Dynamic algorithm transformations (DAT) for low-power adaptive signal processing

Abstract

Presented in this paper are algorithm transformation techniques for adaptive signal processing, which allow dynamic alteration of algorithm properties in response to signal non-stationarities. These transformations, referred to as dynamic algorithm transformations (DAT), jointly optimize algorithm and circuit performance measures such as signal-to-noise ratios (SNR) and power dissipation (P/sub D/), respectively. A DAT-based signal processing system is composed of a signal monitoring algorithm (SMA) block and a signal processing algorithm (SPA) block. First, computation of the theoretical power-optimum SPA configuration incorporating signal transition activity is presented. Next, practical SMA schemes are developed, which achieved power reduction by a combination of powering down the filter taps and modifying the coefficients. The DAT-based adaptive filter is then employed as a near-end cross-talk (NEXT) canceller in 155.52 Mb/s ATM-LAN over category 3 wiring. Simulation results indicate that the power savings for the NEXT canceller range from 21%-62% as the cable length varies from 100 meters to 70 meters.