Prospects and research issues in multi-dimensional all optical networks

Ji, Yuefeng; Zhang, Jiawei; Zhao, Yongli; Yu, Xiaosong; Zhang, Jie; Chen, Xue

doi:10.1007/s11432-016-0324-7

Cited by 48 publications

(11 citation statements)

References 67 publications

(76 reference statements)

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“…1(a)), can solve the problems of scalability, flexibility and smooth upgrade of optical networks. The programmability of network functions and protocols in SDON is beneficial to potentially promote the coordination and orchestration of network services [29]. SDON abstracts the lower layer resource information into the common application programming interface (API) functions of upper layer applications.…”

Section: Self-optimizing Optical Networkmentioning

confidence: 99%

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

Zhao

et al. 2020

IEEE Open J. Comput. Soc.

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As an important bearer network of the fifth generation (5G) mobile communication technology, the optical transport network (OTN) needs to have high-quality network performance and management capabilities. Proof by facts, the combination of artificial intelligence (AI) technology and software-defined networking (SDN) can improve significant optimization effects and management for optical transport networks. However, how to properly deploy AI in optical networks is still an open issue. The training process of AI models depends on a large amount of computing resources and training data, which undoubtedly increases the carrying burden and operating costs of the centralized network controller. With the continuous upgrading of functions and performance, small AI-based chips can be used in optical networks as on-board AI. The emergence of edge computing technology can effectively relieve the computation load of network controllers and provide high-quality AI-based networks optimization functions. In this paper, we describe an architecture called self-optimizing optical network (SOON) with cloud-edge collaboration, which introduces control-layer AI and on-board AI to achieve intelligent network management. In addition, this paper introduces several cloud-edge collaborative strategies and reviews some AI-based network optimization applications to improve the overall network performance.

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Section: Self-optimizing Optical Networkmentioning

confidence: 99%

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

Zhao

et al. 2020

IEEE Open J. Comput. Soc.

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“…Although the eCPRI reliefs the bandwidth pressure, the growing radio frequency band (e.g., 200 MHz) and number of antennas (e.g., 32×32 massive MIMO) still result in huge fronthaul bandwidth demand. Optical technologies with advanced modulation format and multi-dimensional multiplexing [23] are promising solutions for building a high-capacity fronthaul. For example, WDM technology with huge capacity can flexibly provision opical frouthaul [24].…”

Section: High-bandwidth Provisionmentioning

confidence: 99%

Towards converged, collaborative and co-automatic (3C) optical networks

Ji¹,

Zhang²,

Wang³

et al. 2018

Sci. China Inf. Sci.

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The interconnection of all things is developing a new diagram of future information networks. However, it is difficult to realize future applications with only one single technique. Collaboration between multiple advanced techniques is leading the way for the development of future information networks. Optical communication is an enabling technique to achieve high speed, long reach, and low latency communication, which plays an important role on the transformation of information networks. To achieve these advantages that caters to the characteristics of future information networks, collaboration of multiple advanced techniques with optical, which is called "optical plus X", could realize the vision of "all things connected with networks". In this paper, we focus on the collaboration between optical networks with other techniques, mainly discuss four representative aspects, which are "optical plus IP", "optical plus radio", "optical plus computing", and "optical plus AI". We discuss the challenges, timely works, and developing trends. Finally, we give the future visions for optical network towards a collaborative, converged and co-automatic optical network. Keywords 5G optical transport networks, edge computing optical networks, IP over optical networks, AI-assisted optical networks, 3C optical networks

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“…Fiber capacity crunch concerns are driving optical networking toward a spectral-efficiency-conscious design philosophy. Moreover, as the number of various high-capacity services increases such as video delivery service and data centers, transport network flexibility will become important, resulting in the demand of elastic transport optical networking [1][2][3][4][5]. Key methods for empowering the elastic/flexible optical system are: single SDN XCVR, BW increase, flexible spectrum approach, Multicore fiber using the SDM technology.…”

Section: Introductionmentioning

confidence: 99%

All Optical Signal Processing Technologies in Optical Fiber Communication

Anis¹

2020

Optical Fiber Applications

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Due to continued growth of internet at starling rate and the introduction of new broadband services, such as cloud computing, IPTV and high-definition media streaming, there is a requirement for flexible bandwidth infrastructure that supports mobility of data at peta-scale. Elastic networking based on gridless spectrum technology is evolving as a favorable solution for the flexible optical networking supportive next generation traffic requirements. Recently, research is centered on a more elastic spectrum provision methodology than the traditional ITU-T grid. The main issue is the requirement for a transmission connect, capable of accommodating and handling a variety of signals with distinct modulation format, baud rate and spectral occupancy. Segmented use of the spectrum could lead to the shortage of availableness of sufficiently extensive spectrum spaces for high bitrate channels, resulting in wavelength contention. On-demand space assignment creates not only deviation from the ideal course but also have spectrum fragmentation, which reduces spectrum resource utilization. This chapter reviewed the recent research development of feasible solutions for the efficient transport of heterogeneous traffic by enhancing the flexibility of the optical layer for performing allocation of network resources as well as implementation of optical node by all optical signal processing in optical fiber communication.

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Prospects and research issues in multi-dimensional all optical networks

Abstract: Multi-dimensional manipulation of optical field withmetasurfaces and its optimization based on machine learning

Cited by 48 publications

References 67 publications

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

Self-Optimizing Optical Network With Cloud-Edge Collaboration: Architecture and Application

Towards converged, collaborative and co-automatic (3C) optical networks

All Optical Signal Processing Technologies in Optical Fiber Communication

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