Ever increasing demand for higher data rates and better Quality of Service (QoS) for a growing number of users requires new transceiver algorithms and architectures to better exploit the available spectrum and to efficiently counter the impairments of the radio channel. Multiple-Input Multiple-Output (MIMO) communication systems employ multiple antennas at both transmitter and at the receiver to meet the requirements of next-generation wireless systems. It is a promising technology to provide increased data rates while not involving an equivalent increase in the spectral requirements. However, practical implementation of MIMO detectors poses a significant challenge and has been consistently identified as the major bottleneck for realizing the full potential that multiple antenna systems promise. Furthermore, in order to make judicious use of the available bandwidth, the baseband units have to dynamically adapt to different modes (modulation schemes, code rates etc) of operations. Flexibility and high throughput requirements often place conflicting demands on the Very Large Scale Integration (VLSI) system designer. The major focus of this dissertation is to present efficient VLSI architectures for configurable MIMO detectors that can serve as accelerators to enable the realization of next generation wireless devices feasible. Both, hard output and soft output detector architectures are considered.

This dissertation focuses on three major advances on multiple-input multiple-output (MIMO) systems. The first studies and compares decoding algorithms for multi-layered space time coded (MLSTC) systems. These are single user systems that combine spatial multiplexing and transmit diversity. Each layer consists of a space time code. The detection algorithms are based on multi-user detection theory. We consider joint, interference nulling and cancellation, and spatial sequence estimation algorithms. As part of joint detection algorithms, the sphere decoder is studied and its complexity is evaluated over MIMO channels. The second part contributes to the field of space frequency time (SFT) coding for MIMO-OFDM systems. It proposes a full spatial and frequency diversity codes at much lower number of trellis states. The third part proposes and compares uplink scheduling algorithms for multiuser systems with spatial multiplexing. Several scheduling criteria are examined and compared. The capacity and error rate study of MLSTBC reveals the performance of the detection algorithms and their advantage over other open loop MIMO schemes. The results show that the nulling and cancellation operations limit the diversity of the system to the first detected layer in serial algorithms. For parallel algorithms, the diversity of the system is dominated by the performance after parallel nulling. Theoretically, parallel cancellation should provide full receive diversity per layer but error propagations as a result of cancellation prevent the system from reaching this goal. However, parallel cancellation provides some gains but it doesn't increase the diversity. On the other hand, joint detection provides full receive diversity per layer. It could be practically implemented with sphere decoding which has a cubic complexity at high SNR. The results of the SFT coding show the superiority of the IQ-SFT codes over other codes at the same number of sates. The IQ-SFT codes achieve full spatial and frequency diversity at much lower number of trellis states compared to conventional codes. For V-BLAST scheduling, we propose V-BLAST capacity maximizing scheduler and we show that scheduling based on optimal MIMO capacity doesn't work well for V-BLAST. The results also show that maximum minimum singularvalue (MaxMinSV) scheduling performs very close to the V-BLAST capacity maximizing scheduler since it takes into account both the channel power and the orthogonality of the channel. / Ph. D.

The coding and feedback inaccuracies of the channel state information (CSI) in limited feedback multiple-input multiple-output (MIMO) wireless systems can severely impact the achievable data rate and reliability. The CSI is mathematically represented as a Grassmann manifold or manifold of unitary matrices. These are non-Euclidean spaces with special constraints that makes efficient and high fidelity coding especially challenging. In addition, the CSI inaccuracies may occur due to digital representation, time variation, and delayed feedback of the CSI. To overcome these inaccuracies, the manifold structure of the CSI can be exploited. The objective of this dissertation is to develop a new signal processing techniques on the manifolds to harvest the benefits of MIMO wireless systems. First, this dissertation presents the Kerdock codebook design to represent the CSI on the Grassmann manifold. The CSI inaccuracy due to digital representation is addressed by the finite alphabet structure of the Kerdock codebook. In addition, systematic codebook construction is identified which reduces the resource requirement in MIMO wireless systems. Distance properties on the Grassmann manifold are derived showing the applicability of the Kerdock codebook to beam-forming and spatial multiplexing systems. Next, manifold-constrained algorithms to predict and encode the CSI with high fidelity are presented. Two prominent manifolds are considered; the Grassmann manifold and the manifold of unitary matrices. The Grassmann manifold is a class of manifold used to represent the CSI in MIMO wireless systems using specific transmission strategies. The manifold of unitary matrices appears as a collection of all spatial information available in the MIMO wireless systems independent of specific transmission strategies. On these manifolds, signal processing building blocks such as differencing and prediction are derived. Using the proposed signal processing tools on the manifold, this dissertation addresses the CSI coding accuracy, tracking of the CSI under time variation, and compensation techniques for delayed CSI feedback. Applications of the proposed algorithms in single-user and multiuser systems show that most of the spatial benefits of MIMO wireless systems can be harvested. / text