Channel State Information in Multiple Antenna Systems
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In a MIMO system, a transmitter with perfect knowledge of the underlying channel state information (CSI) can achieve a higher channel capacity compared to transmission without CSI. When reciprocity of the wireless channel does not hold, the identification and utilization of partial CSI at the transmitter are important issues. This thesis is focused on partial CSI acquisition and utilization techniques for MIMO 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. 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 per OFDM symbol on the average. We show that in the MISO 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. The feedback channel may have uncertainties such as unexpected delay or error. 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 noncoherent coding design for fast fading channels, where the receiver does not have reliable CSI. 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 training sequence for each data sequence to minimize channel estimation error. The set of training sequences are optimized to minimize pairwise error probability between codewords.