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DSP (digital signal processing) has been widely applied in opticalcommunication systems to mitigate signal distortions and has become one of thekey technologies that have sustained data traffic growth over the past decade.However, the strict energy budget of application-specific integratedcircuit-based DSP chips has prevented the deployment of some powerful butcomputationally costly DSP algorithms. As a result, fiber nonlinearity-inducedsignal distortions impede fiber communications systems, especially inwavelength-division multiplexed (WDM) transmission systems. To solve thesechallenges, photonics hardware (i.e., photonic neural networks) promises tobreak performance limitations in electronics and gain advantages in bandwidth,latency, and power consumption in solving intellectual tasks that areunreachable by conventional digital electronic platforms. This work proposes aphotonic recurrent neural network (RNN) capable of simultaneously resolvingdispersion and both intra and inter-channel fiber nonlinearities in multipleWDM channels in the photonic domain, for the first time to our best knowledge.Furthermore, our photonic RNN can directly process optical WDM signals in thephotonic domain, avoiding prohibitive energy consumption and speed overhead inanalog to digital converters (ADC). We demonstrate in simulation that ourphotonic RNN can process multiple WDM channels simultaneously and achieve areduced bit error rate compared to typical DSP algorithms for all WDM channelsin a pulse-amplitude modulation 4-level (PAM4) transmission system, thanks toits unique capability to address inter-channel fiber nonlinearities. Inaddition to signal quality performance, the proposed system also promises tosignificantly reduce the power consumption and the latency compared to thestate-of-the-art DSP chips, according to our power and latency analysis.

Multi-Wavelength Photonic Neuromorphic Computing for Intra and Inter-Channel Distortion Compensations in WDM Optical Communication Systems