arXiv subjects

Stefan Baumgartner

Publications and source records attributed to Stefan Baumgartner.

3 recordsLinked to original sources

arXiv2024

SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

In recent years data-driven machine learning approaches have been extensively studied to replace or enhance traditionally model-based processing in digital communication systems. In this work, we focus on equalization and propose a novel neural network (NN-)based approach, referred to as SICNN. SICNN is designed by deep unfolding a model-based iterative soft interference cancellation (SIC) method. It eliminates the main disadvantages of its model-based counterpart, which suffers from high computational complexity and performance degradation due to required approximations. We present different variants of SICNN. SICNNv1 is specifically tailored to single carrier frequency domain equalization (SC-FDE) systems, the communication system mainly regarded in this work. SICNNv2 is more universal and is applicable as an equalizer in any communication system with a block-based data transmission scheme. Moreover, for both SICNNv1 and SICNNv2, we present versions with highly reduced numbers of learnable parameters. Another contribution of this work is a novel approach for generating training datasets for NN-based equalizers, which significantly improves their performance at high signal-to-noise ratios. We compare the bit error ratio performance of the proposed NN-based equalizers with state-of-the-art model-based and NN-based approaches, highlighting the superiority of SICNNv1 over all other methods for SC-FDE. Exemplarily, to emphasize its universality, SICNNv2 is additionally applied to a unique word orthogonal frequency division multiplexing (UW-OFDM) system, where it achieves state-of-the-art performance. Furthermore, we present a thorough complexity analysis of the proposed NN-based equalization approaches, and we investigate the influence of the training set size on the performance of NN-based equalizers.

eess.SP↗

arXiv2022

Neural Network Approaches for Data Estimation in Unique Word OFDM Systems

Data estimation is conducted with model-based estimation methods since the beginning of digital communications. However, motivated by the growing success of machine learning, current research focuses on replacing model-based data estimation methods by data-driven approaches, mainly neural networks (NNs). In this work, we particularly investigate the incorporation of existing model knowledge into data-driven approaches, which is expected to lead to complexity reduction and / or performance enhancement. We describe three different options, namely "model-inspired'' pre-processing, choosing an NN architecture motivated by the properties of the underlying communication system, and inferring the layer structure of an NN with the help of model knowledge. Most of the current publications on NN-based data estimation deal with general multiple-input multiple-output communication (MIMO) systems. In this work, we investigate NN-based data estimation for so-called unique word orthogonal frequency division multiplexing (UW-OFDM) systems. We highlight differences between UW-OFDM systems and general MIMO systems one has to be aware of when using NNs for data estimation, and we introduce measures for successful utilization of NN-based data estimators in UW-OFDM systems. Further, we investigate the use of NNs for data estimation when channel coded data transmission is conducted, and we present adaptions to be made, such that NN-based data estimators provide satisfying performance for this case. We compare the presented NNs concerning achieved bit error ratio performance and computational complexity, we show the peculiar distributions of their data estimates, and we also point out their downsides compared to model-based equalizers.

cs.IT↗

arXiv2021

Efficient Majority Voting in Digital Hardware

In recent years, machine learning methods became increasingly important for a manifold number of applications. However, they often suffer from high computational requirements impairing their efficient use in real-time systems, even when employing dedicated hardware accelerators. Ensemble learning methods are especially suitable for hardware acceleration since they can be constructed from individual learners of low complexity and thus offer large parallelization potential. For classification, the outputs of these learners are typically combined by majority voting, which often represents the bottleneck of a hardware accelerator for ensemble inference. In this work, we present a novel architecture that allows obtaining a majority decision in a number of clock cycles that is logarithmic in the number of inputs. We show, that for the example application of handwritten digit recognition a random forest processing engine employing this majority decision architecture implemented on an FPGA allows the classification of more than seven million images per second.

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