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Performance Predict...
Performance Prediction Recipes for Optical Links
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- Agrell, Erik, 1965 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Secondini, M. (author)
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT),Scuola Superiore Sant'Anna di studi universitari e di perfezionamento (SSSUP),Sant'Anna School of Advanced Studies (SSSUP)
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- Alvarado, A. (author)
- Mitsubishi Electric Corporation
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- Yoshida, Tsuyoshi, 1982 (author)
- Osaka University,Technische Universiteit Eindhoven,Eindhoven University of Technology
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(creator_code:org_t)
- 2021
- 2021
- English.
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In: IEEE Photonics Technology Letters. - 1041-1135 .- 1941-0174. ; 33:18, s. 1034-1037
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Abstract
Subject headings
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- Although forward error-correction (FEC) coding is an essential part of modern fiber-optic communication systems, it is impractical to implement and evaluate FEC in transmission experiments and simulations. Therefore, it is desirable to accurately predict the end-to-end link performance including FEC from transmission data recorded without FEC. In this tutorial, we provide ready-to-implement “recipes” for such prediction techniques, which apply to arbitrary channels and require no knowledge of information or coding theory. The appropriate choice of recipe depends on properties of the FEC encoder and decoder. The covered metrics include bit error rate, symbol error rate, achievable information rate, and asymptotic information, in all cases computed using a mismatched receiver. Supplementary software implementations are available.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
Keyword
- Encoding
- optical communication
- probabilistic shaping
- Modulation
- mismatched decoding
- Receivers
- Decoding
- Bit error rate
- bit error rate
- Forward error correction
- Achievable information rate
- asymmetric information
- performance metrics
- Measurement
Publication and Content Type
- art (subject category)
- ref (subject category)
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