# Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs

arXiv: https://arxiv.org/abs/2008.03757 · HTML: https://mlsanity.com/paper/2008.03757/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers](https://mlsanity.com/paper/2102.02993/) (2021)
- [Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems](https://mlsanity.com/paper/2107.11958/) (2021)
- [Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems](https://mlsanity.com/paper/2212.01717/) (2022)
- [Theoretical Analysis of Deep Neural Networks in Physical Layer Communication](https://mlsanity.com/paper/2202.09954/) (2022)
- [Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems: Dithering-Aided Adaptive Approach](https://mlsanity.com/paper/2304.07696/) (2023)
- [Model-Based Learning for DOA Estimation with One-Bit Single-Snapshot Sparse Arrays](https://mlsanity.com/paper/2502.17473/) (2025)
- [Data Detection for Massive MIMO Systems with 1-Bit Quantized Dithered Linear Precoding](https://mlsanity.com/paper/2606.05447/) (2026)

## Abstract (arXiv)

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.