# Deep Learning-Enabled One-Bit DoA Estimation

arXiv: https://arxiv.org/abs/2405.09712 · HTML: https://mlsanity.com/paper/2405.09712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 8 of 8)

- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds](https://mlsanity.com/paper/1808.10038/) (2018)
- [One-Bit MUSIC](https://mlsanity.com/paper/1901.05109/) (2019)
- [On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays](https://mlsanity.com/paper/2012.14051/) (2020)
- [Plug-in Channel Estimation with Dithered Quantized Signals in Spatially Non-Stationary Massive MIMO Systems](https://mlsanity.com/paper/2301.04641/) (2023)
- [Deep Learning Meets Adaptive Filtering: A Stein's Unbiased Risk Estimator Approach](https://mlsanity.com/paper/2307.16708/) (2023)
- [IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays](https://mlsanity.com/paper/2309.08429/) (2023)
- [Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays](https://mlsanity.com/paper/2405.02788/) (2024)

## Built on by (most cited first, 1 of 1)

- [Model-Based Learning for DOA Estimation with One-Bit Single-Snapshot Sparse Arrays](https://mlsanity.com/paper/2502.17473/) (2025)

## Abstract (arXiv)

Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quantization is applied to the measurements. We present a compressed sensing formulation for DoA estimation from one-bit data in which estimating target DoAs requires recovering a sparse signal from a limited number of severely quantized linear measurements. In particular, we exploit covariance recovery from one-bit dither samples. To recover the covariance of transmitted signal, the learned iterative shrinkage and thresholding algorithm (LISTA) is employed fed by one-bit data. We demonstrate that the upper bound of estimation performance is governed by the recovery error of the transmitted signal covariance matrix. Through numerical experiments, we demonstrate the proposed LISTA-based algorithm's capability in estimating target locations. The code employed in this study is available online.