Adaptive Optical Network Research Articles

Experimental control of mode-competition dynamics in a chaotic multimode semiconductor laser for decision making

Photonic computing is widely used to accelerate the computational performance in machine learning. Photonic decision making is a promising approach utilizing photonic computing technologies to solve the multi-armed bandit problems based on reinforcement learning. Photonic decision making using chaotic mode-competition dynamics has been proposed. However, the experimental conditions for achieving a superior decision-making performance have not yet been established. Herein, we experimentally investigate mode-competition dynamics in a chaotic multimode semiconductor laser in the presence of optical feedback and injection. We control the chaotic mode-competition dynamics via optical injection and observe that positive wavelength detuning results in an efficient mode concentration to one of the longitudinal modes with a small optical injection power. We experimentally investigate two-dimensional bifurcation diagram of the total intensity of the laser dynamics. Complex mixed dynamics are observed in the presence of optical feedback and injection. We experimentally conduct decision making to solve the bandit problem using chaotic mode-competition dynamics. A fast mode-concentration property is observed at positive wavelength detunings, resulting in fast convergence of the correct decision rate. Our findings could be useful in accelerating the decision-making performance in adaptive optical networks using reinforcement learning.

Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks

The unprecedented growth of the global Internet traffic, coupled with the large spatio-temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are motivating the evolution from reactive to proactive and eventually towards adaptive optical networks. In these networks, traffic-driven service provisioning can address the problem of network over-provisioning and better adapt to traffic variations, while keeping the quality-of-service at the required levels. Such an approach will reduce network resource over-provisioning and thus reduce the total network cost. This survey provides a comprehensive review of the state of the art on machine learning (ML)-based techniques at the optical layer for traffic-driven service provisioning. The evolution of service provisioning in optical networks is initially presented, followed by an overview of the ML techniques utilized for traffic-driven service provisioning. ML-aided service provisioning approaches are presented in detail, including predictive and prescriptive service provisioning frameworks in proactive and adaptive networks. For all techniques outlined, a discussion on their limitations, research challenges, and potential opportunities is also presented.

Optical signal monitoring using multi-task residual network

Optical signal monitoring, including optical performance monitoring (OPM) and bit rate and modulation format identification (BR-MFI), has attracted considerable interest owing to the advantages in automatic system diagnosis and the establishment of adaptive optical networks. However, the lack of effective multi-parameter shared feature extraction methods makes the optical signal monitoring still elusive and hinders its practical application. Exploiting shortcut connections that allow features to propagate across multiple layers to effectively reduce the low-level feature loss, we introduce a multi-task residual network (MT-ResNet) to extract optical signal feature information from eye diagrams. The results show that the model exhibits a multi-parameter shared feature extraction ability and OPM and BR-MFI can be directly realized. The average root-mean-square errors of optical signal-to-noise ratio, chromatic dispersion, and differential group delay monitoring are 0.84 dB, 1.8 ps/nm, and 0.18 ps, respectively. The accuracies of BR-MFI reach 100%. We demonstrate that the model is robust by investigating its feature extraction ability in the case of increased fiber nonlinearity and untrained samples, and the universality of the model is proved by applying it to a long-reach optical coherent communication system. Our simulation results indicate that this MT-ResNet can be deployed to analyze the signal quality cost-effectively, with a high potential for future dynamic and heterogeneous optical networks.

Machine Learning Based Low-Cost Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature.

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.

Free space optic channel monitoring using machine learning

Free space optic (FSO) is a type of optical communication where the signal is transmitted in free space instead of fiber cables. Because of this, the signal is subject to different types of impairments that affect its quality. Predicting these impairments help in automatic system diagnosis and building adaptive optical networks. Using machine learning for predicting the signal impairments in optical networks has been extensively covered during the past few years. However, for FSO links, the work is still in its infancy. In this paper, we consider predicting three channel parameters in FSO links that are related to amplified spontaneous emission (ASE) noise, turbulence, and pointing errors. To the best of authors knowledge, this work is the first to consider predicting FSO channel parameters under the effect of more than one impairment. First, we report the performance of predicting the FSO parameters using asynchronous amplitude histogram (AAH) and asynchronous delay-tap sampling (ADTS) histogram features. The results show that ADTS histogram features provide better prediction accuracy. Second, we compare the performance of support vector machine (SVM) regressor and convolutional neural network (CNN) regressor using ADTS histogram features. The results show that CNN regressor outperforms SVM regressor for some cases, while for other cases they have similar performance. Finally, we investigate the capability of CNN regressor for predicting the channel parameters for three different transmission speeds. The results show that the CNN regressor has good performance for predicting the OSNR parameter regardless of the value of transmission speed. However, for the turbulence and pointing errors, the prediction under low speed transmission is more accurate than under high speed transmission.

Spectrum Utilization with Increasing Traffic, Based on ILP Model in Elastic Optical Networks

The traffic volume due to bandwidth hungry multimedia and internet applications is growing exponentially and demands a significant change in communication networks. Due to the high speed communication requirements present researchers are giving interest in efficient, adaptive and cost effective optical network design. Recent works in optical networking using optical fiber communication focusing on the efficient utilization of resources by flexibly utilizing the spectrum and adaptive networking to support a better quality of service (QoS). The flexible grid in optical spectrum and adaptive optical networking save spectrum with improvement in energy and spectral efficiency. Simple modulation of a signal can support lower bit rate transmissions, which are more sophisticated for higher bit rates. Recent advances on hardware such as optical transponders and switches make it more feasible to develop a new elastic-optical networking (EON) paradigm. Current research efforts are on new innovations of optical transponders and optical switches to support EON functionalities. The measure of spectrum utilization factor with respect to the value of traffic requirement increases the flexibility of the networks. The Integer Linear Programming (ILP) is used to extract the optimal solution for analysis of utilized frequency slots with available slots and increased traffic in the network. This framework also calculates the spectrum utilization for different traffic in order to choose required frequency slot for the EON to provide uninterrupted network service.

Spectrum Continuity and Contiguity based Dedicated Protection for Flexible Optical Networks

Elastic optical networks (EONs) have been pointed as a promising candidate for transporting data with high transmission rates in adaptive optical networks. Consequently, the occurrence of a failure in a simple element may cause the interruption of various services. Survival mechanisms have been crucial to try to overcome the problems created by possible failures. In this paper, we propose a new dedicated path protection mechanism for link-failure survivability in EONs, referred to as Spectrum Continuity and Contiguity based Dedicated Protection (SCCDP). SCCDP tries to avoid the trap topology problem in search for a link-disjoint route pair. In addition, the calculation of the link-disjoint routes is based on modified version of the Dijkstra's algorithm, referred to as Spectrum Continuity and Contiguity based Shortest Path (SCCSP), which chooses the pair of routes according to the end-to-end spectrum continuity and contiguity. The performance of the proposed algorithm is compared to other well-known algorithms in the Literature. Two different network topologies are used in our simulations for comparison purposes. Our proposal achieved better results concerning blocking probability.

Capacity achieving nonbinary LDPC coded non-uniform shaping modulation for adaptive optical communications.

A mutual information inspired nonbinary coded modulation design with non-uniform shaping is proposed. Instead of traditional power of two signal constellation sizes, we design 5-QAM, 7-QAM and 9-QAM constellations, which can be used in adaptive optical networks. The non-uniform shaping and LDPC code rate are jointly considered in the design, which results in a better performance scheme for the same SNR values. The matched nonbinary (NB) LDPC code is used for this scheme, which further improves the coding gain and the overall performance. We analyze both coding performance and system SNR performance. We show that the proposed NB LDPC-coded 9-QAM has more than 2dB gain in symbol SNR compared to traditional LDPC-coded star-8-QAM. On the other hand, the proposed NB LDPC-coded 5-QAM and 7-QAM have even better performance than LDPC-coded QPSK.

Joint phase noise and frequency offset estimation and mitigation for optically coherent QAM based on adaptive multi-symbol delay detection (MSDD)

This paper extends our prior coherent MSDD Carrier Recovery system from QPSK to QAM operation and also characterizes for the first time the Carrier Frequency Offset (CFO) mitigation capabilities of the novel MSDD for QAM systems. We introduce and numerically investigate the performance of an improved MSDD carrier recovery system (differing from the one disclosed in our MSDD for QPSK prior paper), automatically adapting to the channel statistics for optimal phase-noise mitigation. Remarkably, we do not require a separate structure to estimate and mitigate CFO, but the same adaptive structure originally intended for phase noise mitigation is shown to also automatically provide frequency offset estimation and recovery functionality. The CFO capture range of our system is in principle infinite, whereas prior CFO mitigation systems have CFO capture ranges limited to a small a fraction of the baud-rate. When used for 16-QAM with coherent-grade lasers of 100 KHz linewidth, our MSDD system attains the best tradeoffs between performance and complexity, relative to other carrier recovery systems combining blind-phase-search with maximum likelihood detection. We also present additional MSDD phase-noise recovery system variants whereby substantially reduced complexity is traded off for slightly degraded performance. Our MSDD system is able to switch "on-the-fly" to various m-QAM constellation sizes, e.g. seamlessly transition between 16-QAM and QPSK, which may be useful for dynamically adaptive optical networks.

Elastic and adaptive optical networks: possible adoption scenarios and future standardization aspects

There is growing recognition that we are rapidly approaching the physical capacity limit of standard optical fiber. It is important to make better use of optical network resources to accommodate the ever-increasing traffic demand. One promising way is to introduce elasticity and adaptation into the optical domain through more flexible spectrum allocation, where the required minimum spectral resources are allocated adaptively based on traffic demand and network conditions. In this article, we discuss elastic and adaptive optical networks from the perspective of future standardization. We first overview the architecture, enabling technologies, and benefits of elastic and adaptive optical networks with the new concept of an optical corridor. We then present possible adoption scenarios from current rigid optical networks to elastic and adaptive optical networks. We discuss some possible study items that are relevant to the future standardization activities. These items include optical transport network architecture, structure and mapping of the optical transport unit, automatically switched optical network/generalized multiprotocol label switching control plane issues, and some physical aspects with possible extension of the current frequency grid.

Adaptive Optical Network Unit for Point-to-Point and PON Systems Based on Gigabit Ethernet

This paper proposes an optical network unit (ONU) that works in point-to-point (P2P) optical access systems as well as passive optical network (PON) systems and so reduces the operational expenditure of optical access networks in the full FTTH era. First, we discuss its design and implementation; after clarifying the requirements for the adaptive ONU, we describe how it should work while focusing on Gigabit-Ethernet-based P2P and PON systems. Next, we study its feasibility through experiments on a prototype. Also, we provide some additional considerations regarding the implementation and future application of the adaptive ONU.

Adaptive optical network unit for point-to-point and point-to-multipoint Gigabit Ethernet-based optical access networks

This paper proposes and demonstrates an optical network unit (ONU) that is adaptive in both point-to-point (P2P) and point-to-multipoint (P2MP) optical access networks (OANs) based on Gigabit Ethernet. Use of the proposed ONU makes it possible for network operators to reduce their operational expenditure (OPEX) by controlling/managing necessary inventories as well as handling troubles due to ONU mis-connection.

Microlens arrays with integrated pores

Microlenses are important optical components that image, detect, and couple light. But most synthetic microlenses have fixed position and shape once they are fabricated, so their possible range of tunability and complexity is rather limited. By comparison, biology provides many varied, new paradigms for the development of adaptive optical networks. Here, we discuss inspirational examples of biological lenses and their synthetic analogs. We focus on the fabrication and characterization of biomimetic microlens arrays with integrated pores, whose appearance and function are similar to highly efficient optical elements formed by brittlestars. The complex design can be created by three-beam interference lithography. The synthetic lens has strong focusing ability for use as an adjustable lithographic mask and a tunable optical device coupled with the microfluidic system. Replacing rigid microlenses with soft hydrogels provides a way of changing the lens geometry and refractive index continuously in response to external stimuli, resulting in intelligent, multifunctional, tunable optics.

Multifunctional Biomimetic Microlens Arrays with Integrated Pores

Biology provides a multitude of varied, new paradigms for the development of adaptive optical networks. We discuss a first example of synthetic, biomimetic microlens arrays with integrated pores, whose appearance and function are strikingly similar to their biological prototype &#x2013; a highly efficient optical element formed by brittlestars. The complex microstructure is created directly by three-beam interference lithography in a single exposure from a negative-tone photoresist, SU8. We show that (i) these synthetic polymeric microlenses have strong focusing ability and the structure can be, therefore, (ii) used as an adjustable lithographic mask, and that (iii) light-absorbing liquids can be transported in and out of the pores between the lenses, which provides a wide range of tunability of the lens optical properties.