Sparsity Learning Deep Neural Network Nonlinear Equalization Method for 112Gbps PAM4 Short-Reach Transmission Links

In this paper, we propose and experimentally validate a novel sparsity learning deep neural network nonlinear equalization method (SLDNN-NLM) for a 112-Gbps PAM4 signal using a 25-GHz externally modulated laser (EML) to transmit over 40-km standard single-mode fiber (SSMF) in O-band. Our proposed SLDNN-NLM can reduce computation complexity while still maintain the system performance. This method combines the L2-regularization term into the loss function and adapts the deep neural network (DNN) model by distinguishing the significance of each connection. We next prune away insignificant weight connections using the pruning technique without effectively degrading the system performance. In our experimental validation, we compare the system performance and computation complexity of our approach with conventional deep neural network based nonlinear equalizer (CDNN-NLE), L2-regularized Volterra nonlinear equalizer (L2VNLE), sparse CDNN-NLE, and sparse L2VNLE. The experimental findings imply that the proposed method has great potential to deal with nonlinear distortions arisen from the optical transmission systems. Our results show that the proposed SLDNN-NLM with L2-regularization achieves better BER performance than CDNN-NLE at the same complexity level. Moreover, when comparing with L2VNLE, the computation complexity is reduced by 42% at each received optical power (ROP) after 40-km SSMF transmission. Compared with L2VNLE, CDNN-NLE, sparse L2VNLE, and sparse CDNN-NLE, the overall complexity of the proposed SLDNN-NLM after pruning can be reduced by 86%, 75%, 70%, and 61% for BTB at an ROP of -3dBm, and 79%, 63%, 58%, and 57% for 40-km transmission at an ROP of -5 dBm, respectively, without degrading the system performance.