Multi-band (MB) transmission is being investigated as a cost-effective solution for increasing the capacity of the existing optical networks, while postponing the deployment of new fibers. Thulium–Doped Fiber Amplifiers (TDFAs) represent a viable solution for meeting the continuous bandwidth demand by providing the amplification in the S-band. However, the operation of the optical amplifiers deploying multiple pumps, such as the TDFA, is characterized by the nonlinear behavior, depending on the pumping currents and wavelengths, input power, operating range, etc. In the context of controlling such amplifiers, the main challenge is selecting the pumping configuration that allows the proper operation (e.g., the constant target output power). The numerical optimization, traditionally performed by the analytical models, is demanding due to large number of the unknown parameters in complex differential equations. Moreover, the complexity of storing and managing pre-determined configurations in lookup tables increases when considering the complete spectrum of potential operating conditions. On the other hand, Machine Learning (ML) techniques have the potential to effectively overcome such issues by utilizing amplifier-generated data and enabling fast and efficient pumping selection. In this work, YANG model describing the MB optical amplifiers is proposed to enable the Software-Defined Networking (SDN) control. The experimental TDFA characterization is performed, and the results obtained are recorded in a lookup table. In contrast, we then propose a neural network (NN) – based approach for optimizing pumping currents of the adopted TDFA, allowing the SDN controller to enforce such configuration to achieve the desired target output power. The NN model learns the relationship between the target output power, input power, and wavelength, and the suitable pumping, resulting in the Root Mean Squared Error (RMSE) of 0.23 dB between the target and actual output power. Therefore, a reliable and efficient NN-assisted SDN control is achieved. Moreover, an additional NN is employed to predict the noise figure (NF) based on the wavelength, input power, desired output power, and pumping, achieving an RMSE of 0.12 dB between the predicted and measured NF. Finally, experimental validation of C+S transmission is demonstrated.

A method based on state-driven control for instructing disaggregated transponder local NETCONF agents to select and adapt the optimal Operational (OP) Mode is experimentally demonstrated in a partially disaggregated optical network.

SDN control of digital subcarrier multiplexing is presented for optical multipoint-to-multipoint connectivity using extended spectrum and subcarrier assignment. A dedicated YANG model supports new data plane features introduced in this work. Provisioning is validated with NETCONF agents and physical-layer emulation with per-subcarrier digital signal processing. (c) 2025 The Author(s)

Open RAN (O-RAN) is an architectural framework for 5G and beyond that enables an open, interoperable RAN infrastructure, with the centralized unit handling higher-layer functions and mobility management, and the distributed unit (DU) managing real-time, lower-layer operations near the radio units. Due to its inherently hub-and-spoke architecture, O-RAN is well suited for integrating point-to-multipoint (P2MP) coherent transceivers, enabled by digital subcarrier multiplexing (DSCM), to improve efficiency and reduce costs. On the other hand, the shift toward network disaggregation, driven by software-defined networking, promotes interoperability and automation. However, the control of commercially available P2MP transceivers remains proprietary, highlighting the need for open and standardized management frameworks. Therefore, we first propose an augmentation of the OpenConfig YANG data model for terminal devices to control DSCM and coherent P2MP transceivers. An experimental demonstration is shown in an integrated data and control plane testbed. Then, the first experimental implementation of Open RAN X-haul using P2MP transceivers on horseshoe optical networks is reported. 5G DU and P2MP leaf transceivers are dynamically activated according to cell traffic conditions to optimize energy efficiency.

Multi-band (MB) transmission is being investigated as a cost-effective solution for increasing the capacity of the existing optical networks, while postponing the deployment of new fibers. Thulium-Doped Fiber Amplifiers (TDFAs) represent a viable solution for meeting the continuous bandwidth demand by providing the amplification in the S-band. However, the operation of the optical amplifiers deploying multiple pumps, such as the TDFA, is characterized by the nonlinear behavior, depending on the pumping currents and wavelengths, input power, operating range, etc. In the context of controlling such amplifiers, the main challenge is selecting the pumping configuration that allows the proper operation (e.g., the constant target output power). The numerical optimization, traditionally performed by the analytical models, is demanding due to large number of the unknown parameters in complex differential equations. Moreover, the complexity of storing and managing pre-determined configurations in lookup tables increases when considering the complete spectrum of potential operating conditions. On the other hand, Machine Learning (ML) techniques have the potential to effectively overcome such issues by utilizing amplifier-generated data and enabling fast and efficient pumping selection. In this work, YANG model describing the MB optical amplifiers is proposed to enable the Software-Defined Networking (SDN) control. The experimental TDFA characterization is performed, and the results obtained are recorded in a lookup table. In contrast, we then propose a neural network (NN) - based approach for optimizing pumping currents of the adopted TDFA, allowing the SDN controller to enforce such configuration to achieve the desired target output power. The NN model learns the relationship between the target output power, input power, and wavelength, and the suitable pumping, resulting in the Root Mean Squared Error (RMSE) of 0.23 dB between the target and actual output power. Therefore, a reliable and efficient NN-assisted SDN control is achieved. Moreover, an additional NN is employed to predict the noise figure (NF) based on the wavelength, input power, desired output power, and pumping, achieving an RMSE of 0.12 dB between the predicted and measured NF. Finally, experimental validation of C+S transmission is demonstrated.

The power consumption of telecommunication equipment has been identified as a relevant contributor to global energy consumption. In fact, new-generation optical transponders employ power-intensive electronic application-specific integrated circuits (ASICs) for digital signal processing (DSP). DSP design has traditionally prioritized meeting transmission requirements over power consumption optimization. In general, the evolutions of transmission techniques and network design have always been mainly driven by traffic increase; in this context, in order to operate network resources more efficiently, margin reduction has been investigated in the past few years. Indeed, traditionally, high physical layer margins are used to ensure reliability over an extended period, resulting in overprovisioning the optical connections for both physical layer conditions and capacity. On the other hand, super-channels have emerged as a suitable solution for accommodating the continuous traffic growth. However, power consumption has not been deeply considered in the optimization of super-channel transmission. This paper first investigates the power efficiency of super-channels operated with designed and reduced margins. Low-margin operation is enabled by adapting sub-carrier spacing and filter bandwidth. Power-aware super-channel optimization is then experimentally demonstrated leveraging a 600 Gbit/s transponder in the SDN-controlled elastic optical network (EON). The results have identified a trade-off between power consumption and spectrum efficiency. Furthermore, the ongoing bandwidth demand has motivated the investigation of multi-band (MB) transmission for scaling the capacity of the existing infrastructures. However, novel networking devices (e.g., optical amplifiers operating beyond the C- and L-bands) will affect the overall power consumption. In this context, experimental power analysis of a thulium doped fiber amplifier (TDFA) is performed based on the traffic load and corresponding configuration. The results show that TDFA power consumption varies with configuration and increases with output power.

This study investigates the trade-off between Quality of Transmission (QoT) performance and spectrum efficiency through low-margin operation of multicarriers. Margin reduction is enabled by implementing tight filtering and slight subcarriers overlap. Experiments conducted on a Software Defined Networking (SDN)-controlled Elastic Optical Network (EON) testbed demonstrate spectrum-efficient multicarriers composed of three subcarriers, achieving savings of up to 22% in spectrum occupation for 80 km optical connections.

This study investigates the use of a reconfigurable multi-functional reprogrammable silicon chip as an add/drop network device for a sliceable bandwidth variable transceiver. Software-Defined Networking (SDN) with NETCONF/YANG protocol is used for control and management, leveraging REST API to facilitate communication and coordination between the device local controller and the silicon chip-integrated device.

The complexity of controlling optical amplifiers with multiple pumps, like Thulium-Doped Fiber Amplifiers (TDFAs), stems from the need to select the optimal pumping to ensure proper operation (e.g., maintaining a constant target output power). Therefore, an experimental demonstration of neural network-assisted SDN control of TDFA configuration is presented. The proposed optimization enables efficient automated TDFA control, resulting in an RMSE of 0.23 dBm between target and actual output power.

A YANG model for multi-band amplifiers is proposed. Thulium Doped Fiber Amplifier (TDFA) characterization is performed, then automated control configuring TDFA based on traffic load and C+S transmission are experimentally demonstrated.

Power-aware super-channel optimization is experimentally demonstrated using a 600Gbit/s transponder in the SDN-controlled Elastic Optical Network. The trade-off between power consumption and spectrum efficiency is investigated by operating super-channels at nominal and low-margin conditions. Results show that physical layer and operational modes impact power consumption regardless of the spectrum efficiency. Experiments on 800 Gbit/s super-channels show 7% power saving when super-channels are operated at nominal spectrum usage.

In order to activate high-data-rate connectivity, super-channel transmission strategy is becoming a suitable solution. Optical Software Defined Networking (OSDN) architecture leverages NETCONF protocol for the configuration and management of optical devices. To support a vendor-neutral approach, OpenConfig YANG models are adopted in the NETCONF communication. In OpenConfig, all the proprietary parameters (i.e., Forward Error Correction (FEC), bit rate, modulation format) are mapped to operational modes, maintaining a basic compatibility between vendors. In this work, the experimental analysis of an automatic super-channel optimization is shown. In particular, for each established super-channel, the sub-carriers are partially overlapped and tightly filtered, achieving spectrum saving with margins reduction, while guaranteeing a level of Quality of Transmission (QoT). The procedure has been demonstrated using an SDN Controller with an SDN-based Optical Network, including OpenConfig 600 Gbit/s transponders and emulated ROADMs. After the setup of a lightpath, the optimization procedure is activated by the transponder agents, without involving the SDN controller, to find the optimal super-channel configuration, according to the reach and the desired modulation formats. A spectrum saving of 25% is achieved with respect to the nominal conditions, still guaranteeing the minimum QoT level for the channels involved, in terms of pre-FEC Bit Error Rate (BER).

Optical networks have historically been overprovisioned for physical-layer conditions and capacity and, as such, operate uninterrupted over several years. By reducing system margins, network efficiency and cost savings could be improved without compromising network reliability. On the other hand, super-channel transmission has been identified as a suitable solution for supporting high-data-rate connectivity. NETCONF, along with YANG models, has been recognized as a software defined networking (SDN) configuration and management protocol enabling control in a vendor-neutral way. Moreover, the OpenConfig YANG model defines vendor-specific transmission parameters, such as modulation format and bit rate, within operational modes. This paper investigates the trade-off between super-channel quality of transmission (QoT) and spectrum saving by allowing slight subcarrier overlap and tight filtering, hence reducing system margins. An automatic spectrum-efficient super-channel optimization procedure is experimentally demonstrated using a 600 Gbit/s transponder in an SDN-controlled elastic optical network. The automatic procedure performs the adaptation of filter bandwidth and subcarrier spacing. The latter is effectively operated by transponder agents, without involving the SDN controller, thus reducing the margins while guaranteeing that the QoT enables savings of up to 25% of the spectrum.

This paper presents viable solutions for the control of a multi-band optical network. NETCONF and augmented OpenConfig YANG data models are adopted to configure and monitor the state of network devices. Both connection provisioning and Quality of transmission estimation account for Stimulated Raman Scattering.

Automatic super-channel optimization is experimentally demonstrated using a 600Gb/ s transponder in the SDN-controlled Elastic Optical Network. Margin reduction while guaranteeing Quality of Transmission allows for a spectrum occupation reduction of 25%.