Efficient filter weight computation for mimo system

FIELD: radio engineering, communication.

SUBSTANCE: invention relates to communication engineering and can be used in MIMO systems. Techniques to efficiently derive a spatial filter matrix are described. In a first scheme, a Hermitian matrix is iteratively derived based on a channel response matrix, and a matrix inversion is indirectly calculated by deriving the Hermitian matrix iteratively. The spatial filter matrix is derived based on the Hermitian matrix and the channel response matrix. In a second scheme, multiple rotations are performed to iteratively obtain first and second matrices for a pseudo-inverse matrix of the channel response matrix. The spatial filter matrix is derived based on the first and second matrices. In a third scheme, a matrix is formed based on the channel response matrix and decomposed to obtain a unitary matrix and a diagonal matrix. The spatial filter matrix is derived based on the unitary matrix, the diagonal matrix, and the channel response matrix.

EFFECT: high throughput of transmission channels.

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The technical field to which the invention relates.

The invention in General relates to the field of communications and, more specifically, to methods of calculating the weights of the filters in the communication system.

The level of technology

In the communication system with multiple inputs and multiple outputs (MIMO MVPS) for data transmission using multiple (T) transmit antennas of a transmitting station, and many (R) receiving antennas of the receiving station. A MIMO channel formed by the T transmit antennas and R receiving antennas may be decomposed into S spatial channels, where S≤min {T, R}. S spatial channels may be used to transmit data in such a way to achieve greater overall throughput and/or higher reliability.

The transmitting station can simultaneously transmit T data flows through the T transmit antennas. In these data streams have distortion in accordance with the response of the MIMO channel, and their quality is additionally deteriorates due to exposure to noise and interference. The receiving station receives the transmitted data streams through R receiving antennas. The received signal from each receiving antenna contains a scaled version of the T data streams transmitted by a transmitting station. Transmitted data streams, thus dispersed among the R signals received through R receiving antennas. Reception with anzia then performs spatial processing receiver for R received signals, using the matrix spatial filter, to recover the transmitted data streams.

To determine the weights matrix spatial filter requires a lot of processing. This is because the matrix spatial filter is usually obtained on the basis of the function that contains the inverse of the matrix, and direct calculations of matrix inversion require volumetric calculations.

Thus, this technology requires the development of methods for efficient calculation of the weighting coefficients of the filter.

The invention

Here is described the method of calculating the effective weighting matrix spatial filter. These techniques allow to exclude a direct calculation of matrix inversion.

In the first variant embodiment for obtaining matrixMspatial filter Hermitian matrix P iteration is obtained on the basis of the matrixHresponse channel, and the inverse of the matrix indirectly calculated by iterative obtain a Hermitian matrix. Hermitian matrix can be initialized to the identity matrix. One iteration is then performed for each row of the matrix of the channel response, and effective sequence of calculations performed for each iteration. For the i-th iteration of the receive intermediate vectorailine-based vectorh ithe channel response, which represents the i-th row of the matrix of the channel response. A scalar valueriget on the basis of the intermediate vector and row vector row of the channel response. Intermediate matrixCialso get on the basis of the intermediate vector line. Hermitian matrix, then update based on scalar values and the intermediate matrix. After all iterations, the gain matrix spatial filter based on the Hermitian matrix and the matrix of the channel response.

In the second variant embodiment perform many turns to iteratively obtain a first matrixP1/2and the second matrixBfor pseudouridines matrix of the channel response. One iteration is performed for each row of the matrix of the channel response. For each iteration form a matrixYcontaining the first and second matrix from the previous iteration. Many turns of Givens then perform for the matrixYto zero the elements in the first row of the matrix, to obtain the updated first and second matrix for the next iteration. After all iterations are completed, the receive matrix spatial filter based on the first and second matrices.

In the third variant embodiment form a matrixXbased on the matrix of the channel response and decompose (for example, used the eat decomposition own values) to obtain a unitary matrixVand a diagonal matrixΛ. The decomposition can be obtained in the iterative execution of rotations Jacobi matrixX. Matrix spatial filter is then obtained on the basis of a unitary matrix, a diagonal matrix and the matrix of the channel response.

Various aspects and embodiments of the invention are described in more detail below.

Brief description of drawings

Properties and essence of the present invention will be clearer from the detailed description below, which should be read in conjunction with the drawings in which the same numbers of reference positions indicated corresponding elements in all the drawings.

In figure 1, 2 and 3 shows the processing performed to calculate the matrix of spatial MMSE filter (ISCED, the minimum mean square error), based on the first, second and third variants of the embodiment, respectively.

Figure 4 shows the block diagram of the access point and user terminal.

Detailed description of the invention

The word "exemplary"as used here, means "used as an example, a case or illustration". Any variant execution or design described herein as "exemplary"is not necessarily should be considered as preferred or predominant compared to the other options run or designs.

Described here are methods for calculating weighting coefficients of the filter can be used for MIMO systems with single-carrier and MIMO systems with multiple carriers. Many of bearing can be obtained using multiplexing orthogonal frequency division signals (OFDM), multiple access frequency division with alternation (IFDMA), localized multiple access frequency division (LFDMA), or some other modulation techniques. OFDM, IFDMA and LFDMA effectively share the total bandwidth of the system into multiple (K) orthogonal frequency papolos, which are also called tones, subcarriers, the signal elements and frequency channels. Each podporou associated with the corresponding subcarrier, which can be modulated data. In the system of the OFDM symbols of the modulation transfer in the frequency domain for all or a subset K papolos. In IFDMA transmit the modulation symbols in the field of time popoloca which are evenly distributed on K popoloca. In LFDMA transmit the modulation symbols in the field of time and usually in the neighboring popoloca. For clarity, most of the following description is directed to a MIMO system with a single carrier, which uses one subcarriers.

A MIMO channel formed by multiple (T) transmit antennas at a transmitting station, and numerous ® receiving antennas in the reception of the article is ncii, can be characterized by the matrixHresponse channel size RxT, which can be specified as:

Equation (1)

where hi,j,for i=1,...,R and j=1,...,Tindicates the strengthening of links or complex gain of the channel between the transmit antenna jand receiving antenna i;and

hiis a vector of strings of the channel response 1×T to the receiving antenna i,which represents the i-th row of the matrixH.

For simplicity, in the following description it is assumed that the MIMO channel has full rank and that the number of spatial channels (S) is defined as: S=T≤R

The transmitting station may transmit T modulation symbols simultaneously from the T transmit antennas in each symbol period. The transmitting station may perform or may perform spatial processing for the modulation symbols prior to transmission. For simplicity, the following description assumes that each modulation symbol is passed through the transmitting antenna without any spatial processing.

The receiving station receives R received symbols from the R receiving antennas in each symbol period. The received symbols can be expressed as:

Equation (2)

wheresis a vector of size T×1, where T is the modulation symbols transmitted is erediauwa station;

ris a vector of size R×1, where R is the received characters receive in the receiving station R receiving antennas; and

nis the noise vector of size R×1.

For simplicity, we can assume that the noises are additive white Gaussian noise (AWGN) with zero mean vector and covariance matrix δ2n×Iwhere δ2nisa noise variance, andIrepresents the identity matrix.

In the receiving station may use different methods of spatial processing for recovering modulation symbols transmitted by a transmitting station. For example, the receiving station may perform spatial processing of the receiver with minimum mean square error (MMSE), as follows:

Equation (3)

whereMis a matrix of spatial MMSE filter of size T×R;

Prepresents the Hermitian covariance matrix of size T×T error estimatess-;

is a vector of size T×1, which is an estimate of s; and

"H"denotes conjugate transposition.

MatrixPthe covariance can be specified asP=E[(s-)×(s- )H], where E[] represents an operation of mathematical expectation.Palso is a Hermitian matrix, the off-diagonal elements which have the following properties pi,j=p*i,jwhere "*" denotes a complex conjugate of a number.

As shown in equation (3), matrixMspatial MMSE filter is the calculation of the converted matrix. The direct calculation of the matrix inversion requires a large amount of computer operations. The matrix of spatial MMSE filter can be more effectively obtained on the basis of the embodiments, described below, which allow you to indirectly calculate the inverse of the matrix using an iterative process, instead of directly calculating the conversion matrix.

In the first variant embodiment of the calculation of the matrixMspatial MMSE filter count Hermitian matrixPbased on the Riccati equation. Hermitian matrix P can be expressed as follows:

Equation (4)

Hermitian matrixPithe size of TxT can be defined as:

Equation (5)

Lemma matrix inversion can be applied to equation (5) to obtain the following:

Equation (6)

whererirepresents scalarmultiply value. Equation (6) is called the Riccati equation. MatrixPican be initialized asP0=I. After the execution of R iterations of equation (6), for i=1,...,R are matrixPRas the matrixPorP=PR.

Equation (6) can be multiplied by certain coefficients to obtain the following:

Equation (7)

where matrixPiinitialize asP0=Iand the matrixPget asP=PR.Equations (6) and (7) differ from the solutions of the equation (5). For simplicity used the same variablesPiand rifor both equations (6) and (7), even though these variables have different values in the two equations. The final results obtained by the equations (6) and (7), that is,PRfor equations (6) andPRfor the equation(7),equivalent.However, calculations for the first iteration of equation (7) are simplified thanks to the use ofP0as the identity matrix.

Each iteration of equation (7) can be performed as follows:

Equation (8a)

Equation (8b)

<> Equation (8c)

Equation (8d)

whereaiis a vector of intermediate rows 1×T elements with complex value; and

Crepresents an intermediate Hermitian matrix of size T×T.

In the system (8) equations sequence of operations is structured for efficient calculation using hardware. A scalar value riI hope before the matrixCi. Division by riin equation (7) is achieved by means of an inversion and multiplication. The treatment of rican be performed in parallel with the calculation ofCi. The treatment of riis achieved with a shift for normalization of riand with the use of a reference table to obtain the converted values of ri. The normalization of rican be compensated by multiplying byCi.

MatrixPiinitialize as a Hermitian matrix, orP0=Iand it remains Hermitian matrix in all following iterations. Therefore, only the upper (or lower) diagonal matrix need be calculated for each iteration. After R iterations are matrixPasP=PR.The matrix of spatial MMSE filter can then be calculated as follows:

Equation (9)

Figure 1 shows a process 100 calculation of the matrixMspatial MMSE filter based on the first variant embodiment. MatrixPiinitialized asP0=1 (block 112), and the index i is used to denote the number of iteration is initialized as i=1 (block 114). Then execute R iterations of the Riccati equation.

Each iteration of the Riccati equation is performed by block 120. Foriiteration vectoraithe intermediate line is calculated on the basis of the declared vectorhithe channel response and Hermitian matrixPi+1the previous iteration, as shown in equation (8a) (block 122). A scalar value ricountbased on the variance ofδ2nnoise vectoraiintermediate string and vectorhiprompt response of the channel, as shown in equation (8b) (block 124). Scalarriafter that it is converted (block 126). Intermediate matrixCicalculated on the basis ofaiintermediate line, as shown in equation (8c) (block 128). MatrixPithen update based on the inverted scalar value ofriand the intermediate matrixCias shown in equation (8d) (block 130).

Then determine whether you have performed all the R iterations (block). If the answer is negative, perform a sequential increment index i(block 134), and the process returns to block 122 to perform another iteration. Otherwise, if all R iterations were performed, calculate the matrix M MMSE spatial filter based on the Hermitian matrixPRfor the last iteration, matrixHresponse channels and dispersionδ2nnoiseas shown in equation (9) (block 136). MatrixMyou can then use for spatial processing of the receiver,as shown in equation (3).

In the second variant embodiment of the calculation of the matrixMspatial MMSE filter define a Hermitian matrixPby obtaining the square rootPthat is aP1/2on the basis of an iterative procedure. Spatial processing receiver in equation (3) can be expressed as follows:

Equation (10)

whereU=represents the augmented channel matrix of size (R+T)×T;

Uprepresents pseudouridine matrix of size T×(R+T)resulting from the operation of the treatment or pseudouridine Moore-Penrose forUorUp=(UHU)-1UH;

0Tx1represents the t of a vector of size T×1, containing zeroes; and

Hpδnis Podiatric of size T×R containing the first R columnsUp.

The QR decomposition can be performed for a matrix with augmented channel as follows:

Equation (11)

whereQis a matrix of size (R+T)×T with orthonormal columns;

Ris a matrix of size T×T, which is not the identity matrix;

Bis a matrix of size R×T containing the first R rows of the matrixQ; and

Q2is a matrix of size T×T, containing the last T rows of the matrixQ.

QR (KO, quasiorder) the decomposition in equation (11) decomposes the matrix of the augmented channel orthogonal matrixQand not a single matrixR. Orthogonal matrixQhas the following property:QHQ=Ithat means that the columns of an orthogonal matrix are orthogonal relative to each other, and each column has a single degree. Not the identity matrix is a matrix which can be calculated converts the matrix.

Hermitian matrixPcan then be expressed as:

Equation (12)

Rrepresents decomposition Koleczkowo or Quadra is hydrated root matrix P -1. Therefore,P1/2isR-1and is called the square root matrixP.

Pseudobradya matrix in equation (10) can then be expressed as:

Equation (13)

PediatricaHPδnthat also is a matrix of spatial MMSE filter can then be expressed as:

Equation (14)

Equation (10) can then be expressed as:

Equation (15)

MatrixP1/2andBcan be calculated iteratively as follows:

or Equation (16)

Equation (17)

whereYiis a matrix of size (T+R+1)×(T+1), containing elements derived fromP1/2i-1,Bi-1andhi;

θiis a unitary transformation matrix of size (T+1)×(T+1);

Ziis a transformed matrix of size (T+R+1)×(T+1)containing the elements for Pi1/2,Biandri;

eiis a vector of size R×1 unit (1,0) as the i-th element and with the other zero elements; and

kiis a vector of size T×1 andI/u> iis a vector R×1, and both of them are insignificant.

MatrixP1/2andBinitialize asP01/2=IandB0=0RxT.

The transformation in equation (17) can be performed iteratively, as described below. For clarity, each iteration of equation (17) is called outer iteration. R external iterations of equation (17) is performed for the R vectorhithe channel response for i=1,...,R. For each outer iteration unitary matrixδithe transformation in equation (17) is converted into the transformed matrixZicontaining all zeros in the first row, except the first element. The first column of the transformed matrixZicontains ri1/2,kiandIi. The last T columnsZicontain updatedPi1/2andBi. The first column isZiyou do not need to count, because onlyPi1/2andBiused in the next iteration.Pi1/2is an upper triangular matrix. After R outer iterations getPR1/2asP1/2andBRget asB. MatrixMspatial MMSE filter can then be calculated n the basis P1/2andBas shown in equation (14).

For each outer iterationithe transformation in equation (17) can be performed by successive zero one element in the first rowYiat the same time with 2×2 Givens rotations. T inner iterations Givens rotation can be performed to reset the last T elements in the first rowYi.

For each outer iteration i, matrixYi,jcan be initialized asYi1=Yi. For each inner iteration j for j=1,...,T, the outer iteration i, originally form PediatricoY'i,jsize (T+R+1)×2 containing the first and the (j+1)-th columnsYi,j. Then perform the Givens rotation to pieces and usesY'i,jto generate pieces and usesY"i,jsize (T+R+1)×2 containing zero in the second element in the first row. The Givens rotation can be expressed as:

Equation (18)

whereGi,jis a rotation matrix of the Givens of size 2×2 for the j-th inner iteration of the i-th outer iteration, which is described below. MatrixYi,j+1then form first by settingYi,j+1=Yi,jthen replace the first columnYi,j+1the first columnY"i,jand then replace the (j+1)-th column of the matrixYi,j+1is that the column Y"i,j. The Givens rotation, thus, modifies only two columnsYi,jj-th inner iteration to obtainYi,j+1for the next inner iteration. The Givens rotation can be performed in place of the two columnsYifor each inner iteration, resulting in an intermediate matrixYi,j,Y'i,j,Y"i,jandYi,j+1not needed and described above for clarity.

For the j-th inner iteration of the i-th outer iteration matrixGi,jthe Givens rotation is determined based on the first element (which is always a valid value) and (j+1)-th element in the first row of the matrixYi,j. The first element may be denoted as α, and (j+1)-th element can be designated as b-e. MatrixGi,jturnThe Givens can then be obtained in the following way:

Equation (19)

where c=and s=for equation (19).

Figure 2 shows a process 200 that is designed to calculate the matrixMspatial MMSE filter based on the second variant embodiment. MatrixPi1/2initialize P01/2=Iand the matrixBiinitializer the Ute as B0=0(block 212). The index i to denote the number of external iterations are initialized as i=1 and the index j is used to denote the number of inner iteration, initialize j=1 (block 214). Then execute R outer iterations unitary transformation in accordance with equation (17) (block 220).

For the i-th outer iteration first form a matrixYiwith vectorhiprompt response of the channel matrixPi-11/2andBi-1as shown in equation (17) (block 222). MatrixYithen referred to as matrixYi,jfor the inner iterations (block 224). T inner iterations of the Givens rotation is then performed for the matrixYi,j(block 230).

For the j-th inner iteration gain matrixGi,jthe Givens rotation based on the first and (j+1)-th elements in the first rowYi,jas shown in equation (19) (block 232). MatrixGi,jthe Givens rotation is then applied to the first and the (j+1)-th columnsYi,jto getYi,j+1as shown in equation (18) (block 234). Then determine whether you have performed all the T inner iteration (block 236). If the answer is "No", then the index j is increased by one unit (block 238), and processing returns to block 232 to perform other internal iteration.

If all T inner iteration b is performed for the current outer iteration, and the answer is "Yes" to block 236, then the lastYi,j+1equalZiin equation (17). An updated matrixPi1/2andBiget out the lastYi,j+1(block 240). Then determine whether you have performed all the R outer iterations (block 242). If the answer is "No", then the index i is increased by one unit, and the index j re-initialize j=1 (block 244). Processing then returns to block 222 to perform other external iteration withPi1/2andBi.Otherwise, if all R outer iterations were performed, and the answer is "Yes" to block 242, then calculate the matrixMspatial MMSE filter based onPi1/2andBias shown in equation (14) (block 246). The matrix M can then be used for spatial processing of the receiver as shown in equation (15).

In the third variant embodiment of the calculation of the matrix M MMSE spatial filter performs the decomposition on their own valuesP-1as follows:

Equation (20)

whereVis a unitary matrix T×T eigenvectors; and

Λis a diagonal matrix of size T×T with a valideigenvalues along the diagonal.

RA is a suggestion for eigenvalues Hermitian matrix X2x22×2 can be obtained using different techniques. In a variant embodiment decomposition own valuesX2x2receive by performing complex Jacobi rotation forX2x2to obtain matrixV2x22×2 eigenvectorsX2x2. X2x2andV2x2can be specified as:

Equation (21)

V2x2can be calculated directly fromX2x2as follows:

Equation (22a)

Equation (22b)

Equation (22c)

Equation (22d)

Equation (22e)

Equation (22f)

Equation(22g)

Equation (22h)

Equation (22i)

Equation (22j)

Equation (22k)

Expansion eigenvalues Hermitian matrixXof size T×T, which is greater than 2×2, can be performed in an iterative process. In this iterative process, repeatedly use what is the Jacobi rotation to zero the off-diagonal elements in X. For the iterative process, the index i denotes the iteration number and is initialized as i=1.Xis a Hermitian matrix of size T×T, which must be decomposed, and is installed asX=P-1. MatrixDiis an approximation of a diagonal matrixΛin equation (20) and is initialized asD0=X. MatrixVis an approximation of the unitary matrixVin equation (20) and is initialized asV0=I.

A single iteration of the Jacobi rotation to update matrixDiandVican be performed as follows. First Hermitian matrixDpq2×2 is formed on the basis of the current matrixDias follows:

Equation (23)

where dp,qrepresents the element at location (p,q) matrixDi, p{1,...,T}, g{1,...,T} and p≠q.DpqisPediatricoDi2×2, and four elements of Dpqrepresent the four elements at locations (p, p), (p, q), (q, p) and (q, q) matrixDi.The indices p and q may be selected, as described below.

Then perform the decomposition on their own valuesDpqas shown in equation (22), obtained for the I of the unitary matrix Vpq2×2 eigenvectorsDpq. To decompose on their own valuesDpq,X2x2in equation (21) is replaced byDpqandV2x2from equation (22j) or (22k) are asVpq.

The matrix of Tpqcomplex Jacobi rotation TxT size is then formed withVpq,Tpqand represents the identity matrix with four elements at locations (p, p), (p, q), (q, p) and (q, q), which are replaced by the elements of v1,1v1,2v2,1and v2,2accordingly , matrixYpq.

MatrixDithen updated as follows:

Equation (24)

Equation (24) two zeroes off-diagonal element at locations (p, q) and (q, p)in the matrixDi. The calculation can change the values of the other off-diagonal elements inDi.

MatrixVialso update as follows:

Equation (25)

Vican be viewed as a matrix of cumulative transformations, which contains all matrixTpqturn Jacobi used forDi.

Each iteration of the Jacobi rotation resets the two off-diagonal matrix elementDi. The number of iterations of Jacobi rotation can be performed for different testing the response of the indices p and q, to reset all off-diagonal elementsDi. One pass through all possible values of the indices p and q may be performed as follows. The index p is sequentially changes from 1 up to T-1 in increments of the unit. For each value of p, the index q is sequentially changed from p+1 to T in increments of the unit. The Jacobi rotation performed for each different combination of values of p and q. Many passages can be made up untilDiandViwill not provide reasonably accurate estimatesΛandVrespectively.

Equation (20) can be rewritten as follows:

Equation (26)

whereΛ-1is a diagonal matrix whose elements represent the converted values of the corresponding elements ofΛ. Decomposition own valuesX=P-1is evaluationΛandV.Λcan be inverted to obtainΛ-1.

The matrix of spatial MMSE filter can then be calculated as follows:

Equation (27)

3 shows a process 300 that is designed to calculate the matrixMspatial MMSE filter, on the basis of the third variant embodiment. Hermitian matrixP-1initially get on the basis of the matrix Hresponse of the channel, as shown in equation (20) (block 312). Then perform the decomposition on their own valuesP-1to obtain a unitary matrixVand a diagonal matrixΛas also shown in equation (20) (block 314). Decomposition own values can be performed iterative with multiple twists Jacobi, as described above. MatrixMspatial MMSE filter is then obtained on the basis of a unitary matrixVdiagonal matrixΛand matrixHresponse of the channel, as shown in equation (27) (block 316).

MatrixMspatial MMSE filter based on each of the options above embodiment, represents the biased MMSE solution. Offset matrixMthe spatial filter can be scaled using a diagonal matrixDmmseforobtain unbiased MMSE matrixMmmsespatial filter.MatrixDmmsecan be obtained as Dmmse=[diag[MH]]-1where diag[MH] represents a diagonal matrix containing the diagonal elements ofMH.

The above calculations can also be used to obtain spatial filter matrices for methods with zero significant coefficients (ZF) (also called methods the IR conversion matrix with correlation of the channel (CCMI, OMCC)), methods of combining maximum ratio (MRC, ERC) and so on. For example, the receiving station may perform spatial processing receiver with zero significant coefficients and MRC, as follows:

Equation (28)

Equation (29)

whereMzfis a matrix spatial filter of size T×R were converted to zero is not significant factors;

Mmrcis a matrix of spatial MRC filter size T×R;

Pzf=(HH•H)-1is a Hermitian matrix of size T×T; and

[diag(Pzf)] is a diagonal matrix of size T×T, containing the diagonal elements ofPzf.

The inverse of the matrix is necessary for the direct calculation ofPzf.Pzfcan be calculated using variants of the embodiments described above for the matrix of spatial MMSE filter.

In the above description, it is assumed that the T modulation symbols to transmit simultaneously from the T transmit antennas, without any spatial processing. The transmitting station may perform spatial processing before transmission as follows:

Equation (30)

the de xis a vector of size T×1 T symbols of the transmission, which must be passed through the T transmit antennas; and

Wis the transfer matrix of size T×s MatrixWtransmission can be either (1) the matrix of right singular vectors obtained by performing a decomposition of the singular values ofH, (2) the matrix of eigenvectors obtained by performing a decomposition on their own valuesHHHor (3) the control matrix selected for the spatial distribution of modulation symbols S spatial channels of the MIMO channel. MatrixHeffeffective channel response observed by symbols of the modulation can then be specified asHeff=HW. The above combination can be based on theHeffinstead ofH.

For clarity, the above description, it shows a MIMO system with a single carrier, with one podoloski. For MIMO systems with multiple load-bearing matrixH(k) the channel response can be obtained for each podology k of interest. MatrixM(k) the spatial filter can then be obtained for each podology k based on the matrixH(k) of the channel response for this podology.

The above calculations for the matrix spatial filter can be within the s by using a different processor type, such as the floating-point processor, a processor with a fixed decimal point, processor, digital computer, rotate the coordinates (CORDIC), lookup table, and so forth, or combinations thereof. The CORDIC processor embodies an iterative algorithm that enables quick calculation using hardware trigonometric functions such as sine, cosine, magnitude and phase, using a simple hardware shift and addition/subtraction. The CORDIC processor may iteratively calculate each of the variables r, c1and s1system (22) equations with a large number of iterations, which allows to achieve a higher accuracy for the variable.

Figure 4 shows the block diagram of the point 410 access and terminal 450 of the user in the system 400 MIMO. Point 410 access equipped with Napantennas,and the terminal 450 user is equipped with Nutantennas, where Nap>1 and Nut>1. The top-down transmission channel, at the point 410 access processor 414 transmit (TX) data receives data traffic from source 412 data and other data from a controller/processor 430. The processor 414 TX data formats, encodes, performs interleaving, modulating the data and generates data symbols, which are modulation symbols for data. The spatial processor 420 TX multiplex the imagers and the data symbols with pilot symbols, performs spatial processing matrixWtransfer, if applicable, and provides Napstreams of transmitted symbols. Each module 422 transmitters (TMTR) processes corresponding to the stream of transmitted symbols and generates a modulated signal to a downstream transmission channel. The modulated signals to a downstream transmission channel Napmodules 422a-422ap transmitter transmits via antenna 424a-424ap, respectively.

In the terminal 450 user Nutantennas 452a-452ut receive the transmitted modulated signals downstream of the transmission channel, and each antenna transmits the received signal to the corresponding module (RCVR) 454 receiver. Each module 454 receiver performs processing complementary to the processing performed by the modules 422 transmission, and provides received pilot symbols and received data symbols. Block/processor 478 channel estimation processes the received pilot symbols and provides an assessment of the response of Hdnchannel downstream of the transmission channel. The processor 480 receives the matrixMdnspatial filter downstream of the transmission channel on the basis ofHdnand using any of the variants of the embodiment described above. The spatial processor 460 receiver (RX) performs spatial processing of the receiver (or spatial coherent Phi is Tracey) for the received data symbols from all N utmodules454a-454ut receivermatrixMdnspatial filter downstream of the transmission channel and provides detected data symbols, which are estimates of the data symbols transmitted by the point 410 access. The processor 470 receiver processes (for example, performs the inverse mapping of the symbol, removes the interleaving and decodes) the detected data symbols and provides decoded data to a receiver 472 of data and/or the controller 480.

Treatment for upstream transmission channel may be the same or may be different from the processing for the downward transmission channel. Data from 486 source data and signals from the controller 480 is processed (e.g., encode, perform interleaving and modulate) using a processor 488 TX data, multiplexed with pilot symbols and possibly spatially processed by a spatial processor 490 TX. The characters pass from the spatial processor 490 TX further treated using modules 454a-454ut transmitter for generating Nutmodulated signals upstream transmission channel, which is transmitted via antenna 452a-452ut.

At the point 410 access modulated signals upstream transmission channel is taken with the help of antennas 424a-424ap and process using modules 422a-422ap receiver generated for the I received pilot symbols and received data symbols, for transmission upstream transmission channel. Block/processor 428 channel estimation processes the received pilot symbols and provides an assessment of responseHupchannel ascending transmission. The processor 430 receives the matrixMupspatial filter upstream transmission channel, and using one of the embodiments described above. The spatial processor 440 RX performs spatial processing receiver for the received data symbols with a matrixMupspatial filter upstream transmission channel and provides detected data symbols. The processor 442 data RX advanced processes the detected data symbols and provides decoded data to a receiver 444 data and/or the controller 430.

Controllers 430 and 480 controls the operations at the point 410 access and terminal 450 of the user, respectively. In modules 432 and 482 stores data and program codes used by the controllers 430 and 480, respectively.

The blocks shown in figure 1-4 represent functional blocks, which can be embodied in the form of hardware (one or more devices), built-in programs (one or more devices), software (one or more modules), or combinations thereof. For example, the described methods of calculating the weights is ultra can be embodied as hardware, firmware, software or combinations thereof. When executed in the form of hardware processing modules used to calculate the weighting coefficients of the filter can be embodied in one or more specific integrated circuits (ASIC), digital signal processors (DSP)devices, digital signal processing (DSPD), programmable logic devices (PLD), programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic modules designed to perform the functions described here, or combinations thereof. Different processors at the point 410 access figure 4 can also be embodied using one or more hardware processors. Similarly, the various processors in the terminal 450 of the user can be embodied with one or more hardware processors.

For the variant embodiment using firmware or software, the methods of calculating the weighting factor of the filter can be implemented with modules (e.g., procedures, functions, and so on)that perform the functions described here. Software codes may be stored in the memory module (e.g. module 432 or 482 memory figure 4) and can execute the process is the PR (e.g., the processor 430 or 480). The memory module may be implemented within the processor or external to your processor.

The above description of the disclosed embodiments is provided to enable a person skilled in the art to use the present invention. Various modifications of these options embodiments will be clear to a person skilled in the art, and the General principles defined herein may be applied to other embodiments without going beyond the essence or scope of the invention. Thus, the present invention is not intended to limit the variations of the embodiments described herein, but it should be understood in its broadest scope, which corresponds to the disclosed principles here and new properties.

1. A device for obtaining matrix spatial filter that contains:
the first processor which during operation receives the response matrix of the channel; and
a second processor which during operation receives the first iteration matrix based on the matrix of the channel response and displays the matrix spatial filter based on the first matrix and the matrix of the channel response, the second processor calculates the inverse of the matrix for the matrix obtained from the matrix of the channel response by iterative receipt of the first matrix.

2. The device according to claim 1, which which the second processor during operation initializes the first matrix to the identity matrix.

3. The device according to claim 1, in which the second processor during operation, for each of multiple iterations receives the intermediate vector line based on the first matrix and the vector line of the channel response corresponding to the row of the matrix of the channel response, to obtain a scalar value based on the intermediate vector and row vector row of the channel response to obtain an intermediate matrix based on the intermediate vector line and to update the first matrix on the basis of a scalar value and the intermediate matrix.

4. The device according to claim 1, wherein the first matrix is used to obtain the matrix spatial filter with minimum mean square error (MMSE).

5. The device according to claim 1, in which the second processor during operation receives the first matrix on the basis of the following equation:

wherePirepresents the first matrix for the i-th iteration,hirepresents the i-th row of the matrix of the channel response, rirepresents a scalar value derived fromhiandPi-1and "H" represents conjugate transposition.

6. The device according to claim 1, in which the second processor during operation receives the first matrix on the basis of the following equations:


wherePirepresents the first matrix for the i-th iteration,hirepresents the i-th row of the matrix of the channel response,airepresents an intermediate vector line for the i-th iteration,Cirepresents an intermediate matrix for the i-th iteration,rirepresents a scalar value for the i-th iteration, δ2nrepresents the noise variance, andH"represents conjugate transposition.

7. The device according to claim 1, in which the second processor during operation receives the matrix spatial filter based on the following equation:

whereMis a matrix spatial filter,Prepresents the first matrix, H is a matrix of the channel response, and"H"represents conjugate transposition.

8. A method of obtaining a matrix spatial filter containing phases in which:
iterative get the first matrix based on the matrix of the channel response, and the inverse of the matrix for the matrix obtained from the matrix of the channel response, calculated by iterative obtain a first matrix; and
get the matrix spatial filter based on the first matrix and the matrix of the channel response.

9. The method of claim 8, comprising the step ofwhich initialize the first matrix to the identity matrix.

10. The method according to claim 8, in which the first matrix contains, for each of multiple iterations of stages, which
receive an intermediate vector of strings based on the first matrix and the vector line of the channel response corresponding to the row of the matrix of the channel response,
get a scalar value based on the intermediate vector and row vector row of the channel response,
receive the intermediate matrix based on the intermediate vector line, and
updating the first matrix on the basis of a scalar value and the intermediate matrix.

11. A device for obtaining matrix spatial filter that contains:
the tool iteratively obtain a first matrix based on the matrix of the channel response, and the inverse of the matrix for the matrix obtained from the matrix of the channel response, calculated by iterative obtain a first matrix; and
means for obtaining matrix spatial filter based on the first matrix and the matrix of the channel response.

12. The device according to claim 11, which also contains means for initializing the first matrix to the identity matrix.

13. The device according to claim 11 in which the means for obtaining the first matrix contains, for each of multiple iterations:
means for obtaining an intermediate vector of strings based on the first matrix and the vector line of the channel response corresponding to the row of the matrix on the cliques of the channel
means for obtaining a scalar value based on the intermediate vector and row vector row of the channel response,
means for obtaining an intermediate matrix based on the intermediate vector line and
the update tool of the first matrix on the basis of a scalar value and the intermediate matrix.



 

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