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 ...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.展开更多
基金supported by the National Key R&D Program of China(No.2018YFB1801001)the Royal Society International Exchange Grant(No.IEC\NSFC\211244).
文摘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.
