A semi-blind channel estimation method for multiuser multiantenna OFDM systems

• Published in: IEEE transactions on signal processing Vol. 52; no. 5; pp. 1419 - 1429
• Main Authors: Zeng, Y., Ng, T.-S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ambiguity; Applied sciences; Blocking; Channel estimation; Channels; Computational complexity; Detection, estimation, filtering, equalization, prediction; Equalization; Exact sciences and technology; Frequency domain analysis; Frequency estimation; Information, signal and communications theory; MIMO; Multiplexing; OFDM modulation; Orthogonal Frequency Division Multiplexing; Pilots; Power engineering and energy; Receiving antennas; Scalars; Signal and communications theory; Signal processing algorithms; Signal, noise; Symbols; Telecommunications and information theory; Upper bound; MIMO system; Multiple access; Parameter estimation; Blind channel identification; OFDM; zero-padding; Signal estimation; Subspace method; Blind identification; Equalization; multiantenna; Computational complexity; Multipath channels; Simulation; Channel estimation; Orthogonal frequency division multiplexing; MIMO; multiuser; System identification; semi-blind; Uplink
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2004.826183

A subspace-based blind method is proposed for estimating the channel responses of a multiuser and multiantenna orthogonal frequency division multiplexing (OFDM) uplink system. It gives estimations to all channel responses subject to a scalar matrix ambiguity and does not need precise channel order information (only an upper bound for the orders is required). Furthermore, the scalar ambiguity matrix can be easily resolved by using only one pilot OFDM block, given that the number of users is smaller than the number of symbols in the pilot symbol block. Equalization methods are discussed based on the estimated channels. By using partial knowledge of the channels, a multipath subspace method is proposed that reduces the computational complexity. Simulations show that the methods are effective and robust.