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References in periodicals archive ?
Compute a QR decomposition of [[bar.[beta]].sub.s] = [([Q.sub.s][R.sub.s]).sup.T], [Q.sub.s] [member of] [C.sup.#sxr], [R.sub.s] [member of] [C.sup.rxr].
Kammeyer, "MMSE extension of V-BLAST based on sorted QR decomposition," IEEE Vehicular Technology Conference, Orlando, FL, USA, 2003, vol.1, pp.508-512.
Ghafoor, "QR decomposition based image enhancement for through wall imaging," in Proceedings of the IEEE Radar Conference, pp.
This paper proposes an FPGA based scalable systolic array architecture for QR decomposition based on Givens rotation algorithm using the concept of LUT based Newton-Raphson method for inverse square root and square root and achieves scalability using DPR.
The objective is to get the results from the QR decomposition of [D.sup.*]:
The application of QR decomposition to triangularize the input data matrix results in an alternative method for the implementation of the recursive least-squares (RLS).
The QR decomposition of the extended channel matrix [H.bar] can be expressedby
For this purpose, we evaluated the classical method and the Gram-Schmidt process, by taking Gram-Schmidt QR decomposition as reference.
Wubben et al., "MMSE extension of V-BLAST based on sorted QR decomposition," in Proc.
Since the vector norm is invariant of the orthogonal transformation Q(k)[1], we apply QR decomposition to transform the information matrix to generate an upper right triangular matrixR(k).
We propose, a recursive approach that compute and implement the angle parameter loaded sample-matrix inversion(APLSMI) technique via the inverse QR decomposition using the Givens rotations [1,2,10].The approach is called Angle parameter loaded sample-matrix inversion based inverse QRD-RLS(APLSMIQRDRLS) which is implemented by using the angle parameters and the inverse Cholesky factor as the diagonal loading.
[[PHI].sup.-1.sub.L](n) is the recursive equation for the inverse of the correlation matrix and [[PHI].sub.L](n) can also be written in terms of the matrix product [R.sup.H.sub.L](n)[R.sub.L](n), where [R.sub.L](n) is the upper triangular matrix that results from the application of the QR Decomposition to the exponentially weighted data matrix [Y.sub.L].