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

arXiv: https://arxiv.org/abs/1711.06548 · HTML: https://mlsanity.com/paper/1711.06548/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information](https://mlsanity.com/paper/2505.06900/) (2025)
- [Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding](https://mlsanity.com/paper/2110.12059/) (2021)
- [DDPG-Driven Deep-Unfolding with Adaptive Depth for Channel Estimation with Sparse Bayesian Learning](https://mlsanity.com/paper/2201.08477/) (2022)
- [Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems](https://mlsanity.com/paper/2203.04935/) (2022)
- [Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm](https://mlsanity.com/paper/2411.16043/) (2024)

## Abstract (arXiv)

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.