Adaptive transmission techniques for multiple antenna wireless communication systems

Ameer Dabbagh, Purdue University


The following thesis is divided into four independent chapters. In the first chapter, we study the capacity loss of the point-to-point multiple-input multiple-output (MIMO) wireless system when the covariance matrix of the transmitted signal vector is designed using a low rate feedback channel. We and a closed-form expression for the ergodic capacity loss as a function of the number of bits fed back at each channel realization. These results show that the capacity loss decreases at least as O 2-B/2MMt-2 ) where B is the number of feedback bits, Mt is the number of transmit antennas, M = min{Mr,Mt}, and Mr is the number of receive antennas. ^ In the second chapter, we consider the multiuser MIMO broadcast channel (BC). A new transmission scheme that employs partial interference cancellation at the transmitter with dirty-paper coding is proposed. The maximal achievable throughput of this system is characterized, and its optimality is established in the high and low signal-to-noise ratio (SNR) regimes. We also consider a linear transmission scheme of the type proposed in [72]. We propose a suboptimal precoding algorithm for the sum-rate capacity maximization problem which outperforms the scheme proposed in [72]. ^ In the third chapter, we consider a MIMO BC but with users having single receive antenna. We consider the case when partial CSI is available at the transmitter, and derive an MMSE-based precoding technique that considers channel estimation errors as an integral part of the system design. Compared to previously proposed linear precoding techniques, it is shown that the proposed precoding technique significantly improves the average bit error rate (BER) in the system. ^ In the fourth chapter, we consider the multiuser MIMO multicast channel. Assuming full CSI at the base station, we jointly optimize the transmitter precoding matrix and the set of linear receivers under different design criteria, and propose solutions based on convex optimization theory. The proposed solutions are optimal only when a rank-stream constraint is satisfied. A more efficient iterative joint optimization algorithm is also proposed, and it is shown that in some cases it can outperform solutions which are based on the convex optimization methods. ^




David J. Love, Purdue University.

Subject Area

Engineering, Electronics and Electrical

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