Increasing the Capacity of Optical Nonlinear Interfering Channels (ICONIC)


Fibre optics currently carry more than 99% of the Internet traffic across the globe, and thus, are critical infrastructure for society. Due to the large bandwidth available in optical fibre transmission systems, for a long time, optical fibres were thought to have infinite information carrying capabilities.

With current traffic demands growing by a factor between 10 and 100 every decade, however, this infinite capacity assumption is not true anymore. Optical fibres are now known to be able to carry only a finite amount of information, which sooner or later will lead to a “capacity crunch”. If we reach this situation, the installed infrastructure will not be able to cope with the increasing traffic demands.

The capacity crunch problem naturally leads to two fundamental questions: (i) What is the maximum amount of information that can be reliably transported by optical fibres? and (ii) How to design transmission systems that approach this limit? These questions fall in the area of information and communication theory and still remain as open research problems, mainly due to difficulties in modelling the optical channel in the high-power regime. These two scientific questions will answered in this project.

To answer these questions, accurate channel models for the nonlinear optical channel in the high-power regime will be developed for the first time. Using mathematical tools and the developed models, novel spectrally-efficient nonlinear transmission techniques and algorithms will also be developed. These fundamental theoretical contributions will be complemented with experimental validations at the host organisation.

Due to the central role of information transmission in modern society, the results in this project will have broad societal impact, ranging from enabling novel data-based applications and services to simply faster and better broadband connections at home.


Project number


Main applicant

Dr. A. Alvarado

Affiliated with

University College London, Department of Electronic & Electrical Engineering

Team members

Dr. A. Alvarado, Dr. A. Amari, Ir. V. Oliari


01/10/2017 to 01/10/2022