# AI/ML Life Cycle Management for Interoperable AI Native RAN

arXiv: https://arxiv.org/abs/2507.18538 · HTML: https://mlsanity.com/paper/2507.18538/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Artificial Intelligence in 3GPP 5G-Advanced: A Survey](https://mlsanity.com/paper/2305.05092/) (2023)
- [An Overview of the 3GPP Study on Artificial Intelligence for 5G New Radio](https://mlsanity.com/paper/2308.05315/) (2023)
- [AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective](https://mlsanity.com/paper/2309.10575/) (2023)
- [Deep Learning based Positioning with Multi-task Learning and Uncertainty-based Fusion](https://mlsanity.com/paper/2403.08565/) (2024)
- [Demonstrating Interoperable Channel State Feedback Compression with Machine Learning](https://mlsanity.com/paper/2506.21796/) (2025)

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

- [AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation](https://mlsanity.com/paper/2606.08173/) (2026)
- [Learnware for CSI Feedback: Scene-specific Small Models Can Do Big](https://mlsanity.com/paper/2608.17760/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral to the 5G Radio Access Network (RAN), enabling beam management, channel state information (CSI) feedback, positioning, and mobility prediction. However, without a standardized life-cycle management (LCM) framework, challenges such as model drift, vendor lock-in, and limited transparency hinder large-scale deployment. 3GPP Releases 17--20 have progressively introduced AI/ML management and air-interface support, covering model training, validation, deployment, inference, data collection, performance monitoring, applicability assessment, and feature-specific control. Release 20 further extends these capabilities to two-sided CSI compression and inter-vendor model operation. This article reviews the resulting five-block LCM architecture, KPI-driven monitoring mechanisms, and inter-vendor collaboration schemes. We further propose an enhanced LCM framework with detailed interactions across functional blocks and an integrated procedure for reference-model and vendor-model development in two-sided operation, and identify open challenges in resource-efficient monitoring, environment drift detection, intelligent decision-making, and flexible model training. These developments provide a foundation for AI-native transceivers in 6G.