Bayesian approaches for combining noisy mean and covariance channel information

We studied the ergodic capacity of two models for partial channel knowledge: the pathwise channel model with knowledge of the slow varying parameters at the transmitter and the

We studied the ergodic capacity of two models for partial channel knowledge: the pathwise channel model with knowl- edge of the slow varying parameters at the transmitter and

We studied the ergodic capacity of two models for partial chan- nel knowledge: the pathwise channel model with knowledge of the slow varying parameters at the transmitter and

Existing SM (V-BLAST and similar schemes), based upon channel matrix inversion, rely on the linear independence of antenna channel responses for stream separation and

In [4], a prefiltering approach is proposed as- suming partial Channel State Information at the Transmitter (CSIT), where knowledge of the transmit antenna correla- tions

The novel beamformers are here optimized for the Expected Weighted Sum Rate (EWSR) for the case of Partial Channel State Information at the Transmitters (CSIT).. Gaussian

In order to exploit partial Channel State Information at the Transmitter (CSIT) in MIMO systems, most of the cur- rent precoding schemes exploit either mean [1] or covari- ance

This inference process relies on a dictionary of uplink/downlink covariance matrices, and on interpolation in the corresponding Riemannian space; once the dictionary is known,