Long Short-Term Memory Neural Equalizer

Abstract

A trainable neural equalizer based on Long Short-Term Memory (LSTM) neural network architecture is proposed in this paper to recover the channel output signal. The current widely used solution for the transmission line signal recovering is generally realized through DFE or FFE-DFE combination. The novel learning-based equalizer is suitable for highly non-linear signal restoration thanks to its recurrent design. The effectiveness of the LSTM equalizer is shown through an ADS simulation channel signal equalization task including a quantitative and qualitative comparison with an FFE-DFE combination. The LSTM neural network shows good equalization results compared to that of the FFE-DFE combination. The advantage of a trainable LSTM equalizer lies in its ability to learn its parameters in a flexible manner, to tackle complex scenario without any hardware modification. This can reduce the equalizer implantation cost for variant transmission channels and bring additional portability in practical applications.