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Efficient resource allocation and management can enhance the capacity of an optical backbone network. In this context, spectrum retuning via hitless defragmentation has been presented for elastic optical networks to enhance efficient spectrum accommodation while reducing the unused fragmented spaces in the spectrum. However, the quality of service committed in a service level agreement may be affected due to spectrum retuning. In particular, for transmission beyond the conventional C band, the presence of inter-channel stimulated Raman scattering can severely degrade the quality of the signal during defragmentation. To conquer this problem, this paper proposes, for the first time to our knowledge, a signal-quality-aware proactive defragmentation scheme for the C + L band system. The proposed scheme prioritizes the minimization of the fragmentation index and quality of transmission (QoT) maintenance for two different defragmentation algorithms, namely, nonlinear-impairment (NLI)-aware defragmentation (NAD) and NLI-unaware defragmentation (NUD). We leverage machine learning techniques for QoT estimation of ongoing lightpaths during spectrum retuning. The optical signal-to-noise ratio of a lightpath is predicted for each choice of spectrum retuning, which helps to monitor the effect of defragmentation on the quality of ongoing lightpaths (in terms of assigned modulation format). Numerical results show that, compared to a baseline algorithm (NUD), the proposed NAD algorithm provides up to 15% capacity increment for smaller networks such as BT-UK, while for larger networks such as the 24-node USA network, a capacity benefit of 23% is achieved in terms of the number of served demands at 1% blocking.

Multiband (MB) and multifiber (MF) technologies get significant attention along with their individual pros and cons to solve the optical fiber capacity crunch problem. The MB system is more susceptible to nonlinear transmission impairments such as interchannel stimulated Raman scattering. On the contrary, MF solutions are costly due to the requirement of parallel fiber deployment. Therefore, it is necessary to find a hybrid solution using MB and MF technologies. This paper performs a techno-economic comparison between MF and MB technologies while taking a robust cost model. We propose, for the first time to our knowledge, a domain-knowledge-assisted, cost-effective network upgrade algorithm to enhance the overall network capacity while including minimum capital expenditure (CapEx). The proposed algorithm prioritizes the targeted deployment of cost-effective upgrade solutions in different links of the network to minimize the overall cost of the upgrade. The comparison between optical cable deployment and fiber leasing is captured to upgrade the overall network capacity with minimum cost-per-bit. Reported results show that a single fiber C+L band system can provide approximately 66.67% gain in terms of traffic admissibility compared to MF C band systems in smaller networks such as BT-UK, while considering fiber leasing. If operator owned dark fibers are not available in any of the links, numerical results show that the MF C+L band system in the BT-UK network can still provide CapEx savings of approximately up to 40.5% compared to the MF C band systems while minimizing the cost-per-bit of the network by approximately 21.9% to achieve the targeted network capacity of 150 Tbps. The effect of longer link length on the network upgrade cost is also studied in this paper. Reported result shows that, for larger geography such as the pan-Europe network, the channel launch power needs to be tuned to achieve the benefit of the MF C+L band compared to the MF C band system.

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