A pipelined LMS adaptive filter architecture

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.<
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