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DAS over 1,007-km Hybrid Link with 10-Tb/s DP-16QAM Co-propagation using Frequency- Diverse Chirped Pulses

DAS over 1,007-km Hybrid Link with 10-Tb/s DP-16QAM Co-propagation using Frequency- Diverse Chirped Pulses We report the first distributed acoustic sensing (DAS) experiment with over >1,000 km reach on a hybrid link comprising of a mixture of field and lab fibers with bi-directional inline Raman amplification after each span. We used 20× frequency-diversity chirped-pulses for the probe signal,and recovered the Rayleigh backscatter using a coherent receiver with correlation detection and diversity combining. A measurand resolution of ∼100 pϵ/√ Hz at a gauge length of 20 meters achieved in the offline experiment. We also demonstrate the first real-time FPGA implementation of chirped-pulse DAS without frequency diversity over a range of 210 km.

Weight Pruning Techniques for Nonlinear Impairment Compensation using Neural Networks

Weight Pruning Techniques for Nonlinear Impairment Compensation using Neural Networks Neural networks (NNs) are attractive for nonlinear impairment compensation applications in communication systems, such as optical fiber nonlinearity, nonlinearity of driving amplifiers, and nonlinearity of semiconductor optical amplifiers. Without prior knowledge of the transmission link or the hardware characteristics, optimal parameters are completely constructed from a data-driven approach by exploring training datasets, once the NN structure is given. On the other hand, due to computational power and energy consumption, especially in high-speed communication systems, the computational complexity of the optimized NN needs to be confined to the hardware, such as FPGA or ASIC without sacrificing its performance improvement. In this paper, two approaches are presented to accommodate the NN-based algorithms for high-speed communication systems. The first approach is to reduce computational complexity of the NN-based nonlinearity compensation algorithms on the basis of weight pruning (WP). WP can significantly reduce the computational complexity, especially because the nonlinear compensation task studied here results in a sparse NN. The authors have studied an enhanced approach of WP by imposing an additional restriction on the selection of non-zero weights on each hidden layer. The second approach is to implement NNs onto a silicon-photonic integrated platform, enabling power efficiency to be further improved without sacrificing the high-speed operation.