Large-scale Optical Networks Research Articles

Wavepacket interference of two photons through a beam splitter: from temporal entanglement to wavepacket shaping

Quantum interferences based on beam splitting are widely used for entanglement. However, the quantitative measurement of the entanglement in terms of temporal modes and wavepacket shaping facilitated by this entanglement remain unexplored. Here we analytically study the interference of two photons with different temporal shapes through a beam splitter (BS) and then propose its application in temporal entanglement and shaping of photons. The temporal entanglement described by Von Neumann entropy is determined by the splitting ratio of BS and temporal indistinguishability of input photons. We found that maximum mode entanglement can be achieved with a 50/50 BS configuration, enabling the generation of a Bell state encoded in temporal modes, independent of the exact form of the input photons. Then, detecting one of the entangled photons at a specific time enables the probabilistic shaping of the other photon. This process can shape the exponentially decaying (ED) wavepacket into the ED sine shapes, which can be further shaped into Gaussian shapes with a fidelity exceeding 99%. The temporal entanglement and shaping of photons based on interference may solve the shape mismatch issues in large-scale optical quantum networks.

Cascadable optical nonlinear activation function based on Ge-Si.

To augment the capabilities of optical computing, specialized nonlinear devices as optical activation functions are crucial for enhancing the complexity of optical neural networks. However, existing optical nonlinear activation function devices often encounter challenges in preparation, compatibility, and multi-layer cascading. Here, we propose a cascadable optical nonlinear activation function architecture based on Ge-Si structured devices. Leveraging dual-source modulation, this architecture achieves cascading and wavelength switching by compensating for loss. Experimental comparisons with traditional Ge-Si devices validate the cascading capability of the new architecture. We first verified the versatility of this activation function in a MNIST task, and then in a multi-layer optical dense neural network designed for complex gesture recognition classification, the proposed architecture improves accuracy by an average of 23% compared to a linear network and 15% compared to a network with a traditional activation function architecture. With its advantages of cascadability and high compatibility, this work underscores the potential of all-optical activation functions for large-scale optical neural network scaling and complex task handling.

Biphase routing scheme for optimal throughput in large-scale optical satellite networks

In the large-scale optical satellite network (LS-OSN), hundreds to thousands of low Earth orbit (LEO) satellites will be interconnected via laser links, offering global coverage characterized by high throughput and low latency. LS-OSNs present an attractive strategy to cultivate a comprehensively connected, intelligent world. However, the dynamic nature of the satellites, as they orbit the Earth, results in frequent changes in the LS-OSN topology. Thus, there is a pressing need for efficient routing algorithms that not only cater to massive traffic demands but also swiftly adapt to these constant topological changes. Traditional routing algorithms for services with specific bandwidth requirements often compromise on either computational speed or throughput efficiency. In response, this study introduces a routing scheme based on flow optimization and decomposition (FOND). This seeks to shorten the computation time while preserving optimal network throughput. Expanding upon the FOND scheme, we further devised two heuristic algorithms: the flow-based greedy path (FGP) and the flow-based greedy width (FGW). Simulation results from a 288-satellite constellation network indicate that both the FGP and FGW outpace contemporary methods in terms of the routing computation time while maintaining a consistent throughput equal to 100% of the network capacity. Notably, the FGP has exhibited an impressive capability, reducing the routing computation time to 0.23% compared to the baseline incremental-widest-path (IWP) algorithm, which operates on Dijkstra’s algorithm principles.

Graph model for multiple scattering in lithium niobate on insulator integrated photonic networks

We present a graph-based model for multiple scattering of light in integrated lithium niobate on insulator (LNOI) networks, which describes an open network of single-mode integrated waveguides with tunable scattering at the network nodes. We first validate the model at small scale with experimental LNOI resonator devices and show consistent agreement between simulated and measured spectral data. Then, the model is used to demonstrate a novel platform for on-chip multiple scattering in large-scale optical networks up to few hundred nodes, with tunable scattering behaviour and tailored disorder. Combining our simple graph-based model with material properties of LNOI, this platform creates new opportunities to control randomness in large optical networks.

Design of Medium and Long Distance Satellite Signal Transmission Scheme Based on Baseband Resource Pool

In order to solve the problems such as the delay and jitter in the transmission of medium and long distance satellite signals, as well as the transmission capacity, switching capacity, number of ports and optical crossover capability caused by multiple high rate channels, a signal transmission scheme based on baseband resource pool was proposed in this paper. Firstly, through OTN device, intelligent switching function is introduced to increase optical crossover capability to reduce the long distance transmission delay. On this basis, the baseband resource pool technology was used to construct large-scale optical cross network, and then realized the dynamic sharing and allocation of baseband processing resources. Experimental results show that the proposed scheme can provide deterministic low time delay, and the clock synchronization accuracy can reach less than 50PPB.

Coherent optical neuron control based on reinforcement learning.

Optical neural networks take optical neurons as the cornerstone to achieve complex functions. The coherent optical neuron has become one of the mainstream implementations because it can effectively perform natural and even complex number calculations. However, its state variability and requirement for reliability and effectiveness render traditional control methods no longer applicable. In this Letter, deep reinforcement coherent optical neuron control (DRCON) is proposed, and its effectiveness is experimentally demonstrated. Compared with the standard stochastic gradient descent, the average convergence rate of DRCON is 33% faster, while the effective number of bits increases from less than 2 bits to 5.5 bits. DRCON is a promising first step for large-scale optical neural network control.

Secret-Key Provisioning With Collaborative Routing in Partially-Trusted-Relay-based Quantum-Key-Distribution-Secured Optical Networks

Quantum Key Distribution (QKD) is a promising technology that provides proven unconditional security based on fundamentals of quantum physics, especially for point-to-point communications. It could be applied in large-scale optical networks for long-distance key provisioning mainly by three relaying methods: quantum-repeater-based QKD, trusted-relay-based QKD, and measurement-device-independent QKD (MDI-QKD). However, quantum-repeater-based QKD is still under study because of the immature technologies such as the preliminary quantum memory. The trusted-relay-based QKD is vulnerable since insecurity of non-ideal single-photon sources and detectors cannot be ignored in practical applications. On the other hand, for MDI-QKD, there exists a limitation on its key rate under long-distance communications. According to these limitations, embedding protocols like MDI-QKD into the existing trusted-relay-based QKD secured optical networks (QKD-ON) with usage of untrusted relays is a promising key-provisioning scheme. In this paper, we study such partially-trusted relay scenarios and focus on its routing of keys in different kinds of typical network topologies. A partially-trusted-relay-based QKD method is described, which can allow a pair of optical nodes sharing secret keys under the coexistence of trusted relays and untrusted relays. The secret-key provisioning with collaborative routing ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SKP-CR</i> ) algorithm is proposed to search for the optimal key-relay routing path. We perform the simulations with different proportion of trusted relays versus untrusted relays, initial secret keys in the quantum key pools (QKPs), and traffic load. The simulations verify that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SKP-CR</i> algorithm can significantly outperform the conventional trusted-relay-based scheme in terms of key-distribution success rate with an improvement of up to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">62%</i> with a mesh topology.

Smart Algorithms for Hierarchical Clustering in Optical Network

Network design process is a very important in order to balance between the investment in the network and the supervises offered to the network user, taking into consideration, both minimizing the network investment cost, on the other hand, maximizing the quality of service offered to the customers as well.Partitioning the network to smaller sub-networks called clusters is required during the design process inorder to ease studying the whole network and achieve the design process as a trade-off between several atrtributes such as quality of service, reliability,cost, and management. Under CANON, a large scale optical network is partitioned into a number of geographically limited areas taking into account many different criteria like administrative domains, topological characteristics, traffic patterns, legacy infrastructure etc. An important consideration is that each of these clusters is comprised of a group of nodes in geographical proximity. The clusters can coincide with administrative domains but there could be many cases where two or more clusters belong to the same administrative domain. Therefore, in the most general case the partitioning into specific clusters can be either a off-line or a on-line process. In this work only the off-line case is considered. In this Study, we look at the problem of designing efficient 2- level Hierarchical Optical Networks (HON), in the context of network costs optimization. 2-level HON paradigm only have local rings to connect disjoint sets of nodes and a global sub mesh to interconnect all the local rings. We present an Hierarchical algorithm that is based on two phases. We present results for scenarios containing a set of real optical topologies.

O-Star: An Optical Switching Architecture Featuring Mode and Wavelength-Division Multiplexing for On-Chip Many-Core Systems

In this paper, we propose O-Star, a scalable optical switching architecture for on-chip many-core systems, employing hybrid mode and wavelength division multiplexing technology. The O-Star uses the Benes topology as the core switching module to realize non-blocking switching. Besides, we design a wavelength and mode allocation module to enable each processor to transmit data in parallel. To quantitatively analyze the minimum hardware cost required, we establish a mathematical model for the different number of processors. As a proof of concept, a 64-core optical switching architecture featuring 8-port Benes topology, 2-mode, and 4-wavelength channels is simulated with single channel 25 Gbps data rate. The O-Star is flexible to scale, both for the number of supported processors and the switching capacity. O-Star holds promise for realizing large-scale optical switching networks to address the incoming challenges of high-performance computing (HPC) systems and data center networks (DCNs).

Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits

We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation. We find that tuning a pruned MRR weight banks model gives an equivalent performance in training with the model of random initialization. Results show that the overall accuracy of the optical neural network on the MNIST dataset is 93.49% after pruning six-layer MRR weight banks on the condition of low insertion loss. This work is scalable to much more complex networks, such as convolutional neural networks and recurrent neural networks, and provides a potential guide for truly large-scale optical neural networks.

Fault Localization based on Knowledge Graph in Software-Defined Optical Networks

In the era of the fifth-generation fixed network (F5G), optical networks must be developed to support large bandwidth, low latency, high reliability, and intelligent management. Studies have shown that software-defined optical networks (SDON) and artificial intelligence can help improve the performance and management capabilities of optical networks. Inside a large-scale optical network, many types of alarms are reported that indicate network anomalies. Relationships between the alarms are complicated, making it difficult to accurately locate the source of the fault(s). In this work, we propose a knowledge-guided fault localization method, using network alarm knowledge to analyze network abnormalities. Our method introduces knowledge graphs (KGs) into the alarm analysis process. We also propose a reasoning model based on graph neural network (GNN), to perform relational reasoning on alarm KGs and locate the network faults. We develop an ONOS-based SDON platform for experimental verification, which includes a set of processes for the construction and application of alarm KGs. The experimental results show the proposed method has high accuracy and provide motivation for the industry-scale use of KGs for alarm analysis and fault localization.

Core Function Prepositioning for Point-to-Point Service Slicing in Large-Scale Optical Networks

To accommodate three typical 5G services with satisfied performance requirements, spectrum slicing has been adopted and widely studied in the wireless transmission part. For the wired transmission part, the high-capacity and large-scale optical network is widely used for transferring 5G traffics. To improve the spectrum efficiency and reduce the time delay while accommodating 5G services, optical networks need to adapt to the wireless spectrum slicing. Traditionally, all spectrum resources in optical networks form a resource pool which is equally allocated to applications bit by bit until it is all used up. Although this pattern reduces the resource maintenance complexity, it has a high operation complexity for 5G services. If some important network functions are realized in advance, a 5G service can be easily deployed with simplified operations and satisfied performance requirements. In this letter, a novel framework of core function prepositioning is proposed to efficiently accommodate 5G services in optical networks. It adopts the centralized control model and two network functions of fast connection establishment and disaster-resilient survivability are realized in planned spectrum areas in advance. This framework adapts to the wireless spectrum slicing and has a significant performance boost while accommodating 5G services.

A dynamic quantum memory

Quantum Networks Photons, being robust and fast, are ideal carriers of quantum information in long-distance quantum networks. For a practical communication platform, the photons also need to be manipulated at the local level at nodes or exchange points across the network, and this requires information to be reliably encoded, stored, and retrieved from the photonic qubits. Craiciu et al. demonstrate such functionality using an ensemble of erbium ions embedded within a solid-state matrix of yttrium orthosilicate. With the ions operating as a dynamic quantum memory for photons at communication wavelengths and the approach being compatible with integrated photonics, the results offer promise for possible integration into large-scale optical fiber networks. Optica 8 , 114 (2021).

Monitoring and automatic tuning and stabilization of a 2×2 MZI optical switch for large-scale WDM switch networks.

Large-scale optical switch networks employ wavelength division multiplexing to expand and facilitate multiple input and outputs. Such networks can be implemented with the Mach-Zehnder interferometer (MZI) as the building block. A fully-loaded MZI switch, meaning one with two optical signals at its two inputs and one that is capable of simultaneously switching those inputs to its two outputs, reduces the number building blocks within the network, and as a result makes them more power and area efficient. However, for practical operation, such MZI switches need to be automatically controlled for overcoming fabrication and thermal variations. We present an interference-based monitoring method that enables automatically switching, tuning, and stabilizing of a fully-loaded 2×2 MZI optical switch and demonstrate a prototype on an SOI platform. Using the proposed device and off-the-shelf electronics, we demonstrate automatic tuning and stabilization of an MZI switch with 12.5 Gb/s and 25 Gb/s data rates and channel spacing as small as 1 nm.

Dirty-data-based alarm prediction in self-optimizing large-scale optical networks.

Machine-learning-based solutions are showing promising results for several critical issues in large-scale optical networks. Alarm (caused by failure, disaster, etc.) prediction is an important use-case, where machine learning can assist in predicting events, ahead of time. Accurate prediction enables network administrators to undertake preventive measures. For such alarm prediction applications, high-quality data sets for training and testing are crucial. However, the collected performance and alarm data from large-scale optical networks are often dirty, i.e., these data are incomplete, inconsistent, and lack certain behaviors or trends. Such data are likely to contain several errors, when collected from old-fashioned optical equipment, in particular. Even after appropriate data preprocessing, feature distribution can be extremely unbalanced, limiting the performance of machine learning algorithms. This paper demonstrates a Dirty-data-based Alarm Prediction (DAP) method for Self-Optimizing Optical Networks (SOONs). Experimental results on a commercial large-scale field topology with 274 nodes and 487 links demonstrate that the proposed DAP method can achieve high accuracy for different types of alarms.

Large WDM FBG Sensor Network Based on Frequency-Shifted Interferometry

We combine wavelength-division multiplexing (WDM) and frequency shifted interferometry (FSI) to interrogate a large-scale ultra-weak fiber Bragg grating (FBG) array. Based on Sagnac interference, FSI can location resolve sensors without using pulses or fast detection, therefore considerably lowers the system cost. By combining FSI with WDM, higher spatial resolution can be achieved. We demonstrated simultaneous interrogation of 363 FBG sensors, grouped into 121 identical units of 3 FBGs of different central wavelengths and spaced two meters apart. Based on the performance of the 363 grating system, we show the potential for interrogating 3207 sensors with good signal-to-noise ratio. Stability test and temperature sensing were carried out, and the obtained temperature resolution was ± 0.4 ° C. The results indicate the proposed scheme can greatly enhance the multiplexing capacity and meet the requirements of large-scale optical fiber networks.

Content Placement With Maximum Number of End-to-Content Paths in k-Node (Edge) Content Connected Optical Datacenter Networks

To design survival datacenter networks, the concept of content connectivity, which is defined as the reachability of content from any point of a network after disaster failures, was recently proposed. To quantitatively measure content connectivity, we propose a novel concept of k-node (edge) content connectivity and apply it to optical datacenter networks. Furthermore, a novel disaster-resilient k-node (edge) content connected optical datacenter network (KC-ODN) is proposed in this paper. Design of the KC-ODN is divided into two sub-problems: content placement and independent end-to-content paths calculation. We consider flexible content placement (FCP), in which the content is replicated and maintained in multiple datacenters, rather than fixed content placement (FICP), in which the content is replicated and maintained in only one fixed datacenter, to realize KC-ODN. The integer linear program (ILP) model and heuristic algorithms are developed to achieve KC-ODN. Numerical results show that the method of FCP has lower wavelength consumption than the method of FICP. It is easier to realize high-level k-node (edge) content connectivity in a network with higher connectivity. Compared with the ILP method, heuristic algorithms are more scalable for large-scale optical datacenter networks.

An Convergent WDM-PON Architecture Based on Dual-fiber Tangent Rings Featuring Protection and Extension

A novel wavelength-division-multiplexed passive optical network (WDM-PON) architecture based on dual-fiber multiple tangent rings is proposed. One primary-ring (PR) and certain sub-rings (SRs) are controlled by one optical line terminal (OLT). The design of tangent remote nodes (RNs) guarantees the transmission and convergence of signals between PR and SRs. This network effectively overcomes single-fiber or dual-fiber faults. Signals can be switched between dual fibers by optical route switch (ORS). Simultaneously, the usage of different fibers can reduce the crosstalk in the upstream signals and downstream signals. Tangent RNs help to expand the network, which is highly significant for large-scale optical access network in next generation.

Smart Algorithms for Hierarchical Clustering in Optical Network

Network design process is a very important in order to balance between the investment in the network and the supervises offered to the network user, taking into consideration, both minimizing the network investment cost, on the other hand, maximizing the quality of service offered to the customers as well.Partitioning the network to smaller sub-networks called clusters is required during the design process inorder to ease studying the whole network and achieve the design process as a trade-off between several atrtributes such as quality of service, reliability,cost, and management. Under CANON, a large scale optical network is partitioned into a number of geographically limited areas taking into account many different criteria like administrative domains, topological characteristics, traffic patterns, legacy infrastructure etc. An important consideration is that each of these clusters is comprised of a group of nodes in geographical proximity. The clusters can coincide with administrative domains but there could be many cases where two or more clusters belong to the same administrative domain. Therefore, in the most general case the partitioning into specific clusters can be either a off-line or a on-line process. In this work only the off-line case is considered. In this Study, we look at the problem of designing efficient 2- level Hierarchical Optical Networks ( HON ), in the context of network costs optimization. 2-level HON paradigm only have local rings to connect disjoint sets of nodes and a global sub mesh to interconnect all the local rings. We present an Hierarchical algorithm that is based on two phases. We present results for scenarios containing a set of real optical topologies.

Modeling of Nonlinear Signal Distortion in Fiber-Optic Networks

A low-complexity model for signal quality prediction in a nonlinear fiber-optical network is developed. The model, which builds on the Gaussian noise model, takes into account the signal degradation caused by a combination of chromatic dispersion, nonlinear signal distortion, and amplifier noise. The center frequencies, bandwidths, and transmit powers can be chosen independently for each channel, which makes the model suitable for analysis and optimization of resource allocation, routing, and scheduling in large-scale optical networks applying flexible-grid wavelength-division multiplexing.