Enhanced Multi-Level Signal Recovery in Mobile Fronthaul Network Using DNN Decoder

Abstract

A deep neural network (DNN) decoder which combines the function of both equalization and decoding is proposed and experimentally demonstrated for mobile fronthaul (MFH) transmission. This DNN consists of one input layer, one output layer, and several hidden layers. Adamax algorithm is implemented for finding the global minima while dropout mechanism and early stopping are utilized to avoid overfitting. The DNN accepts the received samples as the input and output the decoded samples directly to recover the transmitted samples. Using this DNN decoder, a record-high data-rate transmissiondistance product at 1800-Gb/s · km based on the directly modulated laser (DML) with intensity-modulation direct-detection is obtained. Besides, the PAM8 modulation with 3-bps/Hz spectral efficiency is implemented. Due to the smaller source spectrum bandwidth compared with traditionally widely used PAM4 and OOK at a certain data rate, the power fading limited transmission distance is extended by 1.5 and three times, respectively. The DML is a low-cost device and this simple transmission system eliminates the need of single-sideband modulation or dispersion compensation, which makes this system an ideal candidate for a low-cost enhanced MFH network.