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Abstract Efficient network management in optical backbone networks is essential to manage continuous traffic growth. To accommodate this growth, network operators need to upgrade their infrastructure at appropriate times. Given the cost constraint of upgrading the entire network at once, upgrading the network periodically in multiple batches is a more pragmatic approach to meet the growing demands. While multi-period, batch-upgrade strategies to increase network capacity from the conventional C band to C+L bands have been proposed, they did not consider so far the possibility to re-provision existing traffic. In this work, we investigate how to selectively re-provision connections from C band to L band during a batch upgrade. This is to ensure greater availability of C-band resources which can help to delay network upgrade and hence reduce upgrade cost, while limiting the number of disrupted connections in the network. This study proposes two re-provisioning strategies, namely, Budget-Based (BB) and Margin-Aware (MA) re-provisioning, which rely on the Quality of Transmission (QoT) of lightpaths. These strategies leverage the knowledge of Generalized Signal-to-Noise Ratio (GSNR) to choose which lightpaths to re-provision. We compare these strategies with a baseline distance-based strategy that uses path length to select and re-provision lightpaths. We also incorporate Machine Learning techniques for QoT estimation of lightpaths to reduce the computational time required for optical-path feasibility check. Numerical results show that, compared to distance-based strategy, BB and MA strategies reduce disruption by about 22% and 27%, respectively, in representative network topologies. 

This paper explores the joint impact of two capacity enhancement schemes in optical backbone networks: multi-band expansion from C+L to C+L+S bands and varying 3R regeneration (no, selective, and full). Using a pay-as-you-grow batch upgrade framework that considers deferral benefits, we evaluate their interaction. In the short-haul BT-UK network, S-band upgrade consistently improves throughput and cost efficiency, with the greatest economic gain with no regeneration. In the long-haul USNET network, S-band upgrade reduces throughput because K-least-loaded routing does not consider path distance, yielding low-quality lightpaths with high blocking probability. Thus, C+L bands with full regeneration are more cost-effective. 

Not AvailableNetwork operators tend to migrate multiband optical networks to wider bands by batch upgrade (i.e., with a pay-as-you-grow strategy affecting only a subset of links at a time). However, temporary interruptions of services routed along the fibers that require equipment upgrades can create significant service blocking, which can only be avoided with interim lightpath re-allocation during the upgrade period. To seamlessly upgrade the network from C+L to C+L+S bands, we propose a batch upgrade strategy to reduce the upgrade cost, and a band-selective re-allocation method during the upgrade period to minimize blocking probability (BP). Simulations on the US-24 topology demonstrate up to nearly 50% cost reduction and BP as low as 0.3%. 

Not AvailableEfficient network management in optical backbone networks is crucial for handling continuous traffic growth. In this work, we address the challenges of managing dynamic traffic in C- and C+L-band optical backbone networks while exploring application flexibility, namely the compressibility and delayability metrics. We propose a strategy, named Delay-Aware and Compression-Aware (DACA) provisioning algorithm, which reduces blocking probability, thereby increasing information-carrying capacity of the network compared to baseline strategies. 

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. Code of this publication is available at github.com/cchen000/CG-Multi-Band. 

Multi-band transmission is a promising solution for capacity enhancement in optical networks. We propose a novel strategy, named C to C+L Upgrade (CLU), to gradually upgrade links from C to C+L bands. We develop a Recurrent Neural Network (RNN)-based model to efficiently predict links for upgrade, based on network state and resource utilization, to reduce blocking and upgrade cost. Our results show that CLU outperforms baseline strategies (which do not employ predictive decisions) by upgrading fewer links at appropriate times. 

We investigate optimized placement of hybrid EDFA/Raman amplifiers in (C+L) networks to avoid lightpath degradation due to ISRS. We numerically compare eight strategies for amplifier deployment showing that an optimized placement of Raman amplification can lead to 40% fewer amplifiers compared to baseline deployment practices.