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Managing O-RAN Networks: xApp Development from Zero to Hero

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arxiv 2407.09619 v3 pith:IPEY7SMU submitted 2024-07-12 cs.NI cs.SYeess.SY

classification cs.NIcs.SYeess.SY
keywords xappso-ranxappopenarchitecturechallengescomprehensivecontrol
verification ladder T0 review T1 audit T2 compute T3 formal
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The Open Radio Access Network (O-RAN) Alliance proposes an open architecture that disaggregates the RAN and supports executing custom control logic in near-real time from third-party applications, the xApps. Despite O-RAN's efforts, the creation of xApps remains a complex and time-consuming endeavor, aggravated by the sometimes fragmented, outdated, or deprecated documentation from the O-RAN Software Community (OSC). These challenges hinder academia and industry from developing and validating solutions and algorithms on O-RAN networks. This tutorial addresses this gap by providing the first comprehensive guide for developing xApps to manage the O-RAN ecosystem from theory to practice. We provide a thorough theoretical foundation of the O-RAN architecture and detail the functionality offered by Near Real-Time RAN Intelligent Controller (Near-RT RIC) components. We examine the xApp design and configuration. We explore the xApp lifecycle and demonstrate how to deploy and manage xApps on a Near-RT RIC. We address the xApps' interfaces and capabilities, accompanied by practical examples. We provide comprehensive details on how xApps can control the RAN. We discuss debugging strategies and good practices to aid the xApp developers in testing their xApps. Finally, we review the current landscape and open challenges for creating xApps.

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Cited by 2 Pith papers

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  1. Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction

    eess.SP 2025-01 conditional novelty 6.0 of 10

    ML-WCP meta-learns a context-dependent likelihood ratio and uses it inside weighted conformal prediction to calibrate wireless AI with zero runtime data.

  2. Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach

    cs.NI 2024-12 conditional novelty 4.0 of 10

    A GraphSAGE-based model reconstructs O-RAN conflict graphs from parameter and KPI data, achieving 100% F1 on a synthetic conflict model.

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