# 5G Positioning Advancements with AI/ML

arXiv: https://arxiv.org/abs/2401.02427 · HTML: https://mlsanity.com/paper/2401.02427/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach](https://mlsanity.com/paper/2210.06294/) (2022)
- [An Overview of the 3GPP Study on Artificial Intelligence for 5G New Radio](https://mlsanity.com/paper/2308.05315/) (2023)
- [Complementary Semi-Deterministic Clusters for Realistic Statistical Channel Models for Positioning](https://mlsanity.com/paper/2207.07837/) (2022)
- [Towards Realistic Statistical Channel Models For Positioning: Evaluating the Impact of Early Clusters](https://mlsanity.com/paper/2207.07838/) (2022)

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

- [5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization](https://mlsanity.com/paper/2505.17684/) (2025)
- [UWB TDoA Error Correction using Transformers: Patching and Positional Encoding Strategies](https://mlsanity.com/paper/2507.03523/) (2025)

## Abstract (arXiv)

This paper provides a comprehensive review of AI/ML-based direct positioning within 5G systems, focusing on its potential in challenging scenarios and conditions where conventional methods often fall short. Building upon the insights from the technical report TR38.843, we examine the Life Cycle Management (LCM) with a focus on to the aspects associated direct positioning process. We highlight significant simulation results and key observations from the report on the direct positioning under the various challenging conditions. Additionally, we discuss selected solutions that address measurement reporting, data collection, and model management, emphasizing their importance for advancing direct positioning.