Robust full-dimension MIMO transmission based on limited feedback angular-domain CSIT

Abstract

In this paper, we propose robust multi-user beamforming and precoding techniques for full-dimension MIMO transmission based on limited feedback. We propose to employ an over-complete basis decomposition in the angular domain to approximate the channel matrices as sums of few dominant specular components, facilitating efficient channel state information (CSI) quantization at the users. The selected expansion vectors of such a sparse approximation, parametrized by azimuth and elevation angles, are relatively robust with respect to channel estimation errors as well as channel variations over time. Based on this CSI feedback, we propose incoherent beamforming/precoding methods that make use only of the azimuth and elevation angles as well as the norm of the expansion coefficients and do not rely on coherent multipath interference to eliminate inter-user interference. Our optimization aims at maximizing the signal power or the achievable rate of a user, while limiting the amount of interference leakage caused to other users. To further improve robustness, we account for uncertainty in the angular channel decomposition in the proposed precoder optimization.

Reliable Multi-Point Transmissions Over Directional MISO TWDP Fading Channels

“…If perfect channel state information (CSI) is available at the transmitters, the SLNR beamformers are obtained by maximizing corresponding Rayleigh quotients involving the instantaneous intended signal and interference leakage powers. However, this is not feasible under the adopted CSIT assumptions of this paper and, similar to [60], we therefore instead consider the expected signal and interference leakage powers, averaged over the unknown diffuse channel components and the random phase shifts of the scattering clusters…”

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“…1; in this figure, the fully white circle illustrates the angular position of the intended user, while the black circles/rectangles show the positions of other users. The leakage bounded beamforming method developed in [3] allows to incorporate angular uncertainty in the transmit beam design. The left and right plots of Fig.…”

Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification

“…When adopting Grassmannian quantization in larger-scale MIMO systems and/or for high resolution quantization, one faces two main challenges: 1) quantization complexity and 2) feedback overhead. The former issue can effectively be tackled, if the channel exhibits structure that can be exploited for quantization; e.g., in the millimeter wave band, the channel is often assumed to be sparse, which allows for efficient parametric CSI quantization by sparse decomposition [12]- [14]. Such techniques, however, are not applicable in i.i.d.…”