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In this paper, we study the achievable rate of the training-based multi-input multi-output (MIMO) systems over time-varying flat fading channels modeled with the $L$-th order autoregressive, AR($L$), process. Using the Bayesian Cram\'{e}r-Rao lower bound (BCRB) to characterize the mean squared error of channel estimation, the achievable rate of the MIMO systems is investigated from the information-theoretical...
This paper examines the use of two-way training in multiple-input multiple-output (MIMO) wireless systems to discriminate the channel estimation (and, thus, data detection) performance between two receivers, namely, a legitimate receiver (LR) and an unauthorized receiver (UR). This work extends upon the discriminatory channel estimation (DCE) proposed in our prior work, where it was previously assumed...
This paper presents a general reduced-rank channel model and a corresponding low-complexity estimation scheme for wideband spatial-correlated multiple-input multiple-output (MIMO) systems. We focus on orthogonal frequency division multiplex (OFDM) based systems. The proposed reduced-rank model is useful for many post-channel-estimation applications such as channel state information (CSI) feedback,...
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