Steady-State Performance Analysis of Incremental Variable Tap-Length Algorithm in WSNs Under Noisy Links condition

Document Type : Research Paper

Authors

1 Engineering Faculty of Khoy, Urmia University of Technology, Urmia, Iran

2 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Abstract

Recently proposed distributed incremental fractional tap-length (FT) variable-length least mean square (LMS) technique do not consider noisy links errors, which occur during the communication of local estimations between nodes. In this paper, we study the noisy links effect on the performance of this algorithm. We derive a mathematical formulation for the steady-state length at each node. Our derived relationship shows how steady-state tap-length is affected by noisy links. Simulations confirm that there is a good match between the theory and simulated tap-length. Furthermore, the critical result is that, as the noise level increases, the steady-state tap-length decreases compared to the ideal link version. However, in low noise conditions, this length is still larger than the optimal filter length.

Keywords

References

1- J. J. Xiao, A. Ribeiro, Z. Q. Luo, and G. B. Giannakis, “Distributed compression-estimation using wireless sensor networks,” IEEE Signal Processing Magazine, vol. 23, no. 4, pp. 27–41, July 2006.
2- A. A. Haghrah, M.A. Tinati, and T.Y. Rezaii, “Analysis of incremental LMS adaptive algorithm over wireless sensor networks with delayed-links,” Digital Signal Processing, vol. 88, pp.80-89, May 2019.
3- L. Li, J. A. Chambers, C. G. Lopes, and A. H. Sayed, “Distributed estimation over an adaptive incremental network based on the affine projection algorithm,” IEEE Trans. Signal Processing, vol. 58, no. 1, pp. 151–164, Jan. 2010.
4- A. H. Sayed and C. G. Lopes, “Distributed recursive least-squares strategies over adaptive networks,” Proc. 40th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, pp.233-237, Oct-Nov 2006.
5- A. Rastegarnia, “Reduced-communication diffusion RLS for distributed estimation over multi-agent networks,” IEEE Trans. Circuits and Systems II: Express Briefs, vol. 67, no. 1, pp. 177-181, Jan. 2020.
6- C. G. Lopes and A. H. Sayed, “Diffusion least-mean squares over adaptive networks: Formulation and performance analysis,” IEEE Trans. Signal Process., vol. 56, no. 7, pp. 3122–3136, July 2008.
7- L. Li and J. A. Chambers, “Distributed adaptive estimation based on the APA algorithm over diffusion networks with changing topology,” in Proc. IEEE Workshop Stat. Signal Process. (SSP), Cardiff, UK, pp. 757–760, Aug./Sept. 2009.
8- Z. Pritzker and A. Feuer, “Variable length stochastic gradient algorithm,” IEEE Trans. Signal Processing, vol. 39, pp. 997–1001, Apr. 1991.
9- Y. K. Won, R.-H. Park, J. H. Park, and B.-U. Lee, “Variable LMS algorithm using the time constant concept,” IEEE Trans. Consumer Electron., vol. 40, pp. 655–661, Aug. 1994.
10- V. H. Nascimento, “Improving the initial convergence of adaptive filters: variable-length LMS algorithms,”In 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002, vol. 2, pp. 667-670, 2002.
11- S. H. Pauline, D. Samiappan, R. Kumar, A. Anand, and A. Kar, “Variable tap-length non-parametric variable step-size NLMS adaptive filtering algorithm for acoustic echo cancellation," Applied Acoustics, vol. 159, p. 107074, Feb. 2020.
12- A. Kar, T. Padhi, B. Majhi, and M. Swamy, “Analyzing the impact of system dimension on the performance of a variable-tap-length adaptive algorithm," Applied Acoustics, vol. 150, pp. 207-215, July 2019.
13- C. Wang, Y. Zhang, Y. Wei, and N. Li, “An effective tap-length NLMS algorithm for network echo cancellers,” Circuits, Systems, and Signal Processing, vol. 36, no. 4, pp. 1686-1699, July 2017.
14- Y. Han, M. Wang, and M. Liu, “An improved variable tap-length algorithm with adaptive parameters," Digital Signal Processing, vol. 74, pp. 111-118, March 2018.
15- R. C. Bilcu, P. Kuosmanen, and K. Egiazarian, “On length adaptation for the least mean square adaptive filters”, Signal Processing-Fractional calculus applications in signals and systems. vol. 86, pp. 3089-3094, Oct. 2006.
16- Y. Wei and Z. Yan, “Variable tap-length LMS algorithm with adaptive step size,” Circuits, Systems, and Signal Processing, vol. 36, no. 7, pp. 2815-2827, 2017.
17- Y. Zhang, J. A. Chambers, S. Sanei, P. Kendrick, and T. J. Cox, “A new variable tap-length LMS algorithm to model an exponential decay impulse response,” IEEE Signal Processing Letters, vol. 14, no. 4, pp. 263–266, Apr. 2007.
18- A. Kar and M. Swamy, “Tap-length optimization of adaptive filters used in stereophonic acoustic echo cancellation," Signal Processing, vol. 131, pp. 422-433, Feb. 2017.
19- K. Mayyas, “Performance analysis of the deficient length LMS adaptive algorithm,” IEEE Trans. Signal Process., vol. 53, no. 8, pp. 2727–2734, Aug. 2005.
20- Y. GU, K. Tang, and H. Cui, “LMS algorithm with gradient descent filter length,” IEEE Signal Process. Letts, vol. 11, no. 3, pp. 305–307, Mar. 2004.
21- Y. Gong and C. F. N. Cowan, “Structure adaptation of linear MMSE adaptive filters,” Proc. Inst. Elect. Eng.—Vision, Image, Signal Process. vol. 151, no. 4, pp. 271–277, Aug. 2004.
22- Y. Gong and C. F. N. Cowan, “An LMS style variable tap-length algorithm for structure adaptation,” IEEE Trans. Signal Process. vol. 53, no. 7, pp. 2400–2407, July 2005.
23- L. Li, Y. Zhang and J. A. Chambers, “Variable length adaptive filtering within incremental learning algorithms for distributed networks,” In2008 42nd Asilomar Conference on Signals, Systems and Computers, pp. 225-229, Oct. 2008.
24- G. Azarnia, and M. A. Tinati, “Steady-State Analysis of the Deficient Length Incremental LMS Adaptive Networks with Noisy links”, AEUE - International Journal of Electronics and Communications, vol. 69, no 1, pp. 153–162, Jan. 2015.
Journal of Communication Engineering
Volume 10, Issue 1 - Serial Number 20
January 2021
Pages 127-143

Files

Share

How to cite

Statistics