# Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB

arXiv: https://arxiv.org/abs/2412.10644 · HTML: https://mlsanity.com/paper/2412.10644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 14 Dec 2024.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [DeepMUSIC: Multiple Signal Classification via Deep Learning](https://mlsanity.com/paper/1912.04357/) (2019)
- [SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect Array](https://mlsanity.com/paper/2203.10231/) (2022)
- [A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling](https://mlsanity.com/paper/2009.12858/) (2020)
- [Carrier Phase Ranging for Indoor Positioning with 5G NR Signals](https://mlsanity.com/paper/2112.11772/) (2021)
- [Efficient Joint DOA and TOA Estimation for Indoor Positioning with 5G Picocell Base Stations](https://mlsanity.com/paper/2206.09748/) (2022)
- [In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks](https://mlsanity.com/paper/2303.04470/) (2023)
- [Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB](https://mlsanity.com/paper/2501.00009/) (2024)
- [5G NR monostatic positioning with array impairments: Data-and-model-driven framework and experiment results](https://mlsanity.com/paper/2412.08095/) (2024)

## Abstract (arXiv)

Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wireless networks heavily rely on pure model-driven techniques to achieve positioning functionality, often succumbing to performance deterioration due to hardware impairments in practical scenarios. Here we reformulate the direction finding or angle-of-arrival (AoA) estimation problem as an image recovery task of the spatial spectrum and propose a new model-driven deep neural network (MoD-DNN) framework. The proposed MoD-DNN scheme comprises three modules: a multi-task autoencoder-based beamformer, a coarray spectrum generation module, and a model-driven deep learning-based spatial spectrum reconstruction module. Our technique enables automatic calibration of angular-dependent phase error thereby enhancing the resilience of direction-finding precision against realistic system non-idealities. We validate the proposed scheme both using numerical simulations and field tests. The results show that the proposed MoD-DNN framework enables effective spectrum calibration and accurate AoA estimation. To the best of our knowledge, this study marks the first successful demonstration of hybrid data-and-model-driven direction finding utilizing readily available commodity 5G gNodeB.