Powder: Platform for Open Wireless Data-driven Experimental Research

Breen, Joe; Buffmire, Andrew; Duerig, Jonathon; Dutt, Kevin; Eide, Eric; Ghosh, Anneswa; Hibler, Mike; Johnson, David E.; Kasera, Sneha Kumar; Lewis, Earl; Maas, Dustin; Martin, Caleb D.; Orange, Alex; Patwari, Neal; Reading, Daniel; Ricci, Robert; Schurig, David; Stoller, Leigh; Todd, Allison; Merwe, Jacobus E. van der; Viswanathan, Naren; Webb, Kirk; Wong, Gary

doi:10.1016/j.comnet.2021.108281

Cited by 40 publications

(30 citation statements)

References 17 publications

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“…Another remote experimentation Platform id the Open Wireless Data-driven Experimental Research (POWDER) that operates as a highly flexible, remotely accessible, end-toend software defined platform supporting a broad range of wireless and mobile related research [235]. This NSF-funded center has many features and capabilities including a massive MIMO base station and Software-Defined Radios (SDRs) that advances competitors in scale, realism, diversity, flexibility, and access.…”

Section: B Experimental Platformsmentioning

confidence: 99%

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook

Rovira-Sugranes¹,

Razi²,

Afghah³

et al. 2021

Preprint

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Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a new breed of unprecedented applications in different domains. This technology's ongoing trend is departing from large remotely-controlled drones to networks of small autonomous drones to collectively complete intricate tasks time and cost-effectively. An important challenge is developing efficient sensing, communication, and control algorithms that can accommodate the requirements of highly dynamic UAV networks with heterogeneous mobility levels. Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions. An important example of this trend is developing learning-powered routing protocols, where machine learning methods are used to model and predict topology evolution, channel status, traffic mobility, and environmental factors for enhanced routing.This paper reviews AI-enabled routing protocols designed primarily for aerial networks, with an emphasis on accommodating highly-dynamic network topology. To this end, we review the basics of UAV technology, different approaches to swarm formation, and commonly-used mobility models, along with their impact on networking paradigms. We proceed with reviewing conventional and AI-enabled routing protocols, including topology-predictive and self-adaptive learning-based routing algorithms. We also discuss tools, simulation environments, remote experimentation platforms, and public datasets that can be used for developing and testing AI-enabled networking protocols for UAV networks. We conclude by presenting future trends, and the remaining challenges in AI-based UAV Networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency, and spectrum sharing.

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Section: B Experimental Platformsmentioning

confidence: 99%

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook

Rovira-Sugranes¹,

Razi²,

Afghah³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…They involve 8 to 10 base stations serving four mobile users each. The locations of the base stations-derived from the OpenCelliD database [21]-match those of real-world cellular deployments in Rome, Italy, in Boston, MA, and in the POWDER testbed [6] of Salt Lake City, UT. The central frequency of these scenarios is set to 1 GHz and they have a duration of 10 minutes each.…”

Section: A Examples Of Available Experimental Scenariosmentioning

confidence: 99%

“…The goal of PAWR is to develop four city-scale programmable wireless platforms open to the research community at large. Among these, POWDER-RENEW focuses on developing a sub-6GHz programmable cellular architecture [6] with massive MIMO capabilities [7]; COSMOS focuses on a combination of mmWave links [8] with low-latency programmable optical backhauls; AERPAW focuses on the intersection between UAV verticals and cellular applications [9]; while ARA will be focused on wireless rural scenarios [10].…”

Section: Introductionmentioning

confidence: 99%

Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation

Bonati¹,

Johari²,

Polese³

et al. 2021

Preprint

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Colosseum is an open-access and publicly-available large-scale wireless testbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, "white-box" platform. Through 256 state-of-theart Software-defined Radios and a Massive Channel Emulator core, Colosseum can model virtually any scenario, enabling the design, development and testing of solutions at scale in a variety of deployments and channel conditions. These Colosseum radiofrequency scenarios are reproduced through high-fidelity FPGAbased emulation with finite-impulse response filters. Filters model the taps of desired wireless channels and apply them to the signals generated by the radio nodes, faithfully mimicking the conditions of real-world wireless environments. In this paper we describe the architecture of Colosseum and its experimentation and emulation capabilities. We then demonstrate the effectiveness of Colosseum for experimental research at scale through exemplary use cases including prevailing wireless technologies (e.g., cellular and Wi-Fi) in spectrum sharing and unmanned aerial vehicle scenarios. A roadmap for Colosseum future updates concludes the paper.

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“…The city-scale platforms of the U.S. National Science Foundation Platforms for Advanced Wireless Research (PAWR) program promise to be a valuable tool to provide the community with the desired diversity of scenarios and scale. The program is currently supporting three open testbeds representative of a variety of wireless usecases, ranging from state-of-the-art Software-defined Radios (SDRs) and massive Multiple Input, Multiple Output (MIMO) communications (POWDER, in Salt Lake City, UT [5]), to ultra-high capacity (e.g., mmWave) and low-latency wireless networks (COSMOS, in New York City [6]), and to aerial wireless communications (AER-PAW, in the Research Triangle of North Carolina [7]). All three platforms provide users with wireless data generation and analysis tools [1].…”

Section: Open Wireless Data Factoriesmentioning

confidence: 99%

Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks

Bonati,

D'Oro,

Polese

et al. 2020

Preprint

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Future, "NextG" cellular networks will be natively cloud-based and built upon programmable, virtualized, and disaggregated architectures. The separation of control functions from the hardware fabric and the introduction of standardized control interfaces will enable the definition of custom closed-control loops, which will ultimately enable embedded intelligence and real-time analytics, thus effectively realizing the vision of autonomous and self-optimizing networks. This article explores the NextG disaggregated architecture proposed by the O-RAN Alliance. Within this architectural context, it discusses potential, challenges, and limitations of data-driven optimization approaches to network control over different timescales. It also provides the first large-scale demonstration of the integration of O-RAN-compliant software components with an open-source full-stack softwarized cellular network. Experiments conducted on Colosseum, the world's largest wireless network emulator, demonstrate closed-loop integration of real-time analytics and control through deep reinforcement learning agents. We also demonstrate for the first time Radio Access Network (RAN) control through xApps running on the near real-time RAN Intelligent Controller (RIC), to optimize the scheduling policies of co-existing network slices, leveraging O-RAN open interfaces to collect data at the edge of the network.

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Powder: Platform for Open Wireless Data-driven Experimental Research

Cited by 40 publications

References 17 publications

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook

Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation

Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks

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