This paper will discuss the characteristics and role of the electronic and photonic technologies for the future networks, including mega data center network, backbone and metro network, and broadband access network as well.
Reconfigurable optical add-drop multiplexers (ROADMs) based on wavelength selective switches (WSSs) are crucial devices in optical networks, facilitating reconfigurable optical routing. In practical systems, soft failures in WSSs may occur, leading to connection disruptions if not addressed promptly. For rapid network diagnosis and maintenance, it is essential to quickly localize the failed WSS and estimate the failure magnitude. However, current approaches rely on scarce real-system failure data or require the installation of additional monitors. To address these limitations, in this paper, we propose a physics-based learning approach (PBLA) to localize the failed WSS and estimate the failure magnitude with two types of failure: filter shift (FS) and filter tightening (FT), without using historical failure data and additional monitors at the repeater nodes. The method leverages the power spectrum (PS) extracted from the chromatic dispersion compensation (CDC) module in receiver digital signal processing (DSP) as input. It formulates an optimization problem to minimize the discrepancy between the extracted PS and the PS calculated through a theoretical model. By solving the optimization problem, the method can effectively localize the failed WSS and estimate the failure magnitude in WSSs. The method is validated through extensive simulations, exhibiting a high localization accuracy and a small estimation error. Furthermore, we thoroughly explore the influence of various factors, including the measurement of PS, failure location, magnitude, the increasing number of WSSs, different symbol rates, and model inaccuracy, on the performance of our method, demonstrating its adaptability across diverse scenarios. Finally, an experimental demonstration further substantiates our method's performance in localizing and estimating WSS-related anomalies in optical networks.
One of the most promising solutions for 100 Gb/s line-rate passive optical networks (PONs) is intensity modulation and direct detection (IMDD) technology together with a digital signal processing- (DSP-) based equalizer for its advantages of system simplicity, cost-effectiveness, and energy-efficiency. However, due to restricted hardware resources, the effective neural network (NN) equalizer and Volterra nonlinear equalizer (VNLE) have the drawback of high implementation complexity. In this paper, we incorporate an NN with the physical principles of a VNLE to construct a white-box low-complexity Volterra-inspired neural network (VINN) equalizer. This equalizer has better performance than a VNLE at the same complexity and attains similar performance with much lower complexity than a VNLE with optimized structural hyperparameter. The effectiveness of the proposed equalizer is verified in 1310 nm band-limited IMDD PON systems. A 30.5-dB power budget is achieved with the 10-G-class transmitter.
As an imperative method of investigating the internal mechanism of femtosecond lasers, traditional femtosecond laser modeling relies on the split-step Fourier method (SSFM) to iteratively resolve the nonlinear Schrodinger equation suffering from the large computation complexity. To realize inverse design and optimization of femtosecond lasers, numerous simulations of mode-locked fiber lasers with different cavity settings are required further highlighting the time-consuming problem induced by the large computation complexity. Here, a recurrent neural network is proposed to realize fast and accurate femtosecond mode-locked fiber laser modeling for the first time. The generalization over different cavity settings is achieved via our proposed prior information feeding method. With the acceleration of GPU, the mean time of the artificial intelligence (AI) model inferring 500 roundtrips is less than 0.1 s. Even on an identical CPU-based hardware platform, the AI model is still 6 times faster than the SSFM method. The proposed AI-enabled method is promising to become a standard approach to femtosecond laser modeling.
A constellation independent look-up table (LUT) method for transceiver nonlinearity predistortion in digital-analog radio-over-fiber system is proposed and experimentally demonstrated, achieving a SNR gain of 1.04dB. The table size can be reduced to 0.3% of the conventional LUT.
We propose a physics-informed EDFA gain model based on the active learning method. Experimental results show that the proposed modelling method can reach a higher optimal accuracy and reduce ~90% training data to achieve the same performance compared with the conventional method.
We propose a novel Load Weighted Scheduling Algorithm (LWSA) to improve the packet aggregation performance for randomly-varied traffic patterns. Simulation results show that the LWSA provides higher utilization than the basic round-robin scheduling algorithm and retains the traffic pattern from IP network to OPS network quite well.
In this paper, a novel in-band optical spectra and filter shape monitoring technique is experimentally demonstrated. Based on swept coherent detection, the proposed technique simultaneously measures the signal and ASE spectra by adjusting the polarization states of the signal and local oscillator. In our experiment, a high resolution of 0.002 nm is achieved.
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