The surge in interest regarding the next generation of optical fiber transmission has stimulated the development of digital signal processing(DSP)schemes that are highly cost-effective with both high performance and l...
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The surge in interest regarding the next generation of optical fiber transmission has stimulated the development of digital signal processing(DSP)schemes that are highly cost-effective with both high performance and low *** benchmarks for nonlinear compensation methods,however,traditional DSP designed with block-by-block modules for linear compensations,could exhibit residual linear effects after compensation,limiting the nonlinear compensation *** we propose a high-efficient design thought for DSP based on the learnable perspectivity,called learnable DSP(LDSP).LDSP reuses the traditional DSP modules,regarding the whole DSP as a deep learning framework and optimizing the DSP parameters adaptively based on backpropagation algorithm from a global *** method not only establishes new standards in linear DSP performance but also serves as a critical benchmark for nonlinear DSP *** comparison to traditional DSP with hyperparameter optimization,a notable enhancement of approximately 1.21 dB in the Q factor for 400 Gb/s signal after 1600 km fiber transmission is experimentally demonstrated by combining LDSP and perturbation-based nonlinear compensation *** from the learnable model,LDSP can learn the best configuration adaptively with low complexity,reducing dependence on initial *** proposed approach implements a symbol-rate DSP with a small bit error rate(BER)cost in exchange for a 48%complexity reduction compared to the conventional 2 samples/symbol *** believe that LDSP represents a new and highly efficient paradigm for DSP design,which is poised to attract considerable attention across various domains of opticalcommunications.
In this paper, we proposed a point-to-multipoint fiber-optic time transfer scheme over ring networks. The proposed scheme is experimentally demonstrated over a 400 km ring fiber network with the communication data tra...
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We demonstrate 1.0-Pb/s CPRI-equivalent rate fronthaul with 1024-QAM by using digital-analog radio-over-fiber and coherent detection based on modulator bias-induced residual carrier for phase tracking. The reach is ex...
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We experimentally demonstrate high-capacity coherent DA-RoF fronthaul leveraging pilot symbol- and BPS-based carrier phase recovery. Up to 4.42- This and 32.6- This CPRI-equivalent rates are achieved with single-carri...
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FEderated Edge Learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server...
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We leverage modulator finite extinction ratio-induced residual carrier for transparent digital signal processing in coherent time-frequency-division-multiplexing PON. We experimentally demonstrate flexible data rates ...
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We propose and experimentally demonstrate an inter-subcarrier crosstalk cancellation algorithm for fasterthan-Nyquist transmission. At 20% HD-FEC threshold, the achievable faster-than-Nyquist rate improves from 0.875 ...
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We propose an optical Hilbert direct detection receiver with an all-pass transfer function for carrier-assisted complex-valued double-sideband signal reception. We experimentally demonstrate single-wavelength line rat...
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We propose a low-complexity and IF-free radio-over-fiber scheme using low-pass delta-sigma modulator and RZ shaping for both sub-6GHz and millimeter-wave bands. Up to 262144-QAM, 65536-QAM and 4096-QAM formats are exp...
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