Beyond Redundancy: Toward Agile Resilience in Optical Networks to Overcome Unpredictable Disasters

IEEE Communications Magazine · 2026-01-01

Resilience in optical networks has traditionally relied on redundancy and pre-planned recovery strategies, both of which assume a certain level of disaster predictability. However, recent environmental changes such as climate shifts, the evolution of communication services, and rising geopolitical risks have increased the unpredictability of disasters, reducing the effectiveness of conventional resilience approaches. To address this unpredictability, this article introduces the concept of agile resilience, which emphasizes dynamic adaptability across multiple operators and layers. We identify key requirements and challenges, and present enabling technologies for the realization of agile resilience. Using a field-deployed transmission system, we demonstrate rapid system characterization, optical path provisioning, and database migration within six hours. These results validate the effectiveness of the proposed enabling technologies and confirm the feasibility of agile resilience.

An Optimization Framework for Monitor Placement in Quantum Network Tomography

Open MIND · 2026-03-06

Quantum Network Tomography (QNT) offers a framework for end-to-end quantum channel characterization by strategically placing monitor nodes within the network. Building upon prior work on single-monitor placement, we study optimal monitor placement and measurement assignments for channel parameter estimation in arbitrary quantum networks. Using an n-node star network as a baseline, we analyze multi-monitor configurations and show that distributing monitors across end nodes can achieve estimation performance comparable to a monitor placed at the hub. Estimation precision is quantified using the Quantum Fisher Information Matrix (QFIM), with channel parameters inferred via Maximum Likelihood Estimation (MLE) and benchmarked against the Quantum Cramer-Rao Bound (QCRB). To generalize, we develop two Integer Linear Program (ILP) formulations: one maximizing estimation accuracy (QF), and another jointly optimizing accuracy and monitoring overhead (QMF). Unlike QF, QMF prevents monitor overloading, enabling scalability and parallelism. We prove optimality for star and analyze applicability to tree-structured quantum networks.

An Optimization Framework for Monitor Placement in Quantum Network Tomography

ArXiv.org · 2026-03-06

Quantum Network Tomography (QNT) offers a framework for end-to-end quantum channel characterization by strategically placing monitor nodes within the network. Building upon prior work on single-monitor placement, we study optimal monitor placement and measurement assignments for channel parameter estimation in arbitrary quantum networks. Using an n-node star network as a baseline, we analyze multi-monitor configurations and show that distributing monitors across end nodes can achieve estimation performance comparable to a monitor placed at the hub. Estimation precision is quantified using the Quantum Fisher Information Matrix (QFIM), with channel parameters inferred via Maximum Likelihood Estimation (MLE) and benchmarked against the Quantum Cramer-Rao Bound (QCRB). To generalize, we develop two Integer Linear Program (ILP) formulations: one maximizing estimation accuracy (QF), and another jointly optimizing accuracy and monitoring overhead (QMF). Unlike QF, QMF prevents monitor overloading, enabling scalability and parallelism. We prove optimality for star and analyze applicability to tree-structured quantum networks.

Control Protocol for Entangled Pair Verification in Quantum Optical Networks

2025-06-08

We consider quantum networks, where entangled-photon pairs are distributed using fibre optic links from a centralized source to entangling nodes. The entanglement is then stored (via an entanglement swap) in entangling nodes' quantum memories until used in, e.g., distributed quantum computing, quantum key distribution, quantum sensing, and other applications. Due to the fibre loss, some photons are lost in transmission. Noise in the transmission link and the quantum memory also reduces fidelity. Thus, entangling nodes must keep updated records of photon-pair arrivals to each destination, and their use by the applications. This coordination requires classical information exchange between each entangled node pair. However, the same fibre link may not admit both classical and quantum transmissions, as the classical channels can generate enough noise (i.e., via spontaneous Raman scattering) to make the quantum link unusable. Here, we consider coordinating entanglement distribution using a standard Internet protocol (IP) network instead, and propose a control protocol to enable such. We analyse the increase in latency from transmission over an IP network, together with the effect of photon loss, quantum memory noise and buffer size, to determine the fidelity and rate of entangled pairs. We characterize the relationship between the latency of the non-ideal IP network and the decoherence time of the quantum memories, providing a comparison of promising quantum memory technologies.

Design and Analysis of Power Consumption Models for Open-RAN Architectures

2025-06-08

The open radio access network (O-RAN) Alliance developed an architecture and specifications for open and disaggregated cellular networks including many elements that are being widely adopted and implemented in both commercial and research networks. In this paper, we develop transaction-based power consumption models of a centralized O-RAN architecture based on commercial hardware and considering the full end-to-end data path from the radio unit to the data center. We focus on recent fanout limitations and early baseband processing requirements related to current implementations of O-RAN and assess the power consumption impact when baseband processing is employed at different centralization points in the network. Additionally, we explore how greater fanout and sharing deeper into the network impact the balance of processing and transmission. Low processing fanout restrictions motivate greater centralization of the processing. At the same time, allowing for more open radio units per open distributed unit will quickly increase the transmission capacity requirements and related energy use.

Load-Balance-Guaranteed DNN Distributed Inference Offloading in MEC Networks Interconnected by Metro Optical Networks

IEEE Transactions on Network Science and Engineering · 2025-11-25

In multi-access edge computing (MEC) networks interconnected by metro optical networks, distributed inference is a promising technique to guarantee user experience for deep neural network (DNN) inference tasks while balancing the load of edge servers. It can partition an entire DNN model into multiple sequentially connected DNN blocks and offload them to distributed edge servers for processing. However, since the number and location of partitioning points are uncertain, the inference delay may be unacceptable due to long transmission delay if DNN inference tasks are divided into too many DNN blocks. Moreover, the computing capacity of edge servers is limited. The inference delay may also be unacceptable due to inadequate computing resources if target edge servers for DNN blocks are heavily loaded or overloaded. In order to accept more DNN inference tasks using limited computing resources, this paper proposes a load-balance-guaranteed DNN distributed inference offloading (LBG-DDIO) scheme to achieve flexible partitioning and offloading, where the partitioning and offloading decisions are determined by jointly considering the inference delay and the imbalanced degree of load (IDL). An efficient heuristic algorithm is developed to determine each DNN block according to the corresponding finish time and IDL, and the selection of target edge servers for DNN blocks is also optimized. LBG-DDIO is compared with four benchmarks, and the simulation results prove that LBG-DDIO can achieve a high acceptance ratio while keeping the load balanced.

Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum

Journal of Optical Communications and Networking · 2025-06-05 · 2 citations

Accurate modeling of the gain spectrum in erbium-doped fiber amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward multi-vendor solutions. In this work, we propose a generalized few-shot transfer learning architecture based on a semi-supervised self-normalizing neural network (SS-NN) that leverages internal EDFA features—such as VOA input/output power and attenuation—to improve gain spectrum prediction. Our SS-NN model employs a two-phase training strategy comprising unsupervised pre-training with noise-augmented measurements and supervised fine-tuning with a custom-weighted MSE loss. Furthermore, we extend the framework with transfer learning (TL) techniques that enable both homogeneous (same-feature space) and heterogeneous (different-feature sets) model adaptation across booster, pre-amplifier, and ILA EDFAs. To address feature mismatches in heterogeneous TL, we incorporate a covariance matching loss to align second-order feature statistics between the source and target domains. Extensive experiments conducted across 26 EDFAs in the COSMOS and Open Ireland testbeds demonstrate that the proposed approach significantly reduces the number of measurement requirements on the system while achieving lower mean absolute errors and improved error distributions compared to benchmark methods.

Beyond Redundancy: Toward Agile Resilience in Optical Networks to Overcome Unpredictable Disasters

ArXiv.org · 2025-09-28

Resilience in optical networks has traditionally relied on redundancy and pre-planned recovery strategies, both of which assume a certain level of disaster predictability. However, recent environmental changes such as climate shifts, the evolution of communication services, and rising geopolitical risks have increased the unpredictability of disasters, reducing the effectiveness of conventional resilience approaches. To address this unpredictability, this paper introduces the concept of agile resilience, which emphasizes dynamic adaptability across multiple operators and layers. We identify key requirements and challenges, and present enabling technologies for the realization of agile resilience. Using a field-deployed transmission system, we demonstrate rapid system characterization, optical path provisioning, and database migration within six hours. These results validate the effectiveness of the proposed enabling technologies and confirm the feasibility of agile resilience.

Design and Analysis of Power Consumption Models for Open-RAN Architectures

ArXiv.org · 2025-05-30

The open radio access network (O-RAN) Alliance developed an architecture and specifications for open and disaggregated cellular networks including many elements that are being widely adopted and implemented in both commercial and research networks. In this paper, we develop transaction-based power consumption models of a centralized O-RAN architecture based on commercial hardware and considering the full end-to-end data path from the radio unit to the data center. We focus on recent fanout limitations and early baseband processing requirements related to current implementations of O-RAN and assess the power consumption impact when baseband processing is employed at different centralization points in the network. Additionally, we explore how greater fanout and sharing deeper into the network impact the balance of processing and transmission. Low processing fanout restrictions motivate greater centralization of the processing. At the same time, allowing for more open radio units per open distributed unit will quickly increase the transmission capacity requirements and related energy use.

Secure Information Exchange Between Optical Network Digital Twin and Optical Transport Network

2025-06-23

This paper explores the role of data spaces in enabling secure, interoperable data exchange between Network Digital Twins and SDN Controllers. This approach emphasizes data space connectors for faster, scalable, and flexible integration of multiple components across digital ecosystems to work in unison. The proposed approach is to use a common trusted platform (in this paper, the platform used is TRUE Connector) which is responsible for secure information exchange between components(e.g: SDN Controller, Network Digital Twins, Software applications, etc.) in a digital ecosystem, thus encouraging collaboration between multiple technology vendors to provide more efficient network services and encourage interoperability.