Reconfigurable Optical Networks Research Articles

Machine Learning-Driven Reconfigurable EONs with Neuromorphic Computing for Network Slicing and On-Demand Service Provisioning: A Review and Survey

Abstract: This paper delves into the substantial potential of integrating advanced technologies within Reconfigurable Elastic Optical Networks (REONs). By leveraging machine learning and neuromorphic computing, these networks can significantly enhance performance, scalability, and efficiency. Machine learning models facilitate dynamic resource management, allowing for the on-demand reconfiguration of optical networks to improve service provisioning and maintain high Quality of Service (QoS). Neuromorphic processors further boost network-slicing capabilities, optimizing bandwidth management and enabling the creation of customized virtual networks. Additionally, the incorporation of neuromorphic computing into REONs contributes to substantial energy savings, a critical factor for sustainable network operations. Techniques such as differential privacy and secure multi-party computation effectively address security and privacy challenges within optical networks. Future research should focus on developing scalable architectures, formulating energy-efficient algorithms, and designing solutions tailored to specific applications to maximize the potential of REONs. In summary, the integration of advanced computing techniques within REONs promises to revolutionize network management and service delivery, equipping future networks to deliver exceptional performance, scalability, and adaptability, thus meeting the evolving demands of modern communication environments.

Transceiver impairment robust and joint monitoring of PDL, DGD, and CD using frequency domain pilot tones for digital subcarrier multiplexing systems

Optical performance monitoring is vital for enabling dynamically reconfigurable optical networks. This paper introduces a monitoring scheme that is robust to transceiver impairments for coherent digital subcarrier multiplexing (DSCM) systems that simultaneously monitors polarization-dependent loss (PDL), differential group delay (DGD), and chromatic dispersion (CD). In the proposed scheme, a pair of frequency domain pilot tones (FPTs) are inserted into the X and Y polarizations of the transmitted dual-polarization signal. Both the signal and the FPTs endure identical channel impairments during fiber transmission. At the receiver side, the transmitted FPTs are extracted to monitor the channel impairments. We begin by establishing a simplified system model using a single frequency tone to analyze the effects of system impairments on the frequency domain pilots. Based on this model, CD, PDL, and DGD are jointly and accurately estimated. In addition, the influences of temporal, amplitude, and phase mismatches between in-phase (I) and quadrature (Q) components of transceivers on channel impairment monitoring are completely eliminated. This achieves a channel impairment monitoring scheme that is robust to transceiver impairments. Experimental verification shows that the proposed scheme can jointly and accurately estimate a wide range of CD, PDL, and DGD. Additionally, the proposed monitoring scheme demonstrates strong robustness against transceiver impairments, rapid polarization fluctuations, and amplified spontaneous emission (ASE) noise.

High‐Speed and Low‐Power 2 × 2 Thermo‐Optic Switch Based on Dual Silicon Topological Nanobeam Cavities

AbstractA 2 × 2 thermo‐optic (TO) switch using dual topological photonic crystal nanobeam (PCN) cavities on the silicon‐on‐insulator (SOI) platform is proposed and experimentally demonstrated. A Fano resonance is observed due to the interference between the topological interface state of the 1D topological PCN cavity and the Fabry‐Perot (F‐P) cavity mode formed between the two facets of the finitely long nanobeam waveguide. Thanks to the sharp rising edge of the spectral response of the Fano resonance and the high confinement of light in the topological PCN cavities, a 2 × 2 TO switch is realized with short switching time and low power consumption. The measured switching power is only 1.55 mW, and the rising time and the falling time are 3 and 5.6 µs, respectively in the on‐off switching experiments. To the best of the knowledge, this is the first time that a dual topological PCN structure is utilized to realize a high‐speed and low‐power TO switch, revealing the possibility of designing high‐performance reconfigurable optical devices and networks using topological photonics.

Photonic neuromorphic architecture for tens-of-task lifelong learning.

Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design. Benefiting from the inherent sparsity and parallelism in massive photonic connections, L2ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field, while incrementally acquiring expertise on various tasks by gradually enlarging the activation. The multi-task optical features are parallelly processed by multi-spectrum representations allocated with different wavelengths. Extensive evaluations on free-space and on-chip architectures confirm that for the first time, L2ONN avoided the catastrophic forgetting issue of photonic computing, owning versatile skills on challenging tens-of-tasks (vision classification, voice recognition, medical diagnosis, etc.) with a single model. Particularly, L2ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks, and 14× larger capacity than existing optical neural networks while maintaining competitive performance on each individual task. The proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme, permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.

Reconfigurable optical neural networks with Plug-and-Play metasurfaces

Reconfigurable optical neural networks with Plug-and-Play metasurfaces

Reconfigurable all-optical pattern-matching system for phase modulation formats based on phase-sensitive amplification in highly nonlinear fiber

All-optical pattern-matching system is to compare the input data with the target sequence to implement the data filtering directly in the optical layer. However, for current all-optical pattern-matching systems, one pattern-matching system can only match one modulation format, which limits its application. In this paper, a reconfigurable all-optical pattern-matching system for phase modulation formats is proposed. Taking advantage of phase-sensitive amplification (PSA) in highly nonlinear fiber (HNLF), the system can match various phase modulation formats through optical switches configured by a controller. We first evaluate the feasibility of the proposed system for binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation formats with the transmission rates of 100 Gbaud and 200 Gbaud. The results show that our proposed system can correctly recognize and locate the position of the target sequence. Next, we analyze the extinction ratio (ER), contrast ratio (CR), Q factor, and bit error rate (BER) by changing the optical signal-to-noise ratio (OSNR) values of BPSK and QPSK signals to evaluate the noise performance. The proposed reconfigurable all-optical pattern-matching system provides an effective method to recognize signals with different phase modulation formats, which is suitable for future dynamically reconfigurable optical networks.

Optical channel aggregation based on modulation format conversion by coherent spectral superposition with electro-optic modulators

Spectrally efficient modulation formats become essential for optical network scaling as the demand for routed data streams exceeds the present wavelength-division multiplexing systems’ throughput. However, achieving high spectral efficiency at high data rates requires complex and bandwidth-intensive electronics. In this study, we propose an all-optical aggregation scheme that combines multiple low spectral efficiency optical wavelength channels from an optical frequency comb based transmitter into fewer channels with higher spectral efficiency. Our method utilizes coherent spectral superposition and optical vector summation, eliminating the need for optical nonlinearities and relying on linear signal processing with an electro-optic modulator. By adjusting the phase of the radio frequency signal driving the modulator, we can easily achieve the required optical phase tuning for vector addition in the I-Q plane. Through experimental demonstrations, we show that the proposed approach enables the generation of 10 GBd PAM-4 and 10 GBd quadrature phase shift keying (QPSK) signals by aggregating two 10 GBd binary phase shift keying signals. Similarly, we aggregate two 10 GBd QPSK signals into one 10 GBd quadrature amplitude modulation-16 (QAM-16) signal. The experiments employ both conventional and sinc-shaped Nyquist signals. Our in-line, all-optical aggregation concept significantly enhances operational capacity while reducing complexity. It offers a promising solution for realizing flexible integrated optical transmitters for advanced modulation format signals using lower-quality electronics. Additionally, it aligns with the requirements of future dynamically reconfigurable optical networks that leverage spectral traffic aggregation. Given its reliance on linear signal processing with an electro-optic modulator, the integration of the method into any integrated photonic platform is straightforward.

Reconfigurable flexible PON downlink by using a wavelength selective switch and single intensity modulator

A reconfigurable passive optical network (PON) downlink has been experimentally demonstrated. A multi-carrier signal is generated in the digital domain and converted to an optical signal by a Mach-Zehnder intensity modulator. A wavelength selective switch (WSS) demultiplexes the sub-carriers for a distribution network. Each sub-carrier signal is a duo-binary filtered binary phase shift keying and directly detected at the receiver side. By changing the sub-carrier allocation and WSS port setting, the PON network can be easily reconfigured without changing the hardware configuration. The maximum capacity of 75-Gb/s with 10-km transmission and 128-way split has been achieved.

Deep Neural Network-Based QoT Estimation for SMF and FMF Links

Quality of transmission (QoT) estimation tools for fiber links are the enabler for the deployment of reconfigurable optical networks. To dynamically set up lightpaths based on traffic request, a centralized controller must base decisions on reliable performance predictions. QoT estimation methods can be categorised in three classes: exact analytical models which provide accurate results with heavy computations, approximate formulas that require less computations but deliver a reduced accuracy, and machine learning (ML)-based methods which potentially have high accuracy with low complexity. To operate an optical network in real-time, beside accurate QoT estimation, the speed in delivering results is a strict requirement. Based on this, only the last two categories are candidates for this application. In this paper, we present a deep neural network (DNN) structure for QoT estimation considering both regular single-mode fiber (SMF) and future few-mode fiber (FMF) proposed to increase the overall network capacity. We comprehensively explore ML-based regression methods for estimating generalized signal-to-noise ratio (GSNR) in partial-load SMF and FMF links. Synthetic datasets have been generated using the enhanced Gaussian noise (EGN) model. Results indicate that the proposed DNN-based regressor can provide better accuracy along with less computation complexity, compared with other state-of-the-art ML methods as well as closed-form-EGN and closed-form-GN models.

Nonvolatile multi-level adjustable optical switch based on the phase change material.

For the advantages of the faster computation speed and lower energy consumption, all-optical computation has attracted great attention compared with the traditional electric computation method. Optical switches are the critical elementary units of optical computation devices. However, the traditional optical switches have two shortcomings, expending the outside energy to keep the switch state and the weak multi-level adjustable ability, which greatly restrict the realization of the large-scale photonic integrated circuits and optical spiking neural networks. In this paper, we use a subwavelength grating slot-ridge (SWGSR) waveguides on the silicon platform to design a nonvolatile multi-level adjustable optical switch based on the phase change material Ge2Sb2Te5 (GST). Changing the phase state of GST can modulate the transmission of the optical switch, and the change of the optical transmittance of the optical switch is about 70%, which is much higher than that of previous optical switches. As no static power is required to maintain the phase state, it can find promising applications in optical switch matrices and reconfigurable optical spiking neural networks.

ReSAW: a reconfigurable and picosecond-synchronized optical data center network based on an AWGR and the WR protocol

The explosive growth of data center (DC) traffic imposes unprecedented challenges on the current electrical switch-based data center networks (DCNs) with the bottleneck of limited bandwidth and high latency. Benefitting from transparency to the data rate and format, optical switching with theoretically infinite bandwidth could overcome the bandwidth bottleneck of electrical switching DCNs. However, DCNs normally deploy multitenant applications with a variety of DC traffic. It is hard to reconfigure the optical interconnections in real-time to provide adaptable bandwidth to the traffic with heterogeneous characteristics. Moreover, to improve the bandwidth utilization, statistical multiplexing is generally deployed in optical DCNs to forward the traffic flow in a time slot, which requires the network time to be precisely synchronized to all network nodes. For fast optical switches with a nanosecond switching configuration time, overall end node times must be synchronized at subnanosecond magnitude. In this paper, we propose and experimentally investigate a reconfigurable and picosecond-synchronized optical DCN (ReSAW) based on an arrayed waveguide grating router (AWGR) and the White Rabbit (WR) protocol. A scheduler based on a distributed field-programmable gate array is implemented in the proposed ReSAW to realize flexible wavelength configuration by controlling the fast laser array based on semiconductor optical amplifiers (SOAs) according to time slot and traffic priority. Moreover, the WR protocol is implemented in optical DCNs for what we believe is the first time to synchronize the time of the distributed top of racks (ToRs). The experimental demonstration validates that ReSAW achieves an average end-to-end latency of 317.44 ns and a precise synchronized time with an average 386 ps skew. When the load is 0.4, the packet loss after ReSAW reconfiguration is less than 1.83 × 10 − 6 , and the network latency is less than 1.73 µs. Based on the experimental parameters and results, the OMNeT++ simulation model is built to further verify the reconfigurability and scalability of the ReSAW network. Results show that the packet loss rate and latency performance increase 8.24% and 12.47%, respectively, at a load of 0.6 as the ReSAW network scales from 2560 to 40,960 servers compared to before the reconfiguration.

High-fidelity and large-scale reconfigurable photonic processor for NISQ applications.

Reconfigurable linear optical networks are a key component for the development of optical quantum information processing platforms in the NISQ era and beyond. We report the implementation of such a device based on an innovative design that uses the mode mixing of a multimode fiber in combination with the programmable wavefront shaping of a SLM. The capabilities of the platform are explored in the classical regime. For up to 8 inputs and a record number of 38 outputs, we achieve fidelities in excess of 93%, and losses below 6.5dB. The device was built inside a standard server rack to allow for real world use and shows consistent performance for 2x8 circuits over a period of 10 days without re-calibration.

High-performance silicon PIN diode switches in the 2-µm wave band.

The 2-µm wave band has attracted significant research interest due to its potential applications for next-generation high-capacity optical communication and sensing. As the key component, fast optical switches are essential for an advanced and reconfigurable optical network. Motivated by this prospect, we propose and demonstrate two typical silicon PIN diode switches at 2 µm. One is based on a coupled microring resonator (CMRR), and the other is based on a Mach-Zehnder interferometer (MZI) with a push-pull-like configuration. The measured insertion loss of the CMRR switch is <2.5 dB, and the cross talk is <-10.8 dB. The insertion loss of the MZI switch is <2 dB, and the cross talk is <-15.6 dB. The switch times of these two structures are both lower than 12.5 ns.

Underwater wireless optical communications based reconfigurable UOWSN for monitoring and discovering continental margin ore deposits.

Changes in the environment, such as landslides, tsunamis, rising or falling sea levels in coastal oceans, and neighboring land surfaces, significantly impact the structure of the ocean and human life. These natural climate-change processes have unanticipated and deadly consequences for coastal areas. The continental margin part of the ocean has recently attracted the most attention because of the mineral sources and human activities such as exploration, navigation, recreation, and fishing. The continental margin stretches from the coastal mountains and plains to continental shelf, slope, and rise, where terrestrial and maritime means meet. In this paper, we propose a reconfigurable underwater optical wireless sensor network (UOWSN) based on underwater wireless optical communication (UWOC) to monitor and discover continental margin ore deposits. In this proposed system, a transceiver on the underwater wireless autonomous vehicle moving around the different regions of the continental margin collects information and transmits it to the seashore control station once it reaches the ocean surface. We investigated the outage probability and average bit error rate of the proposed system at the continental margin and used coding techniques to mitigate the effects of high turbulence in the continental shelf region.

Graphene plasmonic spatial light modulator for reconfigurable diffractive optical neural networks.

Terahertz (THz) diffractive optical neural networks (DONNs) highlight a new route toward intelligent THz imaging, where the image capture and classification happen simultaneously. However, the state-of-the-art implementation mostly relies on passive components and thus the functionalities are limited. The reconfigurability can be achieved through spatial light modulators (SLMs), while it is not clear what device specifications are required and how challenging the associated device implementation is. Here, we show that a complex-valued modulation with a π/2 phase modulation in an active reflective graphene-plasmonics-based SLM can be employed for realizing the reconfigurability in THz DONNs. By coupling the plasmonic resonance in graphene nanoribbons with the reflected Fabry-Pérot (F-P) mode from a back reflector, we achieve a minor amplitude modulation of large reflection and a substantial π/2 phase modulation. Furthermore, the constructed reconfigurable reflective THz DONNs consisting of designed SLMs demonstrate >94.0% validation accuracy of the MNIST dataset. The results suggest that the relaxation of requirements on the specifications of SLMs should significantly simplify and enable varieties of SLM designs for versatile DONN functionalities.

Generation of linearly polarized modes using a digital micromirror device and phase optimization

Linearly polarized modes were generated from the fundamental LP01 using Lee holograms displayed on a digital micromirror device. The phase in the holograms was optimized using simulated annealing algorithm and complex amplitude correlation to improve the quality of the converted modes. The correlation measurements, and comparisons between numerical and experimental results, show the fidelity of the obtained modes and the effectiveness of the optimization. Furthermore, the optimized holograms can be combined to generate multiple modes spatially addressed with individual control. The results, and the use of a digital micromirror device instead of the most common liquid crystal modulators, make this method suitable for Modal Division Multiplexing systems and compatible with other optical telecommunication techniques like Wavelength and Polarization Division multiplexing, and reconfigurable optical networks.

DACON: a reconfigurable application-centric optical network for disaggregated data center infrastructures [Invited

To solve the issues of low resource utilization and performance bottleneck in current server-centric data center networks (DCNs), we propose and experimentally demonstrate a disaggregated application-centric optical network (DACON) for data center infrastructures based on hybrid optical switches. DACON achieves flexible provision and dynamic reconfiguration of hardware nodes exploiting the softwared-define networking (SDN) orchestration plane. A four-node SDN-enabled disaggregated prototype is implemented with a field-programmable-gate-array-based controller of hardware nodes and nanosecond optical switches, performing a minimal node-to-node network latency of 378.6 ns and zero packet loss. Based on the unmodified Linux kernel and two different applications (distributed computing and a Memcached database), the application runtime of the disaggregated prototype is investigated and compared with the server-centric architecture. Experimental results show that the disaggregated prototype performs better with Memcached database applications, achieving a1.46×faster runtime than the server-centric network at a memory node access ratio of 0.9. Based on the customized control plane orchestrator and dynamic resource reallocation, the node-to-node latency is reduced by 21% when CPU nodes access memory nodes. The scalability of DACON is then numerically assessed based on experimentally measured parameters. Results show that the intra-rack node-to-node latency is less than 404.8 ns with a 6144-node network and memory node access ratio of 0.9. Finally, the cost and power consumption are also studied and compared with current DCN architectures. Results indicate DACON saves 13.4% of the cost of an interconnect network compared with current disaggregated architecture and consumes up to 31.1% less power with respect to server-centric DCN architectures.

Automatically reconfigurable optical data center network with dynamic bandwidth allocation

The rapid increasing traffic in data centers (DCs) puts tremendous pressure to the present multi-tier network architectures and electrical switching techniques. Switching traffic in the optical domain featuring ultra-high bandwidth, therefore, has been intensively investigated to build the high capacity data center networks (DCNs). To handle the variable traffic pattern in DCs, the network reconfigurability with adaptable optical bandwidth allocation is of key importance to flexibly assign the optical bandwidth. To this end, we propose and experimentally evaluate a software-defined networking enabled reconfigurable optical DCN with dynamic bandwidth allocation in this work, based on novel optical top of racks exploiting a wavelength selective switch. Experimental assessments show that the proposed solution can automatically reallocate the optical bandwidth in real-time to adapt the dynamic traffic pattern. Compared with the conventional optical DCN with static bandwidth provision, the end-to-end latency performance of the reconfigurable scheme with adaptable bandwidth allocation improves of 58.3% and the average packet loss decreases one order of magnitude. Moreover, the reconfigurable optical DCN features deterministic latency performance, with much lower time variations of packets delivery completion. Based on the experimental parameters, the simulation platform is also built to validate the good scalability of the proposed reconfigurable DCN. Numerical results illustrate the negligible performance degradation (11%) as the network scales from 2560 to 40 960 servers.

Controllable entangled-state distribution in a dual-rail reconfigurable optical network

The authors introduce a new quantum distribution network architecture enabling control of the propagation direction of an entangled state using linear-optical devices and phase shifters and offering reconfigurable connections between multiple quantum nodes.

Dynamic piezoelectric MEMS-based optical metasurfaces.

Optical metasurfaces (OMSs) have shown unprecedented capabilities for versatile wavefront manipulations at the subwavelength scale. However, most well-established OMSs are static, featuring well-defined optical responses determined by OMS configurations set during their fabrication, whereas dynamic OMS configurations investigated so far often exhibit specific limitations and reduced reconfigurability. Here, by combining a thin-film piezoelectric microelectromechanical system (MEMS) with a gap-surface plasmon-based OMS, we develop an electrically driven dynamic MEMS-OMS platform that offers controllable phase and amplitude modulation of the reflected light by finely actuating the MEMS mirror. Using this platform, we demonstrate MEMS-OMS components for polarization-independent beam steering and two-dimensional (2D) focusing with high modulation efficiencies (~50%), broadband operation (~20% near the operating wavelength of 800 nanometers), and fast responses (<0.4 milliseconds). The developed MEMS-OMS platform offers flexible solutions for realizing complex dynamic 2D wavefront manipulations that could be used in reconfigurable and adaptive optical networks and systems.