  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 samples 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.
