Effect of Finite Wordlength on the Performance of an Adaptive Network

In this paper we consider the performance of incremental least mean square (ILMS) adaptive network when it is implemented in finite-precision arithmetic. We show that unlike the infinite-precision case, the steady-state curve, described in terms of mean square deviation (MSD) is not always a monotonic increasing function of step-size parameter. More precisely, when the quantization level is small, reducing the step-size may increase the steady-state MSD.

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