Wireless link adaptation with outdated CSI — a hybrid data-driven and model-based approach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 In this paper, we extend the data-driven MCS selection scheme in our previous work to the case of outdated CSI, by assuming that CSI history is available to the system.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.