2 research outputs found
Learning the CSI Denoising and Feedback Without Supervision
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 overhead incurred when the mobile terminal has to send
the downlink channel state information or corresponding partial information to
the base station, where the complete estimates can subsequently be restored. To
this end, we propose a novel learning-based framework for denoising and
compression of channel estimates. Unlike existing studies, we extend a recently
proposed approach and show that based solely on noisy uplink data available at
the base station, it is possible to learn an autoencoder neural network that
generalizes to downlink data. Subsequently, half of the autoencoder can be
offloaded to the mobile terminals to generate channel feedback there as
efficiently as possible, without any training effort at the terminals or
corresponding transfer of training data. Numerical simulations demonstrate the
excellent performance of the proposed method.Comment: Final versio
Learning the CSI Recovery in FDD Systems
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, the base station reconstructs the full channel state
information in the downlink frequency range based on limited downlink channel
state information feedback from the mobile terminal. The channel state
information recovery is based on a convolutional neural network which is
trained exclusively on collected channel state samples acquired in the uplink
frequency domain. No acquisition of training patterns in the downlink frequency
range is required at all. Finally, after a detailed presentation and analysis
of the proposed technique and its performance, the "transfer learning"'
assumption of the convolutional neural network that is central to the proposed
approach is validated with an analysis based on the maximum mean discrepancy
metric