2D Beam Domain Statistical CSI Estimation for Massive MIMO Uplink

TL;DR

This paper introduces a low-complexity method for estimating 2D beam domain statistical CSI in massive MIMO systems, improving efficiency and performance without relying on instantaneous CSI.

Contribution

A novel, computationally efficient approach to estimate beam domain statistical CSI using KL divergence, applicable to 2D-BSCM in massive MIMO.

Findings

The proposed method outperforms existing algorithms in accuracy.

It significantly reduces computational complexity.

Simulation results confirm its effectiveness and versatility.

Abstract

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two dimensional (2D) beam based statistical channel model (BSCM) in massive MIMO systems.The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple receive pilot signals. A receive model shows the relation between the statistical property of the receive pilot signals and the BDCPMs is derived from the 2D-BSCM. On the basis of the receive model,we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without involving instantaneous CSI is proposed. The proposed method has much lower complexity than the MMV focal underdetermined system solver (M-FOCUSS) algorithm. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application. Simulations results show that the proposed method works well and bring significant performance gain when used in channel estimation.