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Transfer-learning multi-input multi-output equalizer for mode-division multiplexing systems
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DOI：10.3788/COL202422.070602.png)
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Abstract
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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, launching optical powers, and inputting optical signal-to-noise ratios (OSNRs). A dramatic reduction of approximately 40%-83.33% in the required training complexity is achieved in all three scenarios. Furthermore, the good stability of TL-MIMO in both the launched powers and OSNR test bands has also been proved.
Keywords:
mode division multiplexing
multi-input multi-output
transfer learning
training complexity.
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