Complex SVM for Frequency Selective MIMO-OFDM Systems
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In recent years, high speed railways have been developing quickly. Consequently, much more attention has been attracted to high mobility wireless communication than ever before. In high mobility scenarios, we need to reconsider many key technologies for wireless communication such as synchronisation, channel estimation, resource allocation and so on. Among them, channel estimation one of the most important stage to guarantee quality-of-service (QoS) and efficiency of information transmission, is the main problem that this book will concentrate on. The use of Support Vector Machines (SVMs) has ...
In recent years, high speed railways have been developing quickly. Consequently, much more attention has been attracted to high mobility wireless communication than ever before. In high mobility scenarios, we need to reconsider many key technologies for wireless communication such as synchronisation, channel estimation, resource allocation and so on. Among them, channel estimation one of the most important stage to guarantee quality-of-service (QoS) and efficiency of information transmission, is the main problem that this book will concentrate on. The use of Support Vector Machines (SVMs) has shown several advantages in regression, prediction and estimation over some of the classical approaches due to its improved generalization capabilities. Moreover, the introduction of complex algebra in the SVM formulation can provide us with a more natural and flexible framework when dealing with complex symbols and constellations. This book focuses on the study and development of efficient channel estimation algorithms based on complex SVM for Regression (SVR) that are specifically adapted to MIMO-OFDM architecture and applied to LTE Downlink system under high mobility conditions.
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