Error Vector Magnitude (EVM)-Based Constellation Combiner for Cooperative Relay Network

Kuppusamy, Mugelan Ramakrishnan; Bokhari, Syed Moinuddin; Anjaneyalu, Bhagyaveni Marichala

doi:10.1109/lcomm.2015.2504374

Cited by 10 publications

(1 citation statement)

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“…Tracking EVM has the ability to expand the functional capabilities of network modules by supplying network analytics functions with understandable error information about the system. Calculating an EVM has typically necessitated the receipt of millions of symbols [8]. A procedure that accumulates like this is a time-consuming process and makes it difficult to put into practice.…”

Section: Introductionmentioning

confidence: 99%

Low-complexity EVM estimation based on artificial neural networks for coherent optical systems

Jha,

Mishra

2024

J. Opt.

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With continuous growth in modulation formats, the requirement for autonomous devices is becoming more important than ever. Predicting error vector magnitude (EVM) of m-ary quadrature amplitude modulation (mQAM) are intricate issue for the effective design of transmission systems. Existing estimation techniques have survived through repetitive processes that are frequently computationally expensive, and time-consuming. Recently deep learning approaches demonstrated good performance as useful computational tools, offering a different way for accelerating such mQAM simulations. This paper introduces an artificial neural network (ANN) architecture that aims to forecast the EVM of the popular modulation forms including 18 Gbaud 8QAM, 14 Gbaud 16QAM, and 10 Gbaud 64QAM under different transmission conditions. Amplitude histograms (AHs) are produced from constellation diagrams (CDs) obtained with varying launch power, laser linewidth, and OSNR by an offline preprocessing flow. The fully trained framework exhibits superior performance in terms of computing cost compared to the simulation experiments. The overall execution time of the ANN-based modeling method is approximately 234 s as opposed to more than 23000 s when employing the simulation technique, resulting in a 99% reduction in computation time. As a result, this technology opens the door to quick, all-encompassing techniques for characterizing and analyzing optical fiber problems.

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Section: Introductionmentioning

confidence: 99%