FDD Massive MIMO Channel Estimation with Arbitrary 2D-Array Geometry

TL;DR

This paper proposes a novel off-grid sparse Bayesian learning method for downlink channel estimation in FDD massive MIMO systems with arbitrary 2D-array geometries, overcoming limitations of traditional DFT-based approaches.

Contribution

It introduces an off-grid model and an efficient iterative refinement algorithm for accurate channel estimation with arbitrary array geometries, extending to uplink-aided estimation.

Findings

Effective off-grid refinement improves channel estimation accuracy.

Applicable to arbitrary 2D-array geometries, not just ULAs.

Enhanced uplink-aided channel recovery performance.

Abstract

This paper addresses the problem of downlink channel estimation in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The existing methods usually exploit hidden sparsity under a discrete Fourier transform (DFT) basis to estimate the cdownlink channel. However, there are at least two shortcomings of these DFT-based methods: 1) they are applicable to uniform linear arrays (ULAs) only, since the DFT basis requires a special structure of ULAs, and 2) they always suffer from a performance loss due to the leakage of energy over some DFT bins. To deal with the above shortcomings, we introduce an off-grid model for downlink channel sparse representation with arbitrary 2D-array antenna geometry, and propose an efficient sparse Bayesian learning (SBL) approach for the sparse channel recovery and off-grid refinement. The main idea of the proposed off-grid method is to consider the sampled grid points as adjustable parameters. Utilizing an in-exact block majorization-minimization (MM) algorithm, the grid points are refined iteratively to minimize the off-grid gap. Finally, we further extend the solution to uplink-aided channel estimation by exploiting the angular reciprocity between downlink and uplink channels, which brings enhanced recovery performance.