Farhad Arpanaei

Multi-band space-division multiplexing (MB-SDM) is a promising approach for scaling optical backbone capacity; however, dynamic resource allocation in such systems is constrained by the joint impact of inter-core crosstalk (ICXT), inter-channel stimulated Raman scattering (ISRS), amplified spontaneous emission (ASE), and nonlinear interference (NLI). This paper presents a channel-based dynamic quality-of-transmission (QoT)-aware routing, modulation, core, band, and spectrum assignment (RMCBSA) framework for MB-SDM networks. The framework uses pre-calculated channel-level generalized signal-to-noise ratio (GSNR) and ICXT feasibility maps over the C+L+S bands, explicitly accounting for frequency-dependent ICXT and ISRS-induced power redistribution. End-to-end traffic grooming and bandwidth slicing are integrated into the allocation procedure. Two channel allocation policies, core–slot–band (CSB) and band–slot–core (BSC), are evaluated for weakly coupled multi-core fibers (WC-MCFs) and compared with standard single-mode fiber (SSMF)-based bundled multi-fiber-pair (BuMFP) architectures. Results show that neglecting frequency-dependent ICXT introduces systematic band-dependent modulation-feasibility errors in dense-core regimes. Under the adopted fiber parameters, low-core-count WC-MCFs provide lower blocking than the SSMF-based BuMFP baseline due to favorable loss characteristics and sufficiently low ICXT, whereas BuMFPs become advantageous in high-ICXT regimes where crosstalk dominates feasibility. For the 7-core configuration, grooming shifts the blocking onset in WC-MCF to a 77.7% higher traffic load, while the BSC grooming-based policy achieves 44.6% lower blocking probability than BuMFP on average over the traffic-load range of 1837–3675 Tbps. These results highlight the importance of channel-based impairment modeling and grooming for reliable dynamic provisioning in MB-SDM backbone networks.

Multi-band elastic optical networks (MB-EONs) transmit information in multiple bands to increase the available capacity. However, they suffer from quality of transmission (QoT) degradation caused by the inter-channel stimulated Raman scattering effect, which requires addressing through tailored resource assignment. Additionally, dynamically arriving and departing optical service requests generate spectrum fragmentation (SF), where spectrum resources become scattered into non-continuous chunks and aggravate service blocking ratio (SBR) even when the total available bandwidth is sufficient. To jointly address these challenges, we propose an SF- and QoT-aware algorithm for routing, band, modulation format, and spectrum assignment (RBMSA), along with proactive spectrum defragmentation (SD), referred to as SFQA-defrag. The algorithm considers SF metrics and QoT levels of available channels across multiple candidate paths to ensure that the QoT requirements are met while minimizing the SF. The SD process proactively reorganizes spectrum allocation to reduce fragmentation by consolidating the spectrum gaps, which leads to lower blocking of future requests. The SFQA-defrag algorithm is evaluated against benchmark algorithms that independently consider either QoT or SF in three reference backbone topologies. The results demonstrate that SFQA-defrag significantly reduces the SBR and SF compared to benchmarks, albeit with a slight increase in the average path length.

Techno-economic analysis (TEA) plays a vital role in assessing the feasibility and scalability of emerging technologies, especially in the context of innovation and development. Central to any effective TEA is a reliable and detailed model of capital and operational costs. This paper reports the development of such a model for optical networks in the framework of the SEASON project, aimed at supporting a broad spectrum of techno-economic evaluations. The model is constructed using publicly available data and expert insights from project participants. Its generalizable design allows it to be used both within the SEASON project and as a reference for other studies. By harmonizing assumptions and cost parameters, the model fosters consistency across different analyses. It includes cost and power consumption data for a wide range of commercially available optical network components (including transceivers for point-to-multipoint communications), introduces a statistical framework for estimating values for emerging technologies, and provides a cost model for multiband-doped fiber amplifiers. To demonstrate its practical relevance, the paper applies the model to two case studies: an evaluation of how the cost of various multiband node architectures scales with network traffic in meshed topologies and a comparison of different transport solutions to carry fronthaul flows in the radio access network.

Quantum key distribution (QKD) networks offer scalable secure communication, while efficient integration with existing optical infrastructure requires careful consideration. This paper investigates enhancing the security of multipath QKD over multi-band systems by exploring the trade-offs between multi-band separation and multipath techniques. A novel, to our knowledge, framework, incorporating blocking ratio analysis for a single and multipath QKD, is proposed, comparing bitwise product and concatenated key generation methods. Our model considers propagation delays and key pool synchronization’s impact on the secret key rate (SKR). Our simulations using a U.S. long-haul network model demonstrate significant benefits of implementing QKD in alternative spectral bands. The results show substantial improvements in SKR at various span lengths in single-path scenarios. Additionally, increasing the number of quantum channels led to noticeable reductions in network blocking rates, enabling higher classical traffic loads throughout the network infrastructure. We introduce, to our knowledge, new KPIs: the blocking rate ( B _CQ ) and a comprehensive security rate ( C ), assessing the final key’s overall secrecy. For multipath schemes, simulations reveal that concatenated multipath QKD, while exhibiting a superior blocking rate, showed 12% less compromised secrecy at 4000 km (32% to 20%), compared to less than 10% for bitwise product multipath QKD under the same conditions. These findings provide valuable insights into designing efficient and secure quantum-enhanced optical networks, highlighting the complex interplay between security and efficiency in multipath QKD architectures.

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.

Model networks and their underlying topologies have been used as a reference for techno-economic studies for several decades. Existing reference topologies for optical networks may cover different network segments such as backbone, metro core, metro aggregation, access, and/or data center. While telecommunication operators work on the optimization of their own existing deployed optical networks, the availability of different topologies is useful for researchers and technology developers to test their solutions in a variety of scenarios and validate the performance in terms of energy efficiency or cost reduction. This paper presents an open-source tool, MoleNetwork, to generate graphs (backbone, metro core, and metro aggregation) inspired by real network topologies of telecommunication operators that can be used as benchmarks for techno-economic studies. A new topology, to our knowledge, Italy2k, is generated based on the structure of a real operator’s network. Then, the tool is tested by validating that the expected operator parameters are matched in the generated topology and by using the topology to forecast technical demands in a 10-year period in one of its metro regions (core and aggregation 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.

Machine learning (ML) is emerging as a promising tool for estimating the Quality of Transmission (QoT) in optical networks, especially for unestablished lightpaths where traditional methods are limited. However, inaccuracies in ML-based QoT predictions—typically expressed in terms of generalized signal-to-noise ratio (GSNR)—can significantly affect network operation. Overestimation may lead to retransmissions due to overly aggressive modulation format choices, while underestimation results in underutilized spectral resources. To address this, we propose a novel ML architecture that incorporates an embedding layer for link-level features alongside path-and service-level inputs. Using data generated from an accurate analytical model, we show that our approach reduces prediction error by up to 34% compared to standard architectures. Simulated deployment scenarios further demonstrate operational benefits, with a 15.9% decrease in incorrect and a 34.8% reduction in overly conservative modulation format selections.

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.