Efficient Signal Processing for Nonlinear Fibre-Optic Communication Systems

Modern communication theory is founded on the principles of the channel coding and sampling theorems introduced by Claude Shannon in his landmark paper in 1948. Over the years, most efforts in the development of this subject were focused on the analysis of linear communication systems, which accurately describe many practical applications. However, in order to cope with the increasing demands for high-speed Internet and new Internet-based services, the optical core networks should operate at a regime where the nonlinear effects are significant. In such a regime, the optical channel is commonly modelled by nonlinear Schrodinger equation (NLSE) that describes the evolution of the optical signal envelope over the length of fibre. The model defined by NLSE strongly deviates from the conventional linear models. Nevertheless, the current fibre-optic literature is yet dominated by designs based on linear communication theory while treating nonlinearity as a nuisance that needs to be compensated.
In this project, we investigate the application of two important mathematical tools that has recently attracted attention of researchers in different fields in order to tackle the signal processing problem for such a nonlinear communication channel, namely, 1) Nonlinear Fourier transform and 2) Machine Learning. The nonlinear Fourier transform (NFT) is a well-established method (also called inverse scattering) for solving the NLSE although our knowledge about the characteristics of noise in the so-called nonlinear frequency domain is limited. On the other hand, by borrowing mathematical tools from deep learning, it would be possible to train optical receivers efficiently, without an accurate understanding of some effects of fibre nonlinearity on the received optical signal. To achieve this, we are looking for an enthusiastic and strongly motivated student to join our group who will work along with a diverse range of experts and researchers in IDCOM and LiFi R&D Centre.

Entry Qualifications
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. The candidate will be expected to have good programming skills (e.g., Matlab) and a good mathematical background.
For further information on English language requirements for EU/Overseas applicants please see here: https://www.ed.ac.uk/studying/international/english/postgraduate