The lightwave field possesses several dimensional properties,including amplitude,spectrum,phase,and *** measurements of lightwaves have diverse applications ranging from remote sensing to analytical ***,achieving high...
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The lightwave field possesses several dimensional properties,including amplitude,spectrum,phase,and *** measurements of lightwaves have diverse applications ranging from remote sensing to analytical ***,achieving high-resolution simultaneous multi-dimensional measurement of lightwaves remains *** this work,we demonstrate an all-fiber spectropolarimeter based on a speckle pattern obtained from the end of a multi-mode *** proposed system simultaneously achieves a spectral resolution of 100 pm and a polarization resolution of *** polarization measurement errors for three Stokes parameters are 3.37%,1.01%,and 0.84%,respectively,with a mean squared error of 5.3×10^(-5).This work provides novel potential for high-resolution and accurate multi-dimensional lightwave field measurements.
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.
Tensor convolution is important for feature extraction of high-dimensional data in realistic scenarios to obtain fine, high-dimensional features. Photonic convolution in the synthesis frequency dimension, utilizing th...
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Network architectural is indispensable for a wide-area and robust fiber-optic time transfer network, however, there is currently a deficiency in effective scheme. This paper proposed a reconfigurable fiber-optic time ...
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Analog feature extraction(AFE)is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled ***,applying AFE to broadband radio-frequency...
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Analog feature extraction(AFE)is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled ***,applying AFE to broadband radio-frequency(RF)scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic ***,we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog *** feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas,extracting valid features from both temporal and spatial *** of the tunability of the photonic devices,the photonic spatiotemporal feature extractor is trainable,which enhances the validity of the extracted ***,a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature *** validate our scheme,we demonstrate a radar target recognition task with a 4-GHz instantaneous *** results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%.Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing,with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving,robotics,and smart factories.
Flexible coherent passive optical network (PON) is recognized as an advanced capacity optimization strategy for beyond 100G PON due to its flexibility and adaptability, which are pivotal for the evolution of high-capa...
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We experimentally demonstrate single-wavelength 1.6-Tb/s(=8×200Gb/s) data-center interconnects over a 1-km 8-core MCF link. We compare the performance of MLSD and M-BCJR algorithms for FTN transmission under bric...
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Spatial photonic Ising machines, as emerging artificial intelligence hardware solutions by leveraging unique physical phenomena, have shown promising results in solving large-scale combinatorial problems. However,spat...
Spatial photonic Ising machines, as emerging artificial intelligence hardware solutions by leveraging unique physical phenomena, have shown promising results in solving large-scale combinatorial problems. However,spatial light modulator enabled Ising machines still remain bulky, are very power demanding, and have poor stability. In this study, we propose an integrated XY Ising sampler based on a highly uniform multimode interferometer and a phase shifter array, enabling the minimization of both discrete and continuous spin Hamiltonians. We elucidate the performance of this computing platform in achieving fully programmable spin couplings and external magnetic fields. Additionally, we successfully demonstrate the weighted full-rank Ising model with a linear dependence of 0.82 and weighted MaxCut problem solving with the proposed *** results illustrate that the developed structure has significant potential for larger-scale, reduced power consumption and increased operational speed, positioning it as a versatile platform for commercially viable high-performance samplers of combinatorial optimization problems.
Using multiple MEMSes sandwiched between two columns of WSSes, this paper designs a low-loss, nonblocking, and scalab.e OXC that is suitable for the future SDM optical network with a smaller number of wavelengths per ...
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This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile *** aim to maximize the number o...
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This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile *** aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all *** action space of the system is extremely large and grows exponentially with the number of *** this case,single-agent learning will require an overlarge neural network,resulting in insufficient ***,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for *** thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale *** the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this *** the second tier,UAVs utilize multiple agents to plan their *** agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint *** results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific *** particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal ***,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.
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