When operating massive multiple-input multiple-output (MIMO) systems with
uplink (UL) and downlink (DL) channels at different frequencies (frequency
division duplex (FDD) operation), acquisition of channel state information
(CSI) for downlink precoding is a major challenge. Since, barring transceiver
impairments, both UL and DL CSI are determined by the physical environment
surrounding transmitter and receiver, it stands to reason that, for a static
environment, a mapping from UL CSI to DL CSI may exist. First, we propose to
use various neural network (NN)-based approaches that learn this mapping and
provide baselines using classical signal processing. Second, we introduce a
scheme to evaluate the performance and quality of generalization of all
approaches, distinguishing between known and previously unseen physical
locations. Third, we evaluate all approaches on a real-world indoor dataset
collected with a 32-antenna channel sounder