Maximizing Revenue With Adaptive Modulation and Multiple FECs in Flexible Optical Networks

• Published in: IEEE/ACM transactions on networking Vol. 31; no. 1; pp. 220 - 233
• Main Authors: Chen, Cao, Zhou, Fen, Tornatore, Massimo, Xiao, Shilin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; adaptive modulation; Algorithms; Bandwidth; Bandwidths; Crosstalk; Flexible optical networks (FONs); Forward error correction; Heuristic methods; Integer programming; Interference; Maximization; Mixed integer; Modulation; multiple forward-error correction (FEC); Optical communication; Optical fiber networks; Optimization; Revenue; revenue maximization; Signal to noise ratio; Spectrum allocation; Spontaneous emission
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2022.3194982

Flexible optical networks (FONs) are being adopted to accommodate the increasingly heterogeneous traffic in today's Internet. However, in presence of high traffic load, not all offered traffic can be satisfied at all time. As carried traffic load brings revenues to operators, traffic blocking due to limited spectrum resource leads to revenue losses. In this study, given a set of traffic requests to be provisioned, we consider the problem of maximizing operator's revenue, subject to limited spectrum resource and physical layer impairments (PLIs), namely amplified spontaneous emission noise (ASE), self-channel interference (SCI), cross-channel interference (XCI), and node crosstalk. In FONs, adaptive modulation, multiple FEC, and the tuning of power spectrum density (PSD) can be effectively employed to mitigate the impact of PLIs. Hence, in our study, we propose a universal bandwidth-related impairment evaluation model based on channel bandwidth, which allows a performance analysis for different PSD, FEC and modulations. Leveraging this PLI model and a piecewise linear fitting function, we succeed to formulate the revenue maximization problem as a mixed integer linear program. Then, to solve the problem on larger network instances, a fast two-phase heuristic algorithm is also proposed, which is shown to be near-optimal for revenue maximization. Through simulations, we demonstrate that using adaptive modulation enables to significantly increase revenues in the scenario of high signal-to-noise ratio (SNR), where the revenue can even be doubled for high traffic load, while using multiple FECs is more profitable for scenarios with low SNR.