Machine learning for signal processing

We have proposed the use of sparse identification method for optical systems, or SINO. This new approach determines the optimum number of variables in the transmission system required for adaptive mitigation of effects (nonlinearities in fibre optic cable) that limit the throughput of standard optical fibre. Demand for data is high with today’s online culture and the introduction of 8K TV, the Internet of Things and the ever-increasing use of streaming services mean that this demand could outstrip network capacity. Novel techniques, such as SINO, could help to future-proof our broadband infrastructure. The SINO method is significantly less complex than other similar compensation techniques. This bodes well for future commercial deployment. SINO is particularly useful for flexible smart-grid networks, as it does not require a knowledge of system parameters and is scalable to difference power levels. Such networks are more sustainable and more reliable, considering the needs of modern society.


Sorokina M., Sygletos S. & Turitsyn S., Sparse identification for nonlinear optical communication systems: SINO method, Opt. Express 24, 30433-30443 (2016).