Optical Information Capacity Processing

Abstract

The never-stopping increase in demand for information transmission capacity has been met with technological advances in telecommunication systems, such as the implementation of coherent optical systems, advanced multilevel multidimensional modulation formats, fast signal processing, and research into new physical media for signal transmission (e.g. a variety of new types of optical fibers). Since the increase in the signal-to-noise ratio makes fiber communication channels essentially nonlinear (due to the Kerr effect for example), the problem of estimating the Shannon capacity for nonlinear communication channels is not only conceptually interesting, but also practically important. Here we discuss various nonlinear communication channels and review the potential of different optical signal coding, transmission and processing techniques to improve fiber-optic Shannon capacity and to increase the system reach. Introduction: Information Capacity in Optical CommunicationsThe capacity of global and local communication systems has increased dramatically over past decades, with recent laboratory experiments approaching the order of Pbits/s data rates. However, the data demands rise with time at an even higher rate at the moment calling for new technology advances. The efficiency of current transmission systems are limited by a number of physical effects, with optical noise and nonlinearity being the major challenges [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. It is well recognized nowadays that current fiber-optic communication systems and technologies are facing very specific hurdles due to the nonlinear properties of optical fiber channels, problems not existing in wireless and other linear channels. In optical fiber, the dominant

Perturbative discrete-time multivariate fiber channel model with finite memory

“…As averaging leads to information loss about the signal interference, it results in a degraded lower bound of the system capacity in the highly nonlinear regime. More accurate channel capacity estimations will require accurate modelling approaches that take into account the finite response of the nonlinear channel [7][8]. These can be also used for other applications such as coding [6] or pre/post-distortion [9-10] compensation, and suggest system designs that can improve significantly the transmission capacity [11][12].…”