Model-based Deep Learning for Beam Prediction based on a Channel Chart
Taha Yassine Baptiste Chatelier Vincent Corlay Matthieu Crussière Stephane Paquelet Olav Tirkkonen Luc Le Magoarou
Channel charting builds a map of the radio environment in an unsupervised
way. The obtained chart locations can be seen as low-dimensional compressed
versions of channel state information that can be used for a wide variety of
applications, including beam prediction. In non-standalone or cell-free
systems, chart locations computed at a given base station can be transmitted to
several other base stations (possibly operating at different frequency bands)
for them to predict which beams to use. This potentially yields a dramatic
reduction of the overhead due to channel estimation or beam management, since
only the base station performing charting requires channel state information,
the others directly predicting the beam from the chart location. In this paper,
advanced model-based neural network architectures are proposed for both channel
charting and beam prediction. The proposed methods are assessed on realistic
synthetic channels, yielding promising results.