Wireless link adaptation with outdated CSI -- a hybrid data-driven and model-based approach

Link adaptation provides high spectral efficiency in
wireless communications by selecting appropriate transmission
parameters, e.g., the modulation and coding scheme (MCS),
based on the instantaneous wireless channel. However, link adaptation
suffers from impairments due to channel state information
(CSI) feedback delay. In this paper, we extend the data-driven
MCS selection scheme in our previous work to the case of
outdated CSI, by assuming that CSI history is available to the
system. We present two approaches that leverage the CSI history
to optimally select the MCS for the current channel, i.e., i) an
end-to-end (E2E) machine learning approach and ii) a hybrid
data-driven and model-based approach. The E2E method uses
the CSI history as input to a neural network for MCS selection.
Conversely, the hybrid method uses a lower-dimensionality
sufficient statistic for the instantaneous CSI, computed from the
CSI history, as input to a neural network for MCS selection. We
demonstrate that replacing the CSI history with the sufficient
statistic comes without loss of generality. Moreover, by means of
numerical experiments, we show that both approaches effectively
compensate for the feedback delay. However, we advocate the
hybrid approach as it comes with the additional benefits of i) a
smaller neural network, which in turn requires a lower amount
of data and training time, ii) improved explainability, and iii)
better insights into optimization choices.