Multi-User Multi-Antenna Cooperative Cellular Systems

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Date
2013-06-25
Authors
Zheng, Yi
Keyword
MIMO , Cooperative
Abstract
To meet the very high data rate requirements for wireless Internet and multimedia services, cooperative systems with multiple antennas have been proposed for future generation wireless systems. In this thesis, we focus on multiple antennas at the source, relay and destination. We study both downlink and uplink cooperative systems with single antenna relays. For downlink systems, the optimal precoder to minimize the sum transmit power subject to quality of service (QoS) constraints with fixed relay weights is derived. We also study the optimization of relay weights with a fixed precoder. An iterative algorithm is developed to jointly optimize the precoder and relay weights. The performance of the downlink system with imperfect CSI as well as multiple receive antennas is also studied. For the uplink system, we similarly derive the optimum receiver as in the downlink with fixed relay weights. The optimization of relay weights for a fixed receiver is then studied. An iterative algorithm is developed to jointly optimize the receiver and relay weights in the uplink. Systems with imperfect channel estimation are also considered. The study of cooperative MIMO systems is then extended to a multi-cell scenario. In particular, two scenarios are studied. In the first, the cells coordinate their beamformers to find the most suitable cell to serve a specific user. In the second, each base station selectively transmits to a fixed group of users, and the cells coordinate to suppress mutual interference. Finally, our investigation culminates with a study of an uplink cooperative system equipped with multi-antenna relays under a capacity maximization criterion. The specific scheme that users access the base station through a single multi-antenna relay are studied. Iterative capacity maximization algorithm are proposed and shown to converge to local maxima. Numerical results are presented to highlight that the algorithms are able to come close to these bounds after only a few iterations.
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