Abstract
In a frequency division duplexing multiple-input multiple-output
(FDD-MIMO) system, the user equipment (UE) send the downlink channel
state information (CSI) to the base station for performance improvement.
However, with the growing complexity of MIMO systems, this feedback
becomes expensive and has a negative impact on the bandwidth. Although
this problem has been largely studied in the literature, the noisy
nature of the feedback channel is less considered. In this paper, we
introduce PRVNet, a neural architecture based on variational
autoencoders (VAE). VAE gained large attention in many fields (e.g.,
image processing, language models, or recommendation system). However,
it received less attention in the communication domain generally and in
CSI feedback problem specifically. We also introduce a different
regularization parameter for the learning objective, which proved to be
crucial for achieving competitive performance. In addition, we provide
an efficient way to tune this parameter using KL-annealing. Empirically,
we show that the proposed model significantly outperforms
state-of-the-art, including two neural network approaches. The proposed
model is also proved to be more robust against different levels of
noise.