PROJECT TITLE :
Achievable Rates of FDD Massive MIMO Systems With Spatial Channel Correlation
It is well-known that the performance of frequency-division-duplex (FDD) large MIMO systems with i.i.d. channels is disappointing compared with that of your time-division-duplex (TDD) systems, because of the prohibitively large overhead for acquiring channel state info at the transmitter (CSIT). During 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 also the regularized-zero-forcing linear precoder. The achievable rates are optimized by judiciously planning the downlink channel training sequences and user CSIT feedback codebooks, exploiting the multiuser spatial channel correlation. We tend to compare our achievable rates with TDD massive MIMO systems, i.i.d. FDD systems, and also the joint spatial division and multiplexing (JSDM) scheme, by deriving the deterministic equivalents of the achievable rates, primarily based on the one-ring model and therefore the Laplacian model. It is shown that, primarily based on the proposed eigenspace channel estimation schemes, the speed-gap between FDD systems and TDD systems is significantly narrowed, even approached under moderate range of base station antennas. Compared to the JSDM theme, our proposal achieves dimensionality-reduction channel estimation while not channel pre-projection, and higher throughput for moderate range of antennas and moderate to giant channel coherence block length, though at higher computational complexity.
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