Throughput Maximization Leveraging Just-Enough SNR Margin and Channel Spacing Optimization

Flexible optical network is a promising technology to accommodate high-capacity demands in next-generation networks. To ensure uninterrupted communication, existing lightpath provisioning schemes are mainly done with the assumption of worst-case resource under-provisioning and fixed channel spacing, which preserves an excessive signal-to-noise ratio (SNR) margin. However, under a resource over-provisioning scenario, the excessive SNR margin restricts the transmission bit-rate or transmission reach, leading to physical layer resource waste and stranded transmission capacity. To tackle this challenging problem, we leverage an iterative feedback tuning algorithm to provide a just-enough SNR margin, so as to maximize the network throughput. Specifically, the proposed algorithm is implemented in three steps. First, starting from the high SNR margin setup, we establish an integer linear programming model as well as a heuristic algorithm to maximize the network throughput by solving the problem of routing, modulation format, forward error correction, baud-rate selection, and spectrum assignment. Second, we optimize the channel spacing of the lightpaths obtained from the previous step, thereby increasing the available physical layer resources. Finally, we iteratively reduce the SNR margin of each lightpath until the network throughput cannot be increased. Through numerical simulations, we confirm the throughput improvement in different networks and with different baud-rates. In particular, we find that our algorithm enables over 20\% relative gain when network resource is over-provisioned, compared to the traditional method preserving an excessive SNR margin.Comment: submitted to IEEE JLT, Jul. 17th, 2021. 14 pages, 8 figure

A Sensitivity-Enhanced Refractive Index Sensor Using a Single-Mode Thin-Core Fiber Incorporating an Abrupt Taper

A sensitivity-enhanced fiber-optic refractive index (RI) sensor based on a tapered single-mode thin-core diameter fiber is proposed and experimentally demonstrated. The sensor head is formed by splicing a section of tapered thin-core diameter fiber (TCF) between two sections of single-mode fibers (SMFs). The cladding modes are excited at the first SMF-TCF interface, and then interfere with the core mode at the second interface, thus forming an inter-modal interferometer (IMI). An abrupt taper (tens of micrometers long) made by the electric-arc-heating method is utilized, and plays an important role in improving sensing sensitivity. The whole manufacture process only involves fiber splicing and tapering, and all the fabrication process can be achieved by a commercial fiber fusion splicer. Using glycerol and water mixture solution as an example, the experimental results show that the refractive index sensitivity is measured to be 0.591 nm for 1% change of surrounding RI. The proposed sensor structure features simple structure, low cost, easy fabrication, and high sensitivity

Throughput Maximization in Multi-Band Optical Networks with Column Generation

Multi-band transmission is a promising technical direction for spectrum and capacity expansion of existing optical networks. Due to the increase in the number of usable wavelengths in multi-band optical networks, the complexity of resource allocation problems becomes a major concern. Moreover, the transmission performance, spectrum width, and cost constraint across optical bands may be heterogeneous. Assuming a worst-case transmission margin in U, L, and C-bands, this paper investigates the problem of throughput maximization in multi-band optical networks, including the optimization of route, wavelength, and band assignment. We propose a low-complexity decomposition approach based on Column Generation (CG) to address the scalability issue faced by traditional methodologies. We numerically compare the results obtained by our CG-based approach to an integer linear programming model, confirming the near-optimal network throughput. Our results also demonstrate the scalability of the CG-based approach when the number of wavelengths increases, with the computation time in the magnitude order of 10 s for cases varying from 75 to 1200 wavelength channels per link in a 14-node network.Comment: 6 pages, 4 figures, submitted to IEEE International Conference on Communications 2024 (ICC2024). (Note on arXiv: for beginners in the area of column generation, please refer to the example computation in the file . I have uploaded it to this arXiv project along with other source files.

BGP-Reflection Functors and Lusztig's Symmetries: A Ringel–Hall Algebra Approach to Quantum Groups

AbstractAccording to the canonical isomorphisms between the Ringel–Hall algebras (composition algebras) and the quantum groups, we deduce Lusztig's symmetries T″i,1,i∈I, by applying the Bernstein–Gelfand–Ponomarev reflection functors to the Drinfeld doubles of Ringel–Hall algebras. The fundamental properties of T″i,1 including the following can be obtained conceptually. (1) T″i,1,i∈I induce automorphisms of the quantum groups Uq(g) and on the integrable modules. (2) T″i,1,i∈I satisfy the braid group relations. This extends and completes the results of B. Sevenhant and M. Van den Bergh (1999, J. Algebra221, 135–160)

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and high flexibility, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be flexibly changed during the predicting process. The results represent a remarkable improvement in the ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.Comment: 15 pages,9 figure

Programmable long period grating in a liquid core optical fiber

A programmable fiber long-period grating (LPG) is experimentally demonstrated in a liquid core optical fiber with a low insertion loss. The LPG is dynamically formed by a temperature gradient in real time through a micro-heater array. The transmission spectrum of the LPG can be completely reconfigured by digitally changing the grating period, index contrast, length, and design. The phase shift inside the LPG can also be readily defined to enable advanced spectrum shaping. Owing to the high thermo-optic coefficient of the liquid core, it is possible to achieve high coupling efficiencies with driving powers as low as a few tens of milliwatts. The proposed thermo-programmable device provides a potential design solution for dynamic all-fiber optics components

Quantum groups and double quiver algebras

For a finite dimensional semisimple Lie algebra ${\frak{g}}$ and a root $q$ of unity in a field $k,$ we associate to these data a double quiver $\bar{\cal{Q}}.$ It is shown that a restricted version of the quantized enveloping algebras $U_q(\frak g)$ is a quotient of the double quiver algebra $k\bar{\cal Q}.$Comment: 15 page

Kernel mapping for mitigating nonlinear impairments in optical short-reach communications

Nonlinear impairments induced by the opto-electronic components are one of the fundamental performance-limiting factors in high-speed optical short-reach communications, significantly hindering capacity improvement. This paper proposes to employ a kernel mapping function to map the signals in a Hilbert space to its inner product in a reproducing kernel Hilbert space, which has been successfully demonstrated to mitigate nonlinear impairments in optical short-reach communication systems. The operation principle is derived. An intensity modulation/direct detection system with 1.5-mu m vertical cavity surface emitting laser and 10-km 7-core fiber achieving 540.68-Gbps (net-rate 505.31-Gbps) has been carried out. The experimental results reveal that the kernel mapping based schemes are able to realize comparable transmission performance as the Volterra filtering scheme even with a high order. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen