Diversity transmission is an effective technique to combat fading channels and this thesis introduces two main ideas. Firstly, a novel precoding technique is proposed to achieve diversity transmission and improve bit error rate (BER) performance over the existing linear constellation precoding (LCP) techniques. Experimental and theoretical results are presented to show that the proposed precoding schemes can outperform the existing LCP schemes in various fading channels and additive white Gaussian noise channels. Secondly, an interleaving technique to further improve the BER performance is proposed. The proposed diversity transmission techniques are implemented for both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. The second part of the thesis focuses on the pairwise error probability analysis of the proposed and LCP schemes when receivers have imperfect channel state information (CSI). The BER performance of the proposed precoding and interleaver scheme are investigated in OFDM systems with minimum mean square error channel estimators and single-carrier systems with basis expansion model based channel estimators. It is demonstrated that while precoding schemes designed for receivers with perfect CSI yield near-optimum BER performance in the former system, the proposed phase-shift keying based precoding schemes perform well in the latter system. In both cases, the proposed precoding scheme, combined with the novel interleaving technique, outperforms the existing LCP schemes.

Diversity transmission is an effective technique to combat fading channels and this thesis introduces two main ideas. Firstly, a novel precoding technique is proposed to achieve diversity transmission and improve bit error rate (BER) performance over the existing linear constellation precoding (LCP) techniques. Experimental and theoretical results are presented to show that the proposed precoding schemes can outperform the existing LCP schemes in various fading channels and additive white Gaussian noise channels. Secondly, an interleaving technique to further improve the BER performance is proposed. The proposed diversity transmission techniques are implemented for both single-carrier and orthogonal frequency division multiplexing (OFDM) systems. The second part of the thesis focuses on the pairwise error probability analysis of the proposed and LCP schemes when receivers have imperfect channel state information (CSI). The BER performance of the proposed precoding and interleaver scheme are investigated in OFDM systems with minimum mean square error channel estimators and single-carrier systems with basis expansion model based channel estimators. It is demonstrated that while precoding schemes designed for receivers with perfect CSI yield near-optimum BER performance in the former system, the proposed phase-shift keying based precoding schemes perform well in the latter system. In both cases, the proposed precoding scheme, combined with the novel interleaving technique, outperforms the existing LCP schemes.

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This dissertation designs linear transceivers for the multiuser downlink in multiple-input multiple-output (MIMO) systems. The designs rely on an uplink/downlink duality for the mean squared error (MSE) of each individual data stream. We first consider the design of transceivers assuming channel state information (CSI) at the transmitter. We consider minimization of the sum-MSE over all users subject to a sum power constraint on each transmission. Using MSE duality, we solve a computationally simpler convex problem in a virtual uplink. The transformation back to the downlink is simplified by our demonstrating the equality of the optimal power allocations in the uplink and downlink. Our second set of designs maximize the sum throughput for all users. We establish a series of relationships linking MSE to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. We show that minimizing the product of MSE matrix determinants is equivalent to sum-rate maximization, but we demonstrate that this problem does not admit a computationally efficient solution. We simplify the problem by minimizing the product of mean squared errors (PMSE) and propose an iterative algorithm based on alternating optimization with near-optimal performance. The remainder of the thesis considers the more practical case of imperfections in CSI. First, we consider the impact of delay and limited-rate feedback. We propose a system which employs Kalman prediction to mitigate delay; feedback rate is limited by employing adaptive delta modulation. Next, we consider the robust design of the sum-MSE and PMSE minimizing precoders with delay-free but imperfect estimates of the CSI. We extend the MSE duality to the case of imperfect CSI, and consider a new optimization problem which jointly optimizes the energy allocations for training and data stages along with the sum-MSE/PMSE minimizing transceivers. We prove the separability of these two problems when all users have equal estimation error variances, and propose several techniques to address the more challenging case of unequal estimation errors.

This dissertation designs linear transceivers for the multiuser downlink in multiple-input multiple-output (MIMO) systems. The designs rely on an uplink/downlink duality for the mean squared error (MSE) of each individual data stream. We first consider the design of transceivers assuming channel state information (CSI) at the transmitter. We consider minimization of the sum-MSE over all users subject to a sum power constraint on each transmission. Using MSE duality, we solve a computationally simpler convex problem in a virtual uplink. The transformation back to the downlink is simplified by our demonstrating the equality of the optimal power allocations in the uplink and downlink. Our second set of designs maximize the sum throughput for all users. We establish a series of relationships linking MSE to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. We show that minimizing the product of MSE matrix determinants is equivalent to sum-rate maximization, but we demonstrate that this problem does not admit a computationally efficient solution. We simplify the problem by minimizing the product of mean squared errors (PMSE) and propose an iterative algorithm based on alternating optimization with near-optimal performance. The remainder of the thesis considers the more practical case of imperfections in CSI. First, we consider the impact of delay and limited-rate feedback. We propose a system which employs Kalman prediction to mitigate delay; feedback rate is limited by employing adaptive delta modulation. Next, we consider the robust design of the sum-MSE and PMSE minimizing precoders with delay-free but imperfect estimates of the CSI. We extend the MSE duality to the case of imperfect CSI, and consider a new optimization problem which jointly optimizes the energy allocations for training and data stages along with the sum-MSE/PMSE minimizing transceivers. We prove the separability of these two problems when all users have equal estimation error variances, and propose several techniques to address the more challenging case of unequal estimation errors.