# Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays

arXiv: https://arxiv.org/abs/2411.15144 · HTML: https://mlsanity.com/paper/2411.15144/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [Deep Networks for Direction-of-Arrival Estimation in Low SNR](https://mlsanity.com/paper/2011.08848/) (2020)
- [mpNet: variable depth unfolded neural network for massive MIMO channel estimation](https://mlsanity.com/paper/2008.04088/) (2020)
- [Learn to Rapidly and Robustly Optimize Hybrid Precoding](https://mlsanity.com/paper/2301.00369/) (2023)
- [Efficient Deep Unfolding for SISO-OFDM Channel Estimation](https://mlsanity.com/paper/2210.06588/) (2022)
- [SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation](https://mlsanity.com/paper/2306.02271/) (2023)
- [Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments](https://mlsanity.com/paper/2307.04111/) (2023)
- [Semi-Supervised End-to-End Learning for Integrated Sensing and Communications](https://mlsanity.com/paper/2310.09940/) (2023)
- [TransMUSIC: A Transformer-Aided Subspace Method for DOA Estimation with Low-Resolution ADCs](https://mlsanity.com/paper/2309.08174/) (2023)
- [Model-based Deep Learning for Beam Prediction based on a Channel Chart](https://mlsanity.com/paper/2312.02239/) (2023)
- [Neural network approaches to point lattice decoding](https://mlsanity.com/paper/2012.07032/) (2020)
- [Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspective](https://mlsanity.com/paper/2210.15012/) (2022)
- [Modeling and Analysis of OFDM-based 5G/6G Localization under Hardware Impairments](https://mlsanity.com/paper/2301.01042/) (2023)
- [Probabilistic Positioning Via Ray Tracing With Noisy Angle of Arrival Measurements](https://mlsanity.com/paper/2403.00611/) (2024)

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

- [Near Field Localization via AI-Aided Subspace Methods](https://mlsanity.com/paper/2504.00599/) (2025)
- [HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing](https://mlsanity.com/paper/2510.10718/) (2025)
- [Differentiable High-Order Markov Models for Spectrum Prediction](https://mlsanity.com/paper/2412.00328/) (2024)
- [Model-based Implicit Neural Representation for sub-wavelength Radio Localization](https://mlsanity.com/paper/2506.06387/) (2025)
- [Physically constrained unfolded multi-dimensional OMP for large MIMO systems](https://mlsanity.com/paper/2601.10771/) (2026)

## Abstract (arXiv)

Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of the integrated sensing and communication paradigm. To fully exploit the potential of such sensing systems, it is crucial to take into account potential hardware impairments that can negatively impact the obtained performance. This study introduces a joint DoA estimation and hardware impairment learning scheme following a model-based approach. Specifically, a differentiable version of the multiple signal classification (MUSIC) algorithm is derived, allowing efficient learning of the considered impairments. The proposed approach supports both supervised and unsupervised learning strategies, showcasing its practical potential. Simulation results indicate that the proposed method successfully learns significant inaccuracies in both antenna locations and complex gains. Additionally, the proposed method outperforms the classical MUSIC algorithm in the DoA estimation task.