Fiber-optic multi-band transmission (MBT) aims at exploiting the low-loss spectral windows of single-mode fibers (SMFs) for data transport, expanding by ∼11× the available bandwidth of C-band line systems and by ∼5× C+L-band line systems'. MBT offers a high potential for cost-efficient throughput upgrades of optical networks, even in absence of available dark-fibers, as it utilizes more efficiently the existing infrastructures. This represents the main advantage compared to approaches such as multi-mode/-core fibers or spatial division multiplexing. Furthermore, the industrial trend is clear: the first commercial C+L-band systems are entering the market and research has moved toward the neighboring S-band. This article discusses the potential and challenges of MBT covering the ITU-T optical bands O → L. MBT performance is assessed by addressing the generalized SNR (GSNR) including both the linear and non-linear fiber propagation effects. Non-linear fiber propagation is taken into account by computing the generated non-linear interference by using the generalized Gaussian-noise (GGN) model, which takes into account the interaction of nonlinear fiber propagation with stimulated Raman scattering (SRS), and in general considers wavelength-dependent fiber parameters. For linear effects, we hypothesize typical components' figures and discussion on components' limitations, such as transceivers', amplifiers' and filters' are not part of this work. We focus on assessing the transmission throughput that is realistic to achieve by using feasible multi-band components without specific optimizations and implementation discussion. So, results are meant to address the potential throughput scaling by turningon excess fiber transmission bands. As transmission fiber, we focus exclusively on the ITU-T G.652.D, since it is the most widely deployed fiber type worldwide and the mostly suitable to multi-band transmission, thanks to its ultra-wide low-loss singlemode high-dispersion spectral region. Similar analyses could be This work was fundedby the H2020 Metro-Haul project, no. 761727; and by the European Union Horizon 2020 research and innovation program under the Marie Skłodowska-Curie ETN WON, grant agreements 814276.

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