Empowering the 6G Cellular Architecture with Open RAN
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Abstract (arXiv)
Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous development is the RAN with the aim of making it more open, adaptive, functional, and easy to manage. In this paper, we highlight the transformative potential of embracing novel cellular architectures by transitioning from conventional systems to the progressive principles of Open RAN. This promises to make 6G networks more agile, cost-effective, energy-efficient, and resilient. It opens up a plethora of novel use cases, ranging from ubiquitous support for autonomous devices to cost-effective expansions in regions previously underserved. The principles of Open RAN encompass: (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability and orchestration; and (iii) AI-enabled data-centric closed-loop control and automation. We first discuss the transformative role Open RAN principles have played in the 5G era. Then, we adopt a system-level approach and describe how these Open RAN principles will support 6G RAN and architecture innovation. We qualitatively discuss potential performance gains that Open RAN principles yield for specific 6G use cases. For each principle, we outline the steps that research, development and standardization communities ought to take to make Open RAN principles central to next-generation cellular network designs.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Dec 2023.
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Citing papers per year: 2025: 5; 2026: 5.
10 papers.
- 2025XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detectioncited by 4
- 2025AI-Native Open RAN for Non-Terrestrial Networks: An Overviewcited by 1
- 2025Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metricscited by 1
- 2026Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligencecited by 1
- 2025Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networkscited by 0
- 2025XAI-on-RAN: Explainable, AI-native, and GPU-Accelerated RAN Towards 6Gcited by 0
- 2026BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networkscited by 0
- 2026SLA-Aware Distributed LLM Inference Across Device-RAN-Cloudcited by 0
- 2026FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANscited by 0
- 2026WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimizationcited by 0
- 2026WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimization31 Aug 2026
- 2026FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs4 Aug 2026
- 2026Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence27 Feb 2026
- 2026SLA-Aware Distributed LLM Inference Across Device-RAN-Cloud27 Feb 2026
- 2026BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks15 Feb 2026
- 2025XAI-on-RAN: Explainable, AI-native, and GPU-Accelerated RAN Towards 6G7 Oct 2025
- 2025Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics3 Sep 2025
- 2025AI-Native Open RAN for Non-Terrestrial Networks: An Overview16 Jul 2025
- 2025XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection13 Feb 2025
- 2025Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks5 Feb 2025