Performance optimization of MIMO systems with partial channel state information

Multiple-input multiple-output (MIMO) wireless communication systems have the potential to offer high data rates as well as link reliability. The feasibility of these systems in future mobile communication standards depends in great measure on the ability to provide high rates with a reduced amount of channel state information at the transmitter (CSIT), due to limited resource availability on the feedback link. This thesis addresses the problem of optimizing MIMO systems with partial CSIT. On the one hand, we provide methods for obtaining CSIT. On the other hand, we propose techniques to exploit the available sources of CSIT to optimize the system performance. In the first part, point-to-point MIMO channels are considered for the purpose of error rate minimization. Linear precoding techniques are proposed to enhance the performance of space-time coded (STC) MIMO systems, by appropriately combining information on the channel mean and covariance. In the second part of this thesis, we focus on sum-rate performance optimization in MIMO broadcast channels with limited feedback. Low-complexity cross-layer approaches are proposed for systems with joint linear beamforming and multiuser scheduling, optimizing the following parts in the MIMO communications system: linear beamforming techniques, scheduling algorithms, feedback strategies and feedback quantization techniques. Different feedback models are considered. In a simple scenario where feedback consists of only channel quantization indices, we study the benefits of generating quantization codebooks adapted to the channel statistics, exploiting spatial and temporal correlations. A different feedback model considers separate feedback for channel direction information (CDI) and channel quality information (CQI). In such systems, the users need to estimate the amount of multiuser interference, which is a difficult task since the users can not cooperate. We propose a design framework for CQI feedback design in MIMO broadcast channels, based on an estimate on each user's signal-to-interference-plus-noise ratio (SINR). In this framework, a comparative study between space division multiple access (SDMA) and time division multiple access (TDMA) is provided in different asymptotic regimes. In addition, in systems where the available feedback bits need to be shared for CDI and CQI quantization, we present the tradeoff between multiuser diversity and multiplexing gain. The problem of designing linear beamforming techniques for MIMO broadcast channels is also addressed. An iterative optimization method for unitary beamforming is proposed, which exhibits robustness to channel estimation errors. The proposed technique outperforms zero-forcing (ZF) beamforming and even minimum mean-square error (MMSE) beamforming as the variance of the estimation error increases. Our work highlights the importance of linear beamforming optimization in limited feedback scenarios. Rather than designing sophisticated feedback schemes, relying on simple linear beamforming techniques, the system performance can be enhanced by using simple channel quantization strategies combined with optimized linear beamforming techniques.