Other

Intel Corporation

Zao Liu

Yang Liu

Zhaoyin D Wu

Geoffrey Zhang

Alan C Wong

Yu Xu

Todd L Bermensolo

Apparatus and associated methods relate to using a high learning rate to speed up the training of a receiver and switching from a high learning rate to a low learning rate for fine tuning based on exponentially weighted moving average convergence. In an illustrative example, a selection circuit may switch the high learning rate to the low learning rate based on a comparison of a moving average difference eto a predetermined stability criteria Tof the receiver. The moving average difference emay include an exponentially weighted moving average of a difference between two consecutive exponentially weighted moving averages of an operation parameter uof the signal communication channel. By using this method, the training time for the receiver may be advantageously reduced.

Adaptive method to reduce training time of receivers

One embodiment provides an apparatus. The apparatus includes a feedforward equalizer (FFE), an FFE data slicer and an FFE least mean square (LMS) module. The FFE data slicer is to threshold detect a sample of a received analog training signal, a result of the threshold detecting corresponding to an input signal digital decision. The FFE LMS module is to determine a plurality of FFE coefficients based, at least in part, on an output of the FFE data slicer and based, at least in part, on a mean square error corresponding to a difference between an equalized sample and a reference.

Adaptive serdes receiver

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Santa Clara, CA, United States of America

Zao Liu