Wireless Center

Adaptive Receiver Structures

2008-12-24T00:00:00+00:00

Adaptive Receiver Structures We next consider adaptive algorithms for sequentially estimating the blind linear detector. First, we address adaptive implementation of the blind channel estimator discussed above. Suppose that the signal subspace Us is known. Denote by z[i] the projection of the received signal r[i] onto the noise subspace: Equation ...

NAHJ Subspace Tracking

2008-12-24T00:00:00+00:00
NAHJ Subspace Tracking The algorithm we present here was developed in [411, 412]. It is a member of the QR-Jacobi family in the sense that it uses Givens rotations during the updating process. However, this algorithm avoids the QR step entirely. Instead of working with the SVD-type ...

PASTd Algorithm

2008-12-24T00:00:00+00:00
PASTd Algorithm Let be a random vector with autocorrelation matrix Cr = E{r[i]r[i]H}. Consider the scalar function Equation 2.150 with a matrix argument (r < N). It can be shown that [586]: W is a stationary point of if and only if W = ...

Subspace Tracking Algorithms

2008-12-24T00:00:00+00:00
Subspace Tracking Algorithms It is seen from Section 2.5 that the linear multiuser detectors are obtained once the signal subspace components are identified. The classic approach to subspace estimation is through batch eigenvalue decomposition (ED) of the sample autocorrelation matrix or batch singular value decomposition (SVD) of the data ...

Performance of Blind Multiuser Detectors

2008-12-24T00:00:00+00:00
Performance of Blind Multiuser Detectors 2.5.1 Performance Measures In previous sections we have discussed two approaches to blind multiuser detection: the direct method and the subspace method. These two approaches are based primarily on two equivalent expressions for the linear MMSE detector [i.e., (2.26) and (2.78)]. ...