David Larrabeiti

We investigate energy-efficient, traffic-adaptive resource provisioning for 6G x-haul networks by integrating a reconfigurable low-loss switching sub-system (referred to as an interlacer) into the TEFNET24 reference network topology from Telefónica. The interlacer combined with the digital subcarrier multiplexing (DSCM) transceivers forms a WDM-PON approach, enabling subcarrier-level switching at the network access, providing lower distribution loss than conventional power splitter (PS)-based TDMA-PON approaches. Moreover, under the practically low crosstalk levels reported for our interlacer implementations, the reduced distribution loss can be traded for increased modulation cardinality (e.g., 16QAM to 64QAM), allowing for reducing the number of allocated transceivers. We further examine a machine learning (ML)-driven framework for dynamic capacity provisioning and validate it using a real-world operator trace mapped onto the national-scale TEFNET24 topology, capturing 4G/5G load patterns with projections toward 6G. We compare static, semi-static, and fully dynamic strategies and show that ML-aided dynamic provisioning reduces transceiver overallocation by up to 63.7% relative to static methods, with the strongest gains during off-peak hours when traffic variability is highest. Finally, we quantify two complementary energy-saving mechanisms enabled by dynamic operation—subcarrier deactivation and transceiver sleep mode—with the latter yielding up to 21% total network energy savings while maintaining quality of service under time-varying demand.

As optical networks evolve toward dynamic, multi-band (C + L) architectures, efficient and QoT-aware resource management becomes essential to ensure scalable and low-service downtime operation. This paper introduces a novel, to our knowledge, unified hybrid grooming framework that addresses the unique challenges of traffic grooming in dynamic multi-band elastic optical networks (MB-EONs). Motivated by the need for cost-effective and adaptive high-capacity infrastructures, we propose a policy-based framework incorporating three heuristic algorithms tailored to distinct optimization goals. The unique challenges of multi-band optical networks, such as the non-uniform QoT performance caused by inter-channel stimulated Raman scattering (ISRS) are explicitly considered in our design, as they directly impact grooming efficiency, spectrum utilization, and achievable modulation formats. The algorithms include (i) Min–Max Channel, which minimizes spectrum fragmentation and reduces the partial bit rate blocking probability by up to 35%; (ii) Max Grooming Capacity, which improves line card interface (LCI) reuse and reduces deployment by 20%; and (iii) Time Aware, which minimizes reconfiguration counts by up to 80%, significantly lowering control overhead and service downtime. Unlike prior works limited to static or single-band scenarios, our framework is the first, to our knowledge, to dynamically integrate routing, band selection, modulation format, grooming, and spectrum assignment (RBMGSA) in a QoT-aware manner. Simulation results over the NSFNET, Japan, and Spain topologies under dynamic traffic conditions demonstrate that our approach supports flexible trade-offs among performance, cost, and reconfiguration complexity. Notably, the reconfigurable variants of our algorithms consistently outperform non-reconfigurable approaches by enhancing resource utilization and reducing blocking. The proposed system also supports partial grooming, enabling improved service accommodation and laying the groundwork for scalable and efficient operation in future multi-band optical networks.

Quantum key distribution (QKD) combined with quantum-safe encryption algorithms offers a practical path to future-proof communication security. Currently, the BB84 QKD protocol is mature and can be deployed over legacy optical fiber infrastructure using commercial products, with trusted key relaying as a workaround for its distance limitations. However, designing cost-effective QKD networks is essential for widespread adoption by end users and telecom operators (Telcos). To address this challenge, we propose and study a novel, to our knowledge, strategy: periodic time sharing of QKD transceivers. This strategy, especially cost saving at low and medium workloads, allows for generating keys for pairs of nodes at deterministic rates decided a priori. This work presents an offline solution for allocating and scheduling QKD transceivers that are shared by the aforementioned time-division multiplexing (TDM) scheme in a QKD network. We propose a mixed-integer linear programming (MILP)-based scheduling (MBS) method to solve this problem, which is computationally expensive, even for small network topologies. As an alternative, we introduce allocation-driven scheduling (ADS), an algorithm that internally breaks the problem into two steps: allocate first and schedule later. The scheduling can be handled by either a relaxed-MBS (rMBS) or a round-robin scheduling (RRS) approach (ADS-rMBS and ADS-RRS, respectively). Both methods yield results comparable to MBS for small networks. Furthermore, simulations illustrate that both ADS-rMBS and ADS-RRS enable a pay-as-you-grow model, reducing the initial capital expenditure in low-load scenarios. Compared with the non-TDM baseline (i.e., QKD transceivers are non-shared), the cost savings range from 30% to 50%, making QKD deployment more economically viable. Moreover, ADS-rMBS generally outperforms ADS-RRS, but requires a higher runtime, reaching up to 2500 s in large networks. Conversely, ADS-RRS maintains a stable 1 ms runtime across all conditions, making both approaches viable depending on the traffic matrix update interval.

In this special issue, we include eight papers that were extended from the conference papers presented at the 28th International Conference on Optical Networks Design and Modeling 2024 (ONDM 2024), whose motto was “Optical networks at scale: coping with the scalability challenge of future smart networks.” The objective of the conference and this special issue is the selection of the latest research on optical network architectures, control, modeling, and planning techniques driving the design of next-generation networks to meet the challenges of ubiquitous universal green AI. The overall research goal of the works is improving the capacity, efficiency, flexibility, availability, autonomy, resiliency, security, and intelligence of optical access and transport networks in a scalable way. The selected works described below meet the challenge, first looking into the design of digital twins for optical transport networks, investigating advanced mechanisms for failure detection and post-disaster data center content evacuation. Second, through the introduction of quantum technologies. Indeed, the amount of selected works including quantum reveals the growing interest on this topic: on the one hand, the application of quantum-computing-based algorithms to solve classic optical networking problems as an alternative to ILP, and, on the other, the focus on new quantum key distribution technologies capable of coexisting with classical channels either using optical fiber or free space optics.

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.

Both multi-band and space division multiplexing (SDM) independently represent cost-effective approaches for next-generation optical backbone networks, particularly as data exchange between core data centers reaches the petabit-per-second scale. This paper focuses on different strategies for implementing band and SDM elastic optical network (BSDM EON) technology and analyzes the total network capacity of three sizes of backbone metro-core networks: ultra-long-, long-, and medium-distance networks related to the United States, Japan, and Spain, respectively. Two BSDM strategies are considered, namely, multi-core fibers (MCFs) and BSDM based on standard single-mode fiber (SSMF) bundles of multi-fiber pairs (BuMFPs). For MCF-based BSDM, we evaluated the performance of four manufactured trench-assisted weakly coupled (TAWC) MCFs with 4, 7, 13, and 19 cores. Simulation results reveal that, in the regime of ultra-low (UL) loss and inter-core crosstalk (ICXT), MCF-based throughput can be up to 14% higher than SSMF BuMFP-based BSDM when the core pitch exceeds 43 µm and the loss coefficient is lower than that of standard single-mode fibers. However, increasing the number of cores with (non-)standard cladding diameters, UL loss, and ICXT coefficient is not beneficial. As core counts increase up to 13 for non-standard cladding diameters (<230µm), the core pitch and loss coefficient also increase, leading to degraded performance of MCF-based BSDM compared to SSMF BuMFP-based BSDM. The results indicate that, in scenarios with 19 MFPs, SSFM BuMFP-based BSDM outperforms 19-core MCF-based scenarios, increasing the throughput by 55% to 73%, from medium-backbone networks to ultra-long ones.

In this paper, we introduce TEFNET24, a reference multi-layer hierarchical network topology that spans from access to core networks, specifically designed to meet the demands of beyond 5G and prepared for next-generation 6G communication systems. This topology, inspired by the actual network deployments of Telefónica in medium-sized countries (or large federal states) in Europe and America, integrates both IP and optical (DWDM) layers to provide a comprehensive framework for network design, optimization, and analysis. Our primary contribution is the development of an open-source benchmarking network, accessible to both researchers and industry professionals. This resource aims to facilitate the study and advancement of integrated IP and optical networks, allowing researchers to address key challenges such as traffic aggregation, latency reduction, cost efficiency, and support for advanced applications. We provide guidelines for utilizing this benchmark network, enabling users to evaluate and enhance their solutions for AI-driven network management, ultra-reliable low-latency communication, enhanced mobile broadband, and massive machine-type communication. By sharing this detailed and practical benchmarking network, we seek to foster innovation and collaboration within the optical network community, driving forward the capabilities and performance of future communication networks. A dataset with TEFNET24 details is provided.

As probabilistic data structures are widely adopted in computing systems, their privacy is a major issue. Recent works have shown that even though the values stored in these structures look random, information can be extracted from them in some settings. In this paper, we consider the privacy of adaptive cuckoo filters, a probabilistic data structure that implements approximate membership checking. The main novelty and benefit of these filters are that they can adapt to removing false-positives. Unfortunately, our analysis shows that adaptation can dramatically reduce the privacy of the filters, allowing an attacker to extract the set of elements stored in the filter. Indeed, in some settings, the attacker can identify 100% of the elements stored in the filter. This means that the protection of the privacy of adaptive cuckoo filters should be considered. To that end, we propose preprocessing reduction (PR), a scheme that prevents an attacker from extracting the set of elements stored in the filter at the cost of increasing the false-positive probability of the filter. In many settings, the impact on false-positives will be negligible. For example, in a case study with 32-bit universes, the increase in the false-positive probability was smaller than 8% in all the configurations tested. Interestingly, PR is applicable not only to adaptive filters but also to approximate membership check filters in general and thus can be used to protect, for example, Bloom filters.

This JOCN special issue contains extended versions of selected papers presented at the 27th International Conference on Optical Network Design and Modeling (ONDM 2023), which took place on 8–11 May 2023 at the University of Coimbra, Coimbra, Portugal. The articles in this special issue contain several current topics of optical networking research: quality of transmission (QoT) estimation and its importance in network control and optimization, strategies to reduce power consumption in optical networks, analysis of optical network resilience from the link level up to inter-carrier networks, and strategies to upgrade long-haul quantum key distribution networks.