Performance Analysis of Adaptive LMS Filter through Regression Analysis using SystemC

The LMS adaptive filter has several parameters which can affect their performance. From among these parameters, most papers handle the step size parameter for controlling the performance. In this paper, we approach three parameters: step-size, filter tap-size and filter form. The regression analysis is used for defining the relation between parameters and performance of LMS adaptive filter with using the system level simulation results. The results present that all parameters have performance trends in each own particular form, which can be estimated from equations drawn by regression analysis.





References:
[1] Douglas L, Jones, Learning Characteristics of Transposed-Form LMS
Adaptive Filters , IEEE Transactions on Circuits and Systems-2: Analog
and Digital Signal Processing, Vol.39, No. 10, October 1992
[2] Gan, W.S. The non-canonical LMS algorithm(NCLMS): Characteristics
and analysis, Acoustics, Speech, and Signal processing, 1991.
ICASSP-91.,1991 International Conference on. 2137-2140 vol.3
[3] Lok-Kee Ting, Virtex FPGA iplementation of a pipelined Adaptive LMS
predictor for Electronic support Measures Receivers, IEEE Transaction
On Vlsi Systemns, Vol. 13, No. 1, January 2005.
[4] Michael Huston Acoustic Echo Cancellation Using
DigitalSignal Processing, The School of Information
Technology and Electrical Engineering, The University of Queensland.
[5] J.R.Armstrong, Modeling With SystemC : A Case Study, Consultant to
Motorola DSP Core Technology Center.
[6] Varuna Gupta, A Statistical Model for System Components Selection,
IEEE International Systems Conference, 2011.
World Academy of Science, Engineering and Technology 74 2013
[7] Ankur Agarwal, A System-Level modeling Methodology for Performance-Driven Component Selection in Multicore Architectures,
IEEE Systems Journal, Vol.6, No. 2, June 2012.