In cooperative networks, relays cooperate and form a distributed multi-antenna system to provide spatial diversity. In order to achieve high bandwidth efficiency, distributed space-time block codes (DSTBCs) are proposed and have been studied extensively. Among all DSTBCs, this thesis focuses on the codes which are single-symbol maximum likelihood (ML) decodable and can achieve the full diversity order. This thesis presents four works on single-symbol ML decodable DSTBCs. The first work proposes the row-monomial distributed orthogonal space-time block codes (DOSTBCs). We find an upper bound of the data-rate of the row-monomial DOSTBC and construct the codes achieving this upper bound. In the second work, we first study the general DOSTBCs and derive an upper bound of the data-rate of the DOSTBC. Secondly, we propose the row-monomial DOSTBCs with channel phase information (DOSTBCs-CPI) and derive an upper bound of the data-rate of those codes. Furthermore, we find the actual row-monomial DOSTBCs-CPI which achieve the upper bound of the data-rate. In the third and fourth works of this thesis, we focus on error performance analysis of single-symbol ML decodable DSTBCs. Specifically, we study the distributed Alamouti's code in dissimilar cooperative networks. In the third work, we assume that the relays are blind relays and we derive two very accurate approximate bit error rate (BER) expressions of the distributed Alamouti's code. In the fourth work, we assume that the relays are CSI-assisted relays. When those CSI-assisted relays adopt the amplifying coefficients that was proposed in [33] and widely used in many previous publications, upper and lower bounds of the BER of the distributed Alamouti's code are derived. Very surprisingly, the lower bound indicates that the code cannot achieve the full diversity order when the CSI-assisted relays adopt the amplifying coefficients proposed in [33]. Therefore, we propose a new threshold-based amplifying coefficient and it makes the code achieve the full diversity order two. Moreover, three optimum and one suboptimum schemes are developed to calculate the threshold used in this new amplifying coefficient. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-06-27 19:07:47.066

<p>Two topics in fading channels with a strict delay constraint and a resolution-constrained feedback link are treated in this thesis.</p><p>First, a multi-layer variable-rate single-antenna communication system with quantized feedback, where the expected rate is chosen as the performance measure, is studied under both short-term and long-term power constraints. Iterative algorithms exploiting results in the literature of parallel broadcast channels are developed to design the system parameters. A necessary and sufficient condition for single-layer coding to be optimal is derived. In contrast to the ergodic case, it is shown that a few bits of feedback information can improve the expected rate dramatically. The role of multi-layer coding, however, reduces quickly as the resolution of the feedback link increases.</p><p>The other part of the thesis deals with partial power control systems utilizing quantized feedback to minimize outage probability, with an emphasis on the diversity-multiplexing tradeoff. An index mapping with circular structure is shown to be optimal and the design is facilitated with a justified Gaussian approximation. The diversity gain as a function of the feedback resolution is analyzed. The results are then extended to characterize the entire diversity-multiplexing tradeoff curve of multiple-antenna channels with resolution-constrained feedback. Adaptive-rate communication is also studied, where the concept of minimum multiplexing gain is introduced. It is shown that the diversity gain of a system increases significantly even with coarsely quantized feedback, especially at low multiplexing gains.</p>

Two topics in fading channels with a strict delay constraint and a resolution-constrained feedback link are treated in this thesis. First, a multi-layer variable-rate single-antenna communication system with quantized feedback, where the expected rate is chosen as the performance measure, is studied under both short-term and long-term power constraints. Iterative algorithms exploiting results in the literature of parallel broadcast channels are developed to design the system parameters. A necessary and sufficient condition for single-layer coding to be optimal is derived. In contrast to the ergodic case, it is shown that a few bits of feedback information can improve the expected rate dramatically. The role of multi-layer coding, however, reduces quickly as the resolution of the feedback link increases. The other part of the thesis deals with partial power control systems utilizing quantized feedback to minimize outage probability, with an emphasis on the diversity-multiplexing tradeoff. An index mapping with circular structure is shown to be optimal and the design is facilitated with a justified Gaussian approximation. The diversity gain as a function of the feedback resolution is analyzed. The results are then extended to characterize the entire diversity-multiplexing tradeoff curve of multiple-antenna channels with resolution-constrained feedback. Adaptive-rate communication is also studied, where the concept of minimum multiplexing gain is introduced. It is shown that the diversity gain of a system increases significantly even with coarsely quantized feedback, especially at low multiplexing gains. / QC 20101117

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.