# AI-RAN: Transforming RAN with AI-driven Computing Infrastructure

arXiv: https://arxiv.org/abs/2501.09007 · HTML: https://mlsanity.com/paper/2501.09007/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 15 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Hardware Acceleration for Open Radio Access Networks: A Contemporary Overview](https://mlsanity.com/paper/2305.09588/) (2023)

## Built on by (most cited first, 14 of 14)

- [Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G](https://mlsanity.com/paper/2507.06911/) (2025)
- [Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMs](https://mlsanity.com/paper/2507.21193/) (2025)
- [6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence](https://mlsanity.com/paper/2605.01546/) (2026)
- [Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization](https://mlsanity.com/paper/2507.08403/) (2025)
- [Quantization-Aware Collaborative Inference for Large Embodied AI Models](https://mlsanity.com/paper/2602.13052/) (2026)
- [SCA-LLM: Spectral-Attentive LLM-Based Wireless World Modeling for Agentic Communications](https://mlsanity.com/paper/2509.08139/) (2025)
- [DiffPace: Diffusion-based Plug-and-play Augmented Channel Estimation in mmWave and Terahertz Ultra-Massive MIMO Systems](https://mlsanity.com/paper/2511.01867/) (2025)
- [SLA-Aware Distributed LLM Inference Across Device-RAN-Cloud](https://mlsanity.com/paper/2602.23722/) (2026)
- [Orchestrating Multimodal DNN Workloads in Wireless Neural Processing](https://mlsanity.com/paper/2603.02109/) (2026)
- [Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning](https://mlsanity.com/paper/2605.04304/) (2026)
- [Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN](https://mlsanity.com/paper/2605.10036/) (2026)
- [AI-RAN on NPUs: Baseband Processing Without Baseband Chips](https://mlsanity.com/paper/2607.04224/) (2026)
- [ImpactHO: Importance-Aware KV Cache Transfer for Multi-User Edge LLM Handover](https://mlsanity.com/paper/2608.10545/) (2026)
- [CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN](https://mlsanity.com/paper/2609.00590/) (2026)

## Abstract (arXiv)

The radio access network (RAN) landscape is undergoing a transformative shift from traditional, communication-centric infrastructures towards converged compute-communication platforms. This article introduces AI-RAN which integrates both RAN and artificial intelligence (AI) workloads on the same infrastructure. By doing so, AI-RAN not only meets the performance demands of future networks but also improves asset utilization. We begin by examining how RANs have evolved beyond mobile broadband towards AI-RAN and articulating manifestations of AI-RAN into three forms: AI-for-RAN, AI-on-RAN, and AI-and-RAN. Next, we identify the key requirements and enablers for the convergence of communication and computing in AI-RAN. We then provide a reference architecture for advancing AI-RAN from concept to practice. To illustrate the practical potential of AI-RAN, we present a proof-of-concept that concurrently processes RAN and AI workloads utilizing NVIDIA Grace-Hopper GH200 servers. Finally, we conclude the article by outlining future work directions to guide further developments of AI-RAN.