Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata, Javier; Miguel, Ignacio de; Durán, Ramón J.; Merayo, Noemí; Singh, Sandeep Kumar; Jukan, Admela; Chamania, Mohit

doi:10.1016/j.osn.2017.12.006

Cited by 336 publications

(186 citation statements)

References 132 publications

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“…In this case, data-driven ML methods are essential tools for network planning and management, but these methods should be improved to be cost-effective and reliable for deployment. Several previous review works have provided comprehensive summaries of the applications of ML techniques in optical networks [2,[16][17][18][19]. They discuss the ML-based techniques adopted in various domains and point out many possible directions for the future deployment strategies.…”

Section: Monitoringmentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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Section: Monitoringmentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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“…Similarly to the training phase of machine learning algorithms, Algorithm 2 searches for good solutions to the

a c t i v a t e

and

m a s k s

parameters via simulation.…”

Section: Proposed Schemementioning

confidence: 99%

“…Our suggestion to this problem is to diverge to a good solution by pre-processing. Similarly to the training phase of machine learning algorithms, 11,12 Algorithm 2 searches for good solutions to the activate and masks parameters via simulation.…”

Section: Precalculating Hw Parametersmentioning

confidence: 99%

Hop distance–based bandwidth allocation technique for elastic optical networks

Mavridopoulos

Beletsioti

Nicopolitidis

et al. 2020

Int J Communication

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Summary Elastic optical networks (EON) have emerged as a solution to the growing needs of the future internet, by allowing for greater flexibility, spectrum efficiency, and scalability, when compared to WDM solutions. EONs achieve those improvements through finer spectrum allocation granularity. However, due to the continuity and contiguity constrains, distant connections that are routed through multiple hops suffer from increased bandwidth blocking probability (BBP), while more direct connections are easier to form. This paper proposes HopWindows, a novel method that strategically allocates bandwidth to connections based on their hop distance. This new algorithm applies masks that control the range of frequency slots (FSs) allocated to each n‐hop connection. Furthermore, a new network metric is introduced, the normalized bandwidth blocking probability (normalized BBP). Utilization of this metric ensures increased fairness to distant connections. Extended simulation results are presented which indicate that the proposed HopWindows method achieves a superior performance over the well‐known FirstFit algorithm. The proposed algorithm may achieve a decrease in bandwidth blocking probability of up to 50%.

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“…The best approaches to handle such a problem are learning techniques or heuristic methods. Compared with heuristic methods, genetic algorithms (GAs) are known to better handle problems with high dimensionalities [5]. GAs are a subset of the evolutionary algorithms which are key for the optimization of non-linear problems by making use of principles such as natural selection, survival of the fittest and mutation.…”

Section: System Optimizationmentioning

confidence: 99%

“…GAs are a subset of the evolutionary algorithms which are key for the optimization of non-linear problems by making use of principles such as natural selection, survival of the fittest and mutation. In optical communications, GAs have been studied in the context of various optical network optimization problems [5].…”

Section: System Optimizationmentioning

confidence: 99%

Genetic Algorithm Optimization of Multi Core Fibre Transmission Links based on Silicon Photonic Transceivers

Ottino

Saljoghei

Hayashi

et al. 2019

Optical Fiber Communication Conference (OFC) 2019

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We demonstrate a genetic algorithm based system that can optimize optical interconnects using silicon photonic multi-core fibre coupled transceiver. The GA selects 48 parameters to deliver a minimum 6.9x10 -16 BER on channels with diverse losses.OCIS codes: (060.0060) Fiber Optics and optical communication; (060.2360) Fiber optics links and subsystems. IntroductionIt has been envisioned that the speed of the I/Os on semiconductor chips in computing systems will increase to reach capacities beyond 1 Pb/s by 2030 [1]. A front-runner application for fulfilling this purpose is a board-detachable optical transceiver in the form of a mid-board optic (MBO), which offers high bandwidth density and energy efficiency. In a recent work [2] a multi-processor system on chip (MPSoC) based memory disaggregated data center network (DCN) was reported using an optical circuit switched network using MBOs. In these disaggregated systems, high bandwidth density and high capacity optical transceivers are required to support dynamic all-optically routed communication between processors and remote high bandwidth memory (HBM) modules that can support over 1 Tb/s bandwidth.Moreover, to reduce the front panel port and bandwidth density in DCNs, multi-core fibre (MCF) coupled transceivers have been recently explored [3]. MCF based data centre networks (DCNs) have also shown to outperform wave division multiplexing (WDM) systems in terms of performance blocking, cost and energy efficiency [4]. Thus, it can be anticipated that the integration of MCFs with MBOs in future DCNs can lead to substantial performance gains. However, opto-electronic transceivers embedded on disaggregated CPUs and memory modules offer a multitude of control parameters. In practice, transceiver channels might offer different signal quality and experience various levels of degradation and attenuation throughout the network. Thus, the optimum selection of these parameters can maximize system power budget, potentially leading to forward error correction (FEC) free operation, which is essential in low-cost, low-complexity and low-latency DCNs.In this paper, we develop a purpose-made genetic algorithm (GA) and use it in real-time to optimize 48 equalization and amplification parameters for an 8 channels MCF-MBO driven by a Xilinx MPSoC with each channel operating at 10 Gb/s over an optical channel with diversely losses. The process took 13 hours instead of 1.44 x10 33 hours required for a brute force search method. Results suggest significant performance enactment in terms of bit error rate (BER).

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Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Cited by 336 publications

References 132 publications

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Hop distance–based bandwidth allocation technique for elastic optical networks

Genetic Algorithm Optimization of Multi Core Fibre Transmission Links based on Silicon Photonic Transceivers

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