Efficient signal, code, and receiver designs for MIMO communication systems

The so-called diversity-multiplexing tradeoff characterizes the fundamental interaction between the robustness and capacity gains obtainable from multiple-input and multiple-output (MIMO) systems in fading environments. This thesis develops practical schemes for approaching the optimal tradeoff in various delay and complexity regimes. We focus on a two-transmit and two-receive antenna system, in which the receiver has channel knowledge, but the transmitter does not. We first investigate uncoded transmission. We propose a class of lattice-reductionaided low-complexity detectors that can achieve near maximum likelihood performance and the best diversity-multiplexing tradeoff achievable by any length-one code. We also design a family of structured space-time block codes that we call tiltedQAM codes. It achieves the optimal infinite-delay tradeoff with the necessary minimum delay of two, answering a previously open question. It uses constellation rotation ideas to effectively spread information across space and time. We identify rotation angles that are universally optimal at all rates in terms of a determinant criterion. We further develop efficient coding schemes using long error correction codes. In particular, we combine them with tilted-QAM codes using hard and soft decision decoding to obtain good performance at moderate SNR. These new systems are compared to orthogonal space-time coded systems, which we show to achieve near optimal performance at low SNR. We also examine traditional sequential versions and develop new block versions of the Bell Labs layered architecture (BLAST). While some of these can in principle reach the performance limit at all SNRs, we show they also have various practical problems. Finally, for the case where no channel knowledge is available, we present a geometric view of the signal design problem. This view reveals how training based approaches can achieve the optimal (non-coherent) diversity-multiplexing tradeoff. Thesis Supervisor: Gregory W. Wornell Title: Professor