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Báo cáo hóa học: " Decision-Directed Recursive Least Squares MIMO Channels Tracking"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Decision-Directed Recursive Least Squares MIMO Channels Tracking | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2006 Article ID 43275 Pages 1-10 DOI 10.1155 WCN 2006 43275 Decision-Directed Recursive Least Squares MIMO Channels Tracking Ebrahim Karami and Mohsen Shiva Department of Electrical and Computer Engineering Faculty of Engineering University of Tehran Campus No. 2 North Kargar Avenue Tehran 14399 Iran Received 14 June 2005 Revised 22 November 2005 Accepted 22 December 2005 Recommended for Publication by Jonathon Chambers A new approach for joint data estimation and channel tracking for multiple-input multiple-output MIMO channels is proposed based on the decision-directed recursive least squares DD-RLS algorithm. RLS algorithm is commonly used for equalization and its application in channel estimation is a novel idea. In this paper after defining the weighted least squares cost function it is minimized and eventually the RLS MIMO channel estimation algorithm is derived. The proposed algorithm combined with the decision-directed algorithm DDA is then extended for the blind mode operation. From the computational complexity point of view being 0 3 versus the number of transmitter and receiver antennas the proposed algorithm is very efficient. Through various simulations the mean square error MSE of the tracking of the proposed algorithm for different joint detection algorithms is compared with Kalman filtering approach which is one of the most well-known channel tracking algorithms. It is shown that the performance of the proposed algorithm is very close to Kalman estimator and that in the blind mode operation it presents a better performance with much lower complexity irrespective of the need to know the channel model. Copyright 2006 E. Karami and M. Shiva. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. .