CN105846879A - Iterative beam forming method of millimeter wave precoding system - Google Patents

Iterative beam forming method of millimeter wave precoding system Download PDF

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CN105846879A
CN105846879A CN201610439487.2A CN201610439487A CN105846879A CN 105846879 A CN105846879 A CN 105846879A CN 201610439487 A CN201610439487 A CN 201610439487A CN 105846879 A CN105846879 A CN 105846879A
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CN105846879B (en
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娄念念
成先涛
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming

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Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a method for reducing antenna training overhead of iterative beam forming by utilizing inter-channel sparsity in a wireless multiple input multiple output (MIMO) communication system. The invention provides an iterative beam forming method of a millimeter wave precoding system, and is used for overcoming the defect of high antenna training overhead of a power iteration method of a large-scale MIMO system. According to the method, the spatial sparsity of a millimeter wave channel is utilized, and the estimation problem of receiving vectors of millimeter wave MIMO antenna training is converted into a sparse reconstruction problem so that the loss is reduced by utilizing the correlation theory of compression perception.

Description

A kind of iteration beam-forming method in millimeter wave pre-coding system
Technical field
The invention belongs to wireless communication technology field, particularly relate to a kind of at wireless multiple-input and multiple-output (Multiple Input Multiple Output, MIMO) communication system utilize the antenna of interchannel openness reduction iteration beam shaping to instruct The method practicing expense.
Background technology
Beam forming technique is a kind of array signal process technique, is considered as spatial domain linear filtering, in mimo systems May be used for overcoming path loss, improve received signal to noise ratio, promote power system capacity etc..In beam forming technique, it is important to obtain Must be at the optimum array signal weighing vector of the satisfied setting criterion of sending and receiving end under current channel condition.In capacity optiaml ciriterion Under, the singular vector that the beamforming weight vector of sending and receiving end is the singular value decomposition (SVD) by channel matrix and obtains, tool Body principle is described below:
The reception antenna number assuming mimo system is NT, transmitting number of antennas is NR, channel matrixPermissible Carry out SVD decomposition, be expressed as H=U Λ VH, wherein, ()HRepresenting matrix conjugate transpose,WithBe respectively size be NR×NRWith NT×NTUnitary matrice, Λ is a NR×NTDiagonal matrix, its diagonal angle Singular value (the σ of the unit H for arranging in descending order1,σ2,...σm), m=min (NT,NR)。
For NSThe beam shaping of dimension, transmitting terminal and receiving terminal beamforming matrix are respectively adopted described channel matrix H The front m row of right singular matrix V and left singular matrix U, i.e. F=[v1,v2,...,vm], W=[u1,u2,...,um], wherein, NS≤ m。
Assume that sending symbol is x=[x1,x2,...,xm]T, receiving symbol is y=[y1,y2,...,ym]T, noiseThen
Visible, mimo channel is equally divided into m subchannel the most independent, every sub-channels by feature wave-beam shaping All obtain maximized signal to noise ratio.
Generally, receiving terminal is by estimating channel matrix H and carrying out SVD and decompose and obtain the beam shaping square of receiving-transmitting sides Battle array, the beamforming matrix F of transmitting terminal is fed back to transmitting terminal by receiving terminal afterwards.The method of this direct estimation and feedback is suitable for In the situation that number of antennas is less, and (such as, the antenna number of millimeter wave mimo system in the mimo system that number of antennas is more Up to tens, mesh), its computation complexity and training expense all become to bear.
In time division duplex (Time Division Duplex, TDD) system, utilize the mutual of up channel and down channel Yi Xing, it is proposed that a kind of iteration beam-forming method that need not estimate that channel parameter can obtain characteristic vector, i.e. power iteration side Method.The most this method be extend to the beam shaping of multidimensional, i.e. by the way of the stripping of stage one by one, obtain NS Individual beam forming vector, namely beamforming matrix, each stage will experience and take turns power iteration.Tradition dominant eigenvalue exists In the iteration in one stage, during forward iteration, recipient is in order to obtain complete reception vector, it is assumed that recipient uses unit square Battle array is as receiving beamforming matrix, and transmitting terminal must send same training sequence NTSecondary.In like manner, during inverse iteration, receive End must send training sequence NRSecondary.Assume that presetting iterations is NITER, then the iteration transmitting-receiving number of times in a stage is NITER (NT+NR), the expense of iteration and being comprehensively directly proportional of receiving-transmitting sides number of antennas.
Visible, when the number of antennas of receiving-transmitting sides is less, expense is little, but is as the increase of number of antennas, training The expense in stage is multiplied along with number of antennas.
Summary of the invention
In order to overcome the defect that in extensive mimo system, dominant eigenvalue antenna training expense is excessive, the present invention proposes one Planting the iteration beam-forming method in millimeter wave pre-coding system, the method utilizes the spatial sparsity of millimeter wave channel, will The estimation problem receiving vector in the training of millimeter wave mimo antenna is converted into sparse Problems of Reconstruction, thus utilizes the phase of compressed sensing Close theory and reduce loss.
In order to describe present disclosure easily, first the concept used in the present invention and term are defined.
Spatial sparsity: wireless signal is due to higher path loss and the scattering property of extreme difference, and receiving-transmitting sides is only by having Several electromagnetic wave propagation paths of limit are connected, and the signal computational problem relevant with channel can be expressed as sparse reconstruction easily Problem.
Sparse multi-path channel model: Sparse multi-path channel can be modeled as the geometric model with K road multipathWherein,Represent the complex channel gain in the i-th footpath, θiRepresent the i-th footpath leaves angle, φiRepresent the arrival angle in the i-th footpath, aT(φi) it is the antenna-array response of transmitting terminal, aR(θi) be receiving terminal aerial array ring Should, i=1,2 ..., K.Described aerial array uses uniform linear array (ULAs), then the antenna-array response of transmitting terminal is permissible It is expressed asThe antenna-array response of receiving terminal can be expressed asWherein, λ is signal wavelength, and d is antenna spacing, typically takes
A kind of iteration beam-forming method in millimeter wave pre-coding system, step is as follows:
S1, utilize the geometric model of Sparse multi-path channel to carry out sparse modeling, by with channels associated receive estimating of signal Meter problem representation becomes the recovery problem of sparse signal, defines receiving terminal dictionary matrix
Definition transmitting terminal dictionary matrixWherein, N Representing receiving terminal dictionary length, M represents receiving terminal dictionary length；
S2, carrying out the foundation of angular quantification code book, receiving terminal code book isSend out Penetrating end code book isWherein, For Codebook size, NTFor transmission antenna number, NRFor reception antenna number；
S3, starting stage process, specific as follows:
S31, transmitting terminal generate a NT× 1 vector [1,0,0 ..., 0]TAnd be normalized as initial vector, and put Enter code book uses sparse signal recovery algorithms to estimate to obtain initial vector f；
S32, define the transmitting terminal during this and the pendulous frequency Nmr of receiving terminal respectively0,Nmt0；
S33, in O random matrix, choose transmitting terminal optimum calculation matrix ΦT 0, choose the optimum calculation matrix of receiving terminal ΦR 0, wherein, O is the natural number being not zero, and chooses transmitting terminal optimum calculation matrix ΦT 0Square is measured with choosing the optimum of receiving terminal Battle array ΦR 0For micro-judgment process；
S34, reception beamforming vectors training, specific as follows:
S34-1, transmitting terminal continuously transmit Nmt0Secondary vector f is to receiving terminal, and receiving terminal uses Φ during receivingR 0Row make Merging vector for beam shaping weighting, receiving terminal obtainsWherein, nRRepresent the additive white gaussian of receiving terminal Noise vector,
S34-2, receiving terminal use sparse signal recovery algorithms to calculate expression and receive direction of arrival at dictionary described in S1 Matrix ARDIn sparse vector z of positionR, wherein, zRBeing the column vector of N × 1, N represents dictionary ARDLength, zRIn have K nonzero element, K ＜ ＜ N；
S34-3、Hf≈ARDzR, described Hf is stored in NRIn × 1 vector g, i.e. g=ARDzR, wherein, channel matrixVector g is normalized, i.e. by receiving terminalAnd willIt is back to transmitting terminal；
S35, transmission beamforming vectors training, specific as follows:
S35-1, receiving terminal continuously transmit Nmr0Vector described in secondary S34-3To transmitting terminal, transmitting terminal makes during receiving Use ΦT 0Row as beam shaping weighting merge vector, transmitting terminal obtainsWherein, nTRepresent that kth time is repeatedly For the additive white Gaussian noise vector of transmitting terminal,
S35-2, transmitting terminal use sparse signal recovery algorithms to calculate expression and receive direction of arrival at dictionary described in S1 Matrix ATDIn sparse vector z of positionT, wherein, zTBeing the column vector of M × 1, M represents dictionary ATDLength, zTIn have K nonzero element, K ＜ ＜ M；
S35-3、DescribedIt is stored in NTIn × 1 vector f, i.e. f=ATDzT, vector g is carried out by receiving terminal Normalization, i.e.And willIt is back to transmitting terminal；
S4, iterative process, specific as follows:
S41, define the transmitting terminal during this and the pendulous frequency Nmr, Nmt of receiving terminal respectively；
S42, find S24-2, angle corresponding to the position of K nonzero element in S25-2, and K angle is put intoIndividual Phase place finds the most nearest phase place, finally obtains phase place Its transposition is the calculation matrix of transmitting terminal and receiving terminal, wherein,Individual phase place is to be carried out by-π～πIndividual quantization；
S43, receive beamforming vectors training, specific as follows: transmitting terminal continuously transmits Nmt vector f to receiving terminal, connects Receiving end uses Φ during receivingRRow as beam shaping weighting merge vector, receiving terminal obtains r=ΦR H(Hf+nR), connect Receiving end uses method of least square to calculate coefficient hR=(ΦRAR)-1R, and try to achieve v=ARhR, and v is put in code book use dilute Dredging signal recovery algorithms to estimate, the vector v after estimating is normalized, i.e. by receiving terminalAnd willIt is back to send out Sending end；
S44, transmission beamforming vectors training, specific as follows: receiving terminal continuously transmits Nmr vectorTo transmitting terminal, send out Sending end uses Φ during receivingTRow as beam shaping weighting merge vector, receiving terminal obtainsConnect Receiving end uses method of least square to calculate coefficient hT=(ΦTAT)-1T, and try to achieve f=AThT, and f is put in code book use dilute Dredging signal recovery algorithms to estimate, the vector f after estimating is normalized, i.e. by receiving terminalAnd willIt is back to send out Sending end, i.e. returns to S33 and is iterated；
S5, the v that finally will obtain after iteration, f exports.
Further, O=10000 described in S33.
The invention has the beneficial effects as follows:
Present invention preserves the benefit of dominant eigenvalue, i.e. without estimating channel condition information, better astringency.Meanwhile, Utilize the spatial sparsity of millimeter wave mimo channel, without retransmiting and number of antennas one obtaining received signal vector when The same training sequence of the many number of times of sample, and have only to send the number of times far fewer than number of antennas, and this array system have adjusted often The signal phase of individual antenna.
The present invention is similar with dominant eigenvalue, uses the mode of multistage Projection Iteration, can be easily extended to multithread In the antenna training of mimo system.
Accompanying drawing explanation
Fig. 1 millimeter wave MIMO beamforming system figure.
Fig. 2 is the flow chart of simulated program of the present invention.
Fig. 3 is the volumetric properties curve comparison figure that the present invention is applied to the situation of second-rate beam shaping.
Fig. 4 is the volumetric properties curve comparison figure that the present invention is applied to the situation of four stream beam shapings.
Detailed description of the invention
Below in conjunction with embodiment and accompanying drawing, describe technical scheme in detail.
Millimeter wave MIMO beamforming system figure as shown in Figure 1, show in figure is to have NSThe MIMO system of individual data stream System, uses feature wave-beam shaping, then transmitting terminal beamforming matrixReceiving terminal wave beam becomes Shape matrix
Fig. 3 is the volumetric properties curve that the present invention is applied to the situation of second-rate beam shaping, the most respectively SVD bar Curve under part, the curve of the present invention.Fig. 4 is the volumetric properties curve that the present invention is applied to the situation of four stream beam shapings.
Embodiment,
S1, utilize the geometric model of Sparse multi-path channel to carry out sparse modeling, by with channels associated receive estimating of signal Meter problem representation becomes the recovery problem of sparse signal, defines receiving terminal dictionary matrix
Definition transmitting terminal dictionary matrixWherein, N Representing receiving terminal dictionary length, M represents receiving terminal dictionary length, and N is the biggest, represents that quantization is the finest, thus quantization error is the least, M is the biggest, represents that quantization is the finest, thus quantization error is the least；
S2, carrying out the foundation of angular quantification code book, receiving terminal code book isSend out Penetrating end code book isWherein, For Codebook size, NTFor transmission antenna number, NRFor reception antenna number；
S3, starting stage process, specific as follows:
S31, transmitting terminal generate a NT× 1 vector [1,0,0 ..., 0]TAnd be normalized as initial vector, and put Enter code book uses sparse signal recovery algorithms to estimate to obtain initial vector f；
S32, define the transmitting terminal during this and the pendulous frequency Nmr of receiving terminal respectively0,Nmt0；
S33, in 10000 random matrixes, find expression transmitting terminal and the optimum calculation matrix Φ of receiving terminalT 0,ΦR 0；
S34, reception beamforming vectors training, specific as follows:
S34-1, transmitting terminal continuously transmit Nmt0Secondary vector f is to receiving terminal, and receiving terminal uses Φ during receivingR 0Row make Merging vector for beam shaping weighting, receiving terminal obtainsWherein, nRRepresent the additive white gaussian of receiving terminal Noise vector,
S34-2, receiving terminal use sparse signal recovery algorithms to calculate expression and receive direction of arrival at dictionary described in S1 Matrix ARDIn sparse vector z of positionR, wherein, zRBeing the column vector of N × 1, N represents dictionary ARDLength, zRIn have K nonzero element, K ＜ ＜ N；
S34-3、Hf≈ARDzR, described Hf is stored in NRIn × 1 vector g, i.e. g=ARDzR, wherein, channel matrixVector g is normalized, i.e. by receiving terminalAnd willIt is back to transmitting terminal；
S35, transmission beamforming vectors training, specific as follows:
S35-1, receiving terminal continuously transmit Nmr0Vector described in secondary S34-3To transmitting terminal, transmitting terminal makes during receiving Use ΦT 0Row as beam shaping weighting merge vector, transmitting terminal obtainsWherein, nTRepresent that kth time is repeatedly For the additive white Gaussian noise vector of transmitting terminal,
S35-2, transmitting terminal use sparse signal recovery algorithms to calculate expression and receive direction of arrival at dictionary described in S1 Matrix ATDIn sparse vector z of positionT, wherein, zTBeing the column vector of M × 1, M represents dictionary ATDLength, zTIn have K nonzero element, K ＜ ＜ M；
S35-3、DescribedIt is stored in NTIn × 1 vector f, i.e. f=ATDzT, vector g is carried out by receiving terminal Normalization, i.e.And willIt is back to transmitting terminal；
S4, iterative process, specific as follows:
S41, define the transmitting terminal during this and the pendulous frequency Nmr, Nmt of receiving terminal respectively；
S42, find S24-2, angle corresponding to the position of K nonzero element in S25-2, and K angle is put intoIndividual Phase place finds the most nearest phase place, finally obtains phase place Its transposition is the calculation matrix of transmitting terminal and receiving terminal, wherein,Individual phase place is to be carried out by-π～πIndividual quantization；
S43, receive beamforming vectors training, specific as follows: transmitting terminal continuously transmits Nmt vector f to receiving terminal, connects Receiving end uses Φ during receivingRRow as beam shaping weighting merge vector, receiving terminal obtains r=ΦR H(Hf+nR), connect Receiving end uses method of least square to calculate coefficient hR=(ΦRAR)-1R, and try to achieve v=ARhR, and v is put in code book use dilute Dredging signal recovery algorithms to estimate, the vector v after estimating is normalized, i.e. by receiving terminalAnd willIt is back to send out Sending end；
S44, transmission beamforming vectors training, specific as follows: receiving terminal continuously transmits Nmr vectorTo transmitting terminal, send out Sending end uses Φ during receivingTRow as beam shaping weighting merge vector, receiving terminal obtainsConnect Receiving end uses method of least square to calculate coefficient hT=(ΦTAT)-1T, and try to achieve f=AThT, and f is put in code book use dilute Dredging signal recovery algorithms to estimate, the vector f after estimating is normalized, i.e. by receiving terminalAnd willIt is back to send out Sending end, i.e. returns to S33 and is iterated；
S5, the v that finally will obtain after iteration, f exports.

Claims (2)

1. the iteration beam-forming method in millimeter wave pre-coding system, it is characterised in that comprise the steps:
S1, utilize the geometric model of Sparse multi-path channel to carry out sparse modeling, the estimation receiving signal with channels associated is asked Topic is expressed as the recovery problem of sparse signal, defines receiving terminal dictionary matrix
Definition transmitting terminal dictionary matrixWherein, N Representing receiving terminal dictionary length, M represents receiving terminal dictionary length；
S2, carrying out the foundation of angular quantification code book, receiving terminal code book isSend out Penetrating end code book isWherein, For Codebook size, NTFor transmission antenna number, NRFor reception antenna number；
S3, starting stage process, specific as follows:
S31, transmitting terminal generate a NT× 1 vector [1,0,0 ..., 0]TAnd be normalized as initial vector, and put into code Sparse signal recovery algorithms is used to estimate to obtain initial vector f in Ben；
S32, define the transmitting terminal during this and the pendulous frequency Nmr of receiving terminal respectively0,Nmt0；
S33, in O random matrix, choose transmitting terminal optimum calculation matrix ΦT 0, choose the optimum calculation matrix Φ of receiving terminalR 0, Wherein, O is the natural number being not zero, and chooses transmitting terminal optimum calculation matrix ΦT 0With the optimum calculation matrix choosing receiving terminal ΦR 0For micro-judgment process；
S34, reception beamforming vectors training, specific as follows:
S34-1, transmitting terminal continuously transmit Nmt0Secondary vector f is to receiving terminal, and receiving terminal uses Φ during receivingR 0Row as ripple Beam shaping weighting merges vector, and receiving terminal obtainsWherein, nRRepresent the additive white Gaussian noise of receiving terminal Vector,
S34-2, receiving terminal use sparse signal recovery algorithms to calculate expression and receive direction of arrival at dictionary matrix described in S1 ARDIn sparse vector z of positionR, wherein, zRBeing the column vector of N × 1, N represents dictionary ARDLength, zRIn have K Nonzero element, K ＜ ＜ N；
S34-3、Hf≈ARDzR, described Hf is stored in NRIn × 1 vector g, i.e. g=ARDzR, wherein, channel matrixConnect Vector g is normalized, i.e. by receiving endAnd willIt is back to transmitting terminal；
S35, transmission beamforming vectors training, specific as follows:
S35-1, receiving terminal continuously transmit Nmr0Vector described in secondary S34-3To transmitting terminal, transmitting terminal uses during receiving ΦT 0Row as beam shaping weighting merge vector, transmitting terminal obtainsWherein, nTRepresent kth time iteration The additive white Gaussian noise vector of transmitting terminal,
S35-2, transmitting terminal use sparse signal recovery algorithms to calculate expression and receive direction of arrival at dictionary matrix described in S1 ATDIn sparse vector z of positionT, wherein, zTBeing the column vector of M × 1, M represents dictionary ATDLength, zTIn have K Nonzero element, K ＜ ＜ M；
S35-3、DescribedIt is stored in NTIn × 1 vector f, i.e. f=ATDzT, receiving terminal carries out normalizing to vector g Change, i.e.And willIt is back to transmitting terminal；
S4, iterative process, specific as follows:
S41, define the transmitting terminal during this and the pendulous frequency Nmr, Nmt of receiving terminal respectively；
S42, find S24-2, angle corresponding to the position of K nonzero element in S25-2, and K angle is put intoIndividual phase place In find the most nearest phase place, finally obtain phase place By it Transposition is the calculation matrix of transmitting terminal and receiving terminal, wherein,Individual phase place is to be carried out by-π～πIndividual quantization；
S43, reception beamforming vectors training, specific as follows: transmitting terminal continuously transmits Nmt vector f to receiving terminal, receiving terminal Φ is used during receptionRRow as beam shaping weighting merge vector, receiving terminal obtains r=ΦR H(Hf+nR), receiving terminal Method of least square is used to calculate coefficient hR=(ΦRAR)-1R, and try to achieve v=ARhR, and v is put in code book and use sparse letter Number recovery algorithms is estimated, the vector v after estimating is normalized, i.e. by receiving terminalAnd willIt is back to transmitting terminal；
S44, transmission beamforming vectors training, specific as follows: receiving terminal continuously transmits Nmr vectorTo transmitting terminal, transmitting terminal Φ is used during receptionTRow as beam shaping weighting merge vector, receiving terminal obtainsReceiving terminal Method of least square is used to calculate coefficient hT=(ΦTAT)-1T, and try to achieve f=AThT, and f is put in code book and use sparse letter Number recovery algorithms is estimated, the vector f after estimating is normalized, i.e. by receiving terminalAnd willIt is back to send End, i.e. returns to S33 and is iterated；
S5, the v that finally will obtain after iteration, f exports.
A kind of iteration beam-forming method in millimeter wave pre-coding system of volume the most according to claim 1, its feature It is: O=10000 described in S33.
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