20 citations · 35 across the 4 of their papers we have counts for
4 papers
RF Signal Classification with Synthetic Training Data and its Real-World Performance
Stefan Scholl
Neural nets are a powerful method for the classification of radio signals in the electromagnetic spectrum. These neural nets are often trained with synthetically generated data due…
Fourier, Gabor, Morlet or Wigner: Comparison of Time-Frequency Transforms
Stefan Scholl
In digital signal processing time-frequency transforms are used to analyze time-varying signals with respect to their spectral contents over time. Apart from the commonly used shor…
Classification of Radio Signals and HF Transmission Modes with Deep Learning
Stefan Scholl
This paper investigates deep neural networks for radio signal classification. Instead of performing modulation recognition and combining it with further analysis methods, the class…
Efficient Maximum-Likelihood Decoding of Linear Block Codes on Binary Memoryless Channels
Michael Helmling, Eirik Rosnes, Stefan Ruzika +1
In this work, we consider efficient maximum-likelihood decoding of linear block codes for small-to-moderate block lengths. The presented approach is a branch-and-bound algorithm us…