Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/3095

Multiple-input multiple-output detection in wireless communications and data storage systems : performance and implementation issues

Authors Au, Kwok Shum
Issue Date 2007
Summary Over the past decade, there was a concerted research effort on the design and evaluation of multiple-input multiple-output (MIMO) transmission schemes, which has provided substantial insight as well as fundamental principles under certain simplified models of communications environment. However, many MIMO communication systems operate in a more general context that may involve practical aspects that relate to implementation and account for impairments found in real-world systems. In this thesis, we study performance and implementation issues for MIMO detection in wireless communications systems. Our goal is to provide insight on the effects of some practical issues, including channel estimation errors, feedback delay, non-linear amplitude distortion, and carrier offset frequency, on the system performance of several MIMO transmission schemes. For trellis codes and intersymbol interference channels, generating function techniques are attractive in performance analysis since they provide the distance spectrum and a union bound on error probability. However, they can be computationally intensive when the code has large number of states. In view of this, we propose a dynamic state elimination ordering heuristic accelerate the computation. Lastly, we propose an efficient means to analytically evaluate the minimum distance for multiple-head multiple-track recording systems with intertrack interference (ITI). Our results reveal that for small-to-moderate ITI, single-track and two-track error events are the dominant factors in contributing to the minimum distance. We also utilize the generic nature of MIMO channels for the modelling of multiple-head multiple-track systems, and propose using an ordered successive interference cancellation algorithm as a favorable low-complexity alternative to the optimal maximum likelihood sequence detection.
Note Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007
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Language English
Format Thesis
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