FIELD PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF A VARIABLE LEAKY LEAST MEAN SQUARE ADAPTIVE ALGORITHM

Purvika Kalkar; John Sahaya Rani Alex

doi:10.22159/ajpcr.2017.v10s1.19566

Authors

Purvika Kalkar School of Electronics Engineering, VIT University, Chennai, Tamil Nadu, India
John Sahaya Rani Alex School of Electronics Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19566

Keywords:

Field programmable gate array, Noise cancellation, Adaptive filter, Variable leaky least mean square algorithm, System generator

Abstract

Adaptive noise cancellation is an extensively researched area of signal processing. Many algorithms had been studied such as least mean square algorithm (LMS), recursive least square algorithm, and normalized LMS algorithm. The statistical characteristics of noise are fast in nature and the algorithms for noise cancellation should converge fast. Since LMS algorithm has slow convergence; in this paper, a variable leaky LMS (VLLMS) algorithm is explored. VLLMS is implemented using the concept of hardware-software cosimulation using Xilinx System Generator. The design is implemented on Virtex-6 ML605 field programmable gate array board. The implemented design is tested for sinusoidal signal added with an additive
white Gaussian noise. The design summary and the utilization summary are presented.

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References

Subbareddy TV, Reddy SO, Kaushik CS, Elamaran V. Implementation of adaptive filters on TMS320C6713 using LabVIEW â€“ A case study. Indian J Sci Technol 2015;8(22):1-6.

Broujeny BF. Adaptive Filter Theory and Application. Chichester, England: National University of Singapore, John Wiley and Sons Ltd.; p. 1998-7. Table 1: Device utilization summary of variable-LLMS adaptive filter Logic utilization Used Available Utilization Number of slice registers 1 54576 0% Number of fully used LUT-FF pairs 0 1 0% Number of bonded IOBs 1 190 0% Number of BUFG/BUFGCTRLs 1 16 6% LLMS: Leaky least mean square

Honade SJ. Review Paper on Adaptive Filters Using VLSI. 2016. p. 1374-8.

SyuD, Syu S, Ruan S, Huang Y, Yang C. FPGA Implementation of Automatic Speech Recognition System in a Car. 2015. p. 485-6.

Mugdha AC, Rawnaque FS, Ahmed MU. Filter algorithm in noise removal from ECG signals. Informatics, Electronics and Vision (ICIEV), 2015 International Conference. 2015. p. 1-6.

Safarian C, Ogunfunmi T, Kozacky WJ. FPGA Implementation of LMSBased FIR Adaptive Filter for Real Time Digital Signal Processing Applications. 2015. p. 1251-5.

Ajjaiah HB, Hunagund PV, Rao BR. Adaptive Filters in Digital Transmission Based on Improved LMS Algorithm. Wireless

Communications, Signal Processing and Networking (WiSPNET), International Conference. 2016. p. 23-5.

Deb A, Kar A, Chandra M. Advanced linear adaptive filtering methods for active noise control: A technical survey. ICACCS 2015 - Proceeding 2nd International Conference Advance Computing Communication System. 2015. p. 3-7.

Pujari SS. Design and Implementation of Single Order LMS based Adaptive System Identification on Altera based Cyclone II FPGA. 2014. p. 1605-9.

Dong X, Li H, Wang Y. High-speed FPGA Implementation of an Improved LMS Algorithm. No. 61371184. p. 342-5.

Bhoyar DB, Dethe CG, Bera S, Mushrif MM. FPGA Implementation of Adaptive Filter for Noise Cancellation. Electronics and Communication Systems (ICECS), International Conference; 2014.

Kamenetsky M, Widrow B. A Variable Leaky LMS Adaptive Algorithm. Signals, Systems and Computers, Conference Record of the Thirty- Eighth Asilomar Conference; 2004.

Widrow B, Stearns SD. Adaptive Signal Processing. NJ: Prentice Hall; 1985.

Nagal R, Kumar P, Bansal P. Performance Analysis of Least Mean Square Algorithm for Different Step Size Parameters with Different Filter Order and Iterations. International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE); 2015.

Gwadabe TR, Salman MS, Abuhilal H. A modified leaky-LMS algorithm. Int J Comput Electr Eng 2014;6(3):222-5.