14 citations · 29 across the 6 of their papers we have counts for
6 papers
Reverse Ordering Techniques for Attention-Based Channel Prediction
Valentina Rizzello, Benedikt Böck, Michael Joham +1
This work aims to predict channels in wireless communication systems based on noisy observations, utilizing sequence-to-sequence models with attention (Seq2Seq-attn) and transforme…
Variational Inference Aided Estimation of Time Varying Channels
Benedikt Böck, Michael Baur, Valentina Rizzello +1
One way to improve the estimation of time varying channels is to incorporate knowledge of previous observations. In this context, Dynamical VAEs (DVAEs) build a promising deep lear…
Learning Representations for CSI Adaptive Quantization and Feedback
Valentina Rizzello, Matteo Nerini, Michael Joham +2
In this work, we propose an efficient method for channel state information (CSI) adaptive quantization and feedback in frequency division duplexing (FDD) systems. Existing works ma…
Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO
Matteo Nerini, Valentina Rizzello, Michael Joham +2
To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division…
Learning the CSI Denoising and Feedback Without Supervision
Valentina Rizzello, Wolfgang Utschick
In this work, we develop a joint denoising and feedback strategy for channel state information in frequency division duplex systems. In such systems, the biggest challenge is the o…
Learning the CSI Recovery in FDD Systems
Wolfgang Utschick, Valentina Rizzello, Michael Joham +2
We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, t…