Towards Practical FDD Massive MIMO: CSI Extrapolation Driven by Deep Learning and Actual Channel Measurements

A major obstacle for widespread deployment of frequency division duplex (FDD)-based massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the base station (BS), in order to enable closed-loop precoding. In this work, we practically demonstrate an FDD Massive MIMO system that avoids this overhead by employing a deep learning-driven channel extrapolation, i.e., a prediction of the CSI from one frequency to an adjacent frequency band. Further, we compare the resulting performance with other state-of-the-art extrapolation schemes and showcase significant advantages in complex propagation environments, e.g., caused by non-line-of-sight (NLoS) scenarios. Finally, we show, for actual (measured) channels, that a tailored neural network (NN) at the BS can infer DL CSI centered around a frequency f
_DL
by solely observing uplink (UL) CSI on a different, yet adjacent frequency band around f
_UL
, with only a small loss in spectral efficiency.