NDLI: Data detection and soft-Kalman filter based semi-blind channel estimation algorithms for MIMO-OFDM systems

Content Provider	IEEE Xplore Digital Library
Author	Kyeong Jin Kim Reid, T. Iltis, R.A.
Copyright Year	2005
Description	Author affiliation: Nokia Res. Center, Irving, TX, USA (Kyeong Jin Kim; Reid, T.)
Abstract	In MIMO systems, where multiple antennas are used at both transmitter and receiver to achieve high spectral efficiency, channel impulse responses are often assumed to be constant over a block or packet. This assumption of block stationarity on channels is valid for most fixed wireless scenarios. However, for communications in a high mobility environment, the assumption will result in considerable performance degradation. In this paper, we focus on channel estimation for a MIMO system with OFDM transmission technique. In our system, pilots are placed on subcarriers for a novel channel estimation at the receiver with Kalman filters. With the channels estimated by a Kalman filter, we apply the OFDM MIMO soft data detector with a reasonable computational cost. The soft outputs of soft data detection are fed back to another soft Kalman filter for an improved channel estimation. By alternatively and iteratively using these two Kalman filters, a better overall performance can be obtained.
Starting Page	2488
Ending Page	2492
File Size	194581
Page Count	5
File Format	PDF
ISBN	0780389387
DOI	10.1109/ICC.2005.1494785
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-16
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filters Channel estimation MIMO OFDM Receiving antennas Transmitting antennas Transmitters Degradation Detectors Computational efficiency
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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