Institute of Photonics and Quantum Electronics

KIT – The Research University in the Helmholtz Association

Bachelor / Master Thesis:

Neural Network Pre-Distortion

The performance of high-speed optical transmission links is strongly impaired

by a variety of impairments induced by the transmitter components (digital-to-

analog converter, electrical amplifier, electro-optic modulator). Linear digital

pre-distortion is an established concept to reduce the impact of transmitter

bandwidth limitations. For modulation formats of high order, e.g., 64QAM,

however, nonlinear effects become limiting. Neural networks (NNs) are a

promising approach to adress nonlinear distortions, especially nonlinear

intersymbol interference (ISI). Due to their generalization capability, they

theoretically can model and invert any kind of nonlinearity.

In this thesis, novel concepts for a NN based pre-distorter shall be

investigated. For later use in commercial transceivers, an efficient real-time

implementation in an CMOS ASIC is key. Therefore, a detailed comparison of

the NN approach with alternative nonlinear compensation techniques

regarding performance and implementation complexity is one main focus of

this work. Look-up tables, Volterra equalizers, Wiener-Hammerstein systems,

decision-feedback or MLSE equalizers are examples for alternative concepts.

Fig. 1: Principle structure of a neural network

with delay elements (z-1), weights (colored),

sums, and nonlinear functions (𝜎).

For detailed information contact:

M. Sc. Christoph Füllner

christoph.fuellner@kit.edu

Tel. 0721-608-47173

Prof. Dr. Sebastian Randel

sebastian.randel@kit.edu

Tel. 0721-608-42490

Your tasks:

Research on existing ideas and developing of

new pre-distortion NN-based concepts

Implementation of the NN and some alternative

methods in Matlab or Python

Evaluation of the perfomance in simulations

and on experimental data

Analysis of the implementation complexity

z-1

z-1

z-1

z-1

x(n)

.

..

1

1

1

1

1

2

2

2

2

y(n)

x(n-1)

x(n-2)

x(n-3)

x(n-4)

Fig. 2: Constellation diagram for 64QAM. The

right-hand one clearly is nonlinearly distorted.