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

TL;DR

This paper presents a comprehensive model and heuristic solution for maximizing revenue in flexible optical networks by optimizing adaptive modulation, FEC, and power spectrum density, considering physical layer impairments.

Contribution

It introduces a universal impairment evaluation model and formulates the revenue maximization as a mixed integer linear program, with a near-optimal heuristic for large networks.

Findings

Adaptive modulation can double revenue at high SNR and high traffic load.

Multiple FECs are more profitable at low SNR.

The proposed heuristic achieves near-optimal revenue results.

Abstract

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.