Adaptive parallel decision deep neural network for high-speed equalization

Abstract

The equalization plays a pivotal role in modern high-speed optical wire-line transmission. Taking advantage of the digital signal processing architecture, the deep neural network (DNN) is introduced to realize the feedback-free signaling, which has no processing speed ceiling due to the timing constraint on the feedback path. To save the hardware resource of a DNN equalizer, a parallel decision DNN is proposed in this paper. By replacing the soft-max decision layer with hard decision layer, multi-symbol can be processed within one neural network. The neuron increment during parallelization is only linear with the layer count, rather than the neuron count in the case of duplication. The simulation results show that the optimized new architecture has competitive performance with the traditional 2-tap decision feedback equalizer architecture with 15-tap feed forward equalizer at a 28GBd, or even 56GBd, four-level pulse amplitude modulation signal with 30dB loss. And the training convergency of the proposed equalizer is much faster than its traditional counterpart. An adaptive mechanism of the network parameter based on forward error correction is also studied.