Effect of Channel Uncertainty on the Mutual Information of MIMO Fading Channels

Robust Joint Design of Linear Relay Precoder and Destination Equalizer for Dual-Hop Amplify-and-Forward MIMO Relay Systems

“…If the channel estimation algorithm proposed in [27] is used, we have , , , and . The matrices and are the transmit and receive antennas correlation matrices at the source and the relay, respectively, and is the source-relay channel estimation error variance.…”

“…Notice that for the channel estimation algorithms in [27] and [28], , , , and are functions of the second-order statistics of CSI, which can be considered to change very slowly and to be known in prior [3]. However, in the following, the proposed algorithms are developed without assuming any specific form of , , , and .…”

Linear MMSE MIMO Channel Estimation with Imperfect Channel Covariance Information

“…The ability of the so called "Kronecker product model" to reflect the real channel behavior for any transmission scenario is still subject of discussion [11], [12]. In this paper we first consider the general model given by (3) and (4) with the assumption that R has full-rank (positive definite Hermitian matrix). The Kronecker model is used in Sect.…”

“…In fact, imperfect knowledge of the channel precludes optimal coherent signal detection and, consequently, it has a detrimental effect on the overall system capacity [1]. In MIMO systems, even more dramatically, channel estimation errors may constrict multiplexing gain at high SNRs [2], [3]. Moreover, when a feedback link between receiver and transmitter is available, the optimal MIMO power allocation depends on the accuracy of the channel state information [4].…”

“…Linear MMSE (LMMSE) channel estimation is often considered for performance analysis and comparison [2], [3] due to its good performance at any SNR value. A MMSE-based criterion requires second-order statistical knowledge of the radio channel to be estimated.…”