A Detection Guided Normalized Least-Mean-Squares Adaptive Partial Crosstalk Canceller for Multi-User DSL Environments

Abstract

Block crosstalk cancellation techniques in practical multi-user digital subscriber line (DSL) environments may involve a high computational complexity as the channel and noise statistics can vary over time. We follow an adaptive approach by designing a structurally consistent significance-test feature within the normalized least-mean-square (NLMS) adaptive crosstalk canceller, aimed to detect significant crosstalkers within a DSL binder. The proposed detection-guided NLMS adaptive partial crosstalk canceller for DSL targets the dominant crosstalkers across user lines and tones, has low run-time complexity, demonstrates significantly faster convergence, and requires smaller training sequences when compared via simulation to the equivalent standard NLMS adaptive crosstalk canceller.