Title :
A pipelined LMS adaptive filter architecture
Author :
Shanbhag, N.R. ; Parhi, K.K.
Author_Institution :
Dept. of Electr. Eng., Minnesota Univ., Minneapolis, MN, USA
Abstract :
A fine-grain pipelined architecture for least mean-square (LMS) filtering is developed by employing a stochastic form of look-ahead. With the stochastic form of look-ahead one can look for acceptable convergence behavior rather than invariance with respect to the input-output mapping. This architecture offers a trade-off between a variable output latency and adaptation accuracy. Analytical expressions describing the convergence properties are provided. A comparison with previous work indicates that the novel architecture has the least increase in hardware requirements and at the same time has the highest convergence speed in seconds. Simulation results confirm the desired analytical expressions
Keywords :
adaptive filters; digital filters; filtering and prediction theory; least squares approximations; pipeline processing; LMS adaptive filter; adaptation accuracy; convergence properties; convergence speed; fine-grain pipelined architecture; input-output mapping; least mean-square; simulation results; stochastic look-ahead; variable output latency; Adaptive filters; Algorithm design and analysis; Concurrent computing; Convergence; Delay; Hardware; Least squares approximation; Pipeline processing; Signal processing algorithms; Stochastic processes;
Conference_Titel :
Signals, Systems and Computers, 1991. 1991 Conference Record of the Twenty-Fifth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-2470-1
DOI :
10.1109/ACSSC.1991.186532
