CN110138427B - Large-scale multi-input multi-output hybrid beam forming algorithm based on partial connection - Google Patents

Large-scale multi-input multi-output hybrid beam forming algorithm based on partial connection Download PDF

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CN110138427B
CN110138427B CN201910400887.6A CN201910400887A CN110138427B CN 110138427 B CN110138427 B CN 110138427B CN 201910400887 A CN201910400887 A CN 201910400887A CN 110138427 B CN110138427 B CN 110138427B
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precoding matrix
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CN110138427A (en
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庞立华
吴文捷
赵恒�
牛晓娟
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GUANGZHOU ITS COMMUNICATION EQUIPMENT Co.,Ltd.
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Xian University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • 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
    • 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
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
    • 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
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting

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Abstract

The invention discloses a large-scale multi-input multi-output hybrid beam forming algorithm based on partial connection.A given partial connection framework system optimal unconstrained precoder, an initial analog precoding matrix and convergence tolerance, an initial digital precoding matrix is calculated according to the unconstrained precoder and the initial analog precoding matrix, and then an initial error is calculated; when the initial error is smaller than or equal to the convergence tolerance, taking the initial analog precoding matrix and the initial digital precoding matrix as the optimal analog precoding matrix and the optimal digital precoding matrix of the partial connection architecture system; when the initial error is larger than the convergence tolerance, performing iterative computation by taking the minimized error as a target to obtain an optimal analog precoding matrix and an optimal digital precoding matrix; a part of the connection framework system generates and sends out mixed beams according to the optimal analog pre-coding matrix and the optimal digital pre-coding matrix; the invention improves the performance of the algorithm, and leads the performance to be close to the performance of a full digital beam forming system.

Description

Large-scale multi-input multi-output hybrid beam forming algorithm based on partial connection
[ technical field ] A method for producing a semiconductor device
The invention belongs to the technical field of communication, and particularly relates to a large-scale multi-input multi-output hybrid beam forming algorithm based on partial connection.
[ background of the invention ]
The Massive MIMO technology can obviously improve the performance and the capacity of the 5G wireless communication system. The number of the receiving and transmitting antennas of the Massive MIMO system is large, and the digital beam forming technology is used in the system, so that the system implementation cost and the energy consumption are high, and the obstruction is brought to the application of the Massive MIMO beam forming technology.
In order to overcome the problem, a Massive MIMO hybrid beamforming scheme (i.e., a partial connection architecture) is presented, and compared with a digital beamforming technology, using the hybrid beamforming technology can reduce system implementation cost and energy consumption, that is, in the partial connection architecture, each rf chain is only connected to a partial antenna, although the system sacrifices a gain of partial beamforming, the implementation complexity of hardware is greatly reduced, but in the partial connection architecture, performance loss is relatively serious based on some algorithms designed by an encoder.
As shown in fig. 1, it is a downlink communication transmission model of Massive MIMO system under a partial connection structure. In this system, the transmitting end is equipped with NtA receiving end equipped with a single antenna, and a transmitting end for transmitting NsThe stripe data flows to the receiving end. For realizing multi-data stream transmission, the transmitting end is equipped
Figure BDA0002059719250000011
A radio frequency chain, each radio frequency chain is connected with LtThe receiving end of the root antenna is provided with a single radio frequency chain.
Assuming the array antenna model is ULA, the spacing of the antennas is half the wavelength. Defining a dimension vector fiAnd a dimension vector fiEach element in (1) satisfies
Figure BDA0002059719250000012
Then the whole analog precoding matrix at the transmitting end is
Figure BDA0002059719250000021
To simplify the following description, an index set Ω is defined1From FRFThe position composition of zero element is (i, j) ∈ Ω1When the temperature of the water is higher than the set temperature,
Figure BDA0002059719250000022
where i and j represent the number of rows and columns, respectively, of the analog precoding matrix,
Figure BDA0002059719250000023
elements representing ith row and jth column in analog precoding matrix, digital precoding matrix F in transmitting end structureBBHas the dimension of
Figure BDA0002059719250000024
The total power constraint of the transmitting end is normalized by FBBRealization, satisfies | | FRFFBB||＝Ns。
Considering a narrowband block flat fading channel, its received signal may be denoted as y ═ HFRFFBBs + N, y is dimension Nr× 1, H is the dimension Nr×NtChannel matrix of, NrThe number of antennas at the receiving end is shown, s is a transmitted symbol vector, and the mathematical period is satisfied
Figure BDA0002059719250000025
Figure BDA0002059719250000026
With a representation dimension of Ns×NsN is a noise vector, follows an independent identically distributed gaussian distribution with a mean of 0 and a variance of σ2。
In the above partial connection architecture, since the analog domain precoding matrix is constant modulus, the optimization problem is non-convex, so that solving the optimization problem at the transmitting end is very complicated, and a large amount of time is consumed to solve the globally optimal precoding matrix of the optimization problem, which results in increasing the energy loss of the existing partial connection architecture system and greatly improving the network delay.
[ summary of the invention ]
The invention aims to provide a large-scale multi-input multi-output hybrid beam forming algorithm based on partial connection, which improves the performance of the algorithm on the premise of controlling the cost and the energy consumption, so that the performance of the algorithm is close to that of a full digital beam forming system, and the energy consumption of a partial connection architecture system is reduced.
The invention adopts the following technical scheme: a large-scale multiple-input multiple-output hybrid beam forming algorithm based on partial connection comprises the following steps:
giving an optimal unconstrained precoder, an initial analog precoding matrix and a convergence tolerance of a part of connection architecture system, and calculating an initial digital precoding matrix according to the unconstrained precoder and the initial analog precoding matrix so as to calculate an initial error;
when the initial error is smaller than or equal to the convergence tolerance, taking the initial analog precoding matrix and the initial digital precoding matrix as the optimal analog precoding matrix and the optimal digital precoding matrix of the partial connection architecture system;
when the initial error is larger than the convergence tolerance, carrying out iterative calculation by taking the minimized error as a target until the error is smaller than or equal to the convergence tolerance or the maximum iteration times is finished, and taking the corresponding analog precoding matrix and the corresponding digital precoding matrix as an optimal analog precoding matrix and an optimal digital precoding matrix;
and generating and sending out mixed beams by the part of the connection architecture system according to the optimal analog pre-coding matrix and the optimal digital pre-coding matrix.
Further, the optimal unconstrained precoder is obtained by the following steps:
obtaining a channel matrix according to the channel state information of the partial connection architecture system
Figure BDA0002059719250000031
Wherein N iscIndicates the number of clusters, NpIndicating the number of paths in each cluster, αilThe gain factor of the ith transmission path in the ith reflector cluster is expressed by theta for the (i, l) th sub-pathr,ilAnd
Figure BDA0002059719250000032
respectively representing azimuth and elevation angles, theta, of the departure anglet,ilAnd
Figure BDA0002059719250000033
respectively representing the azimuth and elevation angles of the angle of arrival,
Figure BDA0002059719250000034
and
Figure BDA0002059719250000035
respectively representing the azimuth angle thetar,ilAnd
Figure BDA0002059719250000036
pitch angle thetat,ilAnd
Figure BDA0002059719250000037
corresponding receive array responses and transmit array responses;
by H ═ U ∑ VHPerforming singular value decomposition on the channel matrix H to obtain an identity matrix V, and extracting the first N from the identity matrix VsColumn derived matrix V1(ii) a By passing
Figure BDA0002059719250000038
Calculate the diagonal matrix and pass F*＝V1Obtaining the optimal unconstrained precoder F*(ii) a Where U is an identity matrix and Σ is a diagonal matrix.
Further, by
Figure BDA0002059719250000039
Deriving an initial digital precoding matrix
Figure BDA00020597192500000310
And pass through
Figure BDA0002059719250000041
Deriving an initial error0。
Further, the specific process of iterative computation is as follows:
setting an objective function
Figure BDA0002059719250000042
Transformation of | | | F*-FRFFBB||FCan obtain the product
Figure BDA0002059719250000043
Wherein, TnIs F*N-th matrix block of (1), fnDenotes the composition FRFThe nth matrix block of (a) is,
Figure BDA0002059719250000044
is represented by FBBThe nth row vector of (1);
the objective function is optimized to
Figure BDA0002059719250000045
Wherein, tn,mTo represent
Figure BDA0002059719250000046
The (c) th column (c) of (c),
Figure BDA0002059719250000047
to represent
Figure BDA0002059719250000048
The m-th element of (a) is,
Figure BDA0002059719250000049
f for the k-th iterationRFThe nth matrix block of (a) is,
Figure BDA00020597192500000410
is shown as
Figure BDA00020597192500000411
The increment of the phase of the mth element of (c),
Figure BDA00020597192500000412
Figure BDA00020597192500000413
after the k iteration FBBThe nth row vector of (1);
Figure BDA00020597192500000414
to represent
Figure BDA00020597192500000415
An average value of the phase increment;
obtaining a simulation pre-coding matrix after optimizing the objective function
Figure BDA00020597192500000416
And further deriving a digital precoding matrix
Figure BDA00020597192500000417
And error ofk；
Will be wrongkCompared with the convergence tolerance when
Figure BDA00020597192500000423
Then, the corresponding analog precoding matrix is used
Figure BDA00020597192500000418
And a digital precoding matrix
Figure BDA00020597192500000419
As an optimal analog pre-coding matrix and an optimal digital pre-coding matrix; when in use
Figure BDA00020597192500000420
Then, the steps are repeated until the error is less than or equal to the convergence tolerance or the maximum iteration times are finished, and the corresponding simulation precoding matrix is used
Figure BDA00020597192500000421
And a digital precoding matrix
Figure BDA00020597192500000422
As an optimal analog precoding matrix and an optimal digital precoding matrix.
The invention has the beneficial effects that: the invention is based on a Massive MIMO mixed beam forming system, aims at improving the frequency spectrum efficiency, adopts an alternate optimization algorithm based on matrix decomposition to design a mixed pre-coding matrix, carries out singular value decomposition on a channel matrix, designs an optimal unconstrained digital pre-coder and a synthesizer, designs a final pre-coder through alternate optimization, improves the performance of the algorithm on the premise of controlling the cost and energy consumption as much as possible, enables the performance of the algorithm to be close to that of a full-digital beam forming system, solves the problem of performance loss, and effectively verifies the performance of the algorithm.
[ description of the drawings ]
FIG. 1 is a diagram of a downlink communication transmission model of a Massive MIMO system under a partial connection structure in the prior art;
FIG. 2 is a flow chart of the algorithm of the present invention;
FIG. 3 is a graph comparing spectral efficiencies of various algorithms in a validation embodiment of the present invention;
FIG. 4 is a graph illustrating the effect of different data streams on the spectral efficiency of the algorithm in a verification embodiment of the present invention.
[ detailed description ] embodiments
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses a large-scale multi-input multi-output hybrid beam forming algorithm based on partial connection, which is used for acquiring channel state information from a base station to all user terminals
Figure BDA0002059719250000051
Wherein HkAnd the information fading from the base station to a user K is shown, wherein the K is the total number of users in the cell. An optimal digital precoding matrix and a synthesizer matrix of a base station end are designed based on singular value decomposition, and a digital precoding matrix and an analog precoding matrix are designed through alternate optimization. Information data transmission begins by first passing the transmitted signal through a digital precoder FBBThen pass through
Figure BDA0002059719250000052
A radio frequency link, and then
Figure BDA0002059719250000053
An analog precoder, then LtThe root antenna simultaneously feeds the signal to the radio channel. At the receiving end, an analog precoder receives the signal in the channel, via a radio frequency chain, and via a digital precoder WBBA signal is received.
As shown in fig. 2, the method of the present invention specifically comprises:
and giving an optimal unconstrained precoder, an initial analog precoding matrix and a convergence tolerance of the partial connection architecture system, and calculating an initial digital precoding matrix according to the unconstrained precoder and the initial analog precoding matrix so as to calculate an initial error.
The optimal unconstrained precoder is obtained by the following steps:
obtaining a channel matrix according to the channel state information of the partial connection architecture system
Figure BDA0002059719250000061
Wherein N iscIndicates the number of clusters, NpIndicating the number of paths in each cluster, αilThe gain factor of the ith transmission path in the ith reflector cluster is expressed by theta for the (i, l) th sub-pathr,ilAnd
Figure BDA0002059719250000062
respectively representing azimuth and elevation angles, theta, of the departure anglet,ilAnd
Figure BDA0002059719250000063
respectively representing the azimuth and elevation angles of the angle of arrival,
Figure BDA0002059719250000064
and
Figure BDA0002059719250000065
respectively representing the azimuth angle thetar,ilAnd
Figure BDA0002059719250000066
pitch angle thetat,ilAnd
Figure BDA0002059719250000067
corresponding receive array responses and transmit array responses;
by H ═ U ∑ VHSingular value decomposition of a channel matrix HObtaining an identity matrix V, and extracting the first N from the identity matrix VsColumn derived matrix V1(ii) a By passing
Figure BDA0002059719250000068
Calculate the diagonal matrix and pass F*＝V1Obtaining the optimal unconstrained precoder F*(ii) a Where U is an identity matrix and Σ is a diagonal matrix.
By passing
Figure BDA0002059719250000069
Deriving an initial digital precoding matrix
Figure BDA00020597192500000610
And pass through
Figure BDA00020597192500000611
Deriving an initial error0。
And when the initial error is smaller than or equal to the convergence tolerance, taking the initial analog precoding matrix and the initial digital precoding matrix as the optimal analog precoding matrix and the optimal digital precoding matrix of the partial connection architecture system.
And when the initial error is larger than the convergence tolerance, carrying out iterative calculation by taking the minimized error as a target until the error is smaller than or equal to the convergence tolerance or the maximum iteration times is finished, and taking the corresponding analog precoding matrix and the corresponding digital precoding matrix as the optimal analog precoding matrix and the optimal digital precoding matrix.
The specific process of iterative computation is as follows:
setting an objective function
Figure BDA0002059719250000071
And iteratively optimizing two parameters, namely an analog precoding matrix and a digital precoding matrix.
Transformation of | | | F*-FRFFBB||FCan obtain the product
Figure BDA0002059719250000072
Wherein，TnIs F*N-th matrix block of (1), fnDenotes the composition FRFThe nth matrix block of (a) is,
Figure BDA0002059719250000073
is represented by FBBThe nth row vector of (1), the maximum number of iterations in the present invention can be set to Ku100, convergence tolerance is set to
Figure BDA0002059719250000074
Then the objective function is optimized since it should be satisfied in the system
Figure BDA0002059719250000075
So during the optimization process, the normalization constraints are temporarily removed
Figure BDA0002059719250000076
The simplified optimization problem is as follows:
Figure BDA0002059719250000077
defining the kth iteration of hybrid precoding as
Figure BDA0002059719250000078
Assuming that the initial is known
Figure BDA0002059719250000079
Then
Figure BDA00020597192500000710
Closed form solution of
Figure BDA00020597192500000711
On the contrary, when it is known
Figure BDA00020597192500000712
Can update
Figure BDA00020597192500000713
Figure BDA00020597192500000714
In the process of updating
Figure BDA00020597192500000715
When F is greater than F, the following derivation is performedBBAt the time of giving, define
Figure BDA00020597192500000716
Is viThen can obtain
Figure BDA00020597192500000717
Considering FRFThe derivation process is as follows:
Figure BDA0002059719250000081
wherein, TnIs F*Of dimension Lt×Ns。
The optimization problem described above can be translated into
Figure BDA0002059719250000082
Sub-questions, wherein the nth sub-question is
Figure BDA0002059719250000083
To solve the optimization problem, assume that
Figure BDA0002059719250000084
Search pair in a small range
Figure BDA0002059719250000085
Update and define
Figure BDA0002059719250000086
The phase of the m-th element is
Figure BDA0002059719250000087
Can be expressed as
Figure BDA0002059719250000088
Then it is determined that,
Figure BDA0002059719250000089
can be expressed as
Figure BDA00020597192500000810
Wherein the content of the first and second substances,
Figure BDA00020597192500000811
to represent
Figure BDA00020597192500000812
Increment of phase of m-th element when
Figure BDA00020597192500000813
Making Taylor expansion approximation very small
Figure BDA00020597192500000814
Figure BDA00020597192500000815
Wherein the content of the first and second substances,
Figure BDA00020597192500000816
is a vector, a symbol
Figure BDA00020597192500000817
Defined as the Hadamard product, the optimization problem can be reconstructed as
Figure BDA0002059719250000091
The above-mentioned optimization problem is a convex quadratic objective function, and has considered the over-constant modulus constraintThe above formula is based on approximation
Figure BDA0002059719250000092
Is reconstructed only when
Figure BDA0002059719250000093
Is only satisfied when it is very small, therefore
Figure BDA0002059719250000094
Must be added, the optimization problem then translates into
Figure BDA0002059719250000095
This is now a convex optimization problem, once obtained
Figure BDA0002059719250000096
Can obtain
Figure BDA0002059719250000097
Further define the
Figure BDA0002059719250000098
The m-th element of (a) is
Figure BDA0002059719250000099
Is the m-th column of (1) as tn,mThe above equation can be derived as:
Figure BDA00020597192500000910
the optimization problem can be translated into a solution LtA sub-question, wherein the mth sub-question is
Figure BDA00020597192500000911
Obtaining a simulation pre-coding matrix after optimizing the objective function
Figure BDA00020597192500000912
And further deriving a digital precoding matrix
Figure BDA00020597192500000913
And error ofk；
Will be wrongkCompared with the convergence tolerance when
Figure BDA00020597192500000914
Then, the corresponding analog precoding matrix is used
Figure BDA00020597192500000915
And a digital precoding matrix
Figure BDA00020597192500000916
As an optimal analog pre-coding matrix and an optimal digital pre-coding matrix; when in use
Figure BDA00020597192500000917
Then, the steps are repeated until the error is less than or equal to the convergence tolerance or the maximum iteration times are finished, and the corresponding simulation precoding matrix is used
Figure BDA00020597192500000918
And a digital precoding matrix
Figure BDA00020597192500000919
As an optimal analog precoding matrix and an optimal digital precoding matrix.
And generating and sending out mixed beams by the part of the connection architecture system according to the optimal analog pre-coding matrix and the optimal digital pre-coding matrix.
The first verification embodiment:
for the partial connection architecture system of the present invention, the corresponding spectral efficiency is
Figure BDA0002059719250000101
Wherein the content of the first and second substances,
Figure BDA0002059719250000102
is the signal-to-noise ratio, P represents the average transmission power between the base station and the mobile station, sigma1The first dimension representing the diagonal matrix Σ is Ns×NsIs defined as
Figure BDA0002059719250000103
When Gaussian symbols are transmitted in the channel, then the spectral efficiency is
Figure BDA0002059719250000104
Wherein the content of the first and second substances,
Figure BDA0002059719250000105
is Ns×NsP is the average transmission power, NsIs the number of data streams, RnIs a noise covariance matrix, Rn＝σ2FRFFBBσ denotes the variance, HkIs a channel matrix;
hybrid precoding design can be achieved by solving an optimal solution to the optimization problem of the transmitting end, which aims at maximizing spectral efficiency, i.e.
Figure BDA0002059719250000106
Wherein the content of the first and second substances,
Figure BDA0002059719250000107
is the element of the ith row and jth column in the analog precoding matrix,
Figure BDA0002059719250000108
is the element in the ith row and jth column of the digital precoding matrix.
The system adopts a geometric Saleh-Vallenzuela narrowband cluster channel model. The antenna arrays of the base station and the user terminal both adopt uniform linear arrays, and the array response vector of the array is
Figure BDA0002059719250000111
If a uniform linear array is used, the receive array response and the transmit array response in the channel matrix of the above formula are replaced with αULA(θ)Where N denotes the number of array elements in the linear array, λ denotes the carrier wavelength, d denotes the spacing between antennas, and θ denotes the off-angle from/arrival angle.
In the simulation part, not only a millimeter wave cluster channel model but also a Rayleigh channel model are considered. In the rayleigh fading channel model, each element in the normalized channel matrix H follows an independent equal distribution with a mean of 0 and a variance of σ2Complex gaussian distribution.
The numerical result shows that the performance of the hybrid precoding method can be close to that of a high-dimensional full-digital beamforming system and is far higher than that of an analog beamforming system. The analog precoding design of the invention is based on equal gain transmission, and the digital precoding matrix design is based on singular value decomposition. Finally, the design of the hybrid precoder is based on alternate optimization, so that the hybrid precoder is close to an optimal unconstrained matrix, and the system performance is close to the performance of a full digital beamforming algorithm.
Verification example two:
in the embodiment, the advantages of the spectrum efficiency and the performance of other algorithms of the matrix decomposition-based alternating optimization algorithm are verified through Matlab simulation.
In this embodiment, the number of antennas respectively equipped at the transmitting and receiving ends is 128 and 1, the number of radio frequency chains is 4, the number of data streams is 2, and a millimeter wave channel model of the ULA array model is used, and specific values of parameters in the model are as described in table 1.
Parameter assignment in the example of Table 1
Figure BDA0002059719250000112
Figure BDA0002059719250000121
As shown in fig. 3, which shows the results of a comparison of spectral efficiencies for various algorithms. The performance of the analog beamforming system is the worst, and the performance of the digital beamforming system is the best. The performance of the algorithm provided by the invention is obviously superior to the performance of analog beam forming, and is superior to the performance of the algorithm in the prior art, and is closest to the performance of a full digital beam forming system.
As shown in fig. 4, the effect of the number of data streams on the spectral efficiency of the algorithm is shown. Compared with the spectral efficiency of the proposed algorithm with the number of data streams of 1, 2, 4 and 8, as the number of data streams increases, the spectral efficiency of the system increases and the system performance becomes better.

Claims (2)

1. A large-scale MIMO hybrid beam forming method based on partial connection is characterized by comprising the following steps:
giving an optimal unconstrained precoder, an initial analog precoding matrix and a convergence tolerance of a part connection architecture system, and calculating an initial digital precoding matrix according to the optimal unconstrained precoder and the initial analog precoding matrix so as to calculate an initial error;
by passing
Figure FDA0002609163230000011
Deriving an initial digital precoding matrix
Figure FDA0002609163230000012
And pass through
Figure FDA0002609163230000013
Deriving an initial error0；
Figure FDA0002609163230000014
In order to initially model the pre-coding matrix,
Figure FDA0002609163230000015
is composed of
Figure FDA0002609163230000016
By conjugate transpose of (F)*Is an optimal unconstrained precoder;
when the initial error is smaller than or equal to the convergence tolerance, taking the initial analog precoding matrix and the initial digital precoding matrix as an optimal analog precoding matrix and an optimal digital precoding matrix of the partial connection architecture system;
when the initial error is larger than the convergence tolerance, carrying out iterative calculation by taking a minimized error as a target until the error is smaller than or equal to the convergence tolerance or the maximum iteration times is finished, and taking the corresponding analog precoding matrix and the corresponding digital precoding matrix as an optimal analog precoding matrix and an optimal digital precoding matrix;
the specific process of the iterative computation is as follows:
setting an objective function
Figure FDA0002609163230000017
Transformation of | | | F*-FRFFBB||FCan obtain the product
Figure FDA0002609163230000018
Wherein, TnIs F*N-th matrix block of (1), fnDenotes the composition FRFThe nth matrix block of (a) is,
Figure FDA0002609163230000019
is represented by FBBThe nth row vector of (1);
the objective function is optimized to
Figure FDA0002609163230000021
Figure FDA0002609163230000022
Wherein, tn,mTo represent
Figure FDA0002609163230000023
The (c) th column (c) of (c),
Figure FDA0002609163230000024
to represent
Figure FDA0002609163230000025
The m-th element of (a) is,
Figure FDA0002609163230000026
f for the k-th iterationRFThe nth matrix block of (a) is,
Figure FDA0002609163230000027
is shown as
Figure FDA0002609163230000028
The increment of the phase of the mth element of (c),
Figure FDA0002609163230000029
Figure FDA00026091632300000210
after the k iteration FBBThe nth row vector of (1);
Figure FDA00026091632300000211
to represent
Figure FDA00026091632300000212
An average value of the phase increment;
obtaining a simulation pre-coding matrix after optimizing the objective function
Figure FDA00026091632300000213
And further deriving a digital precoding matrix
Figure FDA00026091632300000214
And error ofk；
Will be wrongkCompared with the convergence tolerance when
Figure FDA00026091632300000215
Then, the corresponding analog precoding matrix is used
Figure FDA00026091632300000216
And a digital precoding matrix
Figure FDA00026091632300000217
As an optimal analog pre-coding matrix and an optimal digital pre-coding matrix; when in use
Figure FDA00026091632300000218
Then, the above steps are repeated until the error is less than or equal to the convergence tolerance or the maximum iteration times is finished, and the corresponding analog precoding matrix is used
Figure FDA00026091632300000219
And a digital precoding matrix
Figure FDA00026091632300000220
As an optimal analog pre-coding matrix and an optimal digital pre-coding matrix;
FRFto simulate a precoding matrix, FBBFor a digital precoding matrix, s.t. indicates that … satisfies the condition, i and j indicate the number of rows and columns, Ω, respectively, of the analog precoding matrix1In order to be an index set, the index set,
Figure FDA00026091632300000221
representing the elements of the ith row and jth column of the analog precoding matrix, NtIndicating the number of antennas at the transmitting end,
Figure FDA00026091632300000222
indicating the number of radio frequency chains at the transmitting end,
Figure FDA00026091632300000223
is composed of
Figure FDA00026091632300000224
The phase of the m-th element in (b),
Figure FDA00026091632300000225
is TnThe conjugate transpose of (a) is performed,
Figure FDA00026091632300000226
is a convergence tolerance;
and the part of the connection architecture system generates and sends out mixed beams according to the optimal analog pre-coding matrix and the optimal digital pre-coding matrix.
2. The method of claim 1, wherein the optimal unconstrained precoder is derived by:
obtaining a channel matrix according to the channel state information of the partial connection architecture system
Figure FDA0002609163230000031
Wherein N iscNumber of scattering clusters, N, representing channel matrix HpIndicating the number of paths in each cluster, αilThe gain factor of the ith transmission path in the ith reflector cluster is expressed by theta for the (i, l) th sub-pathr,ilAnd
Figure FDA0002609163230000032
respectively representing azimuth and elevation angles, theta, of the departure anglet,ilAnd
Figure FDA0002609163230000033
respectively representing the azimuth and elevation angles of the angle of arrival,
Figure FDA0002609163230000034
and
Figure FDA0002609163230000035
respectively representing the azimuth angle thetar,ilAnd a pitch angle
Figure FDA0002609163230000036
Azimuth angle thetat,ilAnd a pitch angle
Figure FDA0002609163230000037
Corresponding receive array responses and transmit array responses; n is a radical oftNumber of antennas representing transmitting end, NrThe number of antennas at the receiving end is represented;
by H ═ U ∑ VHPerforming singular value decomposition on the channel matrix H to obtain an identity matrix V, and extracting the first N from the identity matrix VsColumn derived matrix V1(ii) a By passing
Figure FDA0002609163230000038
Calculate the diagonal matrix and pass F*＝V1Obtaining the optimal unconstrained precoder F*(ii) a Where U is an identity matrix and Σ is a diagonal matrix.
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* Cited by examiner, † Cited by third party
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CN108599825A (en) * 2018-02-12 2018-09-28 大连理工大学 A kind of hybrid coding method based on MIMO-OFDM millimeters of wave structures
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* Cited by examiner, † Cited by third party
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