power distribution algorithm mainly utilizes Dinkelbach transformation and determinacy equivalence principles to obtain a globally optimal energy efficiency optimal beam domain multicast power distribution matrix by iteratively solving a series of convex optimization problems. And with the movement of the users, the statistical channel state information between the base station and each user changes, the base station acquires the statistical channel state information according to different application scenes, and the beam domain multicast power distribution with optimal energy efficiency is dynamically implemented.

Description

Large-scale MIMO beam domain multicast transmission method with optimal energy efficiency
Technical Field
The invention belongs to the field of communication, and particularly relates to an energy-efficiency optimal large-scale Input Multiple Output (MIMO) wave beam domain transmission method under a multicast scene by using a large-scale antenna array and channel state information statistics.
Background
In a massive MIMO system, a base station is deployed to serve multiple users simultaneously with massive antenna arrays. 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. In the process of beam division multiple access transmission, a base station side converts a transmitting signal into a beam domain through 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.
And under the multicast communication scene, the base station simultaneously transmits the same information to the target user group. In a large-scale MIMO beam-space multicast scenario, system energy efficiency often needs to be optimized. Such optimization problems are generally difficult to obtain a global optimal solution because the objective function is non-convex, and when the number of base station side antennas is large, the complexity of an expectation process in the process of obtaining the multicast rate of the system is high. Therefore, the invention provides a low-complexity and globally optimal large-scale MIMO wave beam domain multicast transmission method with optimal energy efficiency by utilizing statistical channel state information.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide an energy-efficiency optimal large-scale MIMO beam domain transmission method by utilizing a large-scale antenna array and channel state information statistics under a base station multicast scene.
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 multicast transmission method with optimal energy efficiency comprises the following steps:
(1) the base station configures a large-scale antenna array, generates a large-scale beam set to cover the whole cell through beam forming, and performs multicast communication with users on the generated beam.
(2) And the base station constructs a beam domain energy efficiency optimal multicast power distribution optimization problem by using the acquired beam domain statistical channel state information, solves the optimization problem by using Dinkelbach transformation and deterministic equivalence methods, and distributes power to the transmission signals.
(3) And in the moving process of each user, the base station side dynamically implements beam domain rate allocation with optimal energy efficiency along with the change of the statistical channel state information between the base station and each user.
And (2) configuring an antenna array consisting of a large number of antennas at the base station side in the step (1), wherein the base station generates large-scale beams capable of covering the whole cell by using the same unitary transformation, and each beam is used for dividing space resources. And the base station carries out multicast communication with the target user in the beam domain.
In the step (2), each user sends an uplink detection signal in an uplink channel detection stage, and the base station estimates beam domain statistical channel state information for implementing beam domain power allocation according to the received detection signal. The method for implementing the beam domain power distribution is based on Dinkelbach transformation and deterministic equivalence.
The beam domain power distribution method based on Dinkelbach transformation comprises the following steps:
(a) the objective function for solving the power distribution problem with optimal energy efficiency is a fractional equation, which is a non-convex optimization problem and is often difficult to obtain a global optimal solution. An auxiliary variable is introduced through Dinkelbach transformation, an optimization problem is converted into a convex optimization problem, and a global optimal solution can be obtained while a local optimal solution is obtained.
(b) And (4) solving a convex optimization problem of energy efficiency optimal power distribution, wherein the auxiliary variables are continuously updated along with the iteration process. The iteration process is terminated when the difference between the results of two adjacent iterations is less than some given threshold.
The beam domain power allocation method based on deterministic equivalence comprises the following steps:
(a) according to the large-dimension random matrix theory, channel state information is counted through the wave beam domain of the multicast user, and the deterministic equivalent auxiliary variable of the multicast rate item in the objective function is calculated in an iterative mode until convergence.
(b) And calculating the deterministic equivalent expression of the multicast rate item in the objective function by using the deterministic equivalent auxiliary variable obtained by iteration.
(c) And the deterministic equivalent expression of the multicast rate item is brought into the optimization problem of the multicast power distribution in the wave beam domain with optimal energy efficiency, so that the expectation calculation with high complexity is avoided.
In the step (3), the statistical channel state information between the base station and each user changes along with the movement of the user, and the base station acquires the statistical channel state information at corresponding time intervals according to different application scenes to dynamically implement beam domain power allocation.
Has the advantages that: compared with the prior art, the invention has the following advantages:
1. the base station and each user in the user group implement multicast communication with optimal energy efficiency in a wave beam domain, and can be matched with the spatial characteristics of a wireless channel of the base station, 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 multicast user, the required beam domain statistical channel state information of each user can be obtained by sparse detection signals, and the multicast transmission method is simultaneously suitable for a time division duplex system and a frequency division duplex system.
3. Energy efficiency power distribution is carried out by utilizing Dinkelbach transformation and a deterministic equivalence principle, complexity of solving an optimization problem and realizing a physical layer is obviously reduced, and a global optimal solution can be obtained by the power distribution method.
Drawings
Fig. 1 is a flowchart of a large-scale MIMO beam-domain multicast transmission method with optimal energy efficiency.
Fig. 2 is a diagram of a massive MIMO multicast system.
Fig. 3 is a flow chart of an iterative algorithm based on the Dinkelbach transform.
FIG. 4 is a flow chart of an iterative algorithm based on Dinkelbach and deterministic equivalence.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
As shown in fig. 1, the large-scale MIMO beam-space multicast transmission method with optimal energy efficiency disclosed by 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 multicast communication with optimal energy efficiency with the users on the same time-frequency resource, and the process of the multicast communication is implemented on a wave beam domain.
2) The base station acquires the beam domain statistical channel state information of the multicast user, the beam domain multicast power distribution problem with optimal energy efficiency is established by using the statistical channel state information, the optimization problem is solved by using Dinkelbach transformation and certainty equivalence methods, and power distribution with optimal energy efficiency on the transmitted signals is completed.
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 users in the multicast user group, the base station side dynamically implements beam domain power distribution and the multicast process is dynamically updated.
In the following, taking the massive MIMO multicast system shown in fig. 2 as an example, considering a single-cell scenario, a large-scale antenna array with M transmitting antennas is configured on the base station side (M is 10)2～103Order of magnitude), the antenna spacing is one-half wavelength apart. There are K multicast target users in the cell, each user configures NrThe root receives the antenna.
In the channel detection stage, the multicast user sends uplink detection signals, and the base station estimates each channel according to the received detection signalsBeam field statistical channel state information of individual users
Figure BDA0001756525700000041
Wherein HkThe beam domain channel matrix for the kth multicast user, respectively, operator ⊙ is the Hadamard product of the matrices, the conjugate of the matrices,
Figure BDA0001756525700000042
representing the desired operation.
The base station transforms the space domain signals sent to the users through a unified unitary transformation beam domain, and the base station sends multicast signals to each user in the beam domain. To assume that the beam-domain multicast signal transmitted by the base station is x, the covariance matrix of the transmitted signal is
Figure BDA0001756525700000043
The multicast user rate may be expressed as:
Figure BDA0001756525700000044
wherein the superscript H represents the conjugate transpose of the matrix, min represents the minimum value taking operation, log represents the logarithm operation, and det represents the determinant of the matrix. Considering that the correlation on the base station side of the beam domain channel is low, the base station transmits mutually independent data streams on each beam, namely the matrix Λ is a diagonal matrix.
In order to obtain multicast energy efficiency with higher energy efficiency by paying attention to the energy efficiency problem in beam domain multicast communication, the covariance matrix Λ of the transmitted signals needs to be optimized, that is, the power of the transmitted beams is distributed on the base station side, that is, the following optimization problem is solved:
Figure BDA0001756525700000045
p (Λ) is total transmission power and satisfies P (Λ) ═ μ tr { Λ } + PcWhere tr { Λ } is the signal transmission power, μ (> 1) is the amplification factor, PcFor circuit power dissipated in hardware, PconIs a baseAnd (3) station transmitting signal total power constraint, wherein the tr {. is the operation of taking matrix traces.
The objective function of the problem is non-convex, the global optimal solution is difficult to obtain, and the realization complexity is high. The idea based on Dinkelbach transformation is as follows: the objective function of the power distribution problem with optimal energy efficiency is a fractional equation, which is a non-convex optimization problem that a global optimal solution is often difficult to obtain, an auxiliary variable is introduced through Dinkelbach transformation to convert the optimization problem into a convex optimization problem, and the global optimal solution can be obtained while a local optimal solution is obtained. And solving the convex optimization problem of energy efficiency optimal power distribution through iteration, wherein the auxiliary variables are continuously updated along with the iteration process, and the iteration process is terminated when the difference between two adjacent iteration results is less than a given threshold value.
Fig. 3 shows an implementation flow of the power allocation method based on the Dinkelbach transform, which is implemented by the present invention, and the detailed process is as follows:
step 1: initializing covariance matrix Λ of transmitted signals(0)The iteration number indication i is set to 0. Covariance matrix lambda of signal transmitted in initialization(0)In time, power P can be distributed to N wave beams with strongest wave beam gain according to the wave beam domain statistical channel state informationconN, wherein PconA total power constraint for the base station transmit signal. N may be obtained by using channel statistical state information, and the value mode of N may be as follows: calculating the beam domain channel correlation matrix of each user
Figure BDA0001756525700000051
RkIs an M × M diagonal matrix, each diagonal element is
Figure BDA0001756525700000052
The beam set with the energy coverage for this user up to 80% may be taken, and then the beam sets of all K multicast users are taken and collected to obtain the set y, N being the number of elements in the set y.
Step 2: according to power distribution matrix Λ(0)Calculating corresponding multicast energy efficiency function
Figure BDA0001756525700000053
Is composed of
Figure BDA0001756525700000054
Step 3, introducing Dinkelbach auxiliary variable η to update iteratively in the following way
Figure BDA0001756525700000055
And 4, step 4: the optimization problem is changed into the following form by using Dinkelbach transformation:
Figure BDA0001756525700000056
and 5: and solving the convex optimization problem (5) by using an interior point method or other convex optimization methods.
Step 6: the solution of the convex optimization problem (5) is brought into a multicast energy efficiency function expression (3), and a new multicast energy efficiency function value is calculated
Figure BDA0001756525700000061
And 7: results of the (i + 1) th iterative multicast energy efficiency function
Figure BDA0001756525700000062
And the ith result
Figure BDA0001756525700000063
Making a comparison if the difference between the two times
Figure BDA0001756525700000064
Less than a set threshold epsilon1Then the iteration is terminated; otherwise, adding 1 to the iteration number i, i is i +1, and jumping to step 3.
In the above proposed power allocation method based on the Dinkelbach transform, when solving the multicast energy efficiency function expression (6) and the convex optimization problem (5), the channel is required to be traversed, and the expected value is calculated. Since the expectation has no closed form expressions, Monte-Carlo simulation calculations are required. In order to avoid the expectation operation with high complexity, the deterministic equivalent expression of the multicast rate term is calculated by utilizing a large-dimensional matrix random theory, so that the calculation complexity of the power distribution method based on Dinkelbach transformation is reduced. The deterministic equivalence method can obtain an approximation result of the multicast rate item by iteratively calculating the deterministic equivalence auxiliary variable only by using statistical channel state information. Because the result of the determinacy equivalence can well approach the accurate expression of the multicast rate item, the invention provides a power distribution method based on Dinkelbach transformation and determinacy equivalence.
Fig. 4 shows an implementation flow of a power allocation method based on Dinkelbach transformation and deterministic equivalence, and the detailed process is as follows:
step 1: initializing covariance matrix Λ of transmitted signals(0)The iteration number indication i is set to 0. Covariance matrix lambda of signal transmitted in initialization(0)In time, power P can be distributed to N wave beams with strongest wave beam gain according to the wave beam domain statistical channel state informationcon/N。
Step 2: computing deterministic equivalence initial values for multicast rate items
Figure BDA0001756525700000065
First a deterministic equivalent auxiliary variable is introduced
Γk＝Bk(Φk)-1(7)
Figure BDA0001756525700000066
Figure BDA0001756525700000067
In the iterative process, all three auxiliary variables tend to converge, and stop when the change value of the auxiliary variable is smaller than a given threshold valueAnd (6) iteration. Wherein B iskAnd CkAre all diagonal matrices whose diagonal elements can be represented as
[Bk(X)]i,i＝tr{diag{[Ωk]:,i}X} (10)
Figure BDA0001756525700000071
Hence the multicast rate term RmcR in (Λ)kDeterministic equivalence of (a) can be expressed as
Figure BDA0001756525700000072
Such deterministic equivalence of multicast rate terms may be expressed as
Figure BDA0001756525700000073
And step 3: according to Λ(i)Computing multicast energy efficiency function values
Figure BDA0001756525700000074
Step 4, introducing Dinkelbach auxiliary variable η to update iteratively in the following way
Figure BDA0001756525700000075
And 5: the optimization problem is changed into the following form by using Dinkelbach transformation:
Figure BDA0001756525700000076
step 6: solving the convex optimization problem (16) using an interior point method or other convex optimization method to obtain a solution Λ of the optimization problem for this iteration(i+1)。
And 7: solving of convex optimization problem (16) < lambda >(i+1)Brought into the multicast energy efficiency function expression,computing new multicast energy efficiency function values
Figure BDA0001756525700000077
And 8: multicasting the result of the energy efficiency function for the (i + 1) th iteration
Figure BDA0001756525700000078
And the ith result
Figure BDA0001756525700000079
Making a comparison if the difference between the two times
Figure BDA00017565257000000710
Less than a set threshold epsilon1Then the iteration is terminated; otherwise, adding 1 to the iteration number i, namely i ═ i +1, returning to step 3, bringing the solution of the iteration into the formula (8), and recalculating the deterministic equivalence auxiliary variable Γk、
Figure BDA00017565257000000711
And phikAnd repeating the steps.
And 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 multicast power distribution of the energy efficiency optimal beam domain. Thereby realizing the dynamic update of the 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 (4)

1. A large-scale MIMO wave beam domain multicast transmission method with optimal energy efficiency is characterized in that: the method comprises the following steps:
(1) a base station configured with a large-scale antenna array generates a large-scale beam set to cover the whole cell through beam forming, and performs multicast communication with users on the generated beam;
(2) the base station acquires statistical channel state information of each user, constructs a beam domain energy efficiency optimal multicast power distribution optimization problem according to the statistical channel state information, solves the optimization problem by using Dinkelbach transformation and certainty equivalence methods, and distributes power to the transmission signals; the optimization target of the power distribution optimization problem is to maximize multicast energy efficiency, and the optimization variable is a signal covariance matrix sent by a base station side; the constraint condition is that the covariance matrix of the signals sent by the base station side meets the power constraint; the multicast energy efficiency is the ratio of the multicast rate of the system to the total power of the system; the optimization problem is represented as:
Figure FDA0002463841240000011
s.t.tr{Λ}≤Pcon,Λ≥0
wherein HkIs the beam domain channel matrix of the kth user, Λ is the covariance matrix of the transmitted signal, I is the identity matrix, P (Λ) ═ μ tr { Λ } + PcIs the total transmitted power, mu is the amplification factor, PcFor circuit power dissipated in hardware, PconFor the base station to transmit a total power constraint on the signal,
Figure FDA0002463841240000012
representing the desired operation, det representing the determinant of the matrix, tr (-) representing the traces of the computation matrix;
introducing an auxiliary variable by utilizing Dinkelbach transformation to convert the fractional operation of an original optimization problem objective function into a subtractive operation, so that sub-problems solved in each iteration are convex optimization problems;
the method for reducing the operation complexity of the optimization problem solution by using the deterministic equivalence method specifically comprises the following steps:
(a) according to the large-dimension random matrix theory, the certainty equivalent auxiliary variable gamma of the multicast rate item in the objective function is calculated in an iterative way by counting the channel state information through the wave beam domain of the multicast userk、
Figure FDA0002463841240000013
And phikUntil convergence; wherein, gamma isk＝Bk(Φk)-1，
Figure FDA0002463841240000014
Bk(X) and Ck(X) is a diagonal matrix;
[Bk(X)]i,i＝tr{diag{[Ωk]:,i}X}
Figure FDA0002463841240000015
Figure FDA0002463841240000021
⊙ denotes the Hadamard product of the matrix,. indicates the conjugate of the matrix, the superscript i denotes the number of iterations, and the subscript i denotes the row and column number of the matrix element;
(b) calculating multicast rate terms in objective function using deterministic equal auxiliary variables obtained from iteration
Figure FDA0002463841240000022
Deterministic equivalent expression
Figure FDA0002463841240000023
(c) The deterministic equivalent expression of the multicast rate item is brought into the optimization problem of the multicast power distribution of the wave beam domain with optimal energy efficiency, and the expectation calculation with high complexity is avoided;
(3) and in the moving process of each user, the base station side dynamically implements beam domain power allocation with optimal energy efficiency along with the change of statistical channel state information between the base station and each user.
2. The energy-efficient massive MIMO beam-space multicast transmission method according to claim 1, wherein: in the step (1), the base station uses the same unitary transformation to generate large-scale beams covering the whole cell, and each beam is used for dividing space resources; and the base station carries out multicast communication in the wave beam domain with optimal energy efficiency with the target user in the wave beam domain.
3. The energy-efficient massive MIMO beam-space multicast transmission method according to claim 1, wherein: and the beam domain statistical channel state information is estimated by the base station according to the received uplink detection signal sent by the multicast user.
4. The energy-efficient massive MIMO beam-space multicast transmission method according to claim 1, wherein: in the step (3), the statistical channel state information between the base station and each user changes along with the movement of the user, the base station acquires the statistical channel state information at corresponding time intervals according to different application scenes, and the Dinkelbach transformation and power distribution based on the beam domain with the same certainty and the optimal energy efficiency are dynamically implemented.
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