Combining Optical and Digital Compensation: Neural Network-Based Channel Equalisers in Dispersion-Managed Communications Systems

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Abstract
Machine learning methods, including artificial neural networks, used for mitigation of nonlinear transmission impairments in ultra-long-haul and long-haul unmanaged fibre-optic links feature high complexity due to the accumulated dispersion resulting into large channel memory. Combination of the all-optical dispersion management techniques reducing effective channel memory and low-complexity digital post-processing potentially can offer an attractive trade-off between performance, complexity and costs (or power consumption). This paper demonstrates a feasibility of substantial complexity reduction of machine learning-based channel equalisation in dispersion-managed transmission with acceptable system performance.
Funding Information
  • Engineering and Physical Sciences Research Council
  • EPSRC Core Equipment Fund (EP/V036106/1)

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