Reduced-complexity signal processing techniques for multiple-input multiple-output storage and wireless communication systems

Abstract

This thesis is divided into two parts and dedicated to the development of reduced-complexity detection and channel estimation techniques to facilitate high-speed and high-quality data reception in multiple-input multiple-output systems. In Part 1 of the thesis, we first present a channel model for the two-dimensional optical storage (TwoDOS) system that contains domain bloom and transition jitter. This model will be used in the simulations for evaluating the various techniques developed in this part. Then, we reduce the complexity of the two-dimensional (2D) Viterbi detector (VD) in both the temporal dimension and spatial dimension. Further, we develop a novel 2D target optimization technique and design several suitable targets to compensate for the detection performance loss due to the complexity reduction. In Part 2 of the thesis, we represent multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in the angle domain, where the channel model lends itself to a simple physical interpretation. When no channel stochastic information is available, we propose three channel instantaneous power based angle-domain techniques for channel estimation. When the channel correlation or average power is available, we develop several reduced-complexity, approximated linear minimum mean square error (LMMSE) channel estimation techniques in the angle domain to improve over the channel instantaneous power based techniques.