High dynamic range 100G PON enabled by SOA preamplifier and recurrent neural networks

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Murphy, Stephen L.
Jamali, Fariba
Townsend, Paul D.
Antony, Cleitus
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Institute of Electrical and Electronics Engineers (IEEE)
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In recent years the PON research community has focused on future systems targeting 100 Gb/s/ $\lambda$ and beyond, with digital signal processing seen as a key enabling technology. Spectrally efficient 4-level pulse amplitude modulation (PAM4) is seen as a cost-effective solution that exploits the ready availability of cheaper, low-bandwidth devices, and Semiconductor Optical Amplifiers (SOA) are being investigated as receiver preamplifiers to compensate PAM4’s high signal-to-noise ratio requirements and meet the demanding 29 dB PON loss budget. However, SOA gain saturation-induced patterning distortion is a concern in the context of PON burst-mode signalling, and the 19.5 dB loud-soft packet dynamic range expected by the most recent ITU-T 50G standards. In this article we propose a recurrent neural network equalisation technique based on gated recurrent units (GRU-RNN) to not only mitigate SOA patterning affecting loud packet bursts, but to also exploit their remarkable effectiveness at compensating non-linear impairments to unlock the SOA gain saturated regime. Using such an equaliser we demonstrate $ > 28$ dB system dynamic range in 100 Gb/s PAM4 system by using SOA gain compression in conjunction with GRU-RNN equalisation. We find that our proposed GRU-RNN has similar equalisation capabilities as non-linear Volterra, fully connected neural network, and long short-term memory based equalisers, but observe that feedback-based RNN equalisers are more suited to the varying levels of impairment inherent to PON burst-mode signalling due to their low input tap requirements. Recognising issues surrounding hardware implementation of RNNs, we investigate a multi-symbol equalisation scheme to lower the feedback latency requirements of our proposed GRU-RNN. Finally, we compare equaliser complexities and performances according to trainable parameters and real valued multiplication operations, finding that the proposed GRU-RNN equaliser is more efficient than those based on Volterra, fully connected neural networks or long short-term memory units proposed elsewhere.
Digital signal processing , Four-level pulse amplitude modulation , Machine learning , Optical fiber communications , Passive optical network , Semiconductor optical amplifier
Murphy, S. L., Jamali, F., Townsend, P. D. and Antony, C. (2023) 'High dynamic range 100G PON enabled by SOA preamplifier and recurrent neural networks', Journal of Lightwave Technology, 41(11), pp. 3522-3532. doi: 10.1109/JLT.2023.3249046