# Plug-in Channel Estimation with Dithered Quantized Signals in Spatially Non-Stationary Massive MIMO Systems

arXiv: https://arxiv.org/abs/2301.04641 · HTML: https://mlsanity.com/paper/2301.04641/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 72 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays](https://mlsanity.com/paper/1902.07678/) (2019)
- [One-bit compressive sensing with norm estimation](https://mlsanity.com/paper/1404.6853/) (2014)
- [UNO: Unlimited Sampling Meets One-Bit Quantization](https://mlsanity.com/paper/2301.10155/) (2022)
- [The Bussgang Decomposition of Non-Linear Systems: Basic Theory and MIMO Extensions](https://mlsanity.com/paper/2005.01597/) (2020)
- [Channel Vector Subspace Estimation from Low-Dimensional Projections](https://mlsanity.com/paper/1509.07469/) (2015)
- [Robust one-bit compressed sensing with partial circulant matrices](https://mlsanity.com/paper/1812.06719/) (2018)
- [Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2002.04960/) (2020)
- [Non-Stationarities in Extra-Large Scale Massive MIMO](https://mlsanity.com/paper/1903.03085/) (2019)
- [Structured Channel Covariance Estimation from Limited Samples in Massive MIMO](https://mlsanity.com/paper/1910.14467/) (2019)
- [Deep Learning Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction](https://mlsanity.com/paper/2002.09858/) (2020)
- [A Primer on Near-Field Beamforming for Arrays and Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2110.06661/) (2021)

## Built on by (most cited first, 4 of 4)

- [Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models](https://mlsanity.com/paper/2309.04014/) (2023)
- [A Parameter-Free Two-Bit Covariance Estimator with Improved Operator Norm Error Rate](https://mlsanity.com/paper/2308.16059/) (2023)
- [Deep Learning-Enabled One-Bit DoA Estimation](https://mlsanity.com/paper/2405.09712/) (2024)
- [Bit Efficient Toeplitz Covariance Estimation](https://mlsanity.com/paper/2412.12678/) (2024)

## Abstract (arXiv)

As the array dimension of massive MIMO systems increases to unprecedented levels, two problems occur. First, the spatial stationarity assumption along the antenna elements is no longer valid. Second, the large array size results in an unacceptably high power consumption if high-resolution analog-to-digital converters are used. To address these two challenges, we consider a Bussgang linear minimum mean square error (BLMMSE)-based channel estimator for large scale massive MIMO systems with one-bit quantizers and a spatially non-stationary channel. Whereas other works usually assume that the channel covariance is known at the base station, we consider a plug-in BLMMSE estimator that uses an estimate of the channel covariance and rigorously analyze the distortion produced by using an estimated, rather than the true, covariance. To cope with the spatial non-stationarity, we introduce dithering into the quantized signals and provide a theoretical error analysis. In addition, we propose an angular domain fitting procedure which is based on solving an instance of non-negative least squares. For the multi-user data transmission phase, we further propose a BLMMSE-based receiver to handle one-bit quantized data signals. Our numerical results show that the performance of the proposed BLMMSE channel estimator is very close to the oracle-aided scheme with ideal knowledge of the channel covariance matrix. The BLMMSE receiver outperforms the conventional maximum-ratio-combining and zero-forcing receivers in terms of the resulting ergodic sum rate.