TY - GEN
T1 - Steady-state analysis of the NLMS algorithm with reusing coefficient vector and a method for improving its performance
AU - Kim, Seong Eun
AU - Lee, Jae Woo
AU - Song, Woo Jin
PY - 2011
Y1 - 2011
N2 - The reuse of past coefficient vectors of the NLMS for reducing the steady-state MSD in a low signal-to-noise ratio (SNR) was proposed recently. Its convergence analysis has not been studied yet, so we first derive a steady-state analysis for the NLMS with reusing coefficient vectors for a special case. In addition, this approach slows down the convergence speed while decreasing the steady-state MSD in proportion to the number of reusing coefficient vectors. To address this trade-off, we propose a novel NLMS algorithm which can change the reusing order to achieve both fast convergence speed and low steady-state MSD. The reusing order is decreased or increased by comparing the squared output error with a threshold. The experimental results show that the theoretical results match well with simulation results and the proposed algorithm has fast convergence speed and small steady-state MSD compared to the conventional NLMS.
AB - The reuse of past coefficient vectors of the NLMS for reducing the steady-state MSD in a low signal-to-noise ratio (SNR) was proposed recently. Its convergence analysis has not been studied yet, so we first derive a steady-state analysis for the NLMS with reusing coefficient vectors for a special case. In addition, this approach slows down the convergence speed while decreasing the steady-state MSD in proportion to the number of reusing coefficient vectors. To address this trade-off, we propose a novel NLMS algorithm which can change the reusing order to achieve both fast convergence speed and low steady-state MSD. The reusing order is decreased or increased by comparing the squared output error with a threshold. The experimental results show that the theoretical results match well with simulation results and the proposed algorithm has fast convergence speed and small steady-state MSD compared to the conventional NLMS.
KW - Adaptive filters
KW - coefficient vector reusing
KW - mean-square deviation (MSD)
KW - normalized least-mean-square (NLMS)
KW - steady-state analysis
KW - variable reusing order
UR - https://www.scopus.com/pages/publications/80051643842
U2 - 10.1109/ICASSP.2011.5947259
DO - 10.1109/ICASSP.2011.5947259
M3 - Conference contribution
AN - SCOPUS:80051643842
SN - 9781457705397
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4120
EP - 4123
BT - 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
T2 - 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Y2 - 22 May 2011 through 27 May 2011
ER -