  It is well known that the performance of frequency-division-duplex (FDD)
massive MIMO systems with i.i.d. channels is disappointing compared with that
of time-division-duplex (TDD) systems, due to the prohibitively large overhead
for acquiring channel state information at the transmitter (CSIT). In this
paper, we investigate the achievable rates of FDD massive MIMO systems with
spatially correlated channels, considering the CSIT acquisition dimensionality
loss, the imperfection of CSIT and the regularized-zero-forcing linear
precoder. The achievable rates are optimized by judiciously designing the
downlink channel training sequences and user CSIT feedback codebooks,
exploiting the multiuser spatial channel correlation. We compare our achievable
rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial
division and multiplexing (JSDM) scheme, by deriving the deterministic
equivalents of the achievable rates, based on popular channel models. It is
shown that, based on the proposed eigenspace channel estimation schemes, the
rate-gap between FDD systems and TDD systems is significantly narrowed, even
approached under moderate number of base station antennas. Compared to the JSDM
scheme, our proposal achieves dimensionality-reduction channel estimation
without channel pre-projection, and higher throughput for moderate number of
antennas and moderate to large channel coherence time, though at higher
computational complexity.
