  In this work, we propose a joint adaptive codebook construction and feedback
generation scheme in frequency division duplex (FDD) systems. Both unsupervised
and supervised deep learning techniques are used for this purpose. Based on a
recently discovered equivalence of uplink (UL) and downlink (DL) channel state
information (CSI) in terms of neural network learning, the codebook and
associated deep encoder for feedback signaling is based on UL data only.
Subsequently, the feedback encoder can be offloaded to the mobile terminals
(MTs) to generate channel feedback there as efficiently as possible, without
any training effort at the terminals or corresponding transfer of training and
codebook data. Numerical simulations demonstrate the promising performance of
the proposed method.
