Title :
Unified ML channel estimator for MIMO-OFDM systems with virtual carriers
Author :
Zheng, Kang ; Tian, Feng ; Huang, Guowen ; Min Bei
Abstract :
In this paper, we study the Maximum Likelihood (ML) channel estimation for MIMO-OFDM systems with virtual carriers. A unified ML channel (UML) estimator is proposed to overcome performance loss caused by virtual carriers. The UML is a generalized algorithm applied to both pilot-aided and preamble based systems with arbitrarily placed virtual carriers. To reduce the complexity, Speeded and Compressed ML channel estimation (SCML) is proposed reduce both implementation storage and calculation complexity, to 26% of origin under 4G Gbits V-BLAST MIMO-OFDM system with good performance.
Keywords :
MIMO communication; OFDM modulation; channel estimation; maximum likelihood estimation; MIMO-OFDM systems; V-BLAST; maximum likelihood channel estimator; pilot-aided based systems; preamble based systems; virtual carriers; Channel estimation; Radio access networks;
Conference_Titel :
Communication Technology (ICCT), 2010 12th IEEE International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-6868-3
DOI :
10.1109/ICCT.2010.5688827
