QUANTIZATION EFFECTS IN TRANSFORM-DOMAIN NORMALIZED LMS ALGORITHM

In this paper the quantization effects of the transform-domain normalized least mean square (LMS) algorithm are studied. Our discussion is limited to the systems that employ an orthonormal transform to convert the incoming samples to a set of partially uncorrelated components, to be used as actual inputs to an adaptive linear combiner. The quantization effects of the transformation coefficients and the normalized step-sizes are studied separately. However, the quantization effects of the system variables and its adaptive tap weights are ignored. The new performance index given in [9] is used to derive analytical relations for the performance losses that arise. It is shown that representations of both the normalized step-sizes and the transformation coefficients require fairly low precision. Numerical results that support the developed theories are also given.