963 citations · 3.4k across the 52 of their papers we have counts for
8 papers · 1 filter
Model-free Training of End-to-end Communication Systems
Fayçal Ait Aoudia, Jakob Hoydis
The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model. We present i…
Fundamental Asymptotic Behavior of (Two-User) Distributed Massive MIMO
Luca Sanguinetti, Emil Bjornson, Jakob Hoydis
This paper considers the uplink of a distributed Massive MIMO network where base stations (BSs), each equipped with antennas, receive data from users. We study the as…
Hardware Distortion Correlation Has Negligible Impact on UL Massive MIMO Spectral Efficiency
Emil Björnson, Luca Sanguinetti, Jakob Hoydis
This paper analyzes how the distortion created by hardware impairments in a multiple-antenna base station affects the uplink spectral efficiency (SE), with focus on Massive MIMO. T…
Deep Reinforcement Learning Autoencoder with Noisy Feedback
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis
End-to-end learning of communication systems enables joint optimization of transmitter and receiver, implemented as deep neural network-based autoencoders, over any type of channel…
Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning
Maximilian Arnold, Jakob Hoydis, Stephan ten Brink
With a significant increase in area throughput, Massive MIMO has become an enabling technology for fifth generation (5G) wireless mobile communication systems. Although prototypes…
Can Hardware Distortion Correlation be Neglected When Analyzing Uplink SE in Massive MIMO?
Emil Björnson, Luca Sanguinetti, Jakob Hoydis
This paper analyzes how the distortion created by hardware impairments in a multiple-antenna base station affects the uplink spectral efficiency (SE), with focus on Massive MIMO. T…
End-to-End Learning of Communications Systems Without a Channel Model
Fayçal Ait Aoudia, Jakob Hoydis
The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model. We present…
OFDM-Autoencoder for End-to-End Learning of Communications Systems
Alexander Felix, Sebastian Cammerer, Sebastian Dörner +2
We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cy…