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Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges

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arxiv 2202.01032 v2 pith:DF3JAOAK submitted 2022-02-02 cs.NI eess.SP

Michele Polese , Leonardo Bonati , Salvatore D'Oro , Stefano Basagni , Tommaso Melodia This is my paper · ORCID
classification cs.NIeess.SP
keywords o-raninterfacesresearcharchitecturechallengesdesignnetworkscomponents
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The Open Radio Access Network (RAN) and its embodiment through the O-RAN Alliance specifications are poised to revolutionize the telecom ecosystem. O-RAN promotes virtualized RANs where disaggregated components are connected via open interfaces and optimized by intelligent controllers. The result is a new paradigm for the RAN design, deployment, and operations: O-RAN networks can be built with multi-vendor, interoperable components, and can be programmatically optimized through a centralized abstraction layer and data-driven closed-loop control. Therefore, understanding O-RAN, its architecture, its interfaces, and workflows is key for researchers and practitioners in the wireless community. In this article, we present the first detailed tutorial on O-RAN. We also discuss the main research challenges and review early research results. We provide a deep dive of the O-RAN specifications, describing its architecture, design principles, and the O-RAN interfaces. We then describe how the O-RAN RAN Intelligent Controllers (RICs) can be used to effectively control and manage 3GPP-defined RANs. Based on this, we discuss innovations and challenges of O-RAN networks, including the Artificial Intelligence (AI) and Machine Learning (ML) workflows that the architecture and interfaces enable, security and standardization issues. Finally, we review experimental research platforms that can be used to design and test O-RAN networks, along with recent research results, and we outline future directions for O-RAN development.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing

    cs.LG 2025-05 reject novelty 4.0 of 10

    ORAN-GUIDE couples a domain-specific LLM prompt generator with a frozen GPT-2 encoder and learnable prompt tokens to improve multi-agent SAC sample efficiency in O-RAN slicing.

  2. SLICES, a scientific instrument for the networking community

    cs.NI 2025-02 conditional novelty 4.0 of 10

    The authors propose SLICES, a pan-European research infrastructure to make networking experiments more credible and reproducible by adapting the ESFRI scientific-instrument model.

  3. Industrial Viewpoints on RAN Technologies for 6G

    cs.NI 2025-08 conditional novelty 3.0 of 10

    An industry-authored survey predicts 6G radio access will center on mid-band spectrum, evolved massive MIMO, AI/ML integration, OFDM-compatible waveforms, and native satellite support, explicitly labeled as speculation.

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