Full exploitation of diversity in space-time MMSE receivers

Enhanced MDIR receiver for space--time dispersive channels

“…It is usually the case that the symbols (chip level symbols, scrambled with long codes in the FDD mode of UTRA) in matrix are uncorrelated, so (11) where is . contains (column-wise) the -length response of the physical propagation channel seen at each sensor (12) The MDIR receiver developed in [6] is then given by subject to (13) It may be shown [6] that the choice of the beamformer is obtained as the generalized eigenvector of (14) associated to the minimum eigenvalue , and the impulse response seen at the output of the beamformer is . Note that these vectors are different to those in (7).…”

“…Note that these vectors are different to those in (7). Being non null the signal power at the output of the beamformer through the restriction in (13), takes the value of the inverse of the signal-plus-noise-plus-interference power at the output of the associated beamformer [6]. Since the solution provides beamformers (each giving different SINR at its output), a straightforward question arises: could the different beamformer outputs be efficiently combined as in Fig.…”

Spatial filter decomposition for interference mitigation

“…Due to the presence of the noise, the estimation still remains suboptimal and the result is corrupted by the training noise estimation. It was proven in [13] that combining all eigenvectors, weighted by the inverse of their corresponding output SINR, could lead to an enhanced algorithm, so-called solution-vectors maximum ratio combining (SoMRC). It was shown that this approach gives better performance than the single solution-vector [13,14].…”

“…It was proven in [13] that combining all eigenvectors, weighted by the inverse of their corresponding output SINR, could lead to an enhanced algorithm, so-called solution-vectors maximum ratio combining (SoMRC). It was shown that this approach gives better performance than the single solution-vector [13,14]. Obviously, such kind of algorithms is based on a complex eigendecomposition.…”

“…This result is known and used by many authors. An enhanced maximum signal to interference plus noise constraint (EMSINRC) is proposed in [13]. It is based on the exploitation of the set V z,d of all the eigenvectors of the previous matrix defined as…”