05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Start date: 2020End date: 2022Institute: search.filters.UBSInstitut.Institut für Nachrichtenübertragung

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    Low-complexity adaptive digital equalizers for electronic dispersion compensation in optical fiber links
    (2022) Efinger, Daniel; Speidel, Joachim (Prof. Dr.-Ing.)
    This thesis addresses electronic equalization of intersymbol interference caused by chromatic and polarization mode dispersion in intensity-modulated optical communication links with direct detection. The simple and cost-efficient system setup is, even at high bit rates of 40 Gbit/s and beyond, of interest for short-haul optical links in metropolitan, aggregation or local area networks. Therefore, this thesis investigates preferably simple and low-complexity equalizer structures, which are able to compensate well for the nonlinear characteristics and influences of the intensity-modulated optical communication link with direct detection. Starting with system modeling and the introduction to different equalization methods, we identify low-complexity feed-forward and decision-feedback equalizers in the first part of this thesis. We further put their chromatic and polarization mode dispersion compensation performance to the broader context by comparison to maximum likelihood sequence estimation. Finally, we come to the investigation of adaptation algorithms for equalizer coefficient adjustment, which accounts for the time-variant nature of polarization mode dispersion, while still targeting preferably simple and efficient realization.
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    Efficient FPGA implementation of an ANN-based demapper using cross-layer analysis
    (2022) Ney, Jonas; Hammoud, Bilal; Dörner, Sebastian; Herrmann, Matthias; Clausius, Jannis; Ten Brink, Stephan; Wehn, Norbert
    In the field of communication, autoencoder (AE) refers to a system that replaces parts of the traditional transmitter and receiver with artificial neural networks (ANNs). To meet the system performance requirements, it is necessary for the AE to adapt to the changing wireless-channel conditions at runtime. Thus, online fine-tuning in the form of ANN-retraining is of great importance. Many algorithms on the ANN layer are developed to improve the AE’s performance at the communication layer. Yet, the link of the system performance and the ANN topology to the hardware layer is not fully explored. In this paper, we analyze the relations between the design layers and present a hardware implementation of an AE-based demapper that enables fine-tuning to adapt to varying channel conditions. As a platform, we selected field-programmable gate arrays (FPGAs) which provide high flexibility and allow to satisfy the low-power and low-latency requirements of embedded communication systems. Furthermore, our cross-layer approach leverages the flexibility of FPGAs to dynamically adapt the degree of parallelism (DOP) to satisfy the system-level requirements and to ensure environmental adaptation. Our solution achieves 2000× higher throughput than a high-performance graphics processor unit (GPU), draws 5× less power than an embedded central processing unit (CPU) and is 5800× more energy efficient compared to an embedded GPU for small batch size. To the best of our knowledge, such a cross-layer design approach combined with FPGA implementation is unprecedented.