IETR UMR 6164 : Compressed Sensing Based Channel Estimation for OFDM System

Compressed Sensing Based Channel Estimation for OFDM System

Doctorant : Hui XIE

Directeurs de thèse : Yide WANG

Thèse débutée le : 01/10/2009

Recently, the development of Compressed Sensing (CS) theory attracts great attention among both scientific and engineering field. The core idea of compressed sensing is to reconstruct a sparse vector with high dimension from its projection on a low dimensional space with high probability. CS theory can be widely used in image processing, wireless communication, radar signal processing and geology etc. My research field concentrates on the CS based sparse channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) system. More specifically, how to use limited number of frequency resources (known as pilots in OFDM) to estimate efficiently a sparse channel, including the research of channel reconstruction and threshold based denoising for sparse channel. The main research direction can be divided by two sub-directions :

Threshold determination for the sparse channel estimation by the different usage of pilots ;
The redundant dictionary, which can be employed to improve the channel estimation performance when the path delays of the channel are not sample spaced.





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		Compressed Sensing Based Channel Estimation for OFDM System Doctorant : Hui XIE Directeurs de thèse : Yide WANG Thèse débutée le : 01/10/2009 Recently, the development of Compressed Sensing (CS) theory attracts great attention among both scientific and engineering field. The core idea of compressed sensing is to reconstruct a sparse vector with high dimension from its projection on a low dimensional space with high probability. CS theory can be widely used in image processing, wireless communication, radar signal processing and geology etc. My research field concentrates on the CS based sparse channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) system. More specifically, how to use limited number of frequency resources (known as pilots in OFDM) to estimate efficiently a sparse channel, including the research of channel reconstruction and threshold based denoising for sparse channel. The main research direction can be divided by two sub-directions : Threshold determination for the sparse channel estimation by the different usage of pilots ; The redundant dictionary, which can be employed to improve the channel estimation performance when the path delays of the channel are not sample spaced.