Sequential Number-Theoretic Optimization for High-Dimensional Channel Equalization in Coherent Optical PDM Systems
Polarization-division multiplexing (PDM) in coherent optical communications enhances system capacity but is vulnerable to various channel distortions including transmitter and receiver in-phase/quadrature (IQ) mismatch, rotation of state of polarization (RSOP), frequency offset (FO) and phase noise...
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| Vydané v: | IEEE signal processing letters Ročník 32; s. 2329 - 2333 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1070-9908, 1558-2361 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Polarization-division multiplexing (PDM) in coherent optical communications enhances system capacity but is vulnerable to various channel distortions including transmitter and receiver in-phase/quadrature (IQ) mismatch, rotation of state of polarization (RSOP), frequency offset (FO) and phase noise (PN), which significantly degrade the system performance. To address these challenges, we propose a novel approach using sequential number-theoretic optimization (SNTO) for the joint estimation of these distortions. We further introduce a decision-aided scheme with a window-split structure to accurately track and compensate for time-varying RSOP and PN, thereby implementing signal detection. Through comprehensive mean squared error (MSE) and bit error rate (BER) analysis under different signal-to-noise ratio (SNR) conditions and varying RSOP speeds, our method demonstrates high precision and effectiveness. The SNTO-based algorithm maintains robust performance with superior estimation accuracy and resilience against ultra-fast RSOP. This work introduces an innovative solution for high-dimensional channel equalization in coherent optical PDM systems with not only effectiveness but also robustness. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1070-9908 1558-2361 |
| DOI: | 10.1109/LSP.2025.3575611 |