  In frequency-division duplexing systems, the downlink channel state
information (CSI) acquisition scheme leads to high training and feedback
overheads. In this paper, we propose an uplink-aided downlink channel
acquisition framework using deep learning to reduce these overheads. Unlike
most existing works that focus only on channel estimation or feedback modules,
to the best of our knowledge, this is the first study that considers the entire
downlink CSI acquisition process, including downlink pilot design, channel
estimation, and feedback. First, we propose an adaptive pilot design module by
exploiting the correlation in magnitude among bidirectional channels in the
angular domain to improve channel estimation. Next, to avoid the bit allocation
problem during the feedback module, we concatenate the complex channel and
embed the uplink channel magnitude to the channel reconstruction at the base
station. Lastly, we combine the above two modules and compare two popular
downlink channel acquisition frameworks. The former framework estimates and
feeds back the channel at the user equipment subsequently. The user equipment
in the latter one directly feeds back the received pilot signals to the base
station. Our results reveal that, with the help of uplink, directly feeding
back the pilot signals can save approximately 20% of feedback bits, which
provides a guideline for future research.
