EFFICIENT, RLS, DATA-DRIVEN ECHO CANCELLER FOR FAST INITIALIZATION OF FULL-DUPLEX DATA TRANSMISSION.

Abstract

Computationally efficient recursive-least-squares (RLS) procedures are presented specifically for the adaptive adjustment of the data-driven echo cancelers (DDECs) that are used in voiceband full-duplex data transmission. The methods are shown to yield very short learning times for the DDEC and simultaneously reduce computational requirements to below those required for other least-squares procedures. The new methods can be used with any training sequence over any number of iterations, unlike any of the previous fast-converging methods. The methods are based on fast transversal filter RLS adaptive filtering algorithms that were independently introduced by the authors; however, several special features of the DDEC are introduced and exploited to further reduce computation to the levels that would be required for slower-converging stochastic-gradient solutions. Several tradeoffs between computation, memory, learning-time, and performance are illuminated.