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

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.