Title of Invention  "AN APPARATUS FOR EIGENVALUE DECOMPOSITION AND SINGULAR VALUE DECOMPOSITION OF MATRICES" 

Abstract  Techniques for decomposing matrices using Jacobi rotation are described. Multiple iterations of Jacobi rotation are performed on a first matrix of complex values with multiple Jacobi rotation matrices of complex values to zero out the offdiagonal elements in the first matrix. For each iteration, a submatrix may be formed based on the first matrix and decomposed to obtain eigenvectors for the submatrix, and a Jacobi rotation matrix may be formed with the eigenvectors and used to update the first matrix. A second matrix of complex values, which contains orthogonal vectors, is derived based on the Jacobi rotation matrices. For eigenvalue decomposition, a third matrix of eigenvalues may be derived based on the Jacobi rotation matrices. For singular value decomposition, a fourth matrix with left singular vectors and a matrix of singular values may be derived based on the Jacobi rotation matrices. 
Full Text  EIGENVALUE DECOMPOSITION AND SINGULAR VALUE DECOMPOSITION OF MATRICES USING JACOBI ROTATION I. Claim of Priority under 35 U.S.C. §119 [0001] The present Application for Patent claims priority to Provisional Application Serial No. 60/628,324, entitled "Eigenvalue Decomposition and Singular Value Decomposition of Matrices Using Jacobi Rotation," filed November 15, 2004, assigned to the assignee hereof, and hereby expressly incorporated by reference herein. BACKGROUND I. Field [0002] The present invention relates generally to communication, and more specifically to techniques for decomposing matrices. II. Background [0003] A multipleinput multipleoutput (MIMO) communication system employs multiple (T) transmit antennas at a transmitting station and multiple (R) receive antennas at a receiving station for data transmission. A MIMO channel formed by the T transmit antennas and the R receive antennas may be decomposed into S spatial channels, where S [0004] A MIMO channel response may be characterized by an R xT channel response matrix H, which contains complex channel gains for all of the different pairs of transmit and receive antennas. The channel response matrix H may be diagonalized to obtain S eigenmodes, which may be viewed as orthogonal spatial channels of the MIMO channel. Improved performance may be achieved by transmitting data on the eigenmodes of the MEMO channel. [0005] The channel response matrix H may be diagonalized by performing either singular value decomposition of H or eigenvalue decomposition of a correlation matrix of H. The singular value decomposition provides left and right singular vectors, and the eigenvalue decomposition provides eigenvectors. The transmitting station uses the right singular vectors or the eigenvectors to transmit data on the S eigenmodes. The receiving station uses the left singular vectors or the eigenvectors to receive data transmitted on the S eigenmodes. [0006] Eigenvalue decomposition and singular value decomposition are very computationally intensive. There is therefore a need in the art for techniques to efficiently decompose matrices. SUMMARY [0007] Techniques for efficiently decomposing matrices using Jacobi rotation are described herein. These techniques may be used for eigenvalue decomposition of a Hermitian matrix of complex values to obtain a matrix of eigenvectors and a matrix of eigenvalues for the Hermitian matrix. The techniques may also be used for singular value decomposition of an arbitrary matrix of complex values to obtain a matrix of left singular vectors, a matrix of right singular vectors, and a matrix of singular values for the arbitrary matrix. [0008] In an embodiment, multiple iterations of Jacobi rotation are performed on a first matrix of complex values with multiple Jacobi rotation matrices of complex values to zero out the offdiagonal elements in the first matrix. The first matrix may be a channel response matrix H, a correlation matrix of H, which is R, or some other matrix. For each iteration, a submatrix may be formed based on the first matrix and decomposed to obtain eigenvectors for the submatrix, and a Jacobi rotation matrix may be formed with the eigenvectors and used to update the first matrix. A second matrix of complex values is derived based on the Jacobi rotation matrices. The second matrix contains orthogonal vectors and may be a matrix V, of right singular vectors of H or eigenvectors of R. [0009] For eigenvalue decomposition, a third matrix D of eigenvalues may be derived based on the Jacobi rotation matrices. For singular value decomposition (SVD) based on a first SVD embodiment, a third matrix W, of complex values may be derived based on the Jacobi rotation matrices, a fourth matrix U with orthogonal vectors may be derived based on the third matrix Wi, and a matrix S of singular values may also be derived based on the third matrix Wi. For singular value decomposition based on a second SVD embodiment, a third matrix Ui with orthogonal vectors and a matrix S of singular values may be derived based on the Jacobi rotation matrices. [0010] Various aspects and embodiments of the invention are described in further detail below. BRIEF DESCRIPTION OF THE DRAWINGS [0011] FIG. 1 shows a process for performing eigenvalue decomposition using Jacobi rotation. [0012] FIG. 2 shows a process for performing singular value decomposition using Jacobi rotation in accordance with the first SVD embodiment. [0013] FIG. 3 shows a process for performing singular value decomposition using Jacobi rotation in accordance with the second SVD embodiment. [0014] FIG. 4 shows a process for decomposing a matrix using Jacobi rotation. [0015] FIG. 5 shows an apparatus for decomposing a matrix using Jacobi rotation. [0016] FIG. 6 shows a block diagram of an access point and a user terminal. DETAILED DESCRIPTION [0017] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. [0018] The matrix decomposition techniques described herein may be used for various communication systems such as a singlecarrier communication system with a single frequency subband, a multicarrier communication system with multiple subbands, a singlecarrier frequency division multiple access (SCFDMA) system with multiple subbands, and other communication systems. Multiple subbands may be obtained with orthogonal frequency division multiplexing (OFDM), some other modulation techniques, or some other construct. OFDM partitions the overall system bandwidth into multiple (K) orthogonal subbands, which are also called tones, subcarriers, bins, and so on. With OFDM, each subband is associated with a respective subcarrier that may be modulated with data. An SCFDMA system may utilize interleaved FDMA (IFDMA) to transmit on subbands that are distributed across the system bandwidth, localized FDMA (LFDMA) to transmit on a block of adjacent subbands, or enhanced FDMA (EFDMA) to transmit on multiple blocks of adjacent subbands. In general, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SCFDMA. For clarity, much of the following description is for a MIMO system with a single subband. [0019] A MIMO channel formed by multiple (T) transmit antennas and multiple (R) receive antennas may be characterized by an RxT channel response matrix H, which may be given as: (Equation Removed 1) where entry hi,j, for i=l,...,R and j = l,...,T, denotes the coupling or complex channel gain between transmit antenna j and receive antenna i. [0020] The channel response matrix H may be diagonalized to obtain multiple (S) eigenmodes of H, where S example, performing either singular value decomposition of H or eigenvalue decomposition of a correlation matrix of H. [0021] The eigenvalue decomposition may be expressed as: (Equation Removed 2) where R is a T XT correlation matrix of H; V is a TxT unitary matrix whose columns are eigenvectors of R; A is a T xT diagonal matrix of eigenvalues of R; and "H" denotes a conjugate transpose. The unitary matrix V is characterized by the property VH V = I, where I is the identity matrix. The columns of the unitary matrix are orthogonal to one another, and each column has unit power. The diagonal matrix A contains possible nonzero values along the diagonal and zeros elsewhere. The diagonal elements of A are eigenvalues of R. These eigenvalues are denoted as {λ1, λ2, ...,λS} and represent the power gains for the S eigenmodes. R is a Hermitian matrix whose offdiagonal elements have the following property: ri,j = rji, ,where " * " denotes a complex conjugate. [0022] The singular value decomposition may be expressed as: (Equation Removed 3) where U is an R x R unitary matrix of left singular vectors of H; S is an R x T diagonal matrix of singular values of H; and V is a T x T unitary matrix of right singular vectors of H. U and V each contain orthogonal vectors. Equations (2) and (3) indicate that the right singular vectors of H are also the eigenvectors of R. The diagonal elements of 2 are the singular values of H. These singular values are denoted as {σ1, σ2,...,σS} and represent the channel gains for the S eigenmodes. The singular values of H are also the square roots of the eigenvalues of R, so that σ1. = √λ for i = 1,..., S. [0023] A transmitting station may use the right singular vectors in V to transmit data on the eigenmodes of H. Transmitting data on eigenmodes typically provides better performance than simply transmitting data from the T transmit antennas without any spatial processing. A receiving station may use the left singular vectors in U or the eigenvectors in V to receive the data transmission sent on the eigenmodes of H. Table 1 shows the spatial processing performed by the transmitting station, the received symbols at the receiving station, and the spatial processing performed by the receiving station. In Table 1, s is a Txl vector with up to S data symbols to be transmitted, x is a Txl vector with T transmit symbols to be sent from the T transmit antennas, r is an Rx 1 vector with R received symbols obtained from the R receive antennas, n is an Rxl noise vector, and s is a Txl vector with up to S detected data symbols, which are estimates of the data symbols in s. Table 1 (Table Removed 1) [0024] Eigenvalue decomposition and singular value decomposition of a complex matrix may be performed with an iterative process that uses Jacobi rotation, which is also commonly referred to as Jacobi method and Jacobi transformation. The Jacobi rotation zeros out a pair of offdiagonal elements of the complex matrix by performing a plane rotation on the matrix. For a 2x2 complex Hermitian matrix, only one iteration of the Jacobi rotation is needed to obtain the two eigenvectors and two eigenvalues for this 2x2 matrix. For a larger complex matrix with dimension greater than 2x2, the iterative process performs multiple iterations of the Jacobi rotation to obtain the desked eigenvectors and eigenvalues, or singular vectors and singular values, for the larger complex matrix. Each iteration of the Jacobi rotation on the larger complex matrix uses the eigenvectors of a 2x2 submatrix, as described below. [0025] Eigenvalue decomposition of a 2x2 Hermitian matrix R2X2 may be performed as follows. The Hermitian matrix R2x2 may be expressed as: (Equation Removed 4) where A, B, and D are arbitrary real values, and θb is an arbitrary phase. [0026] The first step of the eigenvalue decomposition of R2x2 is to apply a twosided unitary transformation, as follows: (Equation Removed 5) where Rre is a symmetric real matrix containing real values and having symmetric offdiagonal elements at locations (1, 2) and (2,1). [0027] The symmetric real matrix Rre is then diagonalized using a twosided Jacobi rotation, as follows: (Equation Removed 6) where angle may be expressed as: (Equation Removed 7) A 2x2 unitary matrix V2x2 of eigenvectors of R2x2 may be derived as: (Equation Removed 8) [0029] The two eigenvalues /lt and /12 may be derived based on equation (6), or based on the equation A2x2 = VH2x2 • R2x2 • V2x2, as follows: (Equation Removed 9) [0030] In equation set (9), the ordering of the two eigenvalues is not fixed, and A1 may be larger or smaller than λ2. However, if angle is constrained such that 2Ø 0, and sin 2Ø > 0 if and only if D > A. Thus, the ordering of the two eigenvalues may be determined by the relative magnitudes of A and D. λ1 is the larger eigenvalue if A>D, and λ2 is the larger eigenvalue if D>A. IfA = D, then sin 2Ø = 1 and λ2 is the larger eigenvalue. If λ2 is the larger eigenvalue, then the two eigenvalues in A2x2 may be swapped to maintain a predetermined ordering of largest to smallest eigenvalues, and the first and second columns of V2x2 may also be swapped correspondingly. Maintaining this predetermined ordering for the two eigenvectors in V2x2 results in the eigenvectors of a larger size matrix decomposed using V^ to be ordered from largest to smallest eigenvalues, which is desirable. [0031] The two eigenvalues λ1 and λ2 may also be computed directly from the elements of Rre, as follows: (Equation Removed 10) Equation (10) is the solution to a characteristic equation of R2x2. In equation (10), λ1 is obtained with the plus sign for the second quantity on the right hand side, and λ2 is obtained with the minus sign for the second quantity, where λ1, > λ2. [0032] Equation (8) requires the computation of cos Ø and sin Ø to derive the elements of V2X2. The computation of cos Ø and sin Ø is complex. The elements of V2x2 may be computed directly from the elements of R2x2, as follows: (Equation Removed 11) where r, ri,2 and r2,i are elements of R^, and r is the magnitude of n>2. Since ^ is a complex value, V contains complex values in the second row. [0033] Equation set (11) is designed to reduce the amount of computation to derive V2x2 from R2x2. For example, in equations (11c), (11d), and (11f), division by r is required. Instead, r is inverted to obtain r1, and multiplication by r\ is performed for equations (l1c), (11d), and (ll f). This reduces the number of divide operations, which are computationally more expensive than multiplies. Also, instead of computing the argument (phase) of the complex element r1,2, which requires an arctangent operation, and then computing the cosine and sine of this phase value to obtain c1 and s1, various trigonometric identities are used to solve for c\ and s\ as a function of the real and imaginary parts of r1,2 and using only a square root operation. Furthermore, instead of computing the arctangent in equation (7) and the sine and cosine functions in equation (8), other trigonometric identities are used to solve for c and s as functions of the elements of R2x2. ;0034] Equation set (11) performs a complex Jacobi rotation on R2x2 to obtain V2x2. The set of computations in equation set (11) is designed to reduce the number of multiply, square root, and invert operations required to derive V2x2. This can greatly reduce computational complexity for decomposition of a larger size matrix using V2X2. '0035] The eigenvalues of R2x2 may be computed as follows: (Equation Removed 12) 1. Eigenvalue Decomposition [0036] Eigenvalue decomposition of an NxN Hermitian matrix that is larger than 2x2, as shown in equation (2), may be performed with an iterative process. This iterative process uses the Jacobi rotation repeatedly to zero out the offdiagonal elements in the NxN Hermitian matrix. For the iterative process, NxN unitary transformation matrices are formed based on 2x2 Hermitian submatrices of the NxN Hermitian matrix and are repeatedly applied to diagonalize the NxN Hermitian matrix. Each unitary transformation matrix contains four nontrivial elements (i.e., elements other than 0 or 1) that are derived from elements of a corresponding 2x2 Hermitian submatrix. The transformation matrices are also called Jacobi rotation matrices. After completing all of the Jacobi rotation, the resulting diagonal matrix contains the real eigenvalues of the NxN Hermitian matrix, and the product of all of the unitary transformation matrices is an N x N matrix of eigenvectors for the N x N Hermitian matrix. [0037] In the following description, index i denotes the iteration number and is initialized as i = 0. R is an N x N Hermitian matrix to be decomposed, where N > 2. An N x N matrix D, is an approximation of the diagonal matrix A of eigenvalues of R and is initialized as D0 =R. An NxN matrix V,. is an approximation of the matrix V of eigenvectors of R and is initialized as V0 = I. [0038] A single iteration of the Jacobi rotation to update matrices D, and V, may be performed as follows. First, a 2x2 Hermitian matrix Dpq is formed based on the current, Di as follows: (Equation Removed 13) where dp 9 is the element at location (p,q) in D,; and /?e{l,...,N}, g£{l,...,N},and pq. Dpq is a 2x2 submatrix of Di, and the four elements of Dpq are four elements at locations (p,p}, (p,q), (q,p) and (q,q) in Di The values for indices p and q may be selected in various manners, as described below. [0039] Eigenvalue decomposition of Dpq is then performed, e.g., as shown in equation set (11), to obtain a 2x2 unitary matrix Vpq of eigenvectors of Dpq. For the eigenvalue decomposition of Dpq, R2x2 in equation (4) is replaced with Dpq, and V2x2 from equation (111) is provided as Vpq. [0040] An NxN complex Jacobi rotation matrix Tpq is then formed with matrix Vpq. Tpq is an identity matrix with the four elements at locations (p,p), (p,q), (q,p) and (q,q) replaced with the (1, 1), (1, 2), (2, 1) and (2, 2) elements, respectively, of Vpq. Tpq has the following form: (Equation Removed 14) where V1.1, V1,2, V2,1 and V2,2 are the four elements of Vpq. All of the other offdiagonal elements of Tpq are zeros. Equation (111) indicates that Tpq is a complex matrix containing complex values for V2,1 and Tpq. TP9 is also called a transformation matrix that performs the Jacobi rotation. [0041] Matrix D, is then updated as follows: (Equation Removed 15) Equation (15) zeros out two offdiagonal elements dp,q and dq,p at locations (p,q) and (q,p), respectively, in Di. The computation may alter the values of the other off diagonal elements in Di. [0042] Matrix Vi is also updated as follows: (Equation Removed 16) Vi may be viewed as a cumulative transformation matrix that contains all of the Jacobi rotation matrices Tpq used on Di. [0043] Each iteration of the Jacobi rotation zeros out two offdiagonal elements of Di. Multiple iterations of the Jacobi rotation may be performed for different values of indices p and q to zero out all of the offdiagonal elements of Di. The indices p and q may be selected in a predetermined manner by sweeping through all possible values. [0044] A single sweep across all possible values for indices p and q may be performed as follows. The index p may be stepped from 1 through N 1 in increments of one. For each value of p, the index q may be stepped from p+1 through N in increments of one. An iteration of the Jacobi rotation to update Di. and Vi may be performed for each different combination of values for p and q. For each iteration, Dpq is formed based on the values of p and q and the current Di for that iteration, Vpq is computed for Dpq as shown in equation set (11), Tpg is formed with Vpq as shown in equation (14), D, is updated as shown in equation (15), and V, is updated as shown in equation (16). For a given combination of values for p and q, the Jacobi rotation to update D, and V, may be skipped if the magnitude of the offdiagonal elements at locations (p,q) and (q, p) in Dj is below a predetermined threshold. [0045] A sweep consists of N • (N 1) /2 iterations of the Jacobi rotation to update D, and Vi for all possible values of p and q. Each iteration of the Jacobi rotation zeros out two offdiagonal elements of D, but may alter other elements that might have been zeroed out earlier. The effect of sweeping through indices p and q is to reduce the magnitude of all offdiagonal elements of Di, so that Di approaches the diagonal matrix Λ. V, contains an accumulation of all Jacobi rotation matrices that collectively gives Di. Thus, V, approaches V as Di, approaches A. [0046] Any number of sweeps may be performed to obtain more and more accurate approximations of V and Λ. Computer simulations have shown that four sweeps should be sufficient to reduce the offdiagonal elements of Di. to a negligible level, and three sweeps should be sufficient for most applications. A predetermined number of sweeps (e.g., three or four sweeps) may be performed. Alternatively, the offdiagonal elements of Di may be checked after each sweep to determine whether Di is sufficiently accurate. For example, the total error (e.g., the power in all offdiagonal elements of Di) may be computed after each sweep and compared against an error threshold, and the iterative process may be terminated if the total error is below the error threshold. Other conditions or criteria may also be used to terminate the iterative process. [0047] The values for indices p and q may also be selected in a deterministic manner. As an example, for each iteration i, the largest offdiagonal element of D,. may be identified and denoted as dp,q Jacobi rotation may then be performed with Dp,g containing this largest offdiagonal element dp,q and three other elements at locations (p,p)> (q.p)> and (q,q) in Di. The iterative process may be performed until a termination condition is encountered. The termination condition may be, for example, completion of a predetermined number of iterations, satisfaction of the error criterion described above, or some other condition or criterion. [0048] Upon termination of the iterative process, the final V, is a good approximation of V, and the final D, is a good approximation of Λ. The columns of Vi may be provided as the eigenvectors of R, and the diagonal elements of Di may be provided as the eigenvalues of R. The eigenvalues in the final D, are ordered from largest to smallest because the eigenvectors in Vpq for each iteration are ordered. The eigenvectors in the final Vi, are also ordered based on their associated eigenvalues in D, [0049] FIG. 1 shows an iterative process 100 for performing eigenvalue decomposition of an NxN Hermitian matrix R, where N>2, using Jacobi rotation. Matrices V, and Di. are initialized as V0 = I and D0 = R, and index i is initialized as i = 1 (block 110). [0050] For iteration i, the values for indices p and q are selected in a predetermined manner (e.g., by stepping through all possible values for these indices) or a deterministic manner (e.g., by selecting the index values for the largest offdiagonal element) (block 112). A 2x2 matrix Dpg is then formed with four elements of matrix D, at the locations determined by indices p and q (block 114). Eigenvalue decomposition of Dpq is then performed, e.g., as shown in equation set (11), to obtain a 2x2 matrix Vpq of eigenvectors of Dpq (block 116). An NxN complex Jacobi rotation matrix Tp9 is then formed based on matrix VP9, as shown in equation (14) (block 118). Matrix D, is then updated based on Tpg, as shown in equation (15) (block 120). Matrix V, is also updated based on Tpg, as shown in equation (16) (block 122). [0051] A determination is then made whether to terminate the eigenvalue decomposition of R (block 124). The termination criterion may be based on the number of iterations or sweeps already performed, an error criterion, and so on. If the answer is 'No' for block 124, then index i is incremented (block 126), and the process returns to block 112 for the next iteration. Otherwise, if termination is reached, then matrix Di is provided as an approximation of diagonal matrix A, and matrix Vi is provided as an approximation of matrix V of eigenvectors of R (block 128). [0052] For a MEMO system with multiple subbands (e.g., a MEMO system that utilizes OFDM), multiple channel response matrices H(k) may be obtained for different subbands. The iterative process may be performed for each channel response matrix H(k) to obtain matrices D,(k) and V,(k), which are approximations of diagonal matrix Λ(k) and matrix V(k) of eigenvectors, respectively, of R(k) = HH (k) H(k). [0053] A high degree of correlation typically exists between adjacent subbands in a MEMO channel. This correlation may be exploited by the iterative process to reduce the amount of computation to derive Di(&) and Vi(k) for the subbands of interest. For example, the iterative process may be performed for one subband at a time, starting from one end of the system bandwidth and traversing toward the other end of the system bandwidth. For each subband k except for the first subband, the final solution Vi(k 1) obtained for the prior subband k 1 may be used as an initial solution for the current subband k. The initialization for each subband k may be given as: V0(k) = Vi.(k1) and D0(k) = VHO (k) R(k) V0(k). The iterative process then operates on the initial solutions of D0 (k) and V0(k) for subband k until a termination condition is encountered. [0054] The concept described above may also be used across time. For each time interval t, the final solution Vi (t 1) obtained for a prior time interval t 1 may be used as an initial solution for the current time interval t. The initialization for each time interval t may be given as: VO(t) = Y,.(?l) and D0(t) = VHO(t) R(t)Vo(t), where R(t) = HH(t) H(t) and H(t) is the channel response matrix for time interval t. The iterative process then operates on the initial solutions of D0(t) and Vo(t) for time interval t until a termination condition is encountered. The concept may also be used across both frequency and time. For each subband in each time interval, the final solution obtained for a prior subband and/or the final solution obtained for a prior time interval may be used as an initial solution for the current subband and time interval. Singular Value Decomposition [0055] The iterative process may also be used for singular value decomposition of an arbitrary complex matrix H that is larger than 2x2. The singular value decomposition of H is given as H = U2VH . The following observations may be made regarding H. First, matrix R = HHH and matrix R = H.HH are both Hermitian matrices. Second, right singular vectors of H, which are the columns of V, are also eigenvectors of R. Correspondingly, left singular vectors of H, which are the columns of U, are also eigenvectors of R. Third, the nonzero eigenvalues of R are equal to the nonzero eigenvalues of R, and are the square of corresponding singular values of H. [0056] A 2 x 2 matrix H2x2 of complex values may be expressed as: (Equation Removed 17) where h1 is a 2x1 vector with the elements in the first column of H2x2 ; and h2 is a 2x1 vector with the elements in the second column of H2x2 • [0057] The right singular vectors of H2x2 are the eigenvectors of HH2x2 H2x2 and may be computed using the eigenvalue decomposition described above in equation set (11). A 2x2 Hermitian matrix R2x2 is defined as R2x2 = HH2x2 • H2x2, and the elements of R2x2 may be computed based on the elements of H2x2, as follows: (Equation Removed 18) For Hermitian matrix R2x2, r2l does not need to be computed since r2,1 = r1,2. Equation set (11) may be applied to R2x2 to obtain a matrix V2x2. V2x2 contains the eigenvectors of R2x2, which are also the right singular vectors of H2x2. [0058] The left singular vectors of H2x2 are the eigenvectors of H2x2HH2x2, and may also be computed using the eigenvalue decomposition described above in equation set (11). A 2x2 Hermitian matrix R2x2 is defined as R2x2 =112x2 HH2x2, and the elements of R2x2 may be computed based on the elements of H2X2, as follows: (Equation Removed 19) Equation set (11) may be applied to R2x2 to obtain a matrix V2x2. V2x2 contains the eigenvectors of R2x2, which are also the left singular vectors of H2x2. [0059] The iterative process described above for eigenvalue decomposition of an NxN Hermitian matrix R may be used for singular value decomposition of an arbitrary complex matrix H larger than 2x2. H has a dimension of R xT, where R is the number of rows and T is the number of columns. The iterative process for singular value decomposition (SVD) of H may be performed in several manners. [0060] In a first SVD embodiment, the iterative process derives approximations of the right singular vectors in V and the scaled left singular vectors in UΣ For this embodiment, a TxT matrix Vi is an approximation of V and is initialized as V0 =1. An R xT matrix Wi is an approximation of U Σ and is initialized as W0 = H. [0061] For the first SVD embodiment, a single iteration of the Jacobi rotation to update matrices V, and W, may be performed as follows. First, a 2x2 Hermitian matrix Mpq is formed based on the current Wi.. Mpq is a 2x2 submatrix of WH Wi and contains four elements at locations (p,p), (p,q), (q,p) and (q,q) in WHi Wi. elements of MP9 may be computed as follows: (Equation Removed 20) where wp is column p of Wi, Wq is column q of W,., and wl,p is the element at location (l,p) in Wi. Indices p and q are such that pe {1,..., T}, qe {1,...,T}, and p # q. The values for indices p and q may be selected in various manners, as described below. [0062] Eigenvalue decomposition of Mpq is then performed, e.g., as shown in equation set (11), to obtain a 2x2 unitary matrix MP9 of eigenvectors of Mpq For this eigenvalue decomposition, R2x2 is replaced with Mpq, and V2x2 is provided as V pq. [0063] A TxT complex Jacobi rotation matrix Tpq is then formed with matrix Vpg. Tpq is an identity matrix with the four elements at locations (p,p), (p,q), (q,p) and (q,q) replaced with the (1, 1), (1, 2), (2, 1) and (2, 2) elements, respectively, of VP9. Tpqhas the form shown in equation (14). [0064] Matrix V, is then updated as follows: (Equation Removed 21) [0065] Matrix W,. is also updated as follows: (Equation Removed 22) [0066] For the first SVD embodiment, the iterative process repeatedly zeros out off diagonal elements of WHi • W, without explicitly computing HH H. The indicesp and q may be swept by stepping p from 1 through T 1 and, for each value of p, stepping q from p + 1 through T. Alternatively, the values of p and q for which wHp • wq  is largest may be selected for each iteration. The iterative process is performed until a termination condition is encountered, which may be a predetermined number of sweeps, a predetermined number of iterations, satisfaction of an error criterion, and so on. [0067] Upon termination of the iterative process, the final V, is a good approximation of V, and the final W, is a good approximation of UE. When converged, WHi • Wi = ΣT Σ and U = Wt .Σ1 where " T " denotes a transpose. For a square diagonal matrix, the final solution of Σ may be given as: Σ = (WHi • Wi)½. For a nonsquare diagonal matrix, the nonzero diagonal elements of E are given by the square roots of the diagonal elements of WHi Wi. The final solution of U may be given as: U = Wi.Σ1.. [0068] FIG. 2 shows an iterative process 200 for performing singular value decomposition of an arbitrary complex matrix H that is larger than 2x2 using Jacobi rotation, in accordance with the first SVD embodiment. Matrices Vi, and Wi are initialized as V0 = I and W0 = H, and index i is initialized as i = 1 (block 210). [0069] For iteration i, the values for indices p and q are selected in a predetermined or deterministic manner (block 212). A 2x2 matrix Mpq is then formed with four elements of matrix Wt at the locations determined by indices p and q as shown in equation set (20) (block 214). Eigenvalue decomposition of Mpg is then performed, e.g., as shown in equation set (11), to obtain a 2x 2 matrix Vpg of eigenvectors of Mpq (block 216). A TxT complex Jacobi rotation matrix Tpq is formed based on matrix Vpq, as shown in equation (14) (block 218). Matrix V, is then updated based on Tpq, as shown in equation (21) (block 220). Matrix Wi is also updated based on Tpg, as shown in equation (22) (block 222). [0070] A determination is then made whether to terminate the singular value decomposition of H (block 224). The termination criterion may be based on the number of iterations or sweeps already performed, an error criterion, and so on. If the answer is 'No' for block 224, then index i is incremented (block 226), and the process returns to block 212 for the next iteration. Otherwise, if termination is reached, then post processing is performed on Wi to obtain Σ and U (block 228). Matrix Vi. is provided as an approximation of matrix V of right singular vectors of H, matrix U is provided as an approximation of matrix U of left singular vectors of H, and matrix Σ is provided as an approximation of matrix Σ of singular values of H (block 230) [0071] The left singular vectors of H may be obtained by performing the first SVD embodiment and solving for scaled left singular vectors HV = UΣ and then normalizing. The left singular vectors of H may also be obtained by performing the iterative process for eigenvalue decomposition of HHH . [0072] In a second SVD embodiment, the iterative process directly derives approximations of the right singular vectors in V and the left singular vectors in U. This SVD embodiment applies the Jacobi rotation on a twosided basis to simultaneously solve for the left and right singular vectors. For an arbitrary complex 2x2 matrix H2x2 = [ h1 h2 ], the conjugate transpose of this matrix is HH2x2 = [ hj h2 ], where h1 and h2 are the two columns of HH2x2 and are also the complex conjugates of the rows of H2x2. The left singular vectors of H2x2 are also the right singular vectors of HH2x2. The right singular vectors of H2x2 may be computed using Jacobi rotation, as described above for equation set (18). The left singular vectors of H2x2 may be obtained by computing the right singular vectors of H2x2 using Jacobi rotation, as described above for equation set (19). [0073] For the second SVD embodiment, a TxT matrix V, is an approximation of V and is initialized as V0 =1. An RxR matrix U, is an approximation of U and is initialized as U0 =1. An RxT matrix D, is an approximation of Σ and is initialized as D0=H. [0074] For the second SVD embodiment, a single iteration of the Jacobi rotation to update matrices Vi,, U,. and Di, may be performed as follows. First, a 2x2 Hermitian matrix Xp1q1 is formed based on the current Di. Xp1q1 is a 2x2 submatrix of DHi D1 and contains four elements at locations (pl,pl), (p1,q1) > (q1,P1) DHi Di The four elements of Xp,q may be computed as follows: (Equation Removed 23) where dp, is column p\ of Dt dq1 is column q1 of Di, and dl,p is the element at location (l,p1) in Di. Indices p\ and 171 are such that p1 E {1,..., T}, ql e {1,..., T}, and Pi qi Indices p\ and q\ may be selected in various manners, as described below. [0075] Eigenvalue decomposition of Xp1q1 is then performed, e.g., as shown in equation set (11), to obtain a 2x2 matrix Yp1q1 of eigenvectors of Xp1q1. For this eigenvalue decomposition, R2x2 is replaced with Xp1q1, and V^ is provided as Yp1q1 • A TxT complex Jacobi rotation matrix Tp1q1 is then formed with matrix Vp1q1 and contains the four elements of Vp1q1 atlocations (p^pj), (Pi,4i), (q1p1) and (q1p1)Tp1q1 has the form shown in equation (14). [0076] Another 2x2 Hermitian matrix Yp2q2 is also formed based on the current Dt.. YP2 92 is a 2x2 submatrix of Di, D1H and contains elements at locations (p2,P2), (p2,q2) > (q2,p2) and (q2,q2) in Di.D iH • The elements of Yp2q2 may be computed as follows: (Equation Removed 24) where dp2 is row p2 of D,, dq2 is row q2 of Di, and dp 2rl is the element at location (p2,l) in Di. Indices p2 and q2 are such that p2 {1,...,R}, q2 {l,...,R}, and P2 ≠ q2. Indices p2 and qi may also be selected in various manners, as described below. [0077] Eigenvalue decomposition of YP292 is then performed, e.g., as shown in equation set (11), to obtain a 2x2 matrix Up2q2 of eigenvectors of Yp2q2. For this eigenvalue decomposition, R2x2 is replaced with Yp2q2, and V2x2 is provided as UP292. An RxR complex Jacobi rotation matrix Sp2q2 is then formed with matrix UP292 and contains the four elements of UP292 at locations (p2,p2), (p2,q2), (q2,P2) and (q2,q2). Sp2q2 has the form shown in equation (14). [0078] Matrix V, is then updated as follows: (Equation Removed 25) Matrix Ui. is updated as follows: (Equation Removed 26) [0080] Matrix D, is updated as follows: (Equation Removed 27) [0081] For the second SVD embodiment, the iterative process alternately finds (1) the Jacobi rotation that zeros out the offdiagonal elements with indices p\ and q\ in HH • H and (2) the Jacobi rotation that zeros out the offdiagonal elements with indices p2 and q2 in H HH . The indices p1 and q1 may be swept by stepping p\ from 1 through T 1 and, for each value of p1, stepping q\ from pl +1 through T. The indices p2 and q2 may also be swept by stepping p2 from 1 through R 1 and, for each value of p2, stepping q2 from p2 +1 through R. As an example, for a square matrix H, the indices may be set as p1 = p2 and ql=q2. As another example, for a square or nonsquare matrix H, a set of pi and q\ may be selected, then a set of p2 and q2 may be selected, then a new set of p1 and q1 may be select, then a new set of p2 and q2 may be selected, and so on, so that new values are alternately selected for indices p1 and q1 and indices p2 and q2. Alternatively, for each iteration, the values of p1 and q1 for which dHp1 dq1  is largest may be selected, and the values of p2 and q2 for which dHp1 dq2  is largest may be selected. The iterative process is performed until a termination condition is encountered, which may be a predetermined number of sweeps, a predetermined number of iterations, satisfaction of an error criterion, and so on. [0082] Upon termination of the iterative process, the final V, is a good approximation of V, the final Ut is a good approximation of U, and the final Di, is a good approximation of Σ, where V and Σ may be rotated versions of V and , respectively. The computation described above does not sufficiently constrain the left and right singular vector solutions so that the diagonal elements of the final Di, are positive real values. The elements of the final D, may be complex values whose magnitudes are equal to the singular values of H. V, and Di, may be unrotated as follows: (Equation Removed 28) where P is a TxT diagonal matrix with diagonal elements having unit magnitude and phases that are the negative of the phases of the corresponding diagonal elements of Di. Σ and V are the final approximations of Σ and V, respectively. [0083] FIG. 3 shows an iterative process 300 for performing singular value decomposition of an arbitrary complex matrix H that is larger than 2x2 using Jacobi rotation, in accordance with the second SVD embodiment. Matrices Vi, U,. and D, are initialized as V0 = I, U0 = I and D0 = H, and index i is initialized as i = 1 (block 310). [0084] For iteration z, the values for indices p\, q\, pi and q2 are selected in a predetermined or deterministic manner (block 312). A 2x2 matrix Xp19l is formed with four elements of matrix D, at the locations determined by indices p1 and q1, as shown in equation set (23) (block 314). Eigenvalue decomposition of Xp19l is then performed, e.g., as shown in equation set (11), to obtain a 2x2 matrix Vp19l of eigenvectors of Xp19l (block 316). A TxT complex Jacobi rotation matrix Tp19l is then formed based on matrix Vp19l (block 318). A 2x2 matrix YP2q2 is also formed with four elements of matrix D, at the locations determined by indices p2 and q2, as shown in equation set (24) (block 324). Eigenvalue decomposition of Y_P292 is then performed, e.g., as shown in equation set (11), to obtain a 2x2 matrix Hp2q2 of eigenvectors of YP292 (block 326). An RxR complex Jacobi rotation matrix Sp2q2 is then formed based on matrix Up2q2 (block 328). [0085] Matrix Vi is then updated based on Tp1q1, as shown in equation (25) (block 330). Matrix U, is updated based on Sp2q2 as shown in equation (26) (block 332). Matrix Di. is updated based on Tpi9j and Sp2q2 as shown in equation (27) (block 334). [0086] A determination is then made whether to terminate the singular value decomposition of H (block 336). The termination criterion may be based on the number of iterations or sweeps already performed, an error criterion, and so on. If the answer is 'No' for block 336, then index i is incremented (block 338), and the process returns to block 312 for the next iteration. Otherwise, if termination is reached, then post processing is performed on Di. and V, to obtain Σ and V (block 340). Matrix V is provided as an approximation of V, matrix U, is provided as an approximation of U, and matrix Σ) is provided as an approximation of Σ (block 342) [0087] For both the first and second SVD embodiments, the right singular vectors in the final V, and the left singular vectors in the final U, or U are ordered from the largest to smallest singular values because the eigenvectors in Vpq (for the first SVD embodiment) and the eigenvectors in Yp1q1 and Up2q2 (for the second SVD embodiment) for each iteration are ordered. [0088] For a MIMO system with multiple subbands, the iterative process may be performed for each channel response matrix H(k) to obtain matrices Vi.(k), U,(k), and D, (k), which are approximations of the matrix V(k) of right singular vectors, the matrix U(k) of left singular vectors, and the diagonal matrix Σ(k) of singular values, respectively, for that H(k). The iterative process may be performed for one subband at a time, starting from one end of the system bandwidth and traversing toward the other end of the system bandwidth. For the first SVD embodiment, for each subband k except for the first subband, the final solution Vi(kl) obtained for the prior subband k1 may be used as an initial solution for the current subband k, so that V0(k) = V,.(k 1) and W0(k) = H(k) Vo,(k). For the second SVD embodiment, for each subband k except for the first subband, the final solutions Vi.(k 1) and U,(k 1) obtained for the prior subband k — 1 may be used as initial solutions for the current subband k, so that Vo(k) = Y,(kl), Uo(k) = Ui(kl), and D0(k) = UH(fc)H(k:)V0(k). For both embodiments, the iterative process operates on the initial solutions for subband k until a termination condition is encountered for the subband. The concept may also be used across time or both frequency and time, as described above. [0089] FIG. 4 shows a process 400 for decomposing a matrix using Jacobi rotation. Multiple iterations of Jacobi rotation are performed on a first matrix of complex values with multiple Jacobi rotation matrices of complex values (block 412). The first matrix may be a channel response matrix H, a correlation matrix of H, which is R, or some other matrix. The Jacobi rotation matrices may be Tpq, Tpi9i, Sp2g2, and/or some other matrices. For each iteration, a submatrix may be formed based on the first matrix and decomposed to obtain eigenvectors for the submatrix, and a Jacobi rotation matrix may be formed with the eigenvectors and used to update the first matrix. A second matrix of complex values is derived based on the multiple Jacobi rotation matrices (block 414). The second matrix contains orthogonal vectors and may be matrix Vi of right singular vectors of H or eigenvectors of R. [0090] For eigenvalue decomposition, as determined in block 416, a third matrix D, of eigenvalues may be derived based on the multiple Jacobi rotation matrices (block 420). For singular value decomposition based on the first SVD embodiment or scheme, a third matrix Wi of complex values may be derived based on the multiple Jacobi rotation matrices, a fourth matrix U with orthogonal vectors may be derived based on the third matrix Wi and a matrix Σ of singular values may also be derived based on the third matrix Wi (block 422). For singular value decomposition based on the second SVD embodiment, a third matrix Ui. with orthogonal vectors and a matrix Σ, of singular values may be derived based on the multiple Jacobi rotation matrices (block 424). [0091] FIG. 5 shows an apparatus 500 for decomposing a matrix using Jacobi rotation. Apparatus 500 includes means for performing multiple iterations of Jacobi rotation on a first matrix of complex values with multiple Jacobi rotation matrices of complex values (block 512) and means for deriving a second matrix Vi of complex values based on the multiple Jacobi rotation matrices (block 514). [0092] For eigenvalue decomposition, apparatus 500 further includes means for deriving a third matrix Di of eigenvalues based on the multiple Jacobi rotation matrices (block 520). For singular value decomposition based on the first SVD embodiment, apparatus 500 further includes means for deriving a third matrix W, of complex values based on the multiple Jacobi rotation matrices, a fourth matrix U with orthogonal vectors based on the third matrix, and a matrix Σ of singular values based on the third matrix (block 522). For singular value decomposition based on the second SVD embodiment, apparatus 500 further includes means for deriving a third matrix Ui with orthogonal vectors and a matrix Σ of singular values based on the multiple Jacobi rotation matrices (block 524). 3. System [0093] FIG. 6 shows a block diagram of an embodiment of an access point 610 and a user terminal 650 in a MIMO system 600. Access point 610 is equipped with multiple (Nap) antennas that may be used for data transmission and reception. User terminal 650 is equipped with multiple (Nut) antennas that may be used for data transmission and reception. For simplicity, the following description assumes that MIMO system 600 uses time division duplexing (TDD), and the downlink channel response matrix Hdn(k) for each subband k is reciprocal of the uplink channel response matrix Hup (ft) for that subband, or Hdn (k) = H(k) and Hup (k) = H7 (k). [0094] On the downlink, at access point 610, a transmit (TX) data processor 614 receives traffic data from a data source 612 and other data from a controller/processor 630. TX data processor 614 formats, encodes, interleaves, and modulates the received data and generates data symbols, which are modulation symbols for data. A TX spatial processor 620 receives and multiplexes the data symbols with pilot symbols, performs spatial processing with eigenvectors or right singular vectors if applicable, and provides Nap streams of transmit symbols to Nap transmitters (TMTR) 622a through 622ap. Each transmitter 622 processes its transmit symbol stream and generates a downlink modulated signal. Nap downlink modulated signals from transmitters 622a through 622ap are transmitted from antennas 624a through 624ap, respectively. [0095] At user terminal 650, Nut antennas 652a through 652ut receive the transmitted downlink modulated signals, and each antenna 652 provides a received signal to a respective receiver (RCVR) 654. Each receiver 654 performs processing complementary to the processing performed by transmitters 622 and provides received symbols. A receive (RX) spatial processor 660 performs spatial matched filtering on the received symbols from all receivers 654a through 654ut and provides detected data symbols, which are estimates of the data symbols transmitted by access point 610. An RX data processor 670 further processes (e.g., symbol demaps, deinterleaves, and decodes) the detected data symbols and provides decoded data to a data sink 672 and/or a controller/processor 680. [0096] A channel processor 678 processes received pilot symbols and provides an estimate of the downlink channel response, H(k), for each subband of interest. Processor 678 and/or 680 may decompose each matrix H(k) using the techniques described herein to obtain V(k) and Σ(k), which are estimates of V(k) and Σ(k) for the downlink channel response matrix H(k). Processor 678 and/or 680 may derive a downlink spatial filter matrix Mdn(fc) for each subband of interest based on V(k), as shown in Table 1. Processor 680 may provide Mdn(fc) to RX spatial processor 660 for downlink matched filtering and/or V(k) to a TX spatial processor 690 for uplink spatial processing. [0097] The processing for the uplink may be the same or different from the processing for the downlink. Traffic data from a data source 686 and other data from controller/ processor 680 are processed (e.g., encoded, interleaved, and modulated) by a TX data processor 688, multiplexed with pilot symbols, and further spatially processed by a TX spatial processor 690 with V(k) for each subband of interest. The transmit symbols from TX spatial processor 690 are further processed by transmitters 654a through 654ut to generate Nut uplink modulated signals, which are transmitted via antennas 652a through 652ut. [0098] At access point 610, the uplink modulated signals are received by antennas 624a through 624ap and processed by receivers 622a through 622ap to generate received symbols for the uplink transmission. An RX spatial processor 640 performs spatial matched filtering on the received data symbols and provides detected data symbols. An RX data processor 642 further processes the detected data symbols and provides decoded data to a data sink 644 and/or controller/processor 630. [0099] A channel processor 628 processes received pilot symbols and provides an estimate of either HT(k) or U(k) for each subband of interest, depending on the manner in which the uplink pilot is transmitted. Processor 628 and/or 630 may decompose each matrix HT (k) using the techniques described herein to obtain U(k). Processor 628 and/or 630 may also derive an uplink spatial filter matrix MUp(k) for each subband of interest based on U(k). Processor 680 may provide Mup(k) to RX spatial processor 640 for uplink spatial matched filtering and/or U[(k) to TX spatial processor 620 for downlink spatial processing. [00100] Controllers/processors 630 and 680 control the operation at access point 610 and user terminal 650, respectively. Memories 632 and 682 store data and program codes for access point 610 and user terminal 650, respectively. Processors 628, 630, 678, 680 and/or other processors may perform eigenvalue decomposition and/or singular value decomposition of the channel response matrices. [00101] The matrix decomposition techniques described herein may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. For a hardware implementation, the processing units used to perform matrix decomposition may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. [00102] For a firmware and/or software implementation, the matrix decomposition techniques may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory (e.g., memory 632 or 682 in FIG. 6 and executed by a processor (e.g., processor 630 or 680). The memory unit may be implemented within the processor or external to the processor. [00103] Headings are included herein for reference and to aid in locating certain sections. These headings are not intended to limit the scope of the concepts described therein under, and these concepts may have applicability in other sections throughout the entire specification. [00104] The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. We claim: 1. An apparatus (610, 650) for eigenvalue decomposition and singular value decomposition of matrices in wireless communications comprising: plurality of transmitters (622a; 622ap); plurality of receivers (622a; 622ap); a controller (630) configured to receive traffic data and generating data symbols; a transmit (TX) data processor (614) coupled to said controller (630); a receive (RX) data processor (642) coupled to said controller (630); a channel processor (628) coupled to said controller (630); wherein said channel processor (628) and said controller (630) performs a plurality of iterations of Jacobi rotation on a first matrix of complex values with a plurality of Jacobi rotation matrices of complex values, wherein, for each of the plurality of iterations, said channel processor (628) and said controller (630) is configured to form a submatrix based on the first matrix, to decompose the submatrix to obtain eigenvectors for the submatrix, to form a Jacobi rotation matrix with the eigenvectors, and to update the first matrix with the Jacobi rotation matrix, and to derive a second matrix of complex values based on the plurality of Jacobi rotation matrices, the second matrix comprising orthogonal vectors; and a memory (632) coupled to the said channel processor (628, 630) and said controller (678,680); 2. An apparatus (6 10, 650) as claimed in claim 1, wherein said transmit (TX) data processor (614) is coupled to a data source (612) and said controller (630) for receiving traffic data and generating data symbols. 3. An apparatus (610, 650) as claimed in claim 1, wherein said transmit (TX) data processor (614) is coupled to a TX spatial processor (620), said TX spatial processor (620) multiplexes the data symbols wjth pilot symbols. 4. An apparatus (610, 650) as claimed in claim 3, wherein said TX spatial processor (620) coupled to said channel processor (628) and said controller (630) and provides stream of transmit symbols to said transmitters (622a; 622ap). 5. An apparatus (610, 650) as claimed in claim 1, wherein said plurality of receivers (622a; 622ap) are coupled to a RX spatial processor (640), said RX spatial processor (640) is configured to detects data symbols and the detected data symbols are processed by said receive (RX) data processor (642). 6. An apparatus (610, 650) as claimed in claim 1, wherein said receive (RX) data processor (642) is coupled to a data sink (644) and provides the decoded data to said data sink (644) and said controller (630). 

3807DELNP2007Abstract(08022013).pdf
3807DELNP2007Claims(08022013).pdf
3807DELNP2007Claims(15022012).pdf
3807DELNP2007Correspondence Others(08022013).pdf
3807DELNP2007Correspondence Others(15022012).pdf
3807delnp2007correspondenceothers 1.pdf
3807delnp2007correspondenceothers.pdf
3807delnp2007description (complete).pdf
3807DELNP2007Drawings(15022012).pdf
3807DELNP2007Form13(08022013).pdf
3807DELNP2007Form2(08022013).pdf
3807DELNP2007Form3(15022012).pdf
3807DELNP2007GPA(08022013).pdf
3807DELNP2007Petition137(15022012).pdf
Patent Number  257998  

Indian Patent Application Number  3807/DELNP/2007  
PG Journal Number  48/2013  
Publication Date  29Nov2013  
Grant Date  26Nov2013  
Date of Filing  22May2007  
Name of Patentee  QUALCOMM INCORPORATED  
Applicant Address  5775 MOREHOUSE DRIVE, SAN DIEGO, CALIFORNIA 921211714, USA  
Inventors:


PCT International Classification Number  G06F 17/16  
PCT International Application Number  PCT/US2005/041783  
PCT International Filing date  20051115  
PCT Conventions:
