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Prototyping O-RAN Enabled UAV Experimentation for the AERPAW Testbed

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arxiv 2411.04027 v1 pith:HTGQKV4H submitted 2024-11-06 cs.NI cs.ARcs.SYeess.SY

classification cs.NIcs.ARcs.SYeess.SY
keywords aerpawexperimentationnetworko-ranaerialdataplatformtestbed
verification ladder T0 review T1 audit T2 compute T3 formal
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The Open Radio Access Network (O-RAN) architecture is reshaping the telecommunications landscape by enhancing network flexibility, openness, and intelligence. This paper establishes the requirements, evaluates the design tradeoffs, and introduces a scalable architecture and prototype of an open-source O-RAN experimentation platform within the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW), an at scale testbed that integrates unmanned aerial vehicles (UAVs) with advanced wireless network technologies, offering experimentation in both outdoor testbed and emulation via a custom digital twin (DT). Through a series of aerial experiments, we evaluate FlexRIC, an open-source RAN Intelligent Controller, within the AERPAW hardware-software platform for network data monitoring, providing valuable insights into the proposed integration and revealing opportunities for leveraging O-RAN to create custom service based optimizations for cellular connected UAVs. We discuss the challenges and potential use cases of this integration and demonstrate the use of a generative artificial intelligence model for generating realistic data based on collected real-world data to support AERPAW's DT.

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Forward citations

Cited by 2 Pith papers

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

  1. Driving Innovation in 6G Wireless Technologies: The OpenAirInterface Approach

    cs.NI 2024-12 unverdicted novelty 3.0 of 10

    The paper describes the OpenAirInterface open-source 5G/6G platform, its performance on selected testbeds, and argues that such open-source systems will drive 6G research.

  2. From DeepSense to Open RAN: AI/ML Advancements in Dynamic Spectrum Sensing and Their Applications

    cs.NI 2025-02 unverdicted

    A reading report that synthesizes prior AI/ML work in spectrum sensing and Open RAN, presenting no new results.

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