| The information-theoretic foundation of multiple-input multiple-output (MIMO) systems was laid out by Foschini, Gans, and Telatar [21, 74], who have showed that multiple antennas at the transmitter and the receiver provide significant capacity enhancement over single-antenna systems. Spatial diversity provided by multiple antennas enhances the throughput and reliability of wireless communications [83]. To exploit the enhanced spectral efficiency, space-time coding has been designed to achieve a specific tradeoff between diversity and multiplexing [23,72,73]. Most work on space-time coding deals with the case where no knowledge of the forward channel is available to the transmitter.; In a MIMO system, if the transmitter has perfect knowledge of the underlying channel state information (CSI), power allocation to the right singular subspace of the channel matrix can be used to achieve a higher channel capacity compared to transmission without CSI [27]. When reciprocity of the wireless channel does not hold, as in frequency-division duplex (FDD), perfect CSI at the transmitter requires a high-rate feedback channel, which may not be practical, particularly in fast time-varying environments. Thus, the identification and utilization of partial CSI at the transmitter are important issues.; Much work has been devoted to identifying the benefits of partial CSI at the transmitter and the design of optimal transmission schemes to exploit it. For example, when only the statistics of the channel state are available at the transmitter, an optimal transmit covariance matrix can be designed to achieve higher capacity than transmission without any CSI [76, 85, 86]. Techniques for attaining partial CSI have also been proposed [44, 53, 54].; This thesis is focused on partial CSI acquisition and utilization techniques for MIMO channels. The nature of the CSI feedback problem is a quantization of the underlying matrix channels. We propose a feedback algorithm for tracking the dominant channel subspaces for MIMO systems in a continuously time-varying environment. We exploit the correlation between channel states of adjacent time instants and quantize the variation of channel states. Specifically, we model a subspace as one point in a Grassmann manifold, treat the variations in principal right singular subspaces of the channel matrices as a piecewise-geodesic process in the Grassmann manifold, and quantize the velocity matrix of the geodesic.; As a demonstration of optimal transmitter design given partial CSI feedback, we design a complexity-constrained MIMO OFDM system where the transmitter has knowledge of channel correlations. The transmitter is constrained to perform at most one inverse Discrete Fourier Transform (IDFT) per OFDM symbol on the average. We show that in the multiple input, single output case, time domain beamforming can be used to do two-dimensional eigen-beamforming. For the MIMO case, we derive design criteria for the transmitter beamforming and receiver combining weighting vectors and show some suboptimal solutions.; Most previous papers on CSI feedback did not consider uncertainties in the feedback process, such as unexpected delay or error in the feedback channel. Such uncertainties exist in reality and ignoring them results in suboptimal algorithms. We consider channel mean-feedback with an unknown delay and propose a broadcast approach that is able to adapt to the quality of the feedback.; Having considered CSI feedback problems where the receiver tries to convey its attained CSI to the transmitter, we turn to a different problem; namely, noncoherent coding design for fast fading channels, where the receiver does not have reliable CSI. Unitary space-time codes [31,82] and training based schemes [16] have been proposed historically. We propose a data-dependent superimposed training scheme to improve the performance of training based codes. The transmitter is equipped with multiple training sequences and dynamically selects a... |
