Hybrid analog/digital MIMO architecture in large antenna array systems

Hybrid analog/digital precoding architectures can address the trade-off between achievable spectral efficiency and power consumption in large-scale multiple-input-multiple-output (MIMO) systems. This feature makes the hybrid precoding a promising candidate for millimeter wave systems and massive MIMO systems, which deploy large antenna arrays. One of the major problems with prior work on hybrid architecture is that conventional hybrid precoding methods require full channel state information (CSI), which is difficult to obtain in particular when the hybrid architecture is employed. In the first two contributions of this dissertation, hybrid precoding design methods that require spatial channel covariance information instead of instantaneous full CSI are proposed for the analog part design. Hybrid precoding design with spatial channel covariance for single-user MIMO (SU-MIMO) and multiuser MIMO (MU-MIMO) are proposed in the first and second contribution. Both theoretical and numerical analysis demonstrate that the spatial channel covariance can replace instantaneous full CSI with marginal rate loss for spatially sparse channels. Spatial channel covariance estimation, however, is challenging for the hybrid MIMO architecture because the estimator operating at baseband can only obtain a lower dimensional pre-combined signal through fewer radio frequency (RF) chains than antennas. This challenging task is tackled in the last two contributions. In the third contribution, compressive sensing techniques are applied to spatial channel covariance estimation over time-varying frequency-at channels by exploiting the channel sparsity. Taking time-varying frequency-selective channels into consideration, higher-order tensor decomposition techniques are adopted in the fourth contribution. Simulation and analytical results envince that the proposed methods estimate the spatial channel covariance more accurately and more quickly compared to prior work