An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Advanced Technique and Future Perspective for Next Generation Optical Fiber Communications

Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology

Chirp-based direct phase modulation of VCSELs managed by Neural Networks

VCSEL's capacity of direct modulation and its low cost makes this device a feasible cost-effective transmitter for ultra-dense wavelength division multiplexing (uDWDM) metro-access networks using coherent detection. However, performing direct-phase modulation in semiconductors can be complex due to its nonlinear characteristics. This research presents Neural Network (NN) training techniques for Time-Series analysis in order to describe the correlation between the input current given to the device and its output optical phase, using a 1550nm RayCan SM-VCSEL. Main goal is training a NN capable of predicting an ideal optical power signal for a specific phase result achievable by inverse training, that is: optical phase is the neural network input while the optical power is the desired target. The experiment is done in three stages: (i) VCSEL's characterization, (ii) NN training to predict input current knowing optical power, and (iii) NN training to predict optical power from a known optical phase

Next generation >200 Gb/s multicore fiber short-reach networks employing machine learning

This work proposes and evaluates the use of machine learning (ML) techniques on >200 Gb/s short-reach systems employing weakly coupled multicore fiber (MCF) and Kramers-Kronig (KK) receivers. The short-reach systems commonly found in intra data centers (DC) connections demand low cost-efficient direct detection receivers (DD). The KK receivers allow the combination of higher modulation order, such as 16-QAM used in coherent systems, with the low complexity and low cost of DD. Thus, the use of KK receivers allows to increase the bit rate and spectral efficiency while maintaining the cost of DD systems as this is an important requirement in DC. The use of MCF allows to increase the system capacity as well as the system cable density, although the use of MCF induces additional distortion, known as inter-core crosstalk (ICXT), to the system. Thus, low complexity ML techniques such as k-means clustering, k nearest neighbor (KNN) and artificial neural network (ANN) (estimation feedforward neural network (FNN) and classification feedforward neural network) are proposed to mitigate the effects of random ICXT. The performance improvement provided by the k-means clustering, KNN and the two types of FNN techniques is assessed and compared with the system performance obtained without the use of ML. The use of estimation and classification FNN prove to significantly improve the system performance by mitigating the impact of the ICXT on the received signal. This is achieved by employing only 10 neurons in the hidden layer and four input features. It has been shown that k-means or KNN techniques do not provide performance improvement compared to the system without using ML. These conclusions are valid for direct detection MCF-based short-reach systems with the product between the skew (relative time delay between cores) and the symbol rate much lower than one (skew x symbol rate « 1). By employing the proposed ANNs, the system shows an improvement of approximately 12 dB on the ICXT level, for the same outage probability when comparing with the system without the use of ML. For the BER threshold of 10−1.8 and compared with the standard system operating without employing ML techniques, the system operating with the proposed ANNs show a received optical power improvement of almost 3 dB and a ICXT level improvement of approximately 9 dB when the mean BER is analized.Este trabalho propõe e avalia o uso de técnicas de machine learning (ML) em sistemas de curto alcance com ritmo binário superior a 200 Gb/s utilizando receptores Kramers-Kronig (KK) e fibras multinúcleo (MCF). Os sistemas de curto alcance usualmente encontrados em conexões intra-data centers (DC) exigem receptores de deteção direta (DD) de baixo custo. Os receptores KK permitem a combinação de sistemas de modulação de maior ordem, tais como o 16-QAM, usado em sistemas coerentes, com o baixo custo dos receptores DD. Portanto, o uso de receptores KK permite melhorar o ritmo binário e eficiência espectral e manter a eficiência de custo dos sistemas DD, o que é importante em DC. O uso de fibras multinúcleo permite o aumento da capacidade do sistema, bem como a densidade de cabos. No entanto, o uso de MCF introduz uma distorção adicional no sistema conhecida como inter-core crosstalk (ICXT). Para mitigar os efeitos do ICXT aleatório, são propostas e avaliadas técnicas de ML de baixa complexidade como k-means clustering, k nearest neighbor (KNN) e rede neuronais artificiais (ANN). O desempenho associado à utilização de algoritmos de ML (k-means, KNN e duas redes neuronais do tipo feedforward (FNN): uma para estimação e outra para classificação), é avaliado e comparado com o desempenho do sistema obtido sem o uso de ML. A utilização de FNN para estimação e classificação conduziram a uma melhoria significativa no desempenho do sistema, mitigando o impacto do ICXT no sinal recebido. Isso é alcançado com o uso de uma rede neuronal com uma arquitetura muito simples contendo quatro entradas e 10 neurónios na camada escondida. Foi demonstrado que os algoritmos k-means e KNN não proporcionam melhoria de desempenho em comparação com o sistema sem o uso de ML. Essas conclusões são válidas para sistemas DD de curto alcance baseados em MCF com o produto entre o skew (atraso relativo entre os núcleos) e o ritmo de símbolos muito menor que um (skew x symbol rate « 1). Com o uso das ANNs, o sistema apresenta uma melhoria de aproximadamente 12 dB na probabilidade de indisponibilidade quando comparado com o sistema sem o uso de ML. Para o limite de BER de 10−1.8 , e comparado com o sistema padrão sem o uso de técnicas de ML, o sistema com o uso de ANN mostra uma melhoria na potência óptica recebida de quase 3 dB e uma melhoria no nível de ICXT de aproximadamente 9 dB em relação ao BER médio

Enabling Technology in Optical Fiber Communications: From Device, System to Networking

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking

PID controller based on a self-adaptive neural network to ensure qos bandwidth requirements in passive optical networks

Producción CientíficaIn this paper, a proportional-integral-derivative (PID) controller integrated with a neural network (NN) is proposed to ensure quality of service (QoS) bandwidth requirements in passive optical networks (PONs). To the best of our knowledge, this is the first time an approach that implements a NN to tune a PID to deal with QoS in PONs is used. In contrast to other tuning techniques such as Ziegler-Nichols or genetic algorithms (GA), our proposal allows a real-time adjustment of the tuning parameters according to the network conditions. Thus, the new algorithm provides an online control of the tuning process unlike the ZN and GA techniques, whose tuning parameters are calculated offline. The algorithm, called neural network service level PID (NNSPID), guarantees minimum bandwidth levels to users depending on their service level agreement, and it is compared with a tuning technique based on genetic algorithms (GASPID). The simulation study demonstrates that NN-SPID continuously adapts the tuning parameters, achieving lower fluctuations than GA-SPID in the allocation process. As a consequence, it provides a more stable response than GA-SPID since it needs to launch the GA to obtain new tuning values. Furthermore, NN-SPID guarantees the minimum bandwidth levels faster than GA-SPID. Finally, NN-SPID is more robust than GA-SPID under real-time changes of the guaranteed bandwidth levels, as GA-SPID shows high fluctuations in the allocated bandwidth, especially just after any change is made.Ministerio de Ciencia e Innovación (Projects TEC2014-53071-C3-2-P and TEC2015-71932-REDT

Anwendung von maschinellem Lernen in der optischen Nachrichtenübertragungstechnik

Aufgrund des zunehmenden Datenverkehrs wird erwartet, dass die optischen Netze zukünftig mit höheren Systemkapazitäten betrieben werden. Dazu wird bspw. die kohärente Übertragung eingesetzt, bei der das Modulationsformat erhöht werden kann, erforder jedoch ein größeres SNR. Um dies zu erreichen, wird die optische Signalleistung erhöht, wodurch die Datenübertragung durch die nichtlinearen Beeinträchtigungen gestört wird. Der Schwerpunkt dieser Arbeit liegt auf der Entwicklung von Modellen des maschinellen Lernens, die auf diese nichtlineare Signalverschlechterung reagieren. Es wird die Support-Vector-Machine (SVM) implementiert und als klassifizierende Entscheidungsmaschine verwendet. Die Ergebnisse zeigen, dass die SVM eine verbesserte Kompensation sowohl der nichtlinearen Fasereffekte als auch der Verzerrungen der optischen Systemkomponenten ermöglicht. Das Prinzip von EONs bietet eine Technologie zur effizienten Nutzung der verfügbaren Ressourcen, die von der optischen Faser bereitgestellt werden. Ein Schlüsselelement der Technologie ist der bandbreitenvariable Transponder, der bspw. die Anpassung des Modulationsformats oder des Codierungsschemas an die aktuellen Verbindungsbedingungen ermöglicht. Um eine optimale Ressourcenauslastung zu gewährleisten wird der Einsatz von Algorithmen des Reinforcement Learnings untersucht. Die Ergebnisse zeigen, dass der RL-Algorithmus in der Lage ist, sich an unbekannte Link-Bedingungen anzupassen, während vergleichbare heuristische Ansätze wie der genetische Algorithmus für jedes Szenario neu trainiert werden müssen

Routing and Wavelength Assignment for Multicast Communication in Optical Network-on-Chip

An Optical Network-on-Chip (ONoC) is an emerging chip-level optical interconnection technology to realise high-performance and power-efficient inter-core communication for many-core processors. Within the field, multicast communication is one of the most important inter-core communication forms. It is not only widely used in parallel computing applications in Chip Multi-Processors (CMPs), but also common in emerging areas such as neuromorphic computing. While many studies have been conducted on designing ONoC architectures and routing schemes to support multicast communication, most existing solutions adopt the methods that were initially proposed for electrical interconnects. These solutions can neither fully take advantage of optical communication nor address the special requirements of an ONoC. Moreover, most of them focus only on the optimisation of one multicast, which limits the practical applications because real systems often have to handle multiple multicasts requested from various applications. Hence, this thesis will address the design of a high-performance communication scheme for multiple multicasts by taking into account the unique characteristics and constraints of an ONoC. This thesis studies the problem from a network-level perspective. The design methodology is to optimally route all multicasts requested simultaneously from the applications in an ONoC, with the objective of efficiently utilising available wavelengths. The novelty is to adopt multicast-splitting strategies, where a multicast can be split into several sub-multicasts according to the distribution of multicast nodes, in order to reduce the conflicts of different multicasts. As routing and wavelength assignment problem is an NP-hard problem, heuristic approaches that use the multicast-splitting strategy are proposed in this thesis. Specifically, three routing and wavelength assignment schemes for multiple multicasts in an ONoC are proposed for different problem domains. Firstly, PRWAMM, a Path-based Routing and Wavelength Assignment for Multiple Multicasts in an ONoC, is proposed. Due to the low manufacture complexity requirement of an ONoC, e.g., no splitters, path-based routing is studied in PRWAMM. Two wavelength-assignment strategies for multiple multicasts under path-based routing are proposed. One is an intramulticast wavelength assignment, which assigns wavelength(s) for one multicast. The other is an inter-multicast wavelength assignment, which assigns wavelength(s) for different multicasts, according to the distributions of multicasts. Simulation results show that PRWAMM can reduce the average number of wavelengths by 15% compared to other path-based schemes. Secondly, RWADMM, a Routing and Wavelength Assignment scheme for Distribution-based Multiple Multicasts in a 2D ONoC, is proposed. Because path-based routing lacks flexibility, it cannot reduce the link conflicts effectively. Hence, RWADMM is designed, based on the distribution of different multicasts, which includes two algorithms. One is an optimal routing and wavelength assignment algorithm for special distributions of multicast nodes. The other is a heuristic routing and wavelength assignment algorithm for random distributions of multicast nodes. Simulation results show that RWADMM can reduce the number of wavelengths by 21.85% on average, compared to the state-of-the-art solutions in a 2D ONoC. Thirdly, CRRWAMM, a Cluster-based Routing and Reusable Wavelength Assignment scheme for Multiple Multicasts in a 3D ONoC, is proposed. Because of the different architectures with a 2D ONoC (e.g., the layout of nodes, optical routers), the methods designed for a 2D ONoC cannot be simply extended to a 3D ONoC. In CRRWAMM, the distribution of multicast nodes in a mesh-based 3D ONoC is analysed first. Then, routing theorems for special instances are derived. Based on the theorems, a general routing scheme, which includes a cluster-based routing method and a reusable wavelength assignment method, is proposed. Simulation results show that CRRWAMM can reduce the number of wavelengths by 33.2% on average, compared to other schemes in a 3D ONoC. Overall, the three routing and wavelength assignment schemes can achieve high-performance multicast communication for multiple multicasts of their problem domains in an ONoC. They all have the advantages of a low routing complexity, a low wavelength requirement, and good scalability, compared to their counterparts, respectively. These methods make an ONoC a flexible high-performance computing platform to execute various parallel applications with different multicast requirements. As future work, I will investigate the power consumption of various routing schemes for multicasts. Using a multicast-splitting strategy may increase power consumption since it needs different wavelengths to send packets to different destinations for one multicast, though the reduction of wavelengths used in the schemes can also potentially decrease overall power consumption. Therefore, how to achieve the best trade-off between the total number of wavelengths used and the number of sub-multicasts in order to reduce power consumption will be interesting future research