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: Research Article A Two-Stage Approach for Improving the Convergence of Least-Mean-Square Adaptive Decision-Feedback Equalizers in the Presence of Severe | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 390102 13 pages doi 2008 390102 Research Article A Two-Stage Approach for Improving the Convergence of Least-Mean-Square Adaptive Decision-Feedback Equalizers in the Presence of Severe Narrowband Interference Arun Batra 1 James R. Zeidler 1 and A. A. Louis Beex2 1 Department of Electrical and Computer Engineering University of California at San Diego La Jolla CA 92093-0407 USA 2 Wireless@VT and the DSP Research Laboratory Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA 24061-0111 USA Correspondence should be addressed to Arun Batra abatra@ Received 3 January 2007 Revised 16 April 2007 Accepted 8 August 2007 Recommended by Peter Handel It has previously been shown that a least-mean-square LMS decision-feedback filter can mitigate the effect of narrowband interference . Li and L. Milstein 1983 . An adaptive implementation of the filter was shown to converge relatively quickly for mild interference. It is shown here however that in the case of severe narrowband interference the LMS decision-feedback equalizer DFE requires a very large number of training symbols for convergence making it unsuitable for some types of communication systems. This paper investigates the introduction of an LMS prediction-error filter PEF as a prefilter to the equalizer and demonstrates that it reduces the convergence time of the two-stage system by as much as two orders of magnitude. It is also shown that the steady-state bit-error rate BER performance of the proposed system is still approximately equal to that attained in steady-state by the LMS DFE-only. Finally it is shown that the two-stage system can be implemented without the use of training symbols. This two-stage structure lowers the complexity of the overall system by reducing the number of filter taps that need to be adapted while incurring a slight loss in the .
