arXiv subjects

Rui-Qian Li

Publications and source records attributed to Rui-Qian Li.

4 recordsLinked to original sources

arXiv2026

Fiber Nonlinearity Compensation of Coherent Signals Using Deep Photonic Reservoir Computer

Photonic reservoir computer (PRC) is a promising optical computing framework for high-speed optical signal processing, and various reports have shown its functionality of linear equalization for intensity-modulation direct-detection communication links. However, coherent communication links suffer more from nonlinear impairment, whereas its nonlinear equalization is very challenging. Here we demonstrate the nonlinear equalization of coherent 16-level quadrature amplitude modulation (16-QAM) signals using a deep PRC in experiment. The deep PRC consists of cascading injection-locked semiconductor lasers with optical feedback loops. For 16-QAM signals with a transmission rate of 240 Gbps and a launch power of 12 dBm, the single-channel PRC with 3 hidden layers raises the Q factor by as high as 0.58 dB, while the dual-channel PRC with 2 hidden layers raises the Q factor by 0.55 dB. In addition, we prove that the deep PRC is able to equalize 16-QAM signals of different launch powers and transmission distances.

physics.optics↗

arXiv2025

20-GHz bandwidth optical activation function based on a semiconductor laser

Optical neural networks usually execute the linear multiply-accumulate operation in the optical domain, whereas the nonlinear activation function is mostly implemented in the digital or electrical domain. Here we demonstrate a broadband activation function operated in the optical domain. The optical activation function (OAF) is achieved by a semiconductor laser with optical injection, which is operated in the unstable-locked regime above the Hopf bifurcation. The operation bandwidth of the OAF reaches as high as 20 GHz, which is limited by the relaxation oscillation resonance frequency of the semiconductor laser. The OAF mimics a ReLU function for modulation frequencies below 5 GHz, while resembling an ELU function for frequencies above 5 GHz.

physics.optics↗

arXiv2025

Deep photonic reservoir computer for nonlinear equalization of 16-level quadrature amplitude modulation signals

Photonic reservoir computer (PRC) is a kind of real-time and adaptive recurrent neural network, where only weights in the readout layer require training. PRC is a promising tool to deal with the crucial issue of nonlinear equalization in optical fiber communications. Here we theoretically show a deep PRC for the nonlinear equalization of coherent signals with the format of 16- level quadrature amplitude modulation (16-QAM). The deep PRC consists of cascading injection-locked Fabry-Perot lasers with optical feedback. Both the in-phase component and the quadrature component of the 16-QAM signals are simultaneously injected into the deep PRC in parallel, based on the wavelength multiplexing of Fabry-Perot lasers. It is demonstrated that the deep PRC exhibits strong capability for the nonlinearity compensation of coherent signals. The Q factor is improved by more than 1 dB for 16-QAM signals with launch powers above 10 dBm, associated with a bit rate of 240 Gbps and a transmission distance of 50 km.

physics.optics↗

arXiv2023

Scalable wavelength-multiplexing photonic reservoir computing

Photonic reservoir computing (PRC) is a special hardware recurrent neural network, which is featured with fast training speed and low training cost. This work shows a wavelength-multiplexing PRC architecture, taking advantage of the numerous longitudinal modes in a Fabry-Perot semiconductor laser. These modes construct connected physical neurons in parallel, while an optical feedback loop provides interactive virtual neurons in series. We experimentally demonstrate a four-channel wavelength-multiplexing PRC, which runs four times faster than the single-channel case. It is proved that the multiplexing PRC exhibits superior performance on the task of signal equalization in an optical fiber communication link. Particularly, this scheme is highly scalable owing to the rich mode resources in Fabry-Perot lasers.

physics.optics↗