Chair of Communications

Theses

Offered Theses

Additional topics for Master Theses are available. Please contact Prof. Dr.-Ing. Stephan Pachnicke or the assistants directly.

  1. Nonlinear Fourier Transform
    Motivation: The Nonlinear Fourier Transform (NFT) has the potential to remove the nonlinear limitations for higher capacity with increasing signal to noise ratio (SNR) in the optical fiber transmission system. The time signal can be split into two different nonlinear spectra similar to the Fourier spectrum. The signal can be composed of harmonic waves (continuous spectrum) and nonlinear waves (discrete spectrum), which are orthogonal to each other. These spectra can be modulated separately. The propagation of those spectra along the optical fiber can be characterized through a linear phase shift. It is therefore a linearization process of the nonlinear evolution inside an optical fiber. This makes the demodulation relatively easy.
    However, the numerical calculations for generating (inverse NFT) and demodulating (NFT) of the signals are still computationally complex. Furthermore, modulation formats have to be adapted to the nonlinear spectrum in order to compete with state-of-the-art commercially deployed techniques.
    A part of this research area shall be covered in the course of a master’s thesis. This can be the investigation of different applications as well as the development or evaluation of different modulation formats.
     Keywords: Nonlinear Fourier Transform (NFT), Numerical Calculations, Modeling, complex modulation format
     Supervisor: Jonas Koch, Room C-031, Phone: 880-6305,
    jonas.koch@tf.uni-kiel.de

     

  2. Machine Learning in Optical Communication
    Motivation: Next Generation Digital Signal Processing - The Kerr-Effect leads to nonlinear impairments inside the optical fiber, which impose a limit for the achievable transmission rate with increasing signal-to-noise-ratio (SNR). Nonlinear compensators using state-of-the-art digital signal processing (DSP) often struggle to improve the signal quality. Additionally, the high complexity makes it impractical for real-time use. Moreover, only deterministic nonlinearities are being compensated. There are, however, other nonlinear impairments in a core network, which are the result of the interactions between random noise (e.g. concatenated Erbium-doped amplifiers, EDFA) and the Kerr-Effect.
    Especially stochastic nonlinearities can be identified, characterized and equalized through machine learning algorithms. In addition, we can build a probabilistic model to describe those impairments, which we can use to optimize the demodulation algorithms. A part of this research area shall be covered in the course of a master’s thesis. This can be the investigation of different applications as well as the development or evaluation of different modulation formats.
     Keywords: Machine Learning, Digital Signal Processing, Nonlinear Compensation, Characterization of nonlinear statistical impairments
     Supervisor: Rebekka Weixer, Room C-015, Phone: 880-6312,
    rebekka.weixer@tf.uni-kiel.de

     

  3. Analysis of the Energy Efficiency of Hybrid Optical-Electrical Data Center Connects
     Motivation: In next generation data centers thousands of servers need to be interconnected with very high bit rates. Current architectures mainly rely on so called “fat-tree” approaches using a multi-layer electrical switching matrix. For further increasing data rates such architectures become more and more inefficient and consume a tremendous amount of electrical energy. A potential solution is to use additional optical connects between blades or racks purely in the optical layer without any electrical components in between. The optical switching of data connects will allow a significant reduction of the energy consumption because no optical-electrical conversion is required along the path. As an example an electrical 10 Gb/s Ethernet connection requires approx. 10 W of power whereas a pure photonic solution can be realized with only a fraction of the energy consumption.
     Keywords: Modelling, Optimization, Data Center
     Supervisor: Mihail Balanici, Raum C-014, Telefon: 880-6311,
    mba@tf.uni-kiel.de