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Photonic Network Coding and Partial Protection for Optical Core Networks: Two for a Tango

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arxiv 2105.03503 v2 pith:PJET4FOX submitted 2021-05-07 cs.NI

classification cs.NI
keywords capacityprotectioncodingnetworkpartialdatadigitalefficiency
verification ladder T0 review T1 audit T2 compute T3 formal
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The digital transformation is creating basically a digital version of our physical world and the currency in that digital space is data. Massive amount of data has been generated ranging from wearable devices monitoring our physical health every single millisecond to autonomous vehicles generating roughly 5Tb hourly to even astronomical activities producing an order of Exabytes on daily basis and then ultra-broadband Internet comes into play, moving such data to the cloud. Internet traffic therefore has been experiencing explosive growth and in this context, optical transport networks forming the backbone of the Internet are pushed for transformation in system capacity. While the intuitive solution of deploying multiple fibers can address the pressing demand for increased capacity, doing so does not bring improvement in economic of scales in terms of cost, power consumption and spectral efficiency. This necessitates for a different approach so that the fiber capacity could be utilized in a more efficient manner. In this paper, we focus on innovative techniques, that is, photonic network coding and partial protection, to reduce the effective traffic load in order to achieve greater capacity efficiency for optical transport networks. Specifically, the application of network coding is examined by upgrading the functionalities of intermediate nodes with all-optical processing (i.e., encoding and decoding) capabilities. Besides, partial protection relying on the premise of providing just enough bandwidth in case of failure events is investigated for saving the redundant protection capacity. That it takes two to tango, combining photonic network coding and partial protection therefore bring to light new opportunities and challenges. In mining such new avenue, we present insights on how to derive compounding gains to maximize spectral efficiency via a case study.

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