Results on Canceller Convergence in Nonstationary Noise

摘要

Convergence results for the Sampled Matrix Inversion (SMI)/Gram-Schmidt (GS)canceller algorithm in nonstationary noise is investigated by using the Gram-Schmidt (GS) canceller as an analysis tool. Lower and upper bounds for the convergence rate of the canceller's average output noise power residue normalized to the quiescent average output noise power residue are derived. These bounds are a function of the number of independent samples processed per channel (main and auxiliary), the number of auxiliary input channels, and the external noise environment. The external noise environment was modeled as Gaussian, with a power level specified at each sampling time instant. Furthermore, this model is generalized in the sense that a joint probability distribution function is defined for the power levels over a canceller processing batch. This leads to the capability of modeling and evaluating the SMI/GS canceller in a variety of interference scenarios such as continuous or discrete time processes or a mix of these.