# Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

arXiv: https://arxiv.org/abs/2501.00009 · HTML: https://mlsanity.com/paper/2501.00009/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 10 Dec 2024.

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

- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)
- [IDOL: Inertial Deep Orientation-Estimation and Localization](https://mlsanity.com/paper/2102.04024/) (2021)
- [A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition](https://mlsanity.com/paper/2305.19458/) (2023)
- [Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks](https://mlsanity.com/paper/1811.02759/) (2018)
- [Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12142/) (2020)
- [DeepMUSIC: Multiple Signal Classification via Deep Learning](https://mlsanity.com/paper/1912.04357/) (2019)
- [Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization](https://mlsanity.com/paper/1911.12574/) (2019)
- [Efficient Joint DOA and TOA Estimation for Indoor Positioning with 5G Picocell Base Stations](https://mlsanity.com/paper/2206.09748/) (2022)
- [Fast Direct Localization for Millimeter Wave MIMO Systems via Deep ADMM Unfolding](https://mlsanity.com/paper/2302.02557/) (2023)
- [In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks](https://mlsanity.com/paper/2303.04470/) (2023)
- [Toward 6G TKμ Extreme Connectivity: Architecture, Key Technologies and Experiments](https://mlsanity.com/paper/2208.01190/) (2022)

## Built on by (most cited first, 2 of 2)

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB](https://mlsanity.com/paper/2412.10644/) (2024)

## Abstract (arXiv)

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.