Abstract

A flexible coherent passive optical network (FLCS-CPON) is a promising solution for the future access network. By offering increased speed, sensitivity, and flexibility, it enables more efficient utilization of network resources and allows for serving a larger number of users. However, the past studies often overlook the flexibility of channel equalization. In the FLCS-CPON, customized rate optimization has been achieved to cater to users in different channel conditions. However, in addition to rate optimization, further performance improvement can be achieved by providing customized equalization methods. In this work, we proposed and demonstrated an end-to-end (E2E)-optimization-based FLCS-CPON in a 50 km fiber transmission. It enables tailored signal constellation shaping and equalization, maximizing system efficiency and performance. Finally, we achieved a FLCS-CPON with the net data rate (NDR) varied from 124 to 210 Gbps and the power budget of 40 and 42.4 dB in upstream and downstream, respectively; 3.7 and 2.9 dB power budgets are improved by E2E optimization. In burst-mode, the dynamic range of probabilistic shaping 32 quadrature amplitude modulation (PS-32QAM) at a line rate of 250 Gbps improved by 6.1 to 16.8 dB. Additionally, a dynamic range and net-rate product (DRNRP) of 5779 dB · Gbps is achieved.

© 2023 Optica Publishing Group

Principle and strategy of using probabilistic shaping in a flexible coherent passive optical network without optical amplifiers

Sizhe Xing, Guoqiang Li, An Yan, Wangwei Shen, Zhongya Li, Jiaye Wang, Jianyang Shi, Zhixue He, Nan Chi, and Junwen Zhang
J. Opt. Commun. Netw. 15(8) 507-517 (2023)

Beyond 100G three-dimensional flexible coherent PON with time, frequency, and power resource-allocation capabilities

Wangwei Shen, Guoqiang Li, Sizhe Xing, Zhongya Li, An Yan, Jiaye Wang, Nan Chi, and Junwen Zhang
J. Opt. Commun. Netw. 16(5) 516-526 (2024)

Deep-learning-based multi-user framework for end-to-end fiber-MMW communications

Zhongya Li, Junlian Jia, Guoqiang Li, Boyu Dong, Wangwei Shen, Changle Huang, Jianyang Shi, Nan Chi, and Junwen Zhang
Opt. Express 31(10) 15239-15255 (2023)

Low-complexity end-to-end deep learning framework for 100G-PON

Yongxin Xu, Xiaokai Guan, Wenqing Jiang, Xudong Wang, Weisheng Hu, and Lilin Yi
J. Opt. Commun. Netw. 16(11) 1093-1103 (2024)

Attention-assisted autoencoder neural network for end-to-end optimization of multi-access fiber-terahertz communication systems

Zhongya Li, Boyu Dong, Guoqiang Li, Junlian Jia, Aolong Sun, Wangwei Shen, Sizhe Xing, Jianyang Shi, Nan Chi, and Junwen Zhang
J. Opt. Commun. Netw. 15(9) 711-725 (2023)