Title :
Multichannel blind identification: from subspace to maximum likelihood methods
Author :
Tong, Lang ; Perreau, Sylvie
Author_Institution :
Sch. of Electr. Eng., Cornell Univ., Ithaca, NY, USA
fDate :
10/1/1998 12:00:00 AM
Abstract :
A review of blind channel estimation algorithms is presented. From the (second-order) moment-based methods to the maximum likelihood approaches, under both statistical and deterministic signal models. We outline basic ideas behind several new developments, the assumptions and identifiability conditions required by these approaches, and the algorithm characteristics and their performance. This review serves as an introductory reference for this currently active research area
Keywords :
deterministic algorithms; identification; maximum likelihood estimation; signal processing; statistical analysis; telecommunication channels; blind channel estimation algorithms; deterministic signal models; identifiability conditions; maximum likelihood methods; multichannel blind identification; performance; second-order moment-based methods; signal processing; statistical signal models; subspace methods; Blind equalizers; Computer networks; HDTV; Maximum likelihood estimation; Mobile communication; Signal processing; Signal processing algorithms; System identification; Throughput; Wireless communication;
Journal_Title :
Proceedings of the IEEE
