37 citations · 56 across the 6 of their papers we have counts for
6 papers
From Channel Measurement to Training Data for PHY Layer AI Applications
Michael Zentarra, Julian Ahrens, Lia Ahrens
Learning-based techniques such as artificial intelligence (AI) and machine learning (ML) play an increasingly important role in the development of future communication networks. Th…
Signal Restoration and Channel Estimation for Channel Sounding with SDRs
Julian Ahrens, Lia Ahrens, Michael Zentarra +1
In this paper, the task of channel sounding using software defined radios (SDRs) is considered. In contrast to classical channel sounding equipment, SDRs are general purpose device…
A Machine Learning Method for Prediction of Multipath Channels
Julian Ahrens, Lia Ahrens, Hans D. Schotten
In this paper, a machine learning method for predicting the evolution of a mobile communication channel based on a specific type of convolutional neural network is developed and ev…
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics
Lia Ahrens, Julian Ahrens, Hans D. Schotten
In this paper we propose a novel machine-learning method for anomaly detection applicable to data with periodic characteristics where randomly varying period lengths are explicitly…
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data
Simon Duque Anton, Lia Ahrens, Daniel Fraunholz +1
The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use,…
An AI-driven Malfunction Detection Concept for NFV Instances in 5G
Julian Ahrens, Mathias Strufe, Lia Ahrens +1
Efficient network management is one of the key challenges of the constantly growing and increasingly complex wide area networks (WAN). The paradigm shift towards virtualized (NFV)…