Rate Splitting in FDD Massive MIMO Systems Based on the Second Order Statistics of Transmission Channels

In this work, we present new results for the application of rate splitting multiple access (RSMA) to the downlink (DL) of a massive multiple-input-multiple-output (MaMIMO) system operating in frequency-division-duplex (FDD) mode. Due to the lack of uplink (UL) - DL channel reciprocity in such systems, explicit training in the DL has to be performed in order to gain knowledge about the single-antenna users' channels at the base station (BS). This is ensured through a feedback link from the users to the BS. Dealing with the DL of a massive MIMO system implies that acquiring the DL channel state information (CSI) comes at the cost of a huge training overhead that scales with the number of BS antennas. Therefore, we limit the resources allocated to training by reusing the pilot sequences among the BS antennas that results in a contaminated channel observation. Despite this incomplete channel knowledge at the transmitter side, the proposed RS approach combined with a statistical precoder relying on the channels' second-order information achieves excellent results in terms of spectral efficiency compared to the state-of-art techniques. This is demonstrated via Monte-Carlo simulations of typical communication scenarios.