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.

In the imminent era of bandwidth-abundant optical networks, the concept of hierarchical optical network architectures emerges, decoupling the massive spatial division multiplexing (SDM) layer and the wavelength division multiplexing layer. In this paper, we perform a networking-level analysis of a generalized multiband optical cross-connect (OXC) that realizes switching in two steps: grouping wavelengths and switching them as waveband paths. The performed analysis is simulated for multiband and SDM systems, demonstrating that while conventional single-layer wavelength routing remains effective under certain conditions, waveband routing offers a more scalable and cost-effective solution under specific high-load conditions in bandwidth-abundant optical networks. This positions waveband routing as a promising alternative for future high-capacity networks.

Nowadays, optical transport and data center networks utilize wavelength-division multiplexing (WDM) and band division multiplexing (BDM) as possible solutions to increase the capacity to support the ever-increasing traffic demand. It is an alternative solution to simple spatial division multiplexing (SDM) intended to utilize more than one fiber per link. In this perspective, we propose a new modular photonic integrated multi-band wavelength selective switch (WSS) to implement a novel reconfigurable optical add-drop multiplexer (ROADM) architecture. The proposed WSS can operate on a broad spectrum range, covering the three S+C+L bands, and is potentially scalable to large numbers of output fibers and routed channels while maintaining a small footprint. We investigated the network performance assessment of the ROADM architecture based on the proposed multi-band WSS on a German network, and a detailed comparison is given for both SDM and BDM solutions. Results show that the increase in network capacity by exploiting more bands in the cost-efficient BDM scenario is quite close to the SDM scenario using existing infrastructure replicated over multiple fibers.

Modern elastic optical networking requires additional flexibility at each layer compared to the traditional approach. The application of the Software-defined Networking (SDN) paradigm can provide the required degrees of freedom. The implementation of optical SDN down to the physical layer requires the complete abstraction of network elements to support full control by the centralized controller. In this work, we propose a topological and technological agnostic model based on Machine Learning (ML) to abstract the behavior of optical switches for the computation of Quality-of-transmission (QoT) penalties and the definition of control states. Training and testing datasets are obtained synthetically by software simulation of the photonic switching structure. Results show the capability of the proposed method to predict QoT impairments with high accuracy, and we envision its application in a real-time control plane.

Prior provisioning of optical source technologies have techno-economic importance for the operator during the design and planning of optical network architectonics. Advancement towards the latest technology paradigm such as Elastic Optical Networks (EONs) and Software Defined Networking (SDN) open a gateway for a flexible and re-configurable optical network architecture. In order to achieve the required degree of flexibility, a flexible and dynamic behaviour is required both at the control and data plane. In this regards, SDN-enabled flexible optical transceivers are proposed to provide the required degree of flexibility. Sliceable Bandwidth Variable Transponders (SBVTs) is one of the recent type of flexible optical transceivers. Based on the type/technology of optical carrier source, the SBVTs are categorized into two types; Multi-Laser SBVT (ML-SBVT) and Multi-wavelength SBVT (MW-SBVT). Both architectures have their own pros and cons when it comes to accommodate traffic request. In this paper, we propose a selection model for the SBVTs before its actual deployment in the network. The selection model consider various design and planning phase network characteristics. In addition to this selection model, the comparison of centralized Flex-OCSM architecture is also presented with the already discussed SBVT types. The analysis in this work is performed on random network (20 nodes) and the German Network (17 nodes).