Enforcing Statistical Orthogonality in Massive MIMO Systems via Covariance Shaping

06/15/2021 ∙ by Placido Mursia, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.