CN111010220B - Multi-user multi-stream downlink hybrid precoding method and system based on energy efficiency - Google Patents

Multi-user multi-stream downlink hybrid precoding method and system based on energy efficiency Download PDF

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CN111010220B
CN111010220B CN201911112205.8A CN201911112205A CN111010220B CN 111010220 B CN111010220 B CN 111010220B CN 201911112205 A CN201911112205 A CN 201911112205A CN 111010220 B CN111010220 B CN 111010220B
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陈月云
刘占
简荣灵
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University of Science and Technology Beijing USTB
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • 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
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    • 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
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Abstract

The invention provides a multi-user multi-stream downlink hybrid precoding method and a system based on energy efficiency, wherein the method comprises the following steps: in a large-scale antenna system, simultaneously considering interference among users, interference among multiple data streams of each user and noise, and establishing an energy efficiency model based on a maximized unit power transmission rate; solving an optimal unconstrained simulation pre-coding matrix and an optimal unconstrained simulation combined code matrix through a block diagonalization algorithm based on an energy efficiency model; quantizing the optimal unconstrained analog pre-coding matrix and the optimal unconstrained analog combined code matrix and adding constant modulus constraint to obtain an optimal analog pre-coding matrix and an optimal analog combined code matrix; and fixing the optimal simulation pre-coding matrix and the optimal simulation combined code matrix, and solving the optimal baseband pre-coding matrix and the optimal baseband combined code matrix through a convex optimization algorithm. The invention effectively improves the energy efficiency of the system while ensuring the reliability of the multi-user multi-stream large-scale antenna system.

Description

Multi-user multi-stream downlink hybrid precoding method and system based on energy efficiency
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a multi-user multi-stream downlink hybrid precoding method and system based on energy efficiency.
Background
Large-scale antenna technology requires the deployment of hundreds or thousands of antennas at the base station end. The traditional 4G wireless communication system adopts a full digital precoding technology, and each antenna needs to be configured with a radio frequency link, so that the cost is high. In a 5G wireless communication system, the cost problem can be well solved by hybrid precoding.
In a downlink multi-user multi-stream large-scale antenna hybrid precoding system, information received by a user often has relatively serious interference between data streams and interference between users. Since the problem of maximizing energy efficiency is to make trade-offs between spectral efficiency and total power, the interference effect among users, the interference effect among data streams of each user, and the noise effect need to be considered simultaneously when studying maximizing energy efficiency. However, in the current 5G wireless communication system, there still exist the problems of low energy efficiency, and significant influence on system performance by inter-user interference and inter-per-user multi-data-stream interference.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a multi-user multi-stream downlink hybrid precoding method and system based on energy efficiency, so as to solve the problems that the energy efficiency is low in a 5G wireless communication system, and the system performance is significantly affected by inter-user interference and inter-multiple-data-stream interference of each user.
In order to solve the technical problems, the invention provides the following scheme:
a multi-user multi-stream downlink hybrid precoding method based on energy efficiency comprises the following steps:
in a large-scale antenna system, simultaneously considering interference among users, interference among multiple data streams of each user and noise, and establishing an energy efficiency model based on a maximized unit power transmission rate;
solving an optimal unconstrained simulation precoding matrix and an optimal unconstrained simulation combined code matrix through a Block Diagonalization (BD) algorithm based on the energy efficiency model;
quantizing the optimal unconstrained analog pre-coding matrix and the optimal unconstrained analog combined code matrix and adding constant modulus constraint to obtain an optimal analog pre-coding matrix and an optimal analog combined code matrix;
and fixing the optimal simulation pre-coding matrix and the optimal simulation combined code matrix, and solving the optimal baseband pre-coding matrix and the optimal baseband combined code matrix through a convex optimization algorithm.
Wherein the energy efficiency model based on maximizing the transmission rate per unit power is expressed as:
Figure BDA0002273054540000021
Figure BDA0002273054540000022
Figure BDA0002273054540000023
Figure BDA0002273054540000024
Figure BDA0002273054540000025
wherein, FRFRepresenting an analog precoding matrix, FBBRepresenting the baseband precoding matrix, WRFRepresenting an analog combined code matrix, WBBRepresenting a baseband combined code matrix, PtotalRepresents the total power consumption;
Figure BDA0002273054540000026
represents the total spectral efficiency, log, per unit bandwidth2(|1+γk,i|) represents the spectral efficiency of each data stream,kirepresenting a minimum rate threshold, γ, to meet the user's QoS requirementsk,iRepresenting the signal-to-noise ratio of the ith data stream for user k;
Figure BDA0002273054540000027
representing the constant modulus constraint of the analog precoding, NBSDenotes the number of base station antennas, MBSRepresenting the number of radio frequency links of the base station;
Figure BDA0002273054540000028
representing simulation combined with code constant modulus constraint, NMSRepresenting the number of user antennas, MMSRepresenting the number of radio frequency links of each user; k denotes the number of users, NsRepresenting the number of data streams received by each user and P representing the maximum total transmit power.
Wherein, the signal-to-noise ratio γ of the ith data stream of the user kk,iExpressed as:
Figure BDA0002273054540000029
wherein the content of the first and second substances,
Figure BDA00022730545400000210
to representThe power of the data stream that user k desires to receive;
Figure BDA0002273054540000031
representing the interference power between different data streams received by user k;
Figure BDA0002273054540000032
representing the interference power between different users received by user k;
Figure BDA0002273054540000033
represents the noise power received by user k;
Figure BDA0002273054540000034
a baseband combined code matrix representing the ith data stream for the kth user,
Figure BDA0002273054540000035
an analog combined code matrix representing user k, H represents a channel, HkChannel, F, representing user kRFRepresenting an analog precoding matrix, FBB(ki) a baseband precoding matrix representing the ith data stream for the kth user;
wherein the content of the first and second substances,
Figure BDA0002273054540000036
representing a baseband precoding matrix;
Figure BDA0002273054540000037
representing an analog combined code matrix;
Figure BDA0002273054540000038
a base-band combined code matrix is represented,
Figure BDA0002273054540000039
a baseband combined code matrix representing the kth user.
Wherein the total power consumption PtotalExpressed as:
Figure BDA00022730545400000310
wherein, PRFRepresenting the power consumption, P, of each radio frequency linkcRepresenting the total power consumption of the rest of the base station, excluding the radio frequency link, and alpha represents the power amplification factor.
The solving of the optimal baseband pre-coding matrix and the optimal baseband combined code matrix through the convex optimization algorithm comprises the following steps:
firstly, Dinkelbach method is adopted to convert the energy efficiency model, then the converted energy efficiency model is further converted into a convex function model through the equivalent relation of weighted mean square error and spectral efficiency, and finally a block coordination descent algorithm is adopted to iteratively solve the maximum energy efficiency, the optimal baseband pre-coding matrix and the optimal baseband combination code matrix based on the converted convex function model.
Accordingly, in order to solve the above technical problems, the present invention further provides the following solutions:
an energy-efficient multi-user multi-stream based downlink hybrid precoding system, comprising:
the energy efficiency model establishing module is used for establishing an energy efficiency model based on the transmission rate of the maximized unit power in a large-scale antenna system by simultaneously considering the interference among users, the interference among multiple data streams of each user and noise;
the optimal unconstrained matrix solving module is used for solving an optimal unconstrained simulation precoding matrix and an optimal unconstrained simulation combined code matrix through a block diagonalization algorithm based on the energy efficiency model;
the optimal simulation matrix solving module is used for quantizing the optimal unconstrained simulation precoding matrix and the optimal unconstrained simulation combined code matrix and adding constant modulus constraint to obtain an optimal simulation precoding matrix and an optimal simulation combined code matrix;
and the optimal baseband matrix solving module is used for fixing the optimal analog pre-coding matrix and the optimal analog combined code matrix and solving the optimal baseband pre-coding matrix and the optimal baseband combined code matrix through a convex optimization algorithm.
Wherein the energy efficiency model based on maximizing the transmission rate per unit power is expressed as:
Figure BDA0002273054540000041
Figure BDA0002273054540000042
Figure BDA0002273054540000043
Figure BDA0002273054540000044
Figure BDA0002273054540000045
wherein, FRFRepresenting an analog precoding matrix, FBBRepresenting the baseband precoding matrix, WRFRepresenting an analog combined code matrix, WBBRepresenting a baseband combined code matrix, PtotalRepresents the total power consumption;
Figure BDA0002273054540000046
represents the total spectral efficiency, log, per unit bandwidth2(|1+γk,i|) represents the spectral efficiency of each data stream,kiindicating satisfaction of user QoS requirementsMinimum rate threshold, gammak,iRepresenting the signal-to-noise ratio of the ith data stream for user k;
Figure BDA0002273054540000047
representing the constant modulus constraint of the analog precoding, NBSDenotes the number of base station antennas, MBSRepresenting the number of radio frequency links of the base station;
Figure BDA0002273054540000048
representing simulation combined with code constant modulus constraint, NMSRepresenting the number of user antennas, MMSRepresenting the number of radio frequency links of each user; k denotes the number of users, NsRepresenting the number of data streams received by each user and P representing the maximum total transmit power.
Wherein, the signal-to-noise ratio γ of the ith data stream of the user kk,iExpressed as:
Figure BDA0002273054540000051
wherein the content of the first and second substances,
Figure BDA0002273054540000052
represents the power of the data stream that user k desires to receive;
Figure BDA0002273054540000053
representing the interference power between different data streams received by user k;
Figure BDA0002273054540000054
representing the interference power between different users received by user k;
Figure BDA0002273054540000055
represents the noise power received by user k;
Figure BDA0002273054540000056
a baseband combined code matrix representing the ith data stream for the kth user,
Figure BDA0002273054540000057
an analog combined code matrix representing user k, H represents a channel, HkChannel, F, representing user kRFRepresenting an analog precoding matrix, FBB(ki) a baseband precoding matrix representing the ith data stream for the kth user;
wherein the content of the first and second substances,
Figure BDA0002273054540000058
representing a baseband precoding matrix;
Figure BDA0002273054540000059
representing an analog combined code matrix;
Figure BDA00022730545400000510
a base-band combined code matrix is represented,
Figure BDA00022730545400000511
a baseband combined code matrix representing the kth user.
Wherein the total power consumption PtotalExpressed as:
Figure BDA00022730545400000512
wherein, PRFRepresenting the power consumption, P, of each radio frequency linkcRepresenting the total power consumption of the rest of the base station, excluding the radio frequency link, and alpha represents the power amplification factor.
The optimal baseband matrix solving module is specifically configured to:
firstly, Dinkelbach method is adopted to convert the energy efficiency model, then the converted energy efficiency model is further converted into a convex function model through the equivalent relation of weighted mean square error and spectral efficiency, and finally a block coordination descent algorithm is adopted to iteratively solve the maximum energy efficiency, the optimal baseband pre-coding matrix and the optimal baseband combination code matrix based on the converted convex function model.
The technical scheme of the invention has the following beneficial effects:
in a large-scale antenna system, interference among users, interference among multiple data streams of each user and noise are considered at the same time, and an energy efficiency model based on the maximum unit power transmission rate is established; solving an optimal unconstrained simulation pre-coding matrix and an optimal unconstrained simulation combined code matrix through a block diagonalization algorithm based on an energy efficiency model; quantizing the optimal unconstrained analog pre-coding matrix and the optimal unconstrained analog combined code matrix and adding constant modulus constraint to obtain an optimal analog pre-coding matrix and an optimal analog combined code matrix; and fixing the optimal simulation pre-coding matrix and the optimal simulation combined code matrix, and solving the optimal baseband pre-coding matrix and the optimal baseband combined code matrix when the energy efficiency converges to the maximum value through a convex optimization algorithm. Therefore, interference among data streams can be inhibited, the maximum energy efficiency is realized, and the energy efficiency of the system is effectively improved while the reliability of the multi-user multi-stream large-scale antenna system is ensured.
Drawings
Fig. 1 is a schematic view of a downlink multi-user multi-stream large-scale antenna hybrid precoding system;
fig. 2 is a flowchart illustrating a downlink hybrid precoding method for multi-user and multi-stream based on energy efficiency according to the present invention;
FIG. 3 is a schematic diagram of energy efficiency as a function of signal-to-noise ratio for different algorithms;
fig. 4 is a schematic diagram of the change of the spectral efficiency with the signal-to-noise ratio under different algorithms.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
First embodiment
The present embodiment provides an energy efficiency-based multi-user multi-stream downlink hybrid precoding method for a 5G wireless communication system, in order to improve energy efficiency and reduce inter-user interference and inter-user interference among multiple data streams of each user; the method solves the problems that the energy efficiency is low, and the system performance is influenced by the interference among users and the interference among multiple data streams of each user obviously in the 5G wireless communication system.
First, in order to enable related technical personnel to better understand the scheme of the embodiment, a scenario and a technical principle of a downlink multi-user multi-stream large-scale antenna hybrid precoding system used in the embodiment are briefly described, as shown in fig. 1, a hybrid precoding design is adopted by a base station and each terminal user in the scenario of the downlink multi-user multi-stream large-scale antenna hybrid precoding system. Under the full-connection architecture system, the base station configures MBSBar radio frequency link, NBSA root antenna. Base station transmitting KNSA data stream to K end users, each of which is supposed to receive NSA data stream. At the user end, each end user configures NMSAn antenna, MMSAnd a radio frequency link. In order to ensure the system to normally communicate, KN must be satisfiedS≤MBS≤NBS，NS≤MMS≤NMS。
Based on the above, the execution flow of the energy efficiency-based multi-user multi-stream downlink hybrid precoding method of the embodiment is shown in fig. 2, and includes:
s101, in a large-scale antenna system, simultaneously considering interference among users, interference among multiple data streams of each user and noise, and establishing an energy efficiency model based on a maximized unit power transmission rate;
it should be noted that, in this embodiment, in order to maximize the energy efficiency and ensure the system reliability, the spectral efficiency of each data stream and the transmit power of each data stream need to be constrained:
Figure BDA0002273054540000071
Figure BDA0002273054540000072
where K denotes the number of users, NsIndicating the number of data streams received per user, log2(|1+γk,i|) represents the spectral efficiency of each data stream,kirepresents the minimum rate threshold to meet the user's QoS requirements, P represents the maximum total transmit power, γk,iSignal-to-noise ratio, F, of the ith data stream representing user kRFRepresenting an analog precoding matrix, FBBRepresenting the baseband precoding matrix.
According to the interference received by the user k, including the interference between users and the N received by the user ksInterference and noise between data streams, signal-to-noise ratio gamma of the ith data stream received by the user kk,iCan be expressed as:
Figure BDA0002273054540000073
wherein the content of the first and second substances,
Figure BDA0002273054540000074
represents the power of the data stream that user k desires to receive;
Figure BDA0002273054540000075
representing the interference power between different data streams received by user k;
Figure BDA0002273054540000076
representing the interference power between different users received by user k;
Figure BDA0002273054540000077
represents the noise power received by user k;
Figure BDA0002273054540000078
a baseband combined code matrix representing the ith data stream for the kth user,
Figure BDA0002273054540000079
an analog combined code matrix representing user k, H represents a channel, HkChannel, F, representing user kRFRepresenting an analog precoding matrix, FBB(ki) a baseband precoding matrix representing the ith data stream for the kth user;
wherein the content of the first and second substances,
Figure BDA0002273054540000081
representing a baseband precoding matrix;
Figure BDA0002273054540000082
representing an analog combined code matrix;
Figure BDA0002273054540000083
a base-band combined code matrix is represented,
Figure BDA0002273054540000084
a baseband combined code matrix representing the kth user.
According to the characteristics of a large-scale antenna hybrid precoding system, for an analog domain, only the phase of a signal is changed, and the amplitude of the signal is not changed. Thus, the constant modulus constraint imposed on the analog precoding matrix and the analog combining code matrix can be expressed as:
Figure BDA0002273054540000085
Figure BDA0002273054540000086
wherein N isBSIndicates the number of base station antennas, NMSRepresenting the number of user antennas, MMSRepresenting the number of user radio links, MBSIndicating the number of base station radio links.
Assuming that the power consumption of each radio frequency link is a fixed value, and the power consumption of other hardware of the base station except the radio frequency link is also a fixed value, the total power consumption P istotalCan be expressed as:
Figure BDA0002273054540000087
wherein, PRFRepresenting the power consumption, P, of each radio frequency linkcRepresenting the total power consumption of the rest of the base station, excluding the radio frequency link, and alpha represents the power amplification factor.
In summary, the energy efficiency model based on maximizing the unit power transmission rate established in S101 can be expressed as:
Figure BDA0002273054540000088
Figure BDA0002273054540000089
Figure BDA00022730545400000810
Figure BDA00022730545400000811
Figure BDA00022730545400000812
wherein, WRFRepresenting an analog combined code matrix, WBBA base-band combined code matrix is represented,
Figure BDA0002273054540000091
representing the total spectral efficiency per unit bandwidth.
S102, solving an optimal unconstrained simulation pre-coding matrix and an optimal unconstrained simulation combined code matrix through a block diagonalization algorithm based on an energy efficiency model;
it should be noted that, since the above-mentioned p 1 model contains constant modulus constraint in the constraint condition, the model is non-convex, and the optimal closed-form solution of the precoding matrix and the combined code matrix cannot be found.
To solve the problem, the embodiment first uses the BD algorithm to find the optimal analog precoding matrix and the optimal analog combining code matrix, and the solving process is as follows:
let FRFMN, where M is M1,M2,...,MK]For eliminating inter-user interference, N ═ N1,N2,...,NK]For parallel transmission between different data streams within the same user. Defining interference channels
Figure BDA0002273054540000092
Figure BDA0002273054540000093
Figure BDA0002273054540000094
A set of channel matrices representing the components of the user subchannels other than the k-th user subchannel.
Then, to
Figure BDA0002273054540000095
Performing singular value decomposition
Figure BDA0002273054540000096
Wherein the content of the first and second substances,
Figure BDA0002273054540000097
is that
Figure BDA0002273054540000098
The orthogonal sub-spaces of (a) are,
Figure BDA0002273054540000099
to represent
Figure BDA00022730545400000910
The nuclear space of (a).
Order to
Figure BDA00022730545400000911
The inter-user interference received by the kth user can be cancelled. Definition of
Figure BDA00022730545400000912
Performing singular value decomposition on the equivalent channel of the user k to obtain:
Figure BDA00022730545400000913
further obtain
Figure BDA00022730545400000914
The optimal unconstrained analog precoding matrix is:
Figure BDA00022730545400000915
the optimal unconstrained analog combined code matrix is solved by the same method:
Figure BDA00022730545400000916
s103, quantizing the optimal unconstrained analog pre-coding matrix and the optimal unconstrained analog combined code matrix and adding constant modulus constraint to obtain an optimal analog pre-coding matrix and an optimal analog combined code matrix;
it should be noted that, since the analog precoding matrix and the analog combining code matrix are both constant modulus and the phase shifter is not infinite resolution, the obtained matrix needs to be corrected
Figure BDA0002273054540000101
And
Figure BDA0002273054540000102
the constant modulus constraint is quantized and added,
assuming that the sampling rate of each phase shifter is B bits, the set of phases after sampling can be expressed as:
Figure BDA0002273054540000103
the phase of each element in the simulated precoding and simulated combination codes is selected from the set of phases Θ, namely:
θi,j∈Θ,i＝1,2,...,NBS j＝1,2,...,MBS
definition of < FRFopt(i,j)For unconstrained simulation of precoding matrix FRFoptRow i and column j:
Figure BDA0002273054540000104
due to the fact that FRFopt(i,j)May be negative, thus subtending ^ FRFopt(i,j)The above treatment is carried out, thereby enabling the angle FRFopt(i,j)The value range is obtained within [0,2 pi ]]In the meantime. In order to obtain the optimal quantized phase, the euclidean distance needs to be calculated, and the formula is as follows. And only when the sampled phase is as close to the phase of the optimal unconstrained analog precoding matrix as possible, the Euclidean distance is minimum at the moment, and the phase of the optimal analog precoding matrix is obtained.
Figure BDA0002273054540000105
s.t.θi,j∈Θ,i＝1,2,...,NBS j＝1,2,...,MBS
Finally, adding constant modulus constraint to the analog precoding, an optimal analog precoding matrix can be obtained, which can be expressed as:
Figure BDA0002273054540000106
similarly, the optimal analog combining code matrix can be expressed as:
Figure BDA0002273054540000107
s104, fixing the optimal simulation pre-coding matrix and the optimal simulation combined code matrix, and solving the optimal base band pre-coding matrix and the optimal base band combined code matrix through a convex optimization algorithm.
In addition, S104 includes:
the energy efficiency model was first converted using the "Dinkelbach method", expressed as:
Figure BDA0002273054540000111
Figure BDA0002273054540000112
Figure BDA0002273054540000113
then, the converted model is further converted into a convex function model through the equivalent relation of weighted mean square error and spectral efficiency:
Figure BDA0002273054540000114
Figure BDA0002273054540000115
Figure BDA0002273054540000116
wherein the content of the first and second substances,
Figure BDA0002273054540000117
representing the mean square error of the ith data stream for the kth user.
And finally, iterative calculation of the maximum energy efficiency, the optimal baseband pre-coding matrix and the optimal baseband combined code matrix is carried out by adopting a block coordination descent algorithm based on a convex function model:
solving for the mean square error by weighted mean square error minimization according to model P3
Figure BDA0002273054540000118
And making the partial derivative be 0, the optimal baseband combined code matrix can be obtained.
Figure BDA0002273054540000119
Figure BDA00022730545400001110
Combining the optimized baseband into a matrix
Figure BDA00022730545400001111
Bringing into MSEkiIn (1), obtaining
Figure BDA00022730545400001112
From the matrix calculation equivalence relations, the following conclusions can be drawn:
Figure BDA00022730545400001113
wherein the content of the first and second substances,
Figure BDA00022730545400001114
for model P3, baseband precoding matrix FBB(ki) is as follows:
Figure BDA00022730545400001115
to find the optimal baseband precoding matrix:
Figure BDA0002273054540000121
wherein, λ and μkiAre lagrange multipliers.
Specifically, the solving process of the optimal baseband precoding matrix and the optimal baseband combined code matrix is as follows:
1) fixing the optimal analog precoding matrix and the optimal analog combining code matrix, initializing the energy efficiency eta0Lagrange multiplier mu0，λ0Base band precoding matrix FBBAnd an iteration termination threshold;
2) solving for
Figure BDA0002273054540000122
According to the formula:
Figure BDA0002273054540000123
Figure BDA0002273054540000124
3) solving a baseband precoding matrix FBB(:,ki)；
4) Updating Lagrange multipliers by using Lagrange dual algorithm
Figure BDA0002273054540000126
λlUntil convergence, obtaining the optimal Lagrangian multiplier
Figure BDA0002273054540000125
λ*；
5) Judging | G (eta) ═ R-eta PtotalIf | < is satisfied, if so, the iteration terminates. Otherwise, the energy efficiency η is updatediContinuing the processing of 3) -5) until a convergence condition is satisfied, at which time an optimum energy efficiency η is obtainediThe optimal baseband precoding matrix FBBOptimum baseband combining code matrix WBB
The practical effect of the method of the present embodiment is further illustrated in a comparative manner as follows:
referring to fig. 3, a Maximum Energy Efficiency Hybrid Precoding Model that is based on a Maximum unit power transmission rate in a large-scale antenna system is considered as an Energy Efficiency Model based on a Maximum unit power transmission rate in consideration of a Maximum Energy Efficiency Hybrid Precoding Model between streams (MEE-HP-ISI) Model. The spectral efficiency in the model objective function comprises an inter-user interference power term, an per-user inter-data-stream interference power term, and a noise power term, and the constraint comprises a spectral efficiency constraint.
As can be seen from fig. 3, the energy efficiency of 16 terminal antennas is greater than that of the terminal configuration 8 antennas. For the case of terminal antenna 8, Maximum Energy Efficiency Hybrid Precoding Model that only Considers the Inter-user Interference (MEE-OIUI) Model is used for comparison. This model only contains the inter-user interference power term and does not add spectral efficiency constraints. Meanwhile, a Full Digital Precoding (FDP) algorithm is used as a comparison method of the proposed method.
As can be seen from FIG. 3, the MEE-HP-ISI of the present invention is better suppressedThe interference between data streams and the interference between users of each user has higher energy efficiency along with the increase of the signal to noise ratio. When the signal-to-noise ratio is greater than 0dB, the energy efficiency of MEE-HP-ISI is greater than that of the FDP algorithm, because in the FDP algorithm, the Minimum Mean Square Error (MMSE) is adopted to solve the precoding matrix, and the normalization constraint needs to be satisfied, namely
Figure BDA0002273054540000131
The spectral efficiency of the MEE-HP-ISI increases as the signal-to-noise ratio increases, but the power consumption is less than that of the FDP algorithm.
Furthermore, as can be seen from fig. 4, the proposed model of the present invention has higher spectrum efficiency compared to the MEE-OIUI model, because the proposed model can better suppress the inter-user interference and the inter-data-stream interference in the same user. Furthermore, the spectral efficiency of the FDP algorithm is highest under the same simulation environment.
In the embodiment, in a large-scale antenna system, the interference among users, the interference among multiple data streams of each user and noise are considered at the same time, and an energy efficiency model based on the maximized unit power transmission rate is established; solving an optimal unconstrained simulation pre-coding matrix and an optimal unconstrained simulation combined code matrix through a block diagonalization algorithm based on an energy efficiency model; quantizing the optimal unconstrained analog pre-coding matrix and the optimal unconstrained analog combined code matrix and adding constant modulus constraint to obtain an optimal analog pre-coding matrix and an optimal analog combined code matrix; and fixing the optimal simulation pre-coding matrix and the optimal simulation combined code matrix, and solving the optimal baseband pre-coding matrix and the optimal baseband combined code matrix when the energy efficiency converges to the maximum value through a convex optimization algorithm. The method can inhibit interference among data streams, realize maximum energy efficiency, and effectively improve the energy efficiency of the system while ensuring the reliability of the multi-user multi-stream large-scale antenna system.
Second embodiment
The present embodiment provides an energy efficiency-based multi-user multi-stream downlink hybrid precoding system, where the energy efficiency-based multi-user multi-stream downlink hybrid precoding system includes:
the energy efficiency model establishing module is used for establishing an energy efficiency model based on the transmission rate of the maximized unit power in a large-scale antenna system by simultaneously considering the interference among users, the interference among multiple data streams of each user and noise;
the optimal unconstrained matrix solving module is used for solving an optimal unconstrained simulation precoding matrix and an optimal unconstrained simulation combined code matrix through a block diagonalization algorithm based on the energy efficiency model;
the optimal simulation matrix solving module is used for quantizing the optimal unconstrained simulation precoding matrix and the optimal unconstrained simulation combined code matrix and adding constant modulus constraint to obtain an optimal simulation precoding matrix and an optimal simulation combined code matrix;
and the optimal baseband matrix solving module is used for fixing the optimal analog pre-coding matrix and the optimal analog combined code matrix and solving the optimal baseband pre-coding matrix and the optimal baseband combined code matrix through a convex optimization algorithm.
The energy efficiency-based multi-user multi-stream downlink hybrid precoding system of this embodiment corresponds to the energy efficiency-based multi-user multi-stream downlink hybrid precoding method of the first embodiment; the functions realized by the functional modules of the multi-user multi-stream downlink hybrid precoding system based on energy efficiency correspond to the process steps in the multi-user multi-stream downlink hybrid precoding method based on energy efficiency one to one, and therefore, the description is omitted here.
Furthermore, it should be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (4)

1. A downlink hybrid precoding method of multi-user and multi-stream based on energy efficiency is characterized in that the downlink hybrid precoding method of multi-user and multi-stream based on energy efficiency comprises the following steps:
in a large-scale antenna system, simultaneously considering interference among users, interference among multiple data streams of each user and noise, and establishing an energy efficiency model based on a maximized unit power transmission rate;
solving an optimal unconstrained simulation pre-coding matrix and an optimal unconstrained simulation combined code matrix through a block diagonalization algorithm based on the energy efficiency model;
quantizing the optimal unconstrained analog pre-coding matrix and the optimal unconstrained analog combined code matrix and adding constant modulus constraint to obtain an optimal analog pre-coding matrix and an optimal analog combined code matrix;
fixing the optimal simulation pre-coding matrix and the optimal simulation combined code matrix, and solving the optimal baseband pre-coding matrix and the optimal baseband combined code matrix through a convex optimization algorithm;
wherein the energy efficiency model based on maximizing the transmission rate per unit power is expressed as:
Ρ1：
Figure FDA0002693439250000011
s.t.
Figure FDA0002693439250000012
Figure FDA0002693439250000013
Figure FDA0002693439250000014
Figure FDA0002693439250000015
wherein, FRFRepresenting an analog precoding matrix, FBBRepresenting the baseband precoding matrix, WRFRepresenting an analog combined code matrix, WBBRepresenting a baseband combined code matrix, PtotalRepresents the total power consumption;
Figure FDA0002693439250000016
represents the total spectral efficiency, log, per unit bandwidth2(|1+γk,i|) represents the spectral efficiency of each data stream,kirepresenting a minimum rate threshold, γ, to meet the user's QoS requirementsk,iRepresenting the signal-to-noise ratio of the ith data stream for user k;
Figure FDA0002693439250000017
representing the constant modulus constraint of the analog precoding, NBSDenotes the number of base station antennas, MBSRepresenting the number of radio frequency links of the base station;
Figure FDA0002693439250000018
representing the simulation in combination with the code constant modulus constraint,NMSrepresenting the number of user antennas, MMSRepresenting the number of radio frequency links of each user; k denotes the number of users, NsRepresenting the number of data streams received by each user, P representing the maximum total transmit power;
signal-to-noise ratio gamma of ith data stream of the user kk,iExpressed as:
Figure FDA0002693439250000021
wherein the content of the first and second substances,
Figure FDA0002693439250000022
represents the power of the data stream that user k desires to receive;
Figure FDA0002693439250000023
representing the interference power between different data streams received by user k;
Figure FDA0002693439250000024
representing the interference power between different users received by user k;
Figure FDA0002693439250000025
represents the noise power received by user k;
Figure FDA0002693439250000026
a baseband combined code matrix representing the ith data stream for the kth user,
Figure FDA0002693439250000027
an analog combined code matrix representing user k, H represents a channel, HkChannel, F, representing user kRFRepresenting an analog precoding matrix, FBB(ki) denotes the k-thA base band pre-coding matrix of the ith data stream of the user;
wherein the content of the first and second substances,
Figure FDA0002693439250000028
representing a baseband precoding matrix;
Figure FDA0002693439250000029
representing an analog combined code matrix;
Figure FDA00026934392500000210
a base-band combined code matrix is represented,
Figure FDA00026934392500000211
a baseband combined code matrix representing a kth user;
the total power consumption PtotalExpressed as:
Figure FDA00026934392500000212
wherein, PRFRepresenting the power consumption, P, of each radio frequency linkcRepresenting the total power consumption of the rest of the base station, excluding the radio frequency link, and alpha represents the power amplification factor.
2. The energy-efficiency-based multi-user multi-stream downlink hybrid precoding method of claim 1, wherein the solving of the optimal baseband precoding matrix and the optimal baseband combined code matrix by a convex optimization algorithm comprises:
firstly, Dinkelbach method is adopted to convert the energy efficiency model, then the converted energy efficiency model is further converted into a convex function model through the equivalent relation of weighted mean square error and spectral efficiency, and finally a block coordination descent algorithm is adopted to iteratively solve the maximum energy efficiency, the optimal baseband pre-coding matrix and the optimal baseband combination code matrix based on the converted convex function model.
3. An energy-efficiency-based multi-user multi-stream downlink hybrid precoding system, the energy-efficiency-based multi-user multi-stream downlink hybrid precoding system comprising:
the energy efficiency model establishing module is used for establishing an energy efficiency model based on the transmission rate of the maximized unit power in a large-scale antenna system by simultaneously considering the interference among users, the interference among multiple data streams of each user and noise;
the optimal unconstrained matrix solving module is used for solving an optimal unconstrained simulation precoding matrix and an optimal unconstrained simulation combined code matrix through a block diagonalization algorithm based on the energy efficiency model;
the optimal simulation matrix solving module is used for quantizing the optimal unconstrained simulation precoding matrix and the optimal unconstrained simulation combined code matrix and adding constant modulus constraint to obtain an optimal simulation precoding matrix and an optimal simulation combined code matrix;
the optimal baseband matrix solving module is used for fixing the optimal analog pre-coding matrix and the optimal analog combined code matrix and solving the optimal baseband pre-coding matrix and the optimal baseband combined code matrix through a convex optimization algorithm;
wherein the energy efficiency model based on maximizing the transmission rate per unit power is expressed as:
Ρ1：
Figure FDA0002693439250000031
s.t.
Figure FDA0002693439250000032
Figure FDA0002693439250000033
Figure FDA0002693439250000034
Figure FDA0002693439250000035
wherein, FRFRepresenting an analog precoding matrix, FBBRepresenting the baseband precoding matrix, WRFRepresenting an analog combined code matrix, WBBRepresenting a baseband combined code matrix, PtotalRepresents the total power consumption;
Figure FDA0002693439250000036
represents the total spectral efficiency, log, per unit bandwidth2(|1+γk,i|) represents the spectral efficiency of each data stream,kirepresenting a minimum rate threshold, γ, to meet the user's QoS requirementsk,iRepresenting the signal-to-noise ratio of the ith data stream for user k;
Figure FDA0002693439250000041
representing the constant modulus constraint of the analog precoding, NBSDenotes the number of base station antennas, MBSRepresenting the number of radio frequency links of the base station;
Figure FDA0002693439250000042
representing simulation combined with code constant modulus constraint, NMSRepresenting the number of user antennas, MMSRepresenting the number of radio frequency links of each user; k denotes the number of users, NsRepresenting the number of data streams received by each user, P representing the maximum total transmit power;
signal-to-noise ratio gamma of ith data stream of the user kk,iExpressed as:
Figure FDA0002693439250000043
wherein the content of the first and second substances,
Figure FDA0002693439250000044
represents the power of the data stream that user k desires to receive;
Figure FDA0002693439250000045
representing the interference power between different data streams received by user k;
Figure FDA0002693439250000046
representing the interference power between different users received by user k;
Figure FDA0002693439250000047
represents the noise power received by user k;
Figure FDA0002693439250000048
a baseband combined code matrix representing the ith data stream for the kth user,
Figure FDA0002693439250000049
an analog combined code matrix representing user k, H represents a channel, HkChannel, F, representing user kRFRepresenting an analog precoding matrix, FBB(ki) a baseband precoding matrix representing the ith data stream for the kth user;
wherein the content of the first and second substances,
Figure FDA00026934392500000410
representing a baseband precoding matrix;
Figure FDA00026934392500000411
representing an analog combined code matrix;
Figure FDA00026934392500000412
a base-band combined code matrix is represented,
Figure FDA00026934392500000413
a baseband combined code matrix representing a kth user;
the total power consumption PtotalExpressed as:
Figure FDA00026934392500000414
wherein, PRFRepresenting the power consumption, P, of each radio frequency linkcRepresenting the total power consumption of the rest of the base station, excluding the radio frequency link, and alpha represents the power amplification factor.
4. The energy-efficiency-based multi-user multi-stream downlink hybrid precoding system of claim 3, wherein the optimal baseband matrix solving module is specifically configured to:
firstly, Dinkelbach method is adopted to convert the energy efficiency model, then the converted energy efficiency model is further converted into a convex function model through the equivalent relation of weighted mean square error and spectral efficiency, and finally a block coordination descent algorithm is adopted to iteratively solve the maximum energy efficiency, the optimal baseband pre-coding matrix and the optimal baseband combination code matrix based on the converted convex function model.
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