Artificial Neural-Network-Based Pre-Distortion for High Loss-Budget 60-km Long-Reach Passive Optical Network

Hong Minh Nguyen, Szu Chi Huang, Chia Chien Wei*, Chun Yen Chuang, Jason Jyehong Chen


研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)


High launch optical power can compensate for severe fading and power loss in long-reach passive optical networks (LR-PONs); however, it also aggravates nonlinear degradation, which necessitates the use of complex DSP-based nonlinear compensation techniques at optical network users (ONUs). DSP-related techniques also necessitate the use of additional hardware/software components by the receiver, which can greatly increase implementation costs and energy consumption, particularly when dealing with large-scale ONU deployment. This is the first study to propose artificial neural network (ANN)-based pre-distortion to eliminate the need for complex DSP at ONUs in a high-launch-power LR-PON, thereby permitting the use of a simplified architecture at the user end. In the first phase of the study, the proposed ANN-based pre-distortion scheme was implemented in a single-channel IMDD OFDM LR-PON, which achieved a data rate of >55 Gbps over 60-km transmission with a loss budget of 30 dB without the need for optical inline- or pre-amplification. In the second phase of experiments, the same scheme was applied to a 4-channel wavelength division multiplexing (WDM) OFDM LR-PON. Here, the proposed scheme achieved data rates of >200 Gbps using launch power of 18 dBm per lane, resulting in a loss budget of roughly 29 dB over 60-km single mode fiber transmission.

頁(從 - 到)124824-124832
期刊IEEE Access
出版狀態Published - 2020


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