This paper tackles the problem of downlink transmission in massive
multiple-input multiple-output(MIMO) systems where the equipments (UEs) exhibit
high spatial correlation and the channel estimation is limited by strong pilot
contamination. Signal subspace separation among the UEs is, in fact, rarely
realized in practice and is generally beyond the control of the network
designer (as it is dictated by the physical scattering environment). In this
context, we propose a novel statistical beamforming technique, referred to
asMIMO covariance shaping, that exploits multiple antennas at the UEs and
leverages the realistic non-Kronecker structure of massive MIMO channels to
target a suitable shaping of the channel statistics performed at the UE-side.
To optimize the covariance shaping strategies, we propose a low-complexity
block coordinate descent algorithm that is proved to converge to a limit point
of the original nonconvex problem. For the two-UE case, this is shown to
converge to a stationary point of the original problem. Numerical results
illustrate the sum-rate performance gains of the proposed method with respect
to reference scenarios employing the multiple antennas at the UE for spatial
multiplexing.Comment: Submitted for journal publicatio
