Channel Reconstruction Based Multiuser Precoding with Limited Feedback

We consider the downlink of a multiuser multiple-input multiple-output (MU-MIMO) system, where each user feeds back a partial channel state information (CSI), namely, the quantized version of the dominant eigenvector of its channel covariance matrix, to the base station (BS) for precoding. Specifically, we propose a downlink multiuser precoding scheme by first reconstructing the equivalent channel matrix of each user via a limited feedback, and then by employing a precoder to suppress the multiuser interference at the receivers. For the single stream case, a signal-to-leakage-and-noise ratio (SLNR) based precoding is employed, while for the full stream case with limited feedback, we employ a lattice reduction aided block diagonalization type precoding with suitable modifications at the receiver side. Extensive numerical examples which are provided using the 5G new radio (5G-NR) channel models demonstrate that the proposed schemes outperform the existing eigenvector based algorithms, and they are more robust against the downlink channel estimation errors.

I. Introduction

Knowledge of the channel state information (CSI) at a base station (BS) plays an important role in achieving higher throughputs in future cellular networks. In order for the BS to obtain the downlink CSI, the users need to feed back their downlink channel matrix (e.g., estimated through downlink pilot training). This induces a huge amount of overhead to the system, which in turn decreases the efficacy of CSI utilization. To resolve this issue, limited feedback techniques are employed for which either a quantized version of the channel coefficients (or, a property of channel matrix such as the dominant eigenvector) or their corresponding indices from a predefined codebook are fed back to the BS [1]. The BS then utilizes this information to precode the data for downlink transmission.