Gigabit DSL: A deep-LMS approach

摘要

In this paper we present the Deep-LMS, a novel algorithm for crosstalk cancellation in DSL. The Deep-LMS crosstalk canceler uses an adaptive non-diagonal preprocessing matrix prior to a conventional LMS crosstalk canceler. The role of the preprocessing matrix is to speed-up the convergence of the conventional LMS crosstalk canceler and hence speed-up the convergence of the overall system. The update of the preprocessing matrix is inspired by deep neural networks. However, since all the operations in the Deep-LMS algorithm are linear, we are capable of providing an exact convergence speed analysis. The Deep-LMS is important for crosstalk cancellation in the novel G.fast standard, where traditional LMS converges very slowly due to the large bandwidth. Simulation results support our analysis and show significant reduction in convergence time compared to existing LMS variants.