Abstract
In this paper the delayed least-mean-square (DLMS) algorithm is proposed for training a transversal filter-based decision feedback equalizer (DFE). Delays in the filter coefficient update process are used to pipeline the DFE, thereby increasing the throughput rate, for a given speed of hardware. The filter structures selected for the feedforward and feedback section of the DFE facilitate the use of a shared error signal, thereby reducing communication costs. The new resulting structure is highly modular and is very suitable for very large scale integration (VLSI) implementation. A pipelined form for the normalized least-mean-square algorithm (NMLS) is also obtained which removes the dependency of the convergence speed on the input signal power. The convergence and residual mean-square error characteristics of the different pipelined filters are compared.
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