Future 6G X-haul networks must satisfy strict latency and service reliability requirements, placing significant pressure on metro-access transport architectures. As deployments become denser, longer and more heterogeneous routes intensify physical-layer impairments, making feasibility assurance and Quality-of-Transport (QoT) evaluation increasingly complex. To address these challenges, this work proposes an AI-driven converged metro-access Optical Network-as-a-Service (ONaaS) architecture based on coherent Point-to-Multipoint (P2MP) transmission using Digital Subcarrier Multiplexing (DSCM). An experimentally characterized transceiver impairment model is embedded into a network-level simulator to perform end-to-end feasibility analysis under strict latency and BER constraints. The results show that connectivity is primarily bounded by accumulated impairments, while Distributed Unit (DU) densification improves performance mainly by shortening path lengths, with limited benefit beyond moderate routing depth. To enable scalable operation, a lightweight machine-learning-based BER estimator is developed for rapid QoT prediction. Trained on a minimal deployment scenario, the Random Forest model generalizes across DU densities and topologies with R 2 > 0 . 98 , reducing evaluation time by several orders of magnitude. A techno-economic assessment further indicates up to 75% reduction in DU-site transceivers and 27%–30% energy savings compared to Point-to-Point (P2P) provisioning, demonstrating the efficiency and scalability of AI-enabled P2MP metro-access convergence for 6G.

This article investigates robust design methodologies for cost-effective and resilient design of filterless Point-to-Multipoint (P2MP) optical networks by minimizing the number of amplifiers, which are often compromised by parameter uncertainties, for example inaccurate fiber loss. To overcome the inefficiencies of traditional, overly or insufficiently conservative margin-based designs, this study investigates Deterministic Optimization (DO), Robust Optimization (RO), and Adjustable Robust Optimization (ARO) of optical filterless horseshoe networks. All these techniques are explained in detail using a simple Linear Program (LP) model example. The findings of this manuscript demonstrate that ARO is a significantly superior technique, leveraging a two-stage, “wait-and-see” approach that allows for adaptive adjustments, by modifying amplifier gains after uncertainty is revealed. This adaptability reduces the conservatism of standard RO, achieving full resiliency with a much lower “price of robustness”. Specifically, for the considered scenarios, at a 20% uncertainty level, ARO required only a 16% increase in amplifiers compared to 40% for RO, ultimately providing a more efficient trade-off between performance and robustness.

The extension of elastic optical network (EON) technologies to multi-band transmission (MB-EON) promises enhanced spectral efficiency, greater throughput, and long-term cost benefits for telecom operators. However, designing such networks presents challenges, particularly in optimizing physical parameters like optical power and quality of transmission (QoT) across different frequency bands. This paper introduces a methodology for optimal span-by-span power allocation using two hyper-accelerated power optimization (HPO) modes: flat launch power (FLP) and flat received power (FRP). This methodology significantly accelerate network power optimization while ensuring service stability in scenarios such as changes in network parameters, QoT degradation due to aging, and network re-optimization or upgrading. Through a comprehensive comparison, we find that FRP notably improves signal flatness and GSNR/OSNR, particularly in the S-band, contributing to a network-wide throughput increase in the order of 12% to 75%. Additionally, we demonstrate that HPO applied to global power optimization is simpler and more cost-effective than when applied to local methods for large-scale networks.

The next-generation mobile networks follow the 3GPP disaggregated Radio Access Network (RAN) model, composed of three separate functional units: Centralized Unit (CU), Distributed Unit (DU), and Radio Unit (RU), and its interconnection network infrastructures, front-haul, between RU and DU and mid-haul, between DU and CU. The current focus has been extensively on the front-haul, proposing a myriad of networking technologies and resource allocation solutions. Mid-haul transport infrastructure has received comparably less attention. This paper explores the implementation and performance of optical multi-flow transponders in mobile mid-haul networks. It evaluates the performance of mid-haul mesh topologies with different DU placement methods. Numerical results show the achievable throughput and blocking performance. We also derive the number of Multi-flow optical transponders (MOTP) units deployed, as well as the related trade-offs.

While horseshoe filterless networks with point-to-multipoint (P2MP) transceivers offer a cost-effective solution with inherent survivability against single-link failures, certain scenarios may demand an even higher degree of network resilience. This is particularly true in regions with frequent power outages and/or where load shedding is enforced. Addressing this problem requires a careful design and trade-off between survivability and cost. This paper describes a multi-objective optimization framework based on Integer Linear Programming (ILP) that accurately models the problem and can be used to obtain solutions that prioritize one or the other metric. Moreover, the paper reports a study using a set of randomly generated horseshoe topologies that demonstrates how strategically placing amplifiers and unbalanced couplers can significantly enhance network resilience, while also keeping the minimum number of additional optical amplifiers required.

Horseshoe filterless networks with point-to-multipoint (P2MP) transceivers are cost-effective but may lack resilience in regions with power outages. This paper addresses this gap by strategically placing amplifiers and couplers to enhance network resiliency. We evaluate the effectiveness of this approach through a network availability analysis.

This paper proposes an Integer Linear Programming (ILP) framework to optimize the multi-period planning of metro-aggregation networks exploiting both point-to-multipoint coherent pluggable transceivers and a filterless line system architecture. We investigate several planning strategies to minimize cost while also considering traffic prediction and operational expenditures. The results of a reference network provide evidence of transceiver cost reductions between 20% and 32% compared to the point-to-point approach over a five-year planning period.

Emerging applications are driving traffic requirements to new heights, from IoT and autonomous driving to augmented reality and Industry 4.0. Urban areas, which have a significant fraction of the telecom infrastructure capital expenditures (CAPEX), are experiencing even more growth than other segments. Noteworthy, the traffic pattern in aggregation networks is hub-and-leaf, with only a few hub nodes communicating with multiple leaf nodes. Traditional point-to-point optical solutions are not the best fit for this pattern. A novel optical technology enabling point-to-multipoint operation has recently been proposed to address this limitation. Since this solution relies on broadcasting the optical signal over a virtual tree, its optimal deployment in generic fiber topologies (with failure survivability conditions) requires specific designs. This paper describes an integer linear programming model to optimize the design of survivable metro-aggregation networks exploiting point-to-multipoint coherent transceivers. Results obtained on a mesh network show evidence that transceiver expenditures can be reduced by a figure between 25% and 60%.

Wavelength-division multiplexing (WDM) has historically enabled the increase in the capacity of optical systems by progressively populating the existing optical bandwidth of erbium-doped fiber amplifiers (EDFAs) in the$C$-band. Nowadays, the number of channels—needed in optical systems—is approaching the maximum capacity of standard$C$-band EDFAs. As a result, the industry worked on novel approaches, such as the use of multicore fibers, the extension of the available spectrum of the$C$-band EDFAs, and the development of transmission systems covering$C$- and$L$-bands and beyond. In the context of continuous traffic growth, ultrawideband (UWB) WDM transmission systems appear as a promising technology to leverage the bandwidth of already deployed optical fiber infrastructure and sustain the traffic demand for the years to come. Since the pioneering demonstrations of UWB transmission a few years ago, long strides have been taken toward UWB technologies. In this review article, we discuss how the most recent advances in the design and fabrication of enabling devices, such as lasers, amplifiers, optical switches, and modulators, have improved the performance of UWB systems, paving the way to turn research demonstrations into future products. In addition, we also report on the advances in UWB optical fibers, such as the recently introduced nested antiresonant nodeless fibers (NANFs), whose future implementations could potentially provide up to 300-nm-wide bandwidth at less than 0.2 dB/km loss.

The ever-increasing traffic requirements need cost-efficient and reliable solutions. Metropolitan networks are responsible for a large fraction of the telecom infrastructure CAPEX. Albeit the traffic pattern in these networks tends to be hub-and-spoke, the current transceiver deployments do not leverage this feature. This paper describes and models various survivable metro-aggregation network scenarios using point-to-multipoint capable coherent transceivers. The results obtained over a reference mesh network show that these transceivers can reduce total transceiver costs by ~40% on average.

The massive deployment of 5G and beyond will require high capacity and low latency connectivity services, so network operators will have either to overprovision capacity in their transport networks or to upgrade the optical network controllers to make decisions nearly in real time; both solutions entail high capital and operational expenditures. A different approach could be to move the decision making toward the nodes and subsystems, so they can adapt dynamically the capacity to the actual needs and thus reduce operational costs in terms of energy consumption. To achieve this, several technological challenges need to be addressed. In this paper, we focus on the autonomous operation of Digital Subcarrier Multiplexing (DSCM) systems, which enable the transmission of multiple and independent subcarriers (SC). Herein, we present several solutions enabling the autonomous DSCM operation, including: i) SC quality of transmission estimation; ii) autonomous SC operation at the transmitter side and blind SC configuration recognition at the receiver side; and iii) intent-based capacity management implemented through Reinforcement Learning. We provide useful guidelines for the application of autonomous SC management supported by the extensive results presented.

The performance of optical devices can degrade because of aging and external causes like, for example, temperature variations. Such degradation might start with a low impact on the Quality of Transmission (QoT) of the supported lightpaths (soft-failure). However, it can degenerate into a hard-failure if the device itself is not repaired or replaced, or if an external cause responsible for the degradation is not properly addressed. In this work, we propose comparing the QoT measured in the transponders with the one estimated using a QoT tool. Those deviations can be explained by changes in the value of input parameters of the QoT model representing the optical devices, like noise figure in optical amplifiers and reduced Optical Signal to Noise Ratio in the Wavelength Selective Switches. By applying reverse engineering, the value of those modeling parameters can be estimated as a function of the observed QoT of the lightpaths. Experiments reveal high accuracy estimation of modeling parameters, and results obtained by simulation show large anticipation of soft-failure detection and localization, as well as accurate identification of degradations before they have a major impact on the network.

Both spatial-division multiplexing (SDM) and band-division multiplexing (BDM) emerge as possible solutions to increase the optical network capacity to support the traffic demand which has been rising over time. In this work, two different ROADM (Re-configurable Optical Add Drop Multiplexer) switching techniques, namely SDM-InS (Independent switching) and SDM-CCC (Core Continue Constant) are investigated and the resulting network capacity is compared with the BDM approach. In the BDM case, both L- and S-bands have been used in addition to C-band to increase the network capacity. The launch power is optimized to control the QoT (Quality of Transmission) summarized by the generalized SNR (GSNR) per channel. Due to: stimulated Raman scattering, frequency variation of loss, frequency variation of dispersion coefficient and noise figures, an optimum power tilt and offset are calculated for each band. We show that the total network capacity increased by $\sim 2 \times$ and $\sim 3 \times$, when using the L-band and L+S-bands in addition to the C-band, respectively, in both a reference German and a reference US network. Additionally, it was also shown that using additional bands, the increase in network capacity is close to the result of using additional optical fibers in the SDM case.