963 citations · 3.4k across the 52 of their papers we have counts for
5 papers · 1 filter
Deep Learning-Based Communication Over the Air
Sebastian Dörner, Sebastian Cammerer, Jakob Hoydis +1
End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of…
Massive MIMO has Unlimited Capacity
Emil Björnson, Jakob Hoydis, Luca Sanguinetti
The capacity of cellular networks can be improved by the unprecedented array gain and spatial multiplexing offered by Massive MIMO. Since its inception, the coherent interference c…
Scaling Deep Learning-based Decoding of Polar Codes via Partitioning
Sebastian Cammerer, Tobias Gruber, Jakob Hoydis +1
The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network…
An Introduction to Deep Learning for the Physical Layer
Timothy J. O'Shea, Jakob Hoydis
We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new w…
On Deep Learning-Based Channel Decoding
Tobias Gruber, Sebastian Cammerer, Jakob Hoydis +1
We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori…