Multidimensional Systolic Arrays of LMS Algorithm Adaptive (FIR) Digital Filters
pdf

How to Cite

Multidimensional Systolic Arrays of LMS Algorithm Adaptive (FIR) Digital Filters. (2009). Al-Khwarizmi Engineering Journal, 5(1), 83-93. https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/522

Publication Dates

Abstract

A multidimensional systolic arrays realization of LMS algorithm by a method of mapping regular algorithm onto processor array, are designed. They are based on appropriately selected 1-D systolic array filter that depends on the inner product sum systolic implementation. Various arrays may be derived that exhibit a regular arrangement of the cells (processors) and local interconnection pattern, which are important for VLSI implementation. It reduces latency time and increases the throughput rate in comparison to classical 1-D systolic arrays. The 3-D multilayered array consists of 2-D layers, which are connected with each other only by edges. Such arrays for LMS-based adaptive (FIR) filter may be opposed the fundamental requirements of fast convergence rate in most adaptive filter applications.       

                                                                                                              

pdf

References

[1] L. Xiaojian and T. B. Leonard, “High-Speed Systolic Ladder Structures for Multidimensional Recursive Digital Filters,” IEEE Trans. Signal Processing, vol. 44, no. 4, pp. 1048-1055, Nov. 1996.
[2] F. Lorenzelli and K. Yao, “A linear systolic array for recursive least squares,“IEEE Trans. Signal Processing, vol. 43, no. 12, pp.3014- 484, Apr. 1992.
[3] N. Petkov, Systolic Parallel Processing. Elsevier Science Publishers, North Holland, 1993.
[4] T. P. Plaks, “Mapping Regular Algorithms onto Multilayered 3-D Reconfigurable Processor Array,“ IEEE Proceedings of the 32nd Hawaii International Conference 0n System Sciences, pp.1-10, 1999.
[5] K. M. Ty and A. N. Venestanopoules, “A fast filter for real-time image processing,” IEEE Trans. Circuits Syst. , vol. CAS-33, pp. 948-957, Oct. 1986.
[6] R. Gnanasekaran, “2-D filter implementation for real-time signal processing,” IEEE Trans. Circuits Syst., vol. 35, no. 5, pp. 587-590, May. 1988
[7] W. Luk and G.jones, “Systolic Recursive Filters,” IEEE Trans. Circuits Syst., vol. 35, no. 8 pp. 1067-1068, Aug. 1988.
[8] S. Sunder and V. Ramachandran, “Systolic implementation of multidimensional nonrecursive digital filters,” IEEE Trans. Circuits Syst. Video Technol., vol. 3, no. 6, pp. 399-407, Dec.1993.
[9] JM. Jover and T. Kailath, “A parallel architecture for Kalman filter measurement update and parameter estimation,” Automatica vol. 22, no. 1, pp. 43-57, 1986
[10] S. Haykin, Adaptive Filter Theory, Englewood Cliffs, NJ: Prentice-Hall, 1986.
[11] P. Quinton and Y. Robert, Systolic Algorithms and Architectures. Prentice Hall, Masson, UK, 1991.
[12] S. Rao and T. Kailath, “Regular iterative algorithms and their implementations on processor arrays,” Proc, IEEE, VOL. 76, NO.3, PP. 259-282, Mar. 1988.
[13] J. Teich and L. Thiele, “Partitioning of processor arrays: A piecewise regular approach,” INTEGRATION, VOL. 14, NO.3, PP. 297-332, 1993.
[14] R. A. H. Al-Helali, “Systolic Algorithms of LMS, RLS Adaptive (FIR) Digital Filters for Adaptive Channel Equalization,” M.Sc. Thesis, Univ. of Baghdad, Baghdad, Oct. 2005.
[15] A. V. Oppenheim, and R. W. Schafer, Digital Signal Processing. London: Prentice Hall, 1975.
[16] G. Long, F. Ling, and J .G. Proakis, “The LMS algorithm with delayed coefficient adaptation,”IEEE Trans. Acoustic., Speech, Signal Processing, vol. 37, pp. 1397-1405, Sept. 1989; vol. 40, pp. 230-232, Jan. 1992.
[17] D. L. Jones, “Learning characteristics of transpose-form LMS adaptive filters,” IEEE Trans. Circuits Syst. II, vol. 40, pp. 745-749, Oct.1992
[18] S. C. Douglas, Q. Zhu, and K. F. Smith, “A pipelined LMS Adaptive FIR Filter. Architecture Without Adaptation Delay,” IEEE Trans. Signal Processing, vol. 46, pp. 775-778, Mar. 1998.
[19] A. Antonio, Digital Filters Analysis, Design, and Applications McGraw-Hill, 1993.

Copyright: Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.