Machine learning-assisted nonlinear-impairment-aware proactive defragmentation forC+Lband elastic optical networks

Abstract

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 theC+Lband 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.