Title :
Adaptive Constrained ML Channel SVD Estimation for MIMO-OFDM Systems
Author :
Zamiri-Jafarian, H. ; Eskandari, M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Toronto Univ., Ont.
Abstract :
Singular value decomposition (SVD) of channel matrix is a useful technique to mitigate cochannel interference in multiple-input multiple-output (MIMO) systems. In this paper an adaptive algorithm is proposed to estimate the SVD of channel matrix in MIMO orthogonal frequency division multiplexing (OFDM) communication systems. The two-step adaptive estimation algorithm called ACML is developed based on constrained maximum likelihood criterion. In addition to achieving a good performance regarding convergence rate and mean square error (MSE), the proposed ACML algorithm outperforms the constrained least mean square (CLMS) algorithm
Keywords :
MIMO communication; OFDM modulation; adaptive estimation; channel estimation; cochannel interference; convergence of numerical methods; least mean squares methods; maximum likelihood estimation; singular value decomposition; ACML; CLMS; MIMO-OFDM system; MSE; adaptive estimation algorithm; channel SVD estimation; channel matrix; cochannel interference; constrained least mean square algorithm; constrained maximum likelihood criterion; convergence rate; mean square error methods; multiple-input multiple-output system; orthogonal frequency division multiplexing communication; singular value decomposition; Adaptive algorithm; Adaptive estimation; Frequency estimation; Interchannel interference; Interference constraints; MIMO; Matrix decomposition; Maximum likelihood estimation; OFDM; Singular value decomposition;
Conference_Titel :
Communication systems, 2006. ICCS 2006. 10th IEEE Singapore International Conference on
Conference_Location :
Singapore
Print_ISBN :
1-4244-0411-8
Electronic_ISBN :
1-4244-0411-8
DOI :
10.1109/ICCS.2006.301390
