On the efficient utilization of multiple antennas for multiuser wireless networks

In this dissertation, we consider efficient utilization of multiple antennas in wireless networks that serve multiple users. We primarily focus on scenarios where the antennas are used for spatial multiplexing either by beamforming or antenna assignment. In order to achieve the best network performance, we address the issue jointly with power control and user scheduling.; We first consider minimizing the total power cost of a wireless network while satisfying the signal to interference plus noise ratio (SINR) constraints of all users. This problem is investigated in a MIMO beamforming environment and the network duality principle is developed as an efficient theoretic tool for handling this problem. By using the duality, we propose high performance centralized and decentralized algorithms for conducting beamforming and power control jointly.; We also consider the weighted max-min fair scheduling problem that maximizes the minimum of normalized rates of all users where each rate is normalized by the priority value that the user has. We first consider a MISO downlink in a static channel environment and find an optimal joint beamforming, power control and scheduling algorithm. We compare the performance of the proposed technique with the dirty paper coding technique which is known to achieve the capacity region of a MISO broadcast channel.; We then consider the weighted max-min fair scheduling for a MIMO downlink that dynamically assigns transmit antennas to different users in a fast time-varying channel environment. For this case, we propose an adaptive algorithm for attaining the scheduling goal by exploiting the multiuser diversity (MUD) gain, and prove its convergence. We assume that the antenna assignment/scheduling decisions are made by the rate feedback provided by each receiver. Unlike many other scheduling studies, we investigate the estimation mechanism for providing such feedback and the related resource consumptions. Furthermore, we analyze the interaction of estimation errors with a scheduler that implements the MUD principle, and suggest some general design rules for MIMO systems that employ a MUD scheduler.