# Distributed AI Platform for the 6G RAN

arXiv: https://arxiv.org/abs/2410.03747 · HTML: https://mlsanity.com/paper/2410.03747/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [dApps: Distributed Applications for Real-time Inference and Control in O-RAN](https://mlsanity.com/paper/2203.02370/) (2022)
- [Context-Aware Security for 6G Wireless The Role of Physical Layer Security](https://mlsanity.com/paper/2101.01536/) (2021)
- [Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook](https://mlsanity.com/paper/2302.02107/) (2023)
- [Towards Energy Efficient RAN: From Industry Standards to Trending Practice](https://mlsanity.com/paper/2402.11993/) (2024)

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

- [Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G](https://mlsanity.com/paper/2507.06911/) (2025)
- [Offload or Overload: A Platform Measurement Study of Mobile Robotic Manipulation Workloads](https://mlsanity.com/paper/2603.18284/) (2026)

## Abstract (arXiv)

Cellular Radio Access Networks (RANs) are rapidly evolving towards 6G, driven by the need to reduce costs and introduce new revenue streams for operators and enterprises. In this context, AI emerges as a key enabler in solving complex RAN problems spanning both the management and application domains. Unfortunately, and despite the undeniable promise of AI, several practical challenges still remain, hindering the widespread adoption of AI applications in the RAN space. In this work, we attempt to shed light to these challenges and argue that existing approaches in addressing them are inadequate for realizing the vision of a truly AI-native 6G network. We propose a distributed AI platform architecture, tailored to the needs of an AI-native RAN.