Chinese Optics Letters, Volume. 22, Issue 7, (2024)

Transfer-Learning Multi-Input Multi-Output (TL-MIMO) Equalizer for Mode-Division Multiplexing (MDM) Systems [Early Posting]

Zhao Tianfeng, Wen Feng, Tan Mingming, Wu Baojian, Xu Bo, Qiu Kun
Author Affiliations
  • China
  • Aston Univ
  • 电子科技大学通信与信息工程学院光纤传感与通信教育部重点实验室
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    We propose a transfer-learning multi-input multi-output (TL-MIMO) scheme to significantly reduce the required training complexity for converging the equalizers in mode-division multiplexing (MDM) systems. Based on a built three-mode (LP01, LP11a and LP11b) multiplexed experimental system, we thoughtfully investigate the TL-MIMO performances on the three-typed data, collecting from different sampling times, launched optical powers, and input optical signal-to-noise ratios (OSNRs). The dramatic reduction of 40%~83.33% on the required training complexity is achieved in all of three scenarios. Furthermore, the good stability of TL-MIMO in both the launched power and OSNR test bands has also been proved.

    Paper Information

    Manuscript Accepted: Mar. 29, 2024

    Posted: Mar. 29, 2024

    DOI: COL-0267