A finite precision LMS algorithm for increased quantization robustness

The well known least mean square (LMS) algorithm, or variations thereof, are frequently used in adaptive systems. When the LMS algorithm is implemented in a finite precision environment, it suffers from quantization effects. These effects can severely degrade the performance of the algorithm. This paper proposes a modification of the LMS algorithm that reduces the impact of quantization at virtually no extra computational cost. The paper contains an off-line evaluation of a system identification scheme where the presented algorithm outperforms the classical LMS algorithm yielding a better modelling of the unknown plant. This approach is well suited for adaptive system identification, e.g. beamforming, electrocardiography, and echo cancelling.