Optical Network Design Research Articles

Energy evaluation for a dynamic and reconfigurable TWDM‐PON on a novel network traffic adaptable pattern

SummaryEven with existing methodologies, dynamic resource allocation, service inconsistency, operational costs, and excess energy/power consumption in optical access networks remain the key challenges for the next‐generation passive optical network (NG‐PON) design and development. This paper introduces a novel method for analyzing energy/power consumption in time and wavelength division multiplexed (TWDM) passive optical networks (PONs) with a ring‐based topology. We propose subscriber‐based channel allocation models (CAMs) for on‐demand services, allocate bandwidth dynamically, and evaluate their downstream power consumption. To determine the necessary optical devices for the various CAMs, we investigate subscriber take‐up rates by optimizing the utilization of optical components based on active subscribers. Implementing a novel Poisson distribution‐based network traffic model addresses energy‐related concerns and explores optimization techniques to minimize power and improve energy efficiency. With a take‐up rate of 64 active subscribers, simulation results show significant power consumption reductions ranging from 62.2 to 67.7%, achieved by the proposed subscriber‐based architecture models with varying subscriber counts, when compared with the conventional model. The model with the most subscribers (2048 subscribers) saves the most energy by reducing power consumption to 292 W from the conventional model's (2048 subscribers) 440 W. Furthermore, the model with the most subscribers saves 33.4% of energy, outperforming other subscriber‐based architecture models without affecting QoS even during peak hours of the day. These numerical results show that TWDM‐PONs with the most subscribers exhibit improved energy efficiency than the conventional and other proposed subscriber‐based architecture models.

Pyramid diffractive optical networks for unidirectional image magnification and demagnification

Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational tasks between an input and output field-of-view. Here, we present a pyramid-structured diffractive optical network design (which we term P-D2NN), optimized specifically for unidirectional image magnification and demagnification. In this design, the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or demagnification. This P-D2NN design creates high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction—achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume. Furthermore, the P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths despite being trained with a single wavelength. We also designed a wavelength-multiplexed P-D2NN, where a unidirectional magnifier and a unidirectional demagnifier operate simultaneously in opposite directions, at two distinct illumination wavelengths. Furthermore, we demonstrate that by cascading multiple unidirectional P-D2NN modules, we can achieve higher magnification factors. The efficacy of the P-D2NN architecture was also validated experimentally using terahertz illumination, successfully matching our numerical simulations. P-D2NN offers a physics-inspired strategy for designing task-specific visual processors.

Enhanced Coexistence of Quantum Key Distribution and Classical Communication over Hollow-Core and Multi-Core Fibers.

In this paper, we investigate the impact of classical optical communications in quantum key distribution (QKD) over hollow-core fiber (HCF), multi-core fiber (MCF) and single-core fiber (SCF) and propose wavelength allocation schemes to enhance QKD performance. Firstly, we theoretically analyze noise interference in QKD over HCF, MCF and SCF, such as spontaneous Raman scattering (SpRS) and four-wave mixing (FWM). To mitigate these noise types and optimize QKD performance, we propose a joint noise suppression wavelength allocation (JSWA) scheme. FWM noise suppression wavelength allocation and Raman noise suppression wavelength allocation are also proposed for comparison. The JSWA scheme indicates a significant enhancement in extending the simultaneous transmission distance of classical signals and QKD, reaching approximately 100 km in HCF and 165 km in MCF under a classical power per channel of 10 dBm. Therefore, MCF offers a longer secure transmission distance compared with HCF when classical signals and QKD coexist in the C-band. However, when classical signals are in the C-band and QKD operates in the O-band, the performance of QKD in HCF surpasses that in MCF. This research establishes technical foundations for the design and deployment of QKD optical networks.

Introduction to the ONDM 2023 special issue

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.

Multi-criteria model for selection of optical linear terminals based on FUZZY TOPSIS method

Optical networks are an integral part of modern telecommunication systems. Huge traffic and ever-increasing requirements for data transmission capacity and quality encourage the wider use of modern optical technologies in telecommunications. The rational choice of equipment for the design of optical networks is an urgent task at present. The subject of this study is the process of selecting optical line terminals, which is associated with the evaluation of possible options by a set of indicators. The OLT (optical line terminal) in PON technology implements the function of organizing subscriber lines and is also a node equipment - an L4 switch that combines the functions of routing (IP), traffic fragmentation (VLAN), switching (MAC), quality of service (QoS), and some necessary network service functions. The optimal structuring of PON access networks demands a judicious selection of software and hardware, guided by their tactical and technical characteristics, in view of the substantial information load they entail. One of the effective approaches to solving such problems is the use of MCDM (Multiple Criteria Decision Making) methods. Purpose: to construct a TOPSIS model of the optimal choice of optical linear terminals in the conditions of unclear information for different cases of aggregation of evaluations of decision makers. Task: to formalize the process of selecting optical linear terminals; develop a multi-criteria mathematical model for the effective selection of optical line terminals. Methods used in the study: The fuzzy TOPSIS method, four methods of aggregating the opinions of decision makers. The following results were obtained. Alternatives and criteria for their evaluation are defined. On the basis of interviews with decision makers, evaluations of the degree of importance of the criteria and alternatives to the criteria were determined. Linguistic changes were used to describe decision-makers’ evaluations, which were interpreted as triangular fuzzy numbers. During this study, four methods of aggregating the evaluations of decision makers were used. A fuzzy TOPSIS model for selecting an optical line terminal is constructed. The ranking of the selected optical line terminals is obtained and the best alternative is determined. The results of modeling with different methods of aggregating assessments of decision makers are compared. Conclusions. The use of the Fuzzy TOPSIS method for optimal selection of optical linear terminals is proposed. The influence of the methods of aggregation of evaluations of decision-makers is analyzed.

1.28 Tbps DWDM optical network design using a dispersion compensating distributed Raman amplifier over S-band

1.28 Tbps DWDM optical network design using a dispersion compensating distributed Raman amplifier over S-band

Recent Progress in Optical Network Design and Control towards Human-Centered Smart Society

Recent Progress in Optical Network Design and Control towards Human-Centered Smart Society

Deep learning approach to predict optical attenuation in additively manufactured planar waveguides.

The booming demand for efficient, scalable optical networks has intensified the exploration of innovative strategies that seamlessly connect large-scale fiber networks with miniaturized photonic components. Within this context, our research introduces a neural network, specifically a convolutional neural network (CNN), as a trailblazing method for approximating the nonlinear attenuation function of centimeter-scale multimode waveguides. Informed by a ray tracing model that simulated many flexographically printed waveguide configurations, we cultivated a comprehensive dataset that laid the groundwork for rigorous CNN training. This model demonstrates remarkable adeptness in estimating optical losses due to waveguide curvature, achieving an attenuation standard deviation of 1.5dB for test data over an attenuation range of 50dB. Notably, the CNN model's evaluation speed, at 517µs per waveguide, starkly contrasts the used ray tracing model that demands 5-10min for a similar task. This substantial increase in computational efficiency accentuates the model's paramount significance, especially in scenarios mandating swift waveguide assessments, such as optical network optimization. In a subsequent study, we test the trained model on actual measurements of fabricated waveguides and its optical model. All approaches show excellent agreement in assessing the waveguide's attenuation within measurement accuracy. Our endeavors elucidate the transformative potential of machine learning in revolutionizing optical network design.

Pre- and post-processing techniques for reinforcement-learning-based routing and spectrum assignment in elastic optical networks

Research on the routing and spectrum assignment (RSA) problem has long been conducted with the aim of efficiently utilizing the frequency resources of optical networks. Given the recent progress in machine learning (ML) technology, it has been reported that the application of ML in various areas of optical network design, operation, and management has the potential to bring about new innovations such as autonomous optical network operation and highly accurate estimation of network conditions. With regard to the RSA problem, it is expected that an algorithm that achieves better accommodation efficiency than conventional heuristic methods can be realized by applying reinforcement learning (RL), which well supports training in a simulation environment. In this paper, we introduce and evaluate three techniques devised to apply RL more effectively to elastic optical networks (EONs): two pre-processing techniques called link-axis positional encoding (LPE) and slot-axis positional encoding (SPE) and a post-processing technique named the assignable boundary slot mask. First, we build a simple model in which the state data of the optical network, including frequency slot utilization, are input to the neural network of the RSA agent in RL and show that this model has difficulty outperforming the conventional heuristic RSA algorithm. Next, we build an RSA agent model with the proposed techniques and simulate the accommodation of dynamic optical paths to quantitatively demonstrate that the blocking probability can be reduced by 17% compared to the conventional heuristic.

Building a digital twin for large-scale and dynamic C+L-band optical networks

Bridging the gap between the real and virtual worlds, a digital twin (DT) leverages data, models, and algorithms for comprehensive connectivity. The research on DTs in optical networks has increased in recent years; however, optical networks are evolving toward wideband capabilities, highly dynamic states, and ever-increasing scales, posing huge challenges, including high complexity, extensive computational duration, and limited accuracy for DT modeling. In this study, the DT models are developed based on the Gaussian noise (GN) model and a deep neural network (DNN) to perform efficient and accurate quality of transmission estimations in large-scale C+L-band optical networks, facilitating effective management and control in the digital platform. The DNN-based model obtained the estimated generalized signal-to-noise absolute errors within 0.2 dB in large-scale network simulation, specifically a 77-node network topology. Additionally, compared to the GN-based model, the testing time by using the DNN-based model has been significantly reduced from tens of minutes to 110 ms. Moreover, based on the DT models, multiple potential application scenarios are studied to ensure high-reliability operation and high-efficiency management, including optimization and control of physical layer devices, real-time responses to deterioration alarms and link faults, and network rerouting and resource reallocation. The constructed DT framework integrates practical analysis and deduction functions, with fast operation and accurate calculation to gradually promote the efficient design of optical networks.

A mixed mutation strategy genetic algorithm for the effective training and design of optical neural networks

The optical neural networks (ONNs) have received lots of attention due to their superior speed and energy efficiency in large-scale linear matrix operations compared to electrical neural networks. Undoubtedly, the training and designing weights and other hyperparameters of ONNs to enhance performance is a pivotal concern. We present a mixed mutation strategy genetic algorithm (M2SGA) to deal with the problem of inefficient training. By combining the single-point mutation (SM), the uniform mutation (UM), and the Gaussian mutation (GM) operators, our method can achieve faster convergence speed and better robustness for the parameter training of ONNs than the standard genetic algorithm (SGA). Furthermore, an N-layer feedforward optical neural network based on Mach-Zehnder interferometers (MZIs) and electro-optic modulators (EOMs) is constructed to verify the feasibility of our scheme. And, four datasets are used to further confirm the effect of our proposed scheme on the classification tasks. Additionally, the impact of alternative operators on the performance of the algorithm is evaluated. Experimental results demonstrate that M2SGA exhibits higher accuracy and lower mean squared error compared with SGA. Consequently, M2SGA holds tremendous potential for applications in object detection, image processing and other related fields thanks to its great effect in datasets classification.

Wide-spectrum optical soliton solutions to the time-fractional cubic-quintic resonant nonlinear Schrödinger equation with parabolic law

The time-fractional cubic-quintic resonant nonlinear Schrödinger equation with parabolic law and its soliton solutions have significant implications to examine the dynamics of optical solitons, self-phase modulation, optical beam propagation in nonlinear media, pulse propagation in optical fibers, nonlinear waveguides, and signal transmission systems. This study focuses on the investigation of different soliton solutions related to various aspects of optical solitons, including their formation, propagation traits, and stability analysis of the mentioned equation. Soliton solutions are able to carry out long-distance transmission without frequent amplification and regeneration. We exploit an advanced mathematical technique, the (G′/G,1/G)-expansion method to descend and analyze the soliton solutions. As a result, a significant number of definitive and efficient solitons, such as compacton, bell-shaped, anti-peakon, breather, periodic, and singular solitons have resulted. The uniqueness of the obtained solutions is established by comparing them with previous results. Also, we discuss in detail the implications of fractional derivative and the application of the obtained solutions to optical fiber communication and other relevant fields. The findings of the present study provide valuable insights into the fundamental nature of optical solitons. These insights will contribute to the design and optimization of future optical networks, enabling the development of faster, more reliable, and higher-capacity communication systems.

Design of time division multiplexing/wavelength division multiplexing passive optical network system for high-capacity network

This paper presents the design of time division multiplexing-wavelength division multiplexing-passive optical network (TDM-WDM PON). In this design, the current TDM PON is incorporated with the proposed WDM-PON in order to design a high-capacity network with lower loss requirements. The design has been simulated using OptiSystem software. The upstream wavelength for WDM is between 1,530.334 to 1,542.142 nm while for TDM is 1,310 nm. The downstream wavelength for WDM is from 1,569.865 to 1,581.973 nm, while for TDM is 1,490 nm. Based on the result, it is found that the proposed network is capable to support up to 64 customers with a bit rate of 2.5 Gbps.

Memetic evolutionary algorithms to design optical networks with a local search that improves diversity

The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countries, mainly due to home office, video streaming, hybrid teaching, and others. High-capacity optical networks usually meet this growing demand for Internet services. Thus, investigations that can improve the quality of optical networks are highly relevant in the current context. One of the research problems in this area is related to the physical topology design (PTD) of optical networks, which is classified as NP-hard. Several studies on PTD consider the application of meta-heuristics that obtain suboptimal solutions in a time compatible with engineering applications. However, meta-heuristics and local search techniques have been combined in several other optimization problems, which is not typical for the PTD problem. This paper proposes a solution to the PTD problem that combines a known multipurpose optimization algorithm, the NSGA-III, with operators considering the domain-specific knowledge of the problem to provide superior-quality networks. According to our results, the new proposal presents quality up to 8% higher than previous proposals concerning the hypervolume metrics (HV), maintaining a similar computational cost.

Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks

One of the key performance metrics for optical networks is the maximum achievable throughput for a given network. Determining it, however, is a nondeterministic polynomial time (NP) hard optimization problem, often solved via computationally expensive integer linear programming (ILP) formulations. These are infeasible to implement as objectives, even on very small node scales of a few tens of nodes. Alternatively, heuristics are used although these, too, require considerable computation time for a large number of networks. There is, thus, a need for an ultra-fast and accurate performance evaluation of optical networks. For the first time, we propose the use of a geometric deep learning model, message passing neural networks (MPNNs), to learn the relationship between node and edge features, the network structure, and the maximum achievable network throughput. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by up to five-orders of magnitude compared to the ILP for small networks (10–15 nodes) and compared to a heuristic for large networks (25–100 nodes)—proving their suitability for the design and optimization of optical networks on different time- and distance-scales.

Machine learning enhancement of a digital twin for wavelength division multiplexing network performance prediction leveraging quality of transmission parameter refinement

Digital twins capable of quality of transmission (QoT) estimation and prediction are powerful tools for efficient design and operation of optical networks. In this paper, we employ machine learning techniques to enhance both the QoT estimation and prediction capabilities of an optical network digital twin. By leveraging a method to infer or refine the unknown lumped loss distributions and amplifier gain spectra for accurate characterization of the current optical network state, the accuracy of estimation and prediction is substantially improved. For QoT prediction, we further develop a novel neural-network architecture for erbium-doped fiber amplifiers that, after factory training on a single device and with fully loaded configurations only, generalizes to partially loaded configurations seen after deployment and to different gain and tilt settings and other physical devices of the same type. We combine refined parameters and a novel method that predicts the difference in per-channel power when individual services are added or removed or multiple services are lost due to a fiber cut. The impact of error amplification due to cascading of individual components’ models is shown to be reduced, yielding predictions that are substantially more accurate than simply cascaded predictions. As the network ages, the digital twin can be updated by retraining while using only information available in the current network state.

Introduction to the ONDM 2022 special issue

This JOCN special issue contains extended versions of selected papers presented at the 26th International Conference on Optical Network Design and Modeling (ONDM 2022), which took place 16–19 May 2022 at Warsaw University of Technology, Warsaw, Poland. The topics covered by the papers represent trends in optical networking research: application of machine learning to network management, cross-layer network performance optimization, visible light communication as well as coherent metro networks.

A symmetry-free spectrum allocation heuristic for elastic optical networks

We revisit spectrum allocation (SA), a fundamental problem in optical network design, and we explain that it can be modeled as a permutation problem. This new model eliminates spectrum symmetry, a property that presents a significant challenge to conventional spectrum allocation solutions. Accordingly, we develop parameterized first-fit (PFF), a new symmetry-free heuristic for the SA problem that has several desirable features: it explores a pre-defined subset of the solution space whose size is tailored to the available computational budget; it constructs this subset by sampling from diverse areas of the solution space rather than from the neighborhood of an initial solution; it finds solutions by applying the well-known first-fit (FF) algorithm and thus it can be deployed readily; its execution can be easily parallelized; and it is effective and efficient in finding good quality solutions.

Key physical topology features for optical backbone networks via a multilayer correlation analysis

A communication network is a multilayer network comprising various layered technologies, and the underlying physical topology is an important aspect that determines the upper bound of overall system performance, including total communication capacity, cost, and robustness. We expect that understanding the impact of the physical topology on system performance will lead to better optical communication network design in the future, and we thus focus on clarifying the relationship between physical topology features and system performance. There have been various studies on the relationship between topology features and overall network performance. For example, the average number of hops and the cluster coefficient are well known to change network properties in complex networks. From the perspective of optical communication networks, it is known that the algebraic connectivity and average path length are related to total network capacity, and these findings have been applied in physical topology design models. On the other hand, there have been no quantitative comparisons among various topology features, and the comprehensiveness of the population from which these features are extracted is insufficient. Hence, we have developed a multilayer (physical topology and layer 1) correlation analysis framework that enables a quantitative comparison of topology features. We use this framework to numerically examine the relationships between physical topology features and the total communication capacity, cost, and robustness of optical communication networks, including graph features (especially graph spectra) that have not been investigated. The results show that the Laplacian spectral radius and geodesic distance Laplacian spectral radius are strongly related to system performance, in addition to the conventional average number of hops, cluster coefficient, algebraic connectivity, and average path length. We confirm that these correlations hold for the different network sizes and spatial nonuniformity of real optical backbone networks in different countries and regions. The results show that the average path length and cluster coefficient, or the Laplacian spectral radius and geodesic distance Laplacian spectral radius, are important guidelines for physical topology design of real optical backbone networks.

ELight: Toward Efficient and Aging-Resilient Photonic In-Memory Neurocomputing

Optical phase change material (PCM) has emerged promising to enable photonic in-memory neurocomputing in optical neural network (ONN) designs. However, massive photonic tensor core (PTC) reuse is required to implement large matrix multiplication due to the limited single-core scale. The resultant large number of PCM writes during inference incurs serious dynamic energy costs and overwhelms the fragile PCM with limited write endurance, causing the severe aging issue. Moreover, the aged PCM would distort the stored value and significantly degrade the reliability of PTC. In this work, we propose a holistic solution, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELight</monospace> , to tackle both the aging issue and the post-aging reliability issue, where a proactive aging-aware optimization framework minimizes the overall PCM write cost and a post-aging tolerance scheme overcomes the effect of aged PCM. Specifically, in the aging-aware optimization part, we propose write-aware training to encourage the similarity among weight blocks and combine it with a post-training optimization technique to reduce programming efforts by eliminating redundant writes. Next, an efficient groupwise row-based weight-PTC remapping scheme is introduced to tolerate the reprogrammability degradation due to the aged PCM. Experiments show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELight</monospace> can achieve over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20 \times $ </tex-math></inline-formula> reductions in the total number of write operations and dynamic energy cost with comparable accuracy. Moreover, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELight</monospace> can guarantee significant accuracy recovery under the aged PCM within photonic memories. With our <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELight</monospace> , photonic in-memory neurocomputing will step forward toward practical applications in machine learning with order-of-magnitude longer lifetime, lower programming energy cost, and significant resilience against PCM aging effects.