Widely-Linear MMSE Estimation of Complex-Valued Graph Signals

Non-linear Mmse Estimation

For the first time it is here shown that Symbolby-Symbol Maximum A Posteriori (SbS-MAP) receivers are able to generate Non-Linear Minimum Mean Square Error (NL-MMSE) estimates of the transmitted symbols.

Properties, performance and practical interest of the widely linear MMSE beamformer for nonrectilinear signals

Widely Linear (WL) Minimum Mean Square Error (MMSE) estimation has received a great interest these last 20 years for second order (SO) noncircular signals. In the context of radio communications networks, it has been shown in particular that WL MMSE receivers allow to implement Single Antenna Interference Cancellation (SAIC) of one rectilinear interference, such as Binary Phase Shift Keying (BP...

Complex dual channel estimation: Cost effective widely linear adaptive filtering

Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficien...

Asymptoti Analysis of Widely Linear MMSE Multiuser Dete tion { Complex vs Real

Jointly Gaussian random variables, MMSE and linear MMSE estimation

• Proof: X is j G implies that V = uX is G with mean uμ and variance uΣu. Thus its characteristic function, CV (t) = e ituμe−t 2uTΣu/2. But CV (t) = E[e itV ] = E[e TX ]. If we set t = 1, then this is E[e TX ] which is equal to CX(u). Thus, CX(u) = CV (1) = e iuμe−u TΣu/2. • Proof (other side): we are given that the charac function ofX, CX(u) = E[eiuTX ] = e μe−u TΣu/2. Consider V = uX. Thus, C...