Apparatus and method of non-iterative singular-value decomposition
Method of non-iterative singular-value decomposition (SVD). The method includes receiving, by receiver, a signal; determining, by a channel matrix generator connected to the receiver, a channel matrix for the received signal; reducing, by a singular-value decomposer connected to the channel matrix generator, the dimension of the channel matrix; performing, by the singular-value decomposer, an SVD on the dimension-reduced channel matrix to determine singular vectors and corresponding coefficients that maximize singular values of the singular vectors; and outputting a result of the SVD based on at least one of when the dimension of the dimension-reduced channel matrix is less than or equal to 2 and when two greatest singular values of corresponding singular vectors are determined.
1. A method of non-iterative singular-value decomposition (SVD) in a wireless communication system, comprising:
receiving, by a receiver, a signal;
determining, by a channel matrix generator connected to the receiver, a channel matrix for the received signal;
reducing, by a singular-value decomposer connected to the channel matrix generator, the dimension of the channel matrix;
performing, by the singular-value decomposer, an SVD on the dimension-reduced channel matrix to determine singular vectors and corresponding coefficients that maximize singular values of the singular vectors; and
outputting a result of the SVD based on at least one of when the dimension of the dimension-reduced channel matrix is less than or equal to 2 and when two greatest singular values of corresponding singular vectors are determined.
2. The method of claim 1 , further comprising when the result of the SVD is not output, subtracting, by the singular-value decomposer, the singular vectors from the dimension-reduced channel matrix to reduce rank and returning to performing the SVD.
3. The method of claim 1 , wherein reducing the dimension of the channel matrix is comprised of reducing the dimension of the channel matrix by a rotation in a plane spanned by two coordinate axes.
4. The method of claim 1 , wherein reducing the dimension of the channel matrix is comprised of reducing the dimension of the channel matrix by a linear transformation that describes a reflection about a plane or hyperplane containing an origin.
5. The method of claim 4 , wherein the linear transformation is performed N rx times, where N rx is an integer indicating a number of receive antennas.
6. The method of claim 5 , wherein N rx is equal to 1, 2, or 4.
7. The method of claim 1 , wherein the SVD is performed at a beamformee and the result of the SVD is fed back to a beamformer.
8. The method of claim 1 , wherein the result of the SVD is for single-user multiple-input-multiple output (SU-MIMO).
9. The method of claim 1 , wherein the result of the SVD is for multiple-user multiple-input-multiple output (MU-MIMO).
10. The method of claim 2 , further comprising updating, by a rank reduction processor, the reduced rank channel matrix using orthogonalization, where Gram Schmidt orthogonalization is performed on vectors found from three streams to determine vectors for four streams.
11. An apparatus for non-iterative singular-value decomposition (SVD) in a wireless communication system, comprising:
a receiver configured to receive a signal;
a channel matrix generator connected to the receiver and configured to determine a channel matrix for the received signal; and
a singular-value decomposer connected to the channel matrix generator and configured to:
reduce the dimension of the channel matrix;
perform an SVD on the dimension-reduced channel matrix to determine singular vectors and corresponding coefficients that maximize singular values of the singular vectors; and
output a result of the SVD based on at least one of when the dimension of the dimension-reduced channel matrix is less than or equal to 2 and when two greatest singular values of corresponding singular vectors are determined.
12. The apparatus of claim 11 , wherein the singular-value decomposer is further configured to, when the result of the SVD is not output, subtract the singular vectors from the dimension-reduced channel matrix to reduce rank and returning to performing the SVD.
13. The apparatus of claim 11 , wherein the singular-value decomposer is further configured to reduce the dimension of the channel matrix by a rotation in a plane spanned by two coordinate axes.
14. The apparatus of claim 11 , wherein the singular-value decomposer is further configured to reduce the dimension of the channel matrix by a linear transformation that describes a reflection about a plane or hyperplane containing an origin.
15. The apparatus of claim 14 , wherein the singular-value decomposer is further configured to reduce the dimension of the channel matrix by the linear transformation performed N rx times, where N rx is an integer indicating a number of receive antennas.
16. The apparatus of claim 11 , wherein N rx is equal to 1, 2, or 4.
17. The apparatus of claim 11 , wherein the singular-value decomposer is further configured to perform the SVD at a beamformee and feedback a result of the SVD to a beamformer.
18. The apparatus of claim 11 , wherein the singular-value decomposer is further configured to feedback the result of the SVD for single-user multiple-input-multiple output (SU-MIMO).
19. The apparatus of claim 11 , wherein the singular-value decomposer is further configured to feedback the result of the SVD for multiple-user multiple-input-multiple output (MU-MIMO).
20. The apparatus of claim 12 , wherein the singular-value decomposer is further configured to update the reduced rank channel matrix using orthogonalization, where Gram Schmidt orthogonalization is performed on vectors found from three streams to determine vectors for four streams.