5 papers
On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation
Andreas Toftegaard Kristensen, Alexios Balatsoukas-Stimming, Andreas Burg
In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concur…
Lupulus: A Flexible Hardware Accelerator for Neural Networks
Andreas Toftegaard Kristensen, Robert Giterman, Alexios Balatsoukas-Stimming +1
Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algor…
Identification of Non-Linear RF Systems Using Backpropagation
Andreas Toftegaard Kristensen, Andreas Burg, Alexios Balatsoukas-Stimming
In this work, we use deep unfolding to view cascaded non-linear RF systems as model-based neural networks. This view enables the direct use of a wide range of neural network tools…
Hardware Implementation of Neural Self-Interference Cancellation
Yann Kurzo, Andreas Toftegaard Kristensen, Andreas Burg +1
In-band full-duplex systems can transmit and receive information simultaneously on the same frequency band. However, due to the strong self-interference caused by the transmitter t…
Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios
Andreas Toftegaard Kristensen, Andreas Burg, Alexios Balatsoukas-Stimming
In-band full-duplex systems allow for more efficient use of temporal and spectral resources by transmitting and receiving information at the same time and on the same frequency. Ho…