CN108377160A - MIMO method for precoding based on dynamic channel conditions under a kind of high-speed mobile - Google Patents
MIMO method for precoding based on dynamic channel conditions under a kind of high-speed mobile Download PDFInfo
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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
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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
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
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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
- H04B7/06—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
- H04B7/0619—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 using feedback from receiving side
- H04B7/0621—Feedback content
- H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
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Abstract
The present invention proposes the MIMO method for precoding based on dynamic channel conditions under a kind of high-speed mobile.First, mimo system model is established according to high-speed mobile scene.Secondly, according to the system model established, modular algebra precoding (Tomlison HarashimaPrecoding, THP) scheme is built.Then, the channel matrix of dynamic channel conditions information is built based on channel statistic.Finally, under transmission power constraints, using least mean-square error as optimization aim, weighting matrix G and optimal pre-coding matrix B and F are derived by.MIMO method for precoding under high-speed mobile environment proposed by the present invention, uses dynamic channel conditions information model, it is contemplated that the statistical property of high-speed mobile environment lower channel makes the channel state information that transmitting terminal obtains closer to actual channel.Dynamic channel conditions model is combined with THP and is applied in the precoding under high-speed mobile, multi-user interference can be eliminated and improves precoding accuracy, design reference is provided for the high-speed mobile communications of high quality.
Description
Technical field
The present invention relates to high ferro mobile communication method for precoding, especially one kind under high ferro system of broadband wireless communication
MIMO method for precoding based on dynamic channel conditions under high-speed mobile.
Background technology
With the fast development of mobile communication, demand of the people to telecommunication service quality is continuously increased, traditional low rate
Mobile communication system have been unable to meet the demand of user.This is just to the reliability transmitted in mobile radio communications system and high speed
Property proposes higher challenge.Therefore, next-generation mobile communications long term evolution (Long Term Evolution, LTE) and LTE-
A (LTE-Advanced) technology is suggested.Researcher proposes the concept of LTE-R (LTE-Railway) for high ferro scene, but
It is that there is presently no formation standards, still there are many technological challenges.
MIMO introduces multi-antenna technology, using spatial domain as another new resources.It is transmissible since antenna number increases
Data flow becomes more independent, enhances the reliability of transmission, improves signal quality.Therefore, MIMO technology can inhibit channel
Decline improves spectrum efficiency, to improve the efficiency of transmission and power system capacity of system.However, just because of multiple antennas is used,
Interference is inevitably resulted between different user.This just needs receiving terminal using more complicated detection algorithm to restore number
According to.But in practical applications, the design of detection algorithm is frequently subjected to power consumption and terminal size limitation.Therefore, it is received to simplify
The detection algorithm at end is used to channel state information (Channel State Information, CSI) in transmitting terminal.This
Kind is used to CSI in transmitting terminal, and carrying out pretreated technology to data is called precoding technique.
In recent years, a large amount of research there has been to the method for precoding under high-speed mobile.There are the precoding based on code book, example
Precoding such as based on day line options, the precoding based on discrete fourier, and prelisting based on Householder variations
Code.Precoding also based on non-code book, such as singular value decomposition, geometric mean decomposition and unified channel decomposing algorithm.But it is existing
Some method for precoding are all linear predictive codings, and processing is simple, but performance is unsatisfactory.Meanwhile existing precoding is all false
If the CSI obtained is perfect, and the CSI actually obtained is not perfect, therefore, using the precoding of ideal CSI designs
Larger error can be caused in practical applications.
Under high-speed mobile scene, significant change, traditional channel estimation skill occur within a symbol transmission period for CSI
Art has become no longer accurate, and the fast time variant of channel causes the CSI obtained after experience feedback delay no longer can relatively accurately carve
Draw real channel situation at that time.The statistical information of channel is combined with the time-dependent behavior of channel, it is dynamic to constitute transmitting terminal
State CSI models, instantaneous CSI is compensated using the statistical property of channel, can preferably embody current channel characteristic, so as to
Raising prelists code performance.On the other hand, modular algebra precoding (Tomlison-Harashima Precoding, THP) is a kind of
Nonlinear precoding, the precoding introduce the nonlinear operations such as modulus, feedback, and by reasonably pre-processing, THP can make transmission
Data better adapt to channel time-varying characteristics and reduce the influence that user's interference fringe comes, and improve system performance, and forced on capacity
Nearly mimo channel theoretical value.Therefore, dynamic channel conditions model is combined the precoding applied under high-speed mobile with THP
In, it can effectively eliminate multi-user interference and improve precoding accuracy.
To sum up, for existing method for precoding, their processing method is simple, is not suitable for high-speed mobile scene, in advance
The poor problem of coding efficiency, the present invention propose the MIMO method for precoding based on dynamic channel conditions under a kind of high-speed mobile.
Invention content
The present invention is directed at least solve the technical problems existing in the prior art, a kind of high speed shifting is especially innovatively proposed
MIMO method for precoding based on dynamic channel conditions under dynamic.
In order to realize the present invention above-mentioned purpose, the present invention provides under a kind of high-speed mobile based on dynamic channel conditions
MIMO method for precoding, detailed process are as follows：
It is assumed that in one in a wideband MIMO system single cell multiple-user network downlink, base station (Base
Station, BS) deploy NTRoot transmission antenna；For simplicity, it is assumed that BS and N number of user (N≤NT) between transmission data,
Each user is equipped with 1 antenna receiver；N number of user data N-dimensional vector a=[a1,a2,...,aN]T(symbol []TIt indicates
Transposition operates) it indicates, M-QAM constellations (M indicates modulation system number) are derived from, power is
The block diagram of the THP schemes considered is as shown in Figure 1.It is by a permutation matrix Π, and feedback square formation Β is N number of non-thread
Property modulo operation and NTThe feedforward matrix F compositions of × N-dimensional.Permutation matrix changes user data according to transmission power distribution principle
Precoding sequence, will be assigned to the high user data of transmission power and preferentially carries out precoding.After permutation matrix, weight is obtained
User data after new sortUser data after rearrangement is carried out to precoding again can be with
Greatly improve the performance of precoding.In order to which system physical can be realized, it is based on least mean-square error (Minimum Mean Square
Error, MMSE) multi-user THP in feedback matrix BKIt is set as strictly lower triangular matrix, allows data pre- in a recursive manner
Coding, and modulo operation is independent to the real and imaginary parts that it is inputted, and modulo operation is carried out according to following rules：
Wherein, symbolIndicate that minimum integer is greater than or equal to c.In practice, modulo operation MODM(x) by the reality of x
Number partial periodicity is mapped to intervalIt is interior.In this way, the symbol of precoding processingIt can be constrained to just
Square regionAnd with linear pre-filtering, transmission power correspondingly subtracts
It is small.
According to formula (1), it is seen that the symbol of precodingIt calculates with being iterated, it is as follows
Wherein, []n,lIndicate the line n l column elements of Closed Matrices,Be real and imaginary parts all
It is the plural number of suitable integer, can reducesValue, so that it is dropped into square areaObviously, (a unique p exists
Such a attribute).Formula (2) indicates that the modulo operator in Fig. 1 is equivalent to input data symbol addition vectorSince feedback matrix Β is a strictly lower triangular matrix, and equivalent block diagram is obtained, such as Fig. 2,
We define the vector v for having had modified dataK=aK+dK.Therefore, pre-encode operation formula (2) can be rewritten as in the matrix formOr it is equivalent to
Pre-coded symbolsIt is transmitted to feedforward matrix F.Final NTDimensional vectorPass through the N of base station BSTRoot antenna array
In channel.Channel can pass through N × NTThe H-matrix of dimension carrys out mathematical notation.Particularly, [H]n,iIt indicates to emit from i-th
Channel gain of the antenna to n-th reception antenna.Then, in the discrete of n-th mobile terminal (Mobile Terminal, MT)
Signal can be written as：
Wherein, hnIndicate the line n of H, nnExpression thermal noise, is a zero-mean and variance isStochastic variable, on
Formula can be further represented as：
Wherein, hnnxnnIt is data of the signal of corresponding user n after channel gain, it can be understood as useful data,It is interference of the data to the user of other users.Only eliminate the interference from other users, receiving terminal ability
Solve useful information.
In order to eliminateInfluence, in receiving terminal, each sampled signal rnIt is passed to automatic growth control
(Automatic Gain Control, AGC) unit, then arrives modulo operation identical with transmitting terminal.Finally to output signalEstimated.
All subscriber signals received are added to a vector r=[r1,r2,...,rN]T, convolution (3), we
It can be write as：
R=Hx+n=HF (B+I)-1vK+n (6)
Wherein n=[n1,n2,...,nN]TIt is a zero-mean, variance isGaussian vectors.We are by channel matrix H
Carry out QR decomposition, i.e. H=QHTH=G-1MTH, T is unitary matrice, TTH=I, Q are lower triangular matrixs, and G is that only have member on diagonal line
The matrix of element, M is the diagonal lower triangular matrix of unit, i.e.,We enable precoding feedforward matrix F=T, prelist
Code matrix B+I=M, then received vector can be expressed as
R=G-1MTHF(B+I)-1V+w=G-1v+w (7)
It can be seen that after pre-encode operation, receiving terminal can extract effective data vector, receive signal
The interference between user is eliminated, pre-encode operation depends on pre-coding matrix F and B.Due under high-speed mobile condition, believing
Road has fast time variant, therefore CSI is dynamic change.Using the statistical property dynamic corrections CSI of channel, therefore above formula
In channel matrix H indicated with the two parts for being relatively fixed constant and apparent time-varying：
Wherein, HmIt is the mean value of channel；The part for indicating channel variation, is characterized with the correlation of channel.Channel it is equal
Value and correlation can correspond to the estimated value and error covariance of channel.Therefore, in delivery time s, the CSI of channel can
With by the estimated value of channelAnd its error covariance ReIt constitutes, is characterized as below：
It is now assumed that oneself knows the initial measurement H of 0 moment of transmitting terminal channel0With the statistical information of channel, channel mean value Hm、
Characterize the channel covariancc R of the spatial coherence in mimo system between all transmittings and reception antenna pair0, channel self tuning side
Poor Rs.Using MMSE estimation theories, channel is in moment s optimal estimation valueWith the error covariance R in estimatione,sIt can characterize
For：
Wherein,AsRow vector；Desired operation is sought in E [] expressions；Covariance is sought in cov [] expressions
Operation.Assuming that the time statistical property of all antennas pair is identical, then the spatial coherence of channel is independently to deposit with temporal correlation
, therefore, the auto-covariance R of channelsIt can be expressed as
Rs=βsR0 (12)
Wherein, βsFor the time correlation coefficient of channel.That is, the N of transmitting terminalTBetween root antenna and the N root antennas of receiving terminal
NNTA channel possesses identical time correlation function.
Simplify the time correlation model of above-mentioned (10) (11), simplified model can be effectively isolated channel time variation pair
The influence of transmitting terminal CSI.The estimated value of channelWith the error covariance matrix R in estimationeIt can be rewritten as：
WhereinL is to take the window of CSI mean values long, HkFor in the transient channel measured value of sampling time k.
Therefore, transmitting terminal CSI can briefly portray the time correlation coefficient β for channels, channel measured value Η0, channel mean value
HmWith covariance R0Function.
R0For Positive Semidefinite Hermitian Matrix, the wherein element on diagonal line characterizes NN respectivelyTThe transmission power of a channel
Gain rather than cornerwise element have then reacted intercoupling between each channel.It is built based on Kronecker structures
Channel model in, R0It can be analyzed to transmitting terminal Antenna CorrelationWith receiving terminal Antenna Correlation RrKronecker accumulate shape
Formula：
Wherein, RtWith RrIt is Positive Semidefinite Hermitian Matrix,Similarly,Formula (13)-(15) are substituted into formula (9), the letter based on dynamic CSI can be obtained
Road matrix H：
It is sorted according to best-first, each row vector of channel matrix H is ranked up, the channel square after being sorted
Battle array HKFor
As shown in figure 3, the channel matrix H after sequenceKIt will produce corresponding feedback matrix BK, weighting matrix GKWith feedforward square
Battle array FK.Received vector rKIt can be expressed as
rK=HKxK+nK (18)
Wherein,xKFor NTWhat root transmission antenna was sent
Data vector is expressed as
xK=(x1,x2,...,xNT)T=FK(BK+I)-1(aK+dK) (19)
After the system for sending multi-user THP of the signal after sequence, the data vector r before receiving terminal judgementK' and send
Hold effective input vector v of active feedback channelKCorresponding, error e is expressed as
Wherein,To adjudicate the equivalent noise of leading portion.According to the thought of MMSE, ensureing that sending vector meets
Under conditions of transmission power constraint, seek rational feedforward matrix FK, feedback matrix BKAnd weighting matrix GKSo that error vector
Minimum meets formula (21).Therefore, MMSE object functions and constraints can be built
Wherein, PTIndicate that total emission power, using orthogonality principle, has because direct solution is relatively difficult
That is received vector rKIt is orthogonal with error vector e.By error vectorSubstitution formula (22), has
Wherein,Formula (18) is substituted into above formula again, is obtained
Wherein,Assuming that each element of signal vector is mutually orthogonal, thenFor diagonal matrix.It enablesHave
Wherein feedback matrix BKFor strictly lower triangular matrix.It is not cooperateed between multiuser downstream channel user to meet
Actual conditions, weighting matrix GKIt is set as diagonal matrix.In order not to change the transmission power of transmission data, and also to obtain
The solution of closed form, feedforward matrix FKIt is assumed to be unitary matrice, meetsTherefore above formula abbreviation is：
And it obtains：
Further according toIt obtains：
I.e.：
It enablesAbove formula is represented by：
It is rightMake Cholesky decomposition, you can obtain lower triangular matrix RK.It will
RKThe inverse of main diagonal element can construct weighting matrix G as the elements on diagonal lineK,
Feedback matrix BKIt can be expressed as：
BK=GKRK-I(32)
According to frontIt can obtain feedforward matrix FK：
So far, we have solved corrects the optimal of CSI under transmission power constraints based on channel statistic
Pre-coding matrix F and B, and improve signal-to-noise ratio, meet different user service quality (Quality of Service,
QoS) demand.
As described above, the present invention is based on dynamic channel conditions and THP method for precoding, channel matrix H and have been derived
The expression formula that n user receives signal, using MMSE as optimization aim, is finally obtained optimal under transmission power constraints
The mathematic(al) representation of pre-coding matrix B and F.This method is completely eliminated under high-speed mobile scene between the multi-user of mimo system
Interference, provide design reference for the high-speed mobile communications of high quality.
The beneficial effects of the invention are as follows：
Traditional method for precoding based on perfect CSI is not suitable for high-speed mobile environment, and the code performance that prelists is bad, no
The interference between multi-user can be completely eliminated, and the precoding motion that existing national and foreign standards tissue is put into effect cannot meet
Growing client is solved for a long time, mimo system is more under high-speed mobile environment to communicating QoS demand using the present invention
The problem interfered between user.
Description of the drawings
Fig. 1 downlink transmitting terminal multi-user's modular algebra pre-coding system models；
Fig. 2 downlink transmitting terminal multi-user's modular algebra pre-coding system equivalent models；
Downlink multiuser modular algebra pre-coding system model after Fig. 3 sequences；
The flow chart of MIMO method for precoding based on dynamic channel conditions under Fig. 4 high-speed mobiles.
Specific implementation mode
Hereinafter, description embodiments of the present invention, examples of the embodiments are shown in the accompanying drawings, wherein phase from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached drawing
The embodiment of description is exemplary, and is only used for explaining the present invention, and is not considered as limiting the invention.
In the description of the present invention, unless otherwise specified and limited, it should be noted that term " installation ", " connected ",
" connection " shall be understood in a broad sense, for example, it may be mechanical connection or electrical connection, can also be the connection inside two elements, it can
, can also indirectly connected through an intermediary, for the ordinary skill in the art to be to be connected directly, it can basis
Concrete condition understands the concrete meaning of above-mentioned term.
The MIMO precoding process based on dynamic channel conditions is as shown in figure 4, specific as follows under high-speed mobile：
Step 1：Start.
Step 2：Establish system model.It is assumed that a single cell multiple-user network downlink in a wideband MIMO system
Link, in this network, BS deploys NTRoot transmission antenna.For simplicity, it is assumed that BS and N number of user (N≤NT) between
Transmission data, each user are equipped with 1 antenna receiver.N number of user data N-dimensional vector a=[a1,a2,...,aN]T(symbol
[·]TIndicate transposition operation) it indicates, M-QAM constellations (M indicates modulation system number) are derived from, power is
Step 3：According to the system model that step 2 is established, the precoding model of THP schemes is built.It is by a displacement square
Battle array Π, feeds back square formation Β, N number of non-linear modulo operation and NTThe feedforward matrix F compositions of × N-dimensional.Permutation matrix is according to transmitting work(
Rate distribution principle changes the precoding sequence of user data, will be assigned to the high user data of transmission power and preferentially prelists
Code.After permutation matrix, the user data after being resequencedAfter resequencing
User data carry out precoding again and can greatly improve the performance of precoding.In order to which system physical can be realized, based on MMSE's
Feedback matrix B in multi-user THPKIt is set as strictly lower triangular matrix, allows data precoding in a recursive manner, and modulus is grasped
The real and imaginary parts that it is inputted of opposing are independent, and modulo operation is carried out according to following rules：
SymbolIndicate that minimum integer is greater than or equal to c.In practice, modulo operation MODM(x) by the real part of x
Divide Periodic Maps to intervalIt is interior.In this way, the symbol of precoding processingSquare can be constrained to
RegionAnd with linear pre-filtering, transmission power correspondingly reduces.
According to modulo operation, it is seen that the symbol of precodingIt calculates with being iterated, it is as follows
[·]n,lIndicate the line n l column elements of Closed Matrices,Be real and imaginary parts all it is suitable
Integer plural number, can reduceValue, so that it is dropped into square areaObviously, (a unique p exists in this way
One attribute).Above formula indicates that modulo operator is equivalent to input data symbol addition vectorBy
It is a strictly lower triangular matrix in feedback matrix Β, and obtains equivalent block diagram, we defines the vector for having had modified data
vK=aK+dK.Therefore, pre-encode operation can be rewritten as in the matrix formOr it is equivalent to
Pre-coded symbolsIt is transmitted to feedforward matrix F.Final NTDimensional vectorPass through the N of base station BSTRoot antenna array
In channel.Channel can pass through N × NTThe H-matrix of dimension carrys out mathematical notation.Particularly, [H]n,iIt indicates to emit from i-th
Channel gain of the antenna to n-th reception antenna.Then, it can be written as in the discrete signal of n-th of MT：
Wherein hnIndicate the line n of H, nnExpression thermal noise, is a zero-mean and variance isStochastic variable.In order to
It eliminatesInfluence, in receiving terminal, each sampled signal rnIt is passed to automatic growth control (AGC) unit, then arrives and sends out
The identical modulo operation of sending end.Finally to output signalEstimated.
All subscriber signals received are added to a vector r=[r1,r2,...,rN]T, convolutionWe can be write as：
R=Hx+n=HF (B+I)-1vK+n
Wherein n=[n1,n2,...,nN]TIt is a zero-mean, variance isGaussian vectors, F and B are precoding squares
Battle array, I is unit matrix, vKIt is valid data vector.
Step 4：The channel measurement letter of transmitting terminal is fed back to according to the precoding model of step 3 foundation and from receiving terminal
Breath, obtains the channel matrix H based on dynamic CSI.Channel matrix H is indicated with the two parts for being relatively fixed constant and apparent time-varying：
Wherein, HmIt is the mean value of channel；The part for indicating channel variation, is characterized with the correlation of channel.Channel it is equal
Value and correlation can correspond to the estimated value and error covariance of channel.Therefore, in delivery time s, the CSI of channel can
With by the estimated value of channelAnd its error covariance ReIt constitutes, is characterized as below：
It is now assumed that oneself knows the initial measurement H of 0 moment of transmitting terminal channel0With the statistical information of channel, channel mean value Hm、
Characterize the channel covariancc R of the spatial coherence in mimo system between all transmittings and reception antenna pair0, channel self tuning side
Poor Rs.Using MMSE estimation theories, channel can be characterized as in moment s optimal estimation value and the error covariance in estimation：
Wherein,AsRow vector；Desired operation is sought in E [] expressions；Covariance is sought in cov [] expressions
Operation.Assuming that the time statistical property of all antennas pair is identical, then the spatial coherence of channel is independently to deposit with temporal correlation
, therefore, the auto-covariance R of channelsIt can be expressed as
Rs=βsR0
Wherein, βsFor the time correlation coefficient of channel.That is, the N of transmitting terminalTBetween root antenna and the N root antennas of receiving terminal
NNTA channel possesses identical time correlation function.
Simplify above-mentioned time correlation model, simplified model can be effectively isolated channel time variation to transmitting terminal
The influence of CSI.The estimated value of channelWith the error covariance matrix R in estimationeIt can be rewritten as：
WhereinL is to take the window of CSI mean values long, HkFor in the transient channel measured value of sampling time k.
Therefore, transmitting terminal CSI can briefly portray the time correlation coefficient β for channels, channel measured value Η0, channel mean value
HmWith covariance R0Function.
R0For Positive Semidefinite Hermitian Matrix, the wherein element on diagonal line characterizes NN respectivelyTThe transmission power of a channel
Gain rather than cornerwise element have then reacted intercoupling between each channel.It is built based on Kronecker structures
Channel model in, R0It can be analyzed to transmitting terminal Antenna CorrelationWith receiving terminal Antenna Correlation RrKronecker accumulate shape
Formula：
Wherein, RtWith RrIt is Positive Semidefinite Hermitian Matrix,Together
Reason, Simultaneous formulaWithIt can obtain being based on dynamic CSI
Channel matrix H：
Step 5：It is sorted, each row vector of channel matrix H is ranked up, after being sorted according to best-first
Channel matrix HKFor
Channel matrix H after sequenceKIt will produce corresponding feedback matrix BK, weighting matrix GKWith feedforward matrix FK.It receives
Vectorial rKIt can be expressed as
rK=HKxK+nK
Wherein,xKFor NTWhat root transmission antenna was sent
Data vector is expressed as
After the system for sending multi-user THP of the signal after sequence, the data vector r before receiving terminal judgementK' and send
Hold effective input vector v of active feedback channelKCorresponding, error e is expressed as
Wherein,To adjudicate the equivalent noise of leading portion.According to the thought of MMSE, ensureing that sending vector meets
Under conditions of transmission power constraint, seek rational feedforward matrix FK, feedback matrix BKAnd weighting matrix GKSo that error vector
Minimum meets following formula.Therefore, MMSE object functions and constraints can be built
Wherein, PTIndicate that total emission power, using orthogonality principle, has because direct solution is relatively difficult
That is received vector rKIt is orthogonal with error vector e.By error vectorAbove formula is substituted into, is had
Wherein,Association type r againK=HKxK+nK, obtain
Wherein,Assuming that each element of signal vector is mutually orthogonal, thenFor diagonal matrix.It enablesHave
Wherein feedback matrix BKFor strictly lower triangular matrix.It is not cooperateed between multiuser downstream channel user to meet
Actual conditions, weighting matrix GKIt is set as diagonal matrix.In order not to change the transmission power of transmission data, and also to obtain
The solution of closed form, feedforward matrix FKIt is assumed to be unitary matrice, meetsTherefore formulaAbbreviation is：
And it obtains：
Further according toIt obtains：
I.e.：
It enablesAbove formula is represented by：
It is rightMake Cholesky decomposition, you can obtain lower triangular matrix RK。
By RKThe inverse of main diagonal element can construct weighting matrix G as the elements on diagonal lineK；
Feedback matrix BKIt is represented by：
BK=GKRK-I
According toIt can obtain feedforward matrix FK；
Step 6：Terminate.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiments or example in can be combined in any suitable manner.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
In the case of being detached from the principle of the present invention and objective a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The range of invention is limited by claim and its equivalent.
Claims (3)
1. the MIMO method for precoding based on dynamic channel conditions under a kind of high-speed mobile, which is characterized in that include the following steps：
S1 starts；
S2 establishes system model；It is assumed that in one in a wideband MIMO system single cell multiple-user network downlink,
(Base Station, BS) deploys N for base stationTRoot transmission antenna；For simplicity, it is assumed that BS and N number of user (N≤NT) between
Transmission data, each user are equipped with 1 antenna receiver；N number of user data N-dimensional vector a=[a1,a2,...,aN]T(symbol
[·]TIndicate transposition operation) it indicates, M-QAM constellations (M indicates modulation system number) are derived from, power is
S3, according to the system model established, the precoding of structure modular algebra (Tomlison-Harashima Precoding,
THP) the precoding model of scheme；
S4 feeds back to the channel measurement information of transmitting terminal according to the precoding model established and from receiving terminal, is based on
The channel matrix H of dynamic channel conditions information (Channel State Information, CSI)；
S5, based on the channel matrix H of dynamic CSI, under transmission power constraints, with least mean-square error (Minimum
Mean Square Error, MMSE) it is optimization aim, it is derived by weighting matrix G and optimal pre-coding matrix B and F；
S6 terminates.
2. the MIMO method for precoding based on dynamic channel conditions, feature exist under high-speed mobile according to claim 1
In the S4 includes：
The channel measurement information that transmitting terminal is fed back to according to the precoding model established and from receiving terminal is obtained based on dynamic
The channel matrix H of CSI；Channel matrix H is indicated with the two parts for being relatively fixed constant and apparent time-varying：
Wherein, HmIt is the mean value of channel；The part for indicating channel variation, is characterized with the correlation of channel；The mean value of channel and
Correlation can correspond to the estimated value and error covariance of channel；Therefore, in delivery time s, the CSI of channel can be by believing
The estimated value in roadAnd its error covariance ReIt constitutes, is characterized as below：
It is assumed that oneself knows the initial measurement H of 0 moment of transmitting terminal channel0With the statistical information of channel, channel mean value Hm, characterization MIMO
The channel covariancc R of spatial coherence in system between all transmittings and reception antenna pair0, channel auto-covariance Rs；It utilizes
MMSE estimation theories, channel is in moment s optimal estimation valueWith the error covariance R in estimatione,sIt is characterized as：
Wherein,AsRow vector；Desired operation is sought in E [] expressions；Cov [] expressions ask covariance to grasp
Make；Assuming that the time statistical property of all antennas pair is identical, then the spatial coherence of channel is to be individually present with temporal correlation
, therefore, the auto-covariance R of channelsIt can be expressed as
Rs=βsR0
Wherein, βsFor the time correlation coefficient of channel；That is, the N of transmitting terminalTBetween root antenna and the N root antennas of receiving terminal
NNTA channel possesses identical time correlation function；
Simplify above-mentioned time correlation model, simplified model can be effectively isolated channel time variation to transmitting terminal CSI's
It influences；The estimated value of channelWith the error covariance matrix R in estimationeIt can be rewritten as：
WhereinL is to take the window of CSI mean values long, HkFor in the transient channel measured value of sampling time k；Cause
This, transmitting terminal CSI can briefly portray the time correlation coefficient β for channels, channel measured value Η0, channel mean value Hm
With covariance R0Function；
R0For Positive Semidefinite Hermitian Matrix, the wherein element on diagonal line characterizes NN respectivelyTThe transmission power gain of a channel,
Rather than cornerwise element then reflects intercoupling between each channel；In the channel built based on Kronecker structures
In model, R0It can be analyzed to transmitting terminal Antenna CorrelationWith receiving terminal Antenna Correlation RrKronecker accumulate form：
Wherein, RtWith RrIt is Positive Semidefinite Hermitian Matrix,Similarly,Simultaneous formula WithIt can obtain the channel matrix H based on dynamic CSI：
3. the MIMO method for precoding based on dynamic channel conditions, feature exist under high-speed mobile according to claim 1
In the S5 includes：
It is sorted, each row vector of channel matrix H is ranked up, the channel matrix H after being sorted according to best-firstK
For
Channel matrix H after sequenceKIt will produce corresponding feedback matrix BK, weighting matrix GKWith feedforward matrix FK；Received vector rK
It can be expressed as
rK=HKxK+nK
Wherein,xKFor NTThe data that root transmission antenna is sent
Vector is expressed as
After the system for sending multi-user THP of the signal after sequence, the data vector r before receiving terminal judgementK' and transmitting terminal etc.
Imitate effective input vector v of feedback channelKCorresponding, error e is expressed as
Wherein,For the equivalent noise before judgement；According to the thought of MMSE, ensureing that sending vector meets transmitting work(
Under conditions of rate constraint, seek rational feedforward matrix FK, feedback matrix BKAnd weighting matrix GKSo that error vector is minimum, because
This, can build MMSE object functions and constraints
Wherein, PTIndicate that total emission power, using orthogonality principle, has because direct solution is relatively difficult
That is received vector rKIt is orthogonal with error vector e；By error vector e=rK′-vKSubstitution formulaHave
Wherein,Association type r againK=HKxK+nK, obtain
Wherein,Assuming that each element of signal vector is mutually orthogonal, then
For diagonal matrix；It enablesHave
Wherein feedback matrix BKFor strictly lower triangular matrix；In order to meet the reality not cooperateed between multiuser downstream channel user
Situation, weighting matrix GKIt is set as diagonal matrix；In order not to change the transmission power of transmission data, and also to be closed
The solution of form, feedforward matrix FKIt is assumed to be unitary matrice, meetsTherefore above formula abbreviation is：
And it obtains：
Further according toIt obtains：
I.e.：
It enablesAbove formula is represented by：
It is rightMake Cholesky decomposition, you can obtain lower triangular matrix RK；By RK's
The inverse of main diagonal element can construct weighting matrix G as the elements on diagonal lineK：
Feedback matrix BKIt is represented by：
BK=GKRK-I
According toIt can obtain feedforward matrix FK：
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