Sum-capacity, scheduling, and multi-user diversity in MIMO broadcast systems

Multiple input multiple output (MIMO) communication systems and multiuser diversity are two important foundations that allow future wireless systems to achieve a higher data rate. Earlier studies show that with an efficient use of spatial and user domains, communication systems can achieve data rate close to the fundamental capacity limit. However, these studies are based on the ideal assumption of very high complexity and perfect channel knowledge, which do not manifest themselves in practice. Therefore, this thesis investigates design and performance of capacity-achieving MIMO systems under more practical assumptions.; We first consider a transmission scheme that consists of a simple zero-forcing beamforming (ZFBF) precoder and a smart user selection algorithm, and show that such a system can approach the fundamental sum-capacity limit of MIMO broadcast channels (BC) given a sufficiently large number of users. This result establishes the asymptotic optimality of ZFBF in the large user regime. The user selection scheme, named the semi-orthogonal user selection (SUS) algorithm, relies on the multiuser diversity gain and the orthogonality among chosen users. We further extend the SUS algorithm to incorporate fairness among users, and develop a class of user selection algorithms by converting the user selection problem into a fully connected graph (clique) search problem on a node-weighted graph.; We next consider a MIMO BC with limited channel knowledge at the transmitter due to quantized channel feedback. By analyzing the sum-rate of the system, we show that the use of SUS and ZFBF can achieve the optimal asymptotic sum-capacity in the number of users, and establish the tradeoffs between the number of feedback bits for channel description, the number of users, and the SNR. In particular, we show that the transmitter must have, in addition to directional information, information regarding the quality of each channel that reflects both the channel magnitude and the quantization error.; In our final analysis we investigate a point-to-point MIMO link where channel estimation at the receiver is imperfect. We derive lower and upper bounds of the channel capacity for such a channel, and the optimal power allocation over space and time that achieves the lower bound.